Salience Network Functional Connectivity Mediates Association Between Social Engagement and Cognition in Non-Demented Older Adults: Exploratory Investigation.

Background: Social engagement has beneficial effects during cognitive aging. Large-scale cognitive brain network functions are implicated in both social behaviors and cognition. Objective: We evaluated associations between functional connectivity (FC) of large-scale brain cognitive networks and social engagement, characterized by self-reported social network size and contact frequency. We subsequently tested large-scale brain network FC as a potential mediator of the beneficial relationship between social engagement and cognitive performance. Methods: 112 older adults (70.7±7.3 years, range 54.6-89.7; 84 women) completed the Lubben Social Network Scale 6 (LSNS-6), National Alzheimer's Coordinating Center (NACC) Uniform Data Set 3 (UDS-3) cognitive battery, and resting state fMRI. We completed seed-based correlational analysis in the default mode and salience networks. Significant associations between social engagement scores and cognitive performance, as well as between social engagement and FC of brain networks, informed the construction of mediation models. Results: Social engagement was significantly associated with executive function and global cognition, with greater social engagement associated with better cognitive performance. Social engagement was significantly associated with salience network FC, with greater social engagement associated with higher connectivity. Salience network FC partially mediated associations between social engagement and both executive function and global cognition. Conclusions: Our results suggest that the salience network is a key mediator of the beneficial relationship between social engagement and cognition in older adults.

High-definition transcranial direct current stimulation enhances network segregation during spatial navigation in mild cognitive impairment.

Spatial navigation is essential for everyday life and relies on complex network-level interactions. Recent evidence suggests that transcranial direct current stimulation (tDCS) can influence the activity of large-scale functional brain networks. We characterized brain-wide changes in functional network segregation (i.e. the balance of within vs. between-network connectivity strength) induced by high-definition (HD) tDCS in older adults with mild cognitive impairment (MCI) during virtual spatial navigation. Twenty patients with MCI and 22 cognitively intact older adults (healthy controls-HC) underwent functional magnetic resonance imaging following two counterbalanced HD-tDCS sessions (one active, one sham) that targeted the right parietal cortex (center anode at P2) and delivered 2 mA for 20 min. Compared to HC, MCI patients showed lower brain-wide network segregation following sham HD-tDCS. However, following active HD-tDCS, MCI patients' network segregation increased to levels similar to those in HC, suggesting functional normalization. Follow-up analyses indicated that the increase in network segregation for MCI patients was driven by HD-tDCS effects on the "high-level"/association brain networks, in particular the dorsal-attention and default-mode networks. HD-tDCS over the right parietal cortex may normalize the segregation/integration balance of association networks during spatial navigation in MCI patients, highlighting its potential to restore brain activity in Alzheimer's disease.

Effects of High Definition-Transcranial Direct Current Stimulation on Local GABA and Glutamate Levels Among Older Adults with and without Mild Cognitive Impairment: An Exploratory Study.

BACKGROUND: Prior research, primarily with young adults, suggests transcranial direct current stimulation (tDCS) effects are driven by the primary excitatory and/or inhibitory neurotransmitters, glutamate, and gamma-aminobutyric acid (GABA), respectively. OBJECTIVE: We examined the neurometabolic mechanisms of tDCS in older adults with and without mild cognitive impairment (MCI). METHODS: We used data from a double-blind, cross-over, randomized controlled trial (NCT01958437) in 32 older adults to evaluate high definition (HD)-tDCS-induced changes in glutamate and GABA via magnetic resonance spectroscopy (MRS). Participants underwent MRS following two counterbalanced HD-tDCS sessions (one active, one sham) that targeted the right superior parietal cortex (center anode at P2) and delivered 2mA for 20 minutes. RESULTS: Relative to sham, and when co-varying for MRS voxel overlap and right superior parietal volume, active HD-tDCS significantly increased GABA and decreased the ratio of glutamate to GABA. No changes were observed in a left prefrontal control MRS voxel. Although we did not find a significant correlation between strength of delivered current (measured via MRI-based computational modeling) and neurometabolite change, there was a robust positive relationship between the volume of right superior parietal cortex and neurometabolite change. CONCLUSION: Our preliminary findings of increased GABA and reduced glutamate/GABA ratio raise the possibility that (HD-)tDCS effects differ by age. Moreover, age- and disease-related regional brain volume loss may be especially important to consider when planning future studies. Replication would emphasize the importance of developing population-specific tDCS parameters that consider structural and physiologic changes associated with "normal" and pathological aging.

Aberrant activation of the mentalizing brain system during eye gaze discrimination in bipolar disorder.

