Resting-state EEG Connectivity in Young Children with ADHD.

Objective: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and impairing neurodevelopmental disorder. While early childhood is a crucial time for early intervention, it is characterized by instability of ADHD diagnosis. Neural correlates of ADHD have potential to improve diagnostic accuracy; however, minimal research has focused on early childhood. Research indicates that disrupted neural connectivity is associated with ADHD in older children. Here, we explore network connectivity as a potential neural correlate of ADHD diagnosis in early childhood.Method: We collected EEG data in 52 medication-naïve children with ADHD and in 77 typically developing controls (3-7 years). Data was collected with the EGI 128 HydroCel Sensor Net System, but to optimize the ICA, the data was down sampled to the 10-10 system. Connectivity was measured as the synchronization of the time series of each pair of electrodes. Subsequent analyses utilized graph theoretical methods to further characterize network connectivity.Results: Increased global efficiency, which measures the efficiency of information transfer across the entire brain, was associated with increased inattentive symptom severity. Further, this association was robust to controls for age, IQ, SES, and internalizing psychopathology.Conclusions: Overall, our findings indicate that increased global efficiency, which suggests a hyper-connected neural network, is associated with elevated ADHD symptom severity. These findings extend previous work reporting disruption of neural network connectivity in older children with ADHD into early childhood.

Top-down versus bottom-up attention differentially modulate frontal-parietal connectivity.

The moment-to-moment focus of our mind's eye results from a complex interplay of voluntary and involuntary influences on attention. Previous neuroimaging studies suggest that the brain networks of voluntary versus involuntary attention can be segregated into a frontal-versus-parietal or a dorsal-versus-ventral partition-although recent work suggests that the dorsal network may be involved in both bottom-up and top-down attention. Research with nonhuman primates has provided evidence that a key distinction between top-down and bottom-up attention may be the direction of connectivity between frontal and parietal areas. Whereas typical fMRI connectivity analyses cannot disambiguate the direction of connections, dynamic causal modeling (DCM) can model directionality. Using DCM, we provide new evidence that directed connections within the dorsal attention network are differentially modulated for voluntary versus involuntary attention. These results suggest that the intraparietal sulcus exerts a baseline inhibitory effect on the frontal eye fields that is strengthened during exogenous orienting and attenuated during endogenous orienting. Furthermore, the attenuation from endogenous attention occurs even with salient peripheral cues when those cues are known to be counter predictive. Thus, directed connectivity between frontal and parietal regions of the dorsal attention network is highly influenced by the type of attention that is engaged.

Distinguishing lexical- versus discourse-level processing using event-related potentials.

Two experiments examine the links between neural patterns in EEG (e.g., N400s, P600s) and their corresponding cognitive processes (e.g., lexical access, discourse integration) by varying the lexical and syntactic contexts of co-referential expressions. Experiment 1 examined coreferring expressions when they occurred within the same clause as their antecedents (John/Bill warmly dressed John). Experiment 2 examined between-clause co-referencing with expressions that also varied in lexical frequency (John/Weston went to the store so that John/Weston could buy milk). Evidence of facilitated lexical processing occurred after repeated names, which elicited smaller N400s, as compared with new names. N400s were also attenuated to a greater degree for low-frequency expressions than for high-frequency ones. Repeated names also triggered evidence of postlexical processing, but this emerged as larger P600s for within-clause co-referencing and delayed N400s for between-clause co-referencing. Together, these results suggest that linguistic processes can be distinguished through distinct ERP components or distinct temporal patterns.

From learned value to sustained bias: how reward conditioning changes attentional priority.

Introduction: Attentional bias to reward-associated stimuli can occur even when it interferes with goal-driven behavior. One theory posits that dopaminergic signaling in the striatum during reward conditioning leads to changes in visual cortical and parietal representations of the stimulus used, and this, in turn, sustains attentional bias even when reward is discontinued. However, only a few studies have examined neural activity during both rewarded and unrewarded task phases. Methods: In the current study, participants first completed a reward-conditioning phase, during which responses to certain stimuli were associated with monetary reward. These stimuli were then included as non-predictive cues in a spatial cueing task. Participants underwent functional brain imaging during both task phases. Results: The results show that striatal activity during the learning phase predicted increased visual cortical and parietal activity and decreased ventro-medial prefrontal cortex activity in response to conditioned stimuli during the test. Striatal activity was also associated with anterior cingulate cortex activation when the reward-conditioned stimulus directed attention away from the target. Discussion: Our findings suggest that striatal activity during reward conditioning predicts the degree to which reward history biases attention through learning-induced changes in visual and parietal activities.