Bipolar disorder (BD) is associated with a range of social cognitive deficits. This study investigated the functioning of the mentalizing brain system in BD probed by an eye gaze perception task during fMRI. Compared with healthy controls (n = 21), BD participants (n = 14) showed reduced preferential activation for self-directed gaze discrimination in the medial prefrontal cortex (mPFC) and temporo-parietal junction (TPJ), which was associated with poorer cognition/social cognition. Aberrant functions of the mentalizing system should be further investigated as marker of social dysfunction and treatment targets.

Prediction of Differential Pharmacologic Response in Chronic Pain Using Functional Neuroimaging Biomarkers and a Support Vector Machine Algorithm: An Exploratory Study.

OBJECTIVE: There is increasing demand for prediction of chronic pain treatment outcomes using machine-learning models, in order to improve suboptimal pain management. In this exploratory study, we used baseline brain functional connectivity patterns from chronic pain patients with fibromyalgia (FM) to predict whether a patient would respond differentially to either milnacipran or pregabalin, 2 drugs approved by the US Food and Drug Administration for the treatment of FM. METHODS: FM patients participated in 2 separate double-blind, placebo-controlled crossover studies, one evaluating milnacipran (n = 15) and one evaluating pregabalin (n = 13). Functional magnetic resonance imaging during rest was performed before treatment to measure intrinsic functional brain connectivity in several brain regions involved in pain processing. A support vector machine algorithm was used to classify FM patients as responders, defined as those with a ≥20% improvement in clinical pain, to either milnacipran or pregabalin. RESULTS: Connectivity patterns involving the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC) individually classified pregabalin responders versus milnacipran responders with 77% accuracy. Performance of this classification improved when both PCC and DLPFC connectivity patterns were combined, resulting in a 92% classification accuracy. These results were not related to confounding factors, including head motion, scanner sequence, or hardware status. Connectivity patterns failed to differentiate drug nonresponders across the 2 studies. CONCLUSION: Our findings indicate that brain functional connectivity patterns used in a machine-learning framework differentially predict clinical response to pregabalin and milnacipran in patients with chronic pain. These findings highlight the promise of machine learning in pain prognosis and treatment prediction.

Age differences in functional network reconfiguration with working memory training.

Demanding cognitive functions like working memory (WM) depend on functional brain networks being able to communicate efficiently while also maintaining some degree of modularity. Evidence suggests that aging can disrupt this balance between integration and modularity. In this study, we examined how cognitive training affects the integration and modularity of functional networks in older and younger adults. Twenty three younger and 23 older adults participated in 10 days of verbal WM training, leading to performance gains in both age groups. Older adults exhibited lower modularity overall and a greater decrement when switching from rest to task, compared to younger adults. Interestingly, younger but not older adults showed increased task-related modularity with training. Furthermore, whereas training increased efficiency within, and decreased participation of, the default-mode network for younger adults, it enhanced efficiency within a task-specific salience/sensorimotor network for older adults. Finally, training increased segregation of the default-mode from frontoparietal/salience and visual networks in younger adults, while it diffusely increased between-network connectivity in older adults. Thus, while younger adults increase network segregation with training, suggesting more automated processing, older adults persist in, and potentially amplify, a more integrated and costly global workspace, suggesting different age-related trajectories in functional network reorganization with WM training.

Disrupted Eye Gaze Perception as a Biobehavioral Marker of Social Dysfunction: An RDoC Investigation.

Social dysfunction is an intractable problem in a wide spectrum of psychiatric illnesses, undermining patients' capacities for employment, independent living, and maintaining meaningful relationships. Identifying common markers of social impairment across disorders and understanding their mechanisms are prerequisites to developing targeted neurobiological treatments that can be applied productively across diagnoses and illness stages to improve functional outcome. This project focuses on eye gaze perception, the ability to accurately and efficiently discriminate others' gaze direction, as a potential biomarker of social functioning that cuts across psychiatric diagnoses. This premise builds on both the monkey and human literatures showing gaze perception as a basic building block supporting higher-level social communication and social development, and reports of abnormal gaze perception in multiple psychiatric conditions accompanied by prominent social dysfunction (e.g., psychosis-spectrum disorders, autism-spectrum disorders, social phobia). A large sample (n = 225) of adolescent and young adult (age 14-30) psychiatric patients (regardless of diagnosis) with various degrees of impaired social functioning, and demographically-matched healthy controls (n = 75) will be recruited for this study. Participant's psychiatric phenotypes, cognition, social cognition, and community functioning will be dimensionally characterized. Eye gaze perception will be assessed using a psychophysical task, and two metrics (precision, self-referential bias) that respectively tap into gaze perception disturbances at the visual perceptual and interpretation levels, independent of general deficits, will be derived using hierarchical Bayesian modeling. A subset of the participants (150 psychiatric patients, 75 controls) will additionally undergo multimodal fMRI to determine the functional and structural brain network features of altered gaze perception. The specific aims of this project are three-fold: (1) Determine the generality of gaze perception disturbances in psychiatric patients with prominent social dysfunction; (2) Map behavioral indices of gaze perception disturbances to dimensions of psychiatric phenotypes and core functional domains; and (3) Identify the neural correlates of altered gaze perception in psychiatric patients with social dysfunction. Successfully completing these specific aims will identify the specific basic deficits, clinical profile, and underlying neural circuits associated with social dysfunction that can be used to guide targeted, personalized treatments, thus advancing NIMH's Strategic Objective 1 (describe neural circuits associated with mental illnesses and map the connectomes for mental illnesses) and Objective 3 (develop new treatments based on discoveries in neuroscience and behavioral science).

Neuromodulation of brain activation associated with addiction: A review of real-time fMRI neurofeedback studies.

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has emerged in recent years as an imaging modality used to examine volitional control over targeted brain activity. rtfMRI-nf has also been applied clinically as a way to train individuals to self-regulate areas of the brain, or circuitry, involved in various disorders. One such application of rtfMRI-nf has been in the domain of addictive behaviors, including substance use. Given the pervasiveness of substance use and the challenges of existing treatments to sustain abstinence, rtfMRI-nf has been identified as a promising treatment tool. rtfMRI-nf has also been used in basic science research in order to test the ability to modulate brain function involved in addiction. This review focuses first on providing an overview of recent rtfMRI-nf studies in substance-using populations, specifically nicotine, alcohol, and cocaine users, aimed at reducing craving-related brain activation. Next, rtfMRI-nf studies targeting reward responsivity and emotion regulation in healthy samples are reviewed in order to examine the extent to which areas of the brain involved in addiction can be self-regulated using neurofeedback. We propose that future rtfMRI-nf studies could be strengthened by improvements to study design, sample selection, and more robust strategies in the development and assessment of rtfMRI-nf as a clinical treatment. Recommendations for ways to accomplish these improvements are provided. rtfMRI-nf holds much promise as an imaging modality that can directly target key brain regions involved in addiction, however additional studies are needed in order to establish rtfMRI-nf as an effective, and practical, treatment for addiction.

Neural correlates of working memory training: Evidence for plasticity in older adults.

Brain activity typically increases with increasing working memory (WM) load, regardless of age, before reaching an apparent ceiling. However, older adults exhibit greater brain activity and reach ceiling at lower loads than younger adults, possibly reflecting compensation at lower loads and dysfunction at higher loads. We hypothesized that WM training would bolster neural efficiency, such that the activation peak would shift towards higher memory loads after training. Pre-training, older adults showed greater recruitment of the WM network than younger adults across all loads, with decline at the highest load. Ten days of adaptive training on a verbal WM task improved performance and led to greater brain responsiveness at higher loads for both groups. For older adults the activation peak shifted rightward towards higher loads. Finally, training increased task-related functional connectivity in older adults, both within the WM network and between this task-positive network and the task-negative/default-mode network. These results provide new evidence for functional plasticity with training in older adults and identify a potential signature of improvement at the neural level.

Network segregation varies with neural distinctiveness in sensorimotor cortex.

Normal aging is associated with declines in sensorimotor function. Previous studies have linked age-related behavioral declines to decreases in neural differentiation (i.e., dedifferentiation), including decreases in the distinctiveness of neural activation patterns and in the segregation of large-scale neural networks at rest. However, no studies to date have explored the relationship between these two neural measures and whether they explain the same aspects of behavior. To investigate these issues, we collected a battery of sensorimotor behavioral measures in older and younger adults and estimated (a) the distinctiveness of neural representations in sensorimotor cortex and (b) sensorimotor network segregation in the same participants. Consistent with prior findings, sensorimotor representations were less distinct and sensorimotor resting state networks were less segregated in older compared to younger adults. We also found that participants with the most distinct sensorimotor representations exhibited the most segregated sensorimotor networks. However, only sensorimotor network segregation was associated with individual differences in sensorimotor performance, particularly in older adults. These novel findings link network segregation to neural distinctiveness, but also suggest that network segregation may play a larger role in maintaining sensorimotor performance with age.

Neighborhood poverty predicts altered neural and behavioral response inhibition.