Blind Subgrouping of Task-based fMRI.

Significant heterogeneity in network structures reflecting individuals' dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME's ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.

Mild traumatic brain injury history is associated with lower brain network resilience in soldiers.

Special Operations Forces combat soldiers sustain frequent blast and blunt neurotrauma, most often classified as mild traumatic brain injuries. Exposure to repetitive mild traumatic brain injuries is associated with persistent behavioural, cognitive, emotional and neurological symptoms later in life. Identifying neurophysiological changes associated with mild traumatic brain injury exposure, in the absence of present-day symptoms, is necessary for detecting future neurological risk. Advancements in graph theory and functional MRI have offered novel ways to analyse complex whole-brain network connectivity. Our purpose was to determine how mild traumatic brain injury history, lifetime incidence and recency affected whole-brain graph theoretical outcome measures. Healthy male Special Operations Forces combat soldiers (age = 33.2 ± 4.3 years) underwent multimodal neuroimaging at a biomedical research imaging centre using 3T Siemens Prisma or Biograph MRI scanners in this cross-sectional study. Anatomical and functional scans were preprocessed. The blood-oxygen-level-dependent signal was extracted from each functional MRI time series using the Big Brain 300 atlas. Correlations between atlas regions were calculated and Fisher z-transformed to generate subject-level correlation matrices. The Brain Connectivity Toolbox was used to obtain functional network measures for global efficiency (the average inverse shortest path length), local efficiency (the average global efficiency of each node and its neighbours), and assortativity coefficient (the correlation coefficient between the degrees of all nodes on two opposite ends of a link). General linear models were fit to compare mild traumatic brain injury lifetime incidence and recency. Nonparametric ANOVAs were used for tests on non-normally distributed data. Soldiers with a history of mild traumatic brain injury had significantly lower assortativity than those who did not self-report mild traumatic brain injury (t148 = 2.44, P = 0.016). The assortativity coefficient was significantly predicted by continuous mild traumatic brain injury lifetime incidence [F1,144 = 6.51, P = 0.012]. No differences were observed between recency groups, and no global or local efficiency differences were observed between mild traumatic brain injury history and lifetime incidence groups. Brain networks with greater assortativity have more resilient, interconnected hubs, while those with lower assortativity indicate widely distributed, vulnerable hubs. Greater lifetime mild traumatic brain injury incidence predicted lower assortativity in our study sample. Less resilient brain networks may represent a lack of physiological recovery in mild traumatic brain injury patients, who otherwise demonstrate clinical recovery, more vulnerability to future brain injury and increased risk for accelerated age-related neurodegenerative changes. Future longitudinal studies should investigate whether decreased brain network resilience may be a predictor for long-term neurological dysfunction.

Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data.

Research questions in the human sciences often seek to answer if and when a process changes across time. In functional MRI studies, for instance, researchers may seek to assess the onset of a shift in brain state. For daily diary studies, the researcher may seek to identify when a person's psychological process shifts following treatment. The timing and presence of such a change may be meaningful in terms of understanding state changes. Currently, dynamic processes are typically quantified as static networks where edges indicate temporal relations among nodes, which may be variables reflecting emotions, behaviors, or brain activity. Here we describe three methods for detecting changes in such correlation networks from a data-driven perspective. Networks here are quantified using the lag-0 pair-wise correlation (or covariance) estimates as the representation of the dynamic relations among variables. We present three methods for change point detection: dynamic connectivity regression, max-type method, and a PCA-based method. The change point detection methods each include different ways to test if two given correlation network patterns from different segments in time are significantly different. These tests can also be used outside of the change point detection approaches to test any two given blocks of data. We compare the three methods for change point detection as well as the complementary significance testing approaches on simulated and empirical functional connectivity fMRI data examples.

Effect of Place-Based Versus Default Mapping Procedures on Masked Speech Recognition: Simulations of Cochlear Implant Alone and Electric-Acoustic Stimulation.