Socioeconomic disadvantage during childhood is associated with a myriad of negative adult outcomes. One mechanism through which disadvantage undermines positive outcomes may be by disrupting the development of self-control. The goal of the present study was to examine pathways from three key indicators of socioeconomic disadvantage - low family income, low maternal education, and neighborhood poverty - to neural and behavioral measures of response inhibition. We utilized data from a representative cohort of 215 twins (ages 7-18 years, 70% male) oversampled for exposure to disadvantage, who participated in the Michigan Twins Neurogenetics Study (MTwiNS), a study within the Michigan State University Twin Registry (MSUTR). Our child-friendly Go/No-Go task activated the bilateral inferior frontal gyrus (IFG), and activation during this task predicted behavioral inhibition performance, extending prior work on adults to youth. Critically, we also found that neighborhood poverty, assessed via geocoding, but not family income or maternal education, was associated with IFG activation, a finding that we replicated in an independent sample of disadvantaged youth. Further, we found that neighborhood poverty predicted response inhibition performance via its effect on IFG activation. These results provide the first mechanistic evidence that disadvantaged contexts may undermine self-control via their effect on the brain. The broader neighborhood, beyond familial contexts, may be critically important for this association, suggesting that contexts beyond the home have profound effects on the developing brain and behaviors critical for future health, wealth, and wellbeing.

Sensorimotor network segregation declines with age and is linked to GABA and to sensorimotor performance.

Aging is typically associated with declines in sensorimotor performance. Previous studies have linked some age-related behavioral declines to reductions in network segregation. For example, compared to young adults, older adults typically exhibit weaker functional connectivity within the same functional network but stronger functional connectivity between different networks. Based on previous animal studies, we hypothesized that such reductions of network segregation are linked to age-related reductions in the brain's major inhibitory transmitter, gamma aminobutyric acid (GABA). To investigate this hypothesis, we conducted graph theoretical analyses of resting state functional MRI data to measure sensorimotor network segregation in both young and old adults. We also used magnetic resonance spectroscopy to measure GABA levels in the sensorimotor cortex and collected a battery of sensorimotor behavioral measures. We report four main findings. First, relative to young adults, old adults exhibit both less segregated sensorimotor brain networks and reduced sensorimotor GABA levels. Second, less segregated networks are associated with lower GABA levels. Third, less segregated networks and lower GABA levels are associated with worse sensorimotor performance. Fourth, network segregation mediates the relationship between GABA and performance. These findings link age-related differences in network segregation to age-related differences in GABA levels and sensorimotor performance. More broadly, they suggest a neurochemical substrate of age-related dedifferentiation at the level of large-scale brain networks.

Amygdala habituation and uncinate fasciculus connectivity in adolescence: A multi-modal approach.

Despite prior extensive investigations of the interactions between the amygdala and prefrontal cortex, few studies have simultaneously considered activation and structural connectivity in this circuit, particularly as it pertains to adolescent socioemotional development. The current multi-modal study delineated the correspondence between uncinate fasciculus (UF) connectivity and amygdala habituation in a large adolescent sample that was drawn from a population-based sample. We then examined the influence of demographic variables (age, gender, and pubertal status) on the relation between UF connectivity and amygdala habituation. 106 participants (15-17 years) completed DTI and an fMRI emotional face processing task. Left UF fractional anisotropy was associated with left amygdala habituation to fearful faces, suggesting that increased structural connectivity of the UF may facilitate amygdala regulation. Pubertal status moderated this structure-function relation, such that the association was stronger in those who were less mature. Therefore, UF connectivity may be particularly important for emotion regulation during early puberty. This study is the first to link structural and functional limbic circuitry in a large adolescent sample with substantial representation of ethnic minority participants, providing a more comprehensive understanding of socioemotional development in an understudied population.

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training.

Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on "resting-state" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.

Support vector machine classification of arterial volume-weighted arterial spin tagging images.

INTRODUCTION: In recent years, machine-learning techniques have gained growing popularity in medical image analysis. Temporal brain-state classification is one of the major applications of machine-learning techniques in functional magnetic resonance imaging (fMRI) brain data. This article explores the use of support vector machine (SVM) classification technique with motor-visual activation paradigm to perform brain-state classification into activation and rest with an emphasis on different acquisition techniques. METHODS: Images were acquired using a recently developed variant of traditional pseudocontinuous arterial spin labeling technique called arterial volume-weighted arterial spin tagging (AVAST). The classification scheme is also performed on images acquired using blood oxygenation-level dependent (BOLD) and traditional perfusion-weighted arterial spin labeling (ASL) techniques for comparison. RESULTS: The AVAST technique outperforms traditional pseudocontinuous ASL, achieving classification accuracy comparable to that of BOLD contrast images. CONCLUSION: This study demonstrates that AVAST has superior signal-to-noise ratio and improved temporal resolution as compared with traditional perfusion-weighted ASL and reduced sensitivity to scanner drift as compared with BOLD. Owing to these characteristics, AVAST lends itself as an ideal choice for dynamic fMRI and real-time neurofeedback experiments with sustained activation periods.

Dynamic filtering improves attentional state prediction with fNIRS.

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%).

Prolongation of ERP latency and reaction time (RT) in simultaneous EEG/fMRI data acquisition.

BACKGROUND: Recording EEG and fMRI data simultaneously inside a fully-operating scanner has been recognized as a novel approach in human brain research. Studies have demonstrated high concordance between the EEG signals and hemodynamic response. However, a few studies reported altered cognitive process inside the fMRI scanner such as delayed reaction time (RT) and reduced and/or delayed N100 and P300 event-related brain potential (ERP) components. NEW METHOD: The present study investigated the influence of electromagnetic field (static magnetic field, radio frequency (RF) pulse, and gradient switching) and experimental environment on posterior N100 and P300 ERP components in four different settings with six healthy subjects using a visual oddball task: (1) classic fMRI acquisition inside the scanner (e.g., supine position, mirror glasses for stimulus presentation), (2) standard behavioral experiment outside the scanner (e.g., seated position, keyboard response), (3) controlled fMRI acquisition inside the scanner (e.g., organic light-emitting diode (OLED) goggles for stimulus presentation) inside; and (4) modified behavioral experiment outside the scanner (e.g., supine position, OLED goggles). RESULTS: The study findings indicated that the experimental environment in simultaneous EEG/fMRI acquisition could substantially delay N1P, P300 latency, and RT inside the scanner, and was associated with a reduced N1P amplitude. COMPARISON WITH EXISTING METHODS: There was no effect of electromagnetic field in the prolongation of RT, N1P and P300 latency inside the scanner. N1P, but not P300, latency was sensitive to stimulus presentation method inside the scanner. CONCLUSION: Future simultaneous EEG/fMRI data collection should consider experimental environment in both design and analysis.

Pharmacologic attenuation of cross-modal sensory augmentation within the chronic pain insula.

Pain can be elicited through all mammalian sensory pathways yet cross-modal sensory integration, and its relationship to clinical pain, is largely unexplored. Centralized chronic pain conditions such as fibromyalgia are often associated with symptoms of multisensory hypersensitivity. In this study, female patients with fibromyalgia demonstrated cross-modal hypersensitivity to visual and pressure stimuli compared with age- and sex-matched healthy controls. Functional magnetic resonance imaging revealed that insular activity evoked by an aversive level of visual stimulation was associated with the intensity of fibromyalgia pain. Moreover, attenuation of this insular activity by the analgesic pregabalin was accompanied by concomitant reductions in clinical pain. A multivariate classification method using support vector machines (SVM) applied to visual-evoked brain activity distinguished patients with fibromyalgia from healthy controls with 82% accuracy. A separate SVM classification of treatment effects on visual-evoked activity reliably identified when patients were administered pregabalin as compared with placebo. Both SVM analyses identified significant weights within the insular cortex during aversive visual stimulation. These data suggest that abnormal integration of multisensory and pain pathways within the insula may represent a pathophysiological mechanism in some chronic pain conditions and that insular response to aversive visual stimulation may have utility as a marker for analgesic drug development.

Stable long-range interhemispheric coordination is supported by direct anatomical projections.

The functional interaction between the brain's two hemispheres includes a unique set of connections between corresponding regions in opposite hemispheres (i.e., homotopic regions) that are consistently reported to be exceptionally strong compared with other interhemispheric (i.e., heterotopic) connections. The strength of homotopic functional connectivity (FC) is thought to be mediated by the regions' shared functional roles and their structural connectivity. Recently, homotopic FC was reported to be stable over time despite the presence of dynamic FC across both intrahemispheric and heterotopic connections. Here we build on this work by considering whether homotopic FC is also stable across conditions. We additionally test the hypothesis that strong and stable homotopic FC is supported by the underlying structural connectivity. Consistent with previous findings, interhemispheric FC between homotopic regions were significantly stronger in both humans and macaques. Across conditions, homotopic FC was most resistant to change and therefore was more stable than heterotopic or intrahemispheric connections. Across time, homotopic FC had significantly greater temporal stability than other types of connections. Temporal stability of homotopic FC was facilitated by direct anatomical projections. Importantly, temporal stability varied with the change in conductive properties of callosal axons along the anterior-posterior axis. Taken together, these findings suggest a notable role for the corpus callosum in maintaining stable functional communication between hemispheres.