PURPOSE: Cochlear implant (CI) recipients demonstrate variable speech recognition when listening with a CI-alone or electric-acoustic stimulation (EAS) device, which may be due in part to electric frequency-to-place mismatches created by the default mapping procedures. Performance may be improved if the filter frequencies are aligned with the cochlear place frequencies, known as place-based mapping. Performance with default maps versus an experimental place-based map was compared for participants with normal hearing when listening to CI-alone or EAS simulations to observe potential outcomes prior to initiating an investigation with CI recipients. METHOD: A noise vocoder simulated CI-alone and EAS devices, mapped with default or place-based procedures. The simulations were based on an actual 24-mm electrode array recipient, whose insertion angles for each electrode contact were used to estimate the respective cochlear place frequency. The default maps used the filter frequencies assigned by the clinical software. The filter frequencies for the place-based maps aligned with the cochlear place frequencies for individual contacts in the low- to mid-frequency cochlear region. For the EAS simulations, low-frequency acoustic information was filtered to simulate aided low-frequency audibility. Performance was evaluated for the AzBio sentences presented in a 10-talker masker at +5 dB signal-to-noise ratio (SNR), +10 dB SNR, and asymptote. RESULTS: Performance was better with the place-based maps as compared with the default maps for both CI-alone and EAS simulations. For instance, median performance at +10 dB SNR for the CI-alone simulation was 57% correct for the place-based map and 20% for the default map. For the EAS simulation, those values were 59% and 37% correct. Adding acoustic low-frequency information resulted in a similar benefit for both maps. CONCLUSIONS: Reducing frequency-to-place mismatches, such as with the experimental place-based mapping procedure, produces a greater benefit in speech recognition than maximizing bandwidth for CI-alone and EAS simulations. Ongoing work is evaluating the initial and long-term performance benefits in CI-alone and EAS users. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19529053.

Effectiveness of Place-based Mapping in Electric-Acoustic Stimulation Devices.

BACKGROUND: The default mapping procedure for electric-acoustic stimulation (EAS) devices uses the cochlear implant recipient's unaided detection thresholds in the implanted ear to derive the acoustic settings and assign the lowest frequency filter of electric stimulation. Individual differences for speech recognition with EAS may be due to discrepancies between the electric frequency filters of individual electrode contacts and the cochlear place of stimulation, known as a frequency-to-place mismatch. Frequency-to-place mismatch of greater than 1/2 octave has been demonstrated in up to 60% of EAS users. Aligning the electric frequency filters via a place-based mapping procedure using postoperative imaging may improve speech recognition with EAS. METHODS: Masked sentence recognition was evaluated for normal-hearing subjects (n = 17) listening with vocoder simulations of EAS, using a place-based map and a default map. Simulation parameters were based on audiometric and imaging data from a representative 24-mm electrode array recipient and EAS user. The place-based map aligned electric frequency filters with the cochlear place frequency, which introduced a gap between the simulated acoustic and electric output. The default map settings were derived from the clinical programming software and provided the full speech frequency range. RESULTS: Masked sentence recognition was significantly better for simulated EAS with the place-based map as compared with the default map. CONCLUSION: The simulated EAS place-based map supported better performance than the simulated EAS default map. This indicates that individualizing maps may improve performance in EAS users by helping them achieve better asymptotic performance earlier and mitigate the need for acclimatization.

Detecting Task-Dependent Functional Connectivity in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions.

Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. Impact statement Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.

Neural basis of visual distraction.

The ability to maintain focus and avoid distraction by goal-irrelevant stimuli is critical for performing many tasks and may be a key deficit in attention-related problems. Recent studies have demonstrated that irrelevant stimuli that are consciously perceived may be filtered out on a neural level and not cause the distraction triggered by subliminal stimuli. However, in everyday situations, suprathreshold stimuli often do capture attention, but the neural mechanisms by which some stimuli rapidly and automatically trigger distraction remain unknown. Here, we investigated the neural basis of distraction by utilizing a particularly strong form of distractor: the abrupt appearance of a new object. Our results revealed a competitive relation between brain regions coding the locations of the target and the distractor, with distractor processing increasing and target processing decreasing, but only when the distractor was a new object; an equivalent luminance change to an existing object neither generated distraction nor affected target processing. Results also revealed changes in neural activity in intraparietal sulcus (IPS) and temporo-parietal junction (TPJ) that were unique to the new object distractor condition. The strongest relations between behavioral distraction and neural activity were observed in these parietal regions. Furthermore, participants who were less susceptible to distraction showed a more consistent, albeit more moderate, level of activity in IPS and TPJ. The present results thus provide new evidence regarding the neural mechanisms underlying distraction and resistance to it.

Attentional Control and Executive Function.

The processes of attentional control and executive function are critical for navigating and operating efficiently in everyday life, and deficits in these core processes have serious consequences. Despite a long history of research into these topics, much is still unknown about the brain mechanisms supporting these processes. This special issue of Cognitive Neuroscience: Current Debates, Research & Reports presents nine new empirical papers investigating the dynamic neural mechanisms of attentional selection, working memory, and executive control. The papers in this special issue utilize electrophysiological and neuroimaging methods, along with advanced analysis techniques, to identify the neural substrates and dynamic mechanisms underlying the orienting and shifting of attention, as well as the representation and maintenance of information in working memory. These articles inform theories of attentional selection by providing a deeper understanding of social influences on the allocation of attention as well as illuminating the role of selection history in biasing neural activity and behavior. Finally, the research presented here has broader impacts on the field of cognitive neuroscience, as results from studies investigating the coupling between bands of oscillatory neural activity provide exciting new insights into the coordination between widespread brain networks.

Reward history impacts attentional orienting and inhibitory control on untrained tasks.

It has been robustly shown that stimuli with reward history receive attentional priority. However, the majority of this research tests reward history effects on attentional bias using similar tasks for both the reward learning phase and the unrewarded testing phase, which limits our understanding of how the effects of reward history generalize beyond the trained tasks and mental sets. Across two new experiments, the current study addresses these issues by first associating reward with a stimulus in a visual search paradigm, and then testing value-driven effects of that stimulus in untrained and unrewarded tasks, including a cueing paradigm, a go/no-go task, and a delay discounting task. Results of Experiment 1 demonstrate that history of reward association in a visual search task generalizes to value-driven attentional bias in a different attention paradigm (i.e., cueing), indicating these effects are indeed attributable to imbued value that can transfer to other tasks beyond that in which the reward was trained. The results of Experiment 2 demonstrate that in addition to eliciting attentional orienting on untrained tasks, reward history can lead to better inhibitory control in the go/no-go task. We find no evidence for reward history effects in the delay discounting task. Together, these experiments demonstrate that when the reward association task is in the attention domain, reward history modulates attentional priority, and this effect generalizes to untrained and unrewarded tasks that utilize both spatial and nonspatial attention.

Neural bases for impaired social cognition in schizophrenia and autism spectrum disorders.

Schizophrenia and autism both feature significant impairments in social cognition and social functioning, but the specificity and mechanisms of these deficits remain unknown. Recent research suggests that social cognitive deficits in both disorders may arise from dysfunctions in the neural systems that underlie social cognition. We explored the neural activation of discrete brain regions implicated in social cognitive and face processing in schizophrenia subgroups and autism spectrum disorders during complex social judgments of faces. Twelve individuals with autism spectrum disorders (ASD), 12 paranoid individuals with schizophrenia (P-SCZ), 12 non-paranoid individuals with schizophrenia (NP-SCZ), and 12 non-clinical healthy controls participated in this cross sectional study. Neural activation, as indexed by blood oxygenation level dependent (BOLD) contrast, was measured in a priori regions of interest while individuals rated faces for trustworthiness. All groups showed significant activation of a social cognitive network including the amygdala, fusiform face area (FFA), superior temporal sulcus (STS), and ventrolateral prefrontal cortex (VLPFC) while completing a task of complex social cognition (i.e. trustworthiness judgments). ASD and P-SCZ individuals showed significantly reduced neural activation in the right amygdala, FFA, and left VLPFC as compared to controls and in the left VLPFC as compared to NP-SCZ individuals during this task. These findings lend support to models hypothesizing well-defined neural substrates of social cognition and suggest a specific neural mechanism that may underlie social cognitive impairments in both autism and paranoid schizophrenia.

An investigation of the relationship between activation of a social cognitive neural network and social functioning.

Previous work examining the neurobiological substrates of social cognition in healthy individuals has reported modulation of a social cognitive network such that increased activation of the amygdala, fusiform gyrus, and superior temporal sulcus are evident when individuals judge a face to be untrustworthy as compared with trustworthy. We examined whether this pattern would be present in individuals with schizophrenia who are known to show reduced activation within these same neural regions when processing faces. Additionally, we sought to determine how modulation of this social cognitive network may relate to social functioning. Neural activation was measured using functional magnetic resonance imaging with blood oxygenation level dependent contrast in 3 groups of individuals--nonparanoid individuals with schizophrenia, paranoid individuals with schizophrenia, and healthy controls--while they rated faces as either trustworthy or untrustworthy. Analyses of mean percent signal change extracted from a priori regions of interest demonstrated that both controls and nonparanoid individuals with schizophrenia showed greater activation of this social cognitive network when they rated a face as untrustworthy relative to trustworthy. In contrast, paranoid individuals did not show a significant difference in levels of activation based on how they rated faces. Further, greater activation of this social cognitive network to untrustworthy faces was significantly and positively correlated with social functioning. These findings indicate that impaired modulation of neural activity while processing social stimuli may underlie deficits in social cognition and social dysfunction in schizophrenia.

Hold it! Memory affects attentional dwell time.

The allocation of attention, including the initial orienting and the subsequent dwell time, is affected by several bottom-up and top-down factors. How item memory affects these processes, however, remains unclear. Here, we investigated whether item memory affects attentional dwell time by using a modified version of the attentional blink (AB) paradigm. Across four experiments, our results revealed that the AB was significantly affected by memory status (novel vs. old), but critically, this effect depended on the ongoing memory context. Specifically, items that were unique in terms of memory status demanded more resources, as measured by a protracted AB. The present findings suggest that a more comprehensive understanding of memory's effects on attention can be obtained by accounting for an item's memorial context, as well as its individual item memory strength. Our results provide new evidence that item memory and memory context play a significant role in the temporal allocation of attention.

Impaired conflict monitoring near the hands: Neurophysiological evidence.

Recent evidence suggests that hand-stimulus proximity enhances the visuo-motor Simon effect. The present study used event-related potentials (ERP) to examine the timing at which hand-stimulus proximity modulates the Simon effect. The results show that the P1 and N1 components were not modulated by hand-stimulus proximity, suggesting that early sensory processing is not altered by hand-stimulus proximity. However, the interference effect (the difference between incompatible versus compatible trials) on the N2 component was significantly attenuated near the hands compared to far from the hands, indicating that hand-stimulus proximity impairs conflict monitoring. We also found significant effects on a later component, as the P3 was reduced and had a shorter latency for the hand-proximal condition relative to the hand-distal condition. These new findings suggest that the critical stage at which hand-stimulus proximity affects cognitive processing lies past the early perceptual processing, acting instead on later stages of processing related to executive functioning.

Exogenous vs. endogenous attention: Shifting the balance of fronto-parietal activity.

Despite behavioral and electrophysiological evidence for dissociations between endogenous (voluntary) and exogenous (reflexive) attention, fMRI results have yet to consistently and clearly differentiate neural activation patterns between these two types of attention. This study specifically aimed to determine whether activity in the dorsal fronto-parietal network differed between endogenous and exogenous conditions. Participants performed a visual discrimination task in endogenous and exogenous attention conditions while undergoing fMRI scanning. Analyses revealed robust and bilateral activation throughout the dorsal fronto-parietal network for each condition, in line with many previous results. In order to investigate possible differences in the balance of neural activity within this network with greater sensitivity, a priori regions of interest (ROIs) were selected for analysis, centered on the frontal eye fields (FEF) and intraparietal sulcus (IPS) regions identified in previous studies. The results revealed a significant interaction between region, condition, and hemisphere. Specifically, in the left hemisphere, frontal areas were more active than parietal areas, but only during endogenous attention. Activity in the right hemisphere, in contrast, remained relatively consistent for these regions across conditions. Analysis of this activity over time indicates that this left-hemispheric regional imbalance is present within the FEF early, at 3-6.5 s post-stimulus presentation, whereas a regional imbalance in the exogenous condition is not evident until 6.5-8 s post-stimulus presentation. Overall, our results provide new evidence that although the dorsal fronto-parietal network is indeed associated with both types of attentional orienting, regions of the network are differentially engaged over time and across hemispheres depending on the type of attention.

Replication and innovation versus a perfect '.05'.

Slotnick (this issue) makes a strong case that any attempt to assess the reliabiltiy of statistical correction procedures should use truly random data. In addition, however, there is an important side effect of the over-reliance on any given threshold to determine the worth of an experiment. Placing too much faith in any method of correction obscures the point that replication across labs remains a most critical part of scientific study. Especially for expensive methods, such as fMRI, an overemphasis on increasingly conservative thresholds can negatively impact the potential for replication of studies and the pursuit and reporting of innovative results.

Relation of higher-frequency oscillatory activity to white matter changes and to core mechanisms of attention.

Voelker et al. discuss the potentially critical role of white matter changes underlying the effects of training. In regard to the specific types of neural activities and processes related to these changes, the authors focus on theta rhythms and the speed of manual response times. However, white matter changes likely affect brain oscillatory activity at multiple frequencies, and recent findings suggest structural connections may be even more important for higher frequency functional connectivity. Furthermore, activity in the gamma frequency range has been implicated in basic mechanisms of attention, and changes in these core processes could underlie improvements across multiple tasks.