Brain Activity is Influenced by How High Dimensional Data are Represented: An EEG Study of Scatterplot Diagnostic (Scagnostics) Measures.

Visualization and visual analytic tools amplify one's perception of data, facilitating deeper and faster insights that can improve decision making. For multidimensional data sets, one of the most common approaches of visualization methods is to map the data into lower dimensions. Scatterplot matrices (SPLOM) are often used to visualize bivariate relationships between combinations of variables in a multidimensional dataset. However, the number of scatterplots increases quadratically with respect to the number of variables. For high dimensional data, the corresponding enormous number of scatterplots makes data exploration overwhelmingly complex, thereby hindering the usefulness of SPLOM in human decision making processes. One approach to address this difficulty utilizes Graph-theoretic Scatterplot Diagnostic (Scagnostics) to automatically extract a subset of scatterplots with salient features and of manageable size with the hope that the data will be sufficient for improving human decisions. In this paper, we use Electroencephalogram (EEG) to observe brain activity while participants make decisions informed by scatterplots created using different visual measures. We focused on 4 categories of Scagnostics measures: Clumpy, Monotonic, Striated, and Stringy. Our findings demonstrate that by adjusting the level of difficulty in discriminating between data sets based on the Scagnostics measures, different parts of the brain are activated: easier visual discrimination choices involve brain activity mostly in visual sensory cortices located in the occipital lobe, while more difficult discrimination choices tend to recruit more parietal and frontal regions as they are known to be involved in resolving ambiguities. Our results imply that patterns of neural activity are predictive markers of which specific Scagnostics measures most assist human decision making based on visual stimuli such as ours.

Seeking the Amygdala: Novel Use of Diffusion Tensor Imaging to Delineate the Basolateral Amygdala.

The amygdaloid complex, including the basolateral nucleus (BLA), contributes crucially to emotional and cognitive brain functions, and is a major target of research in both humans and rodents. However, delineating structural amygdala plasticity in both normal and disease-related contexts using neuroimaging has been hampered by the difficulty of unequivocally identifying the boundaries of the BLA. This challenge is a result of the poor contrast between BLA and the surrounding gray matter, including other amygdala nuclei. Here, we describe a novel diffusion tensor imaging (DTI) approach to enhance contrast, enabling the optimal identification of BLA in the rodent brain from magnetic resonance (MR) images. We employed this methodology together with a slice-shifting approach to accurately measure BLA volumes. We then validated the results by direct comparison to both histological and cellular-identity (parvalbumin)-based conventional techniques for defining BLA in the same brains used for MRI. We also confirmed BLA connectivity targets using DTI-based tractography. The novel approach enables the accurate and reliable delineation of BLA. Because this nucleus is involved in and changed by developmental, degenerative and adaptive processes, the instruments provided here should be highly useful to a broad range of neuroimaging studies. Finally, the principles used here are readily applicable to numerous brain regions and across species.

Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach.

Introduction: Prenatal maternal stress (PNMS), including exposure to natural disasters, has been shown to serve as a risk factor for future child psychopathology and suboptimal brain development, particularly among brain regions shown to be sensitive to stress and trauma exposure. However, statistical approaches deployed in most studies are usually constrained by a limited number of variables for the sake of statistical power. Explainable machine learning, on the other hand, enables the study of high data dimension and offers novel insights into the prominent subset of behavioral phenotypes and brain regions most susceptible to PNMS. In the present study, we aimed to identify the most important child neurobehavioral and brain features associated with in utero exposure to Superstorm Sandy (SS). Methods: By leveraging an explainable machine learning technique, the Shapley additive explanations method, we tested the marginal feature effect on SS exposures and examined the individual variable effects on disaster exposure. Results: Results show that certain brain regions are especially sensitive to in utero exposure to SS. Specifically, in utero SS exposure was associated with larger gray matter volume (GMV) in the right caudate, right hippocampus, and left amygdala and smaller GMV in the right parahippocampal gyrus. Additionally, higher aggression scores at age 5 distinctly correlated with SS exposure. Discussion: These findings suggest in utero SS exposure may be associated with greater aggression and suboptimal developmental alterations among various limbic and basal ganglia brain regions.

Robust enhancement of motor sequence learning with 4 mA transcranial electric stimulation.

BACKGROUND AND OBJECTIVES: Motor learning experiments with transcranial direct current stimulation (tDCS) at 2 mA have produced mixed results. We hypothesize that tDCS boosts motor learning provided sufficiently high field intensity on the motor cortex. METHODS: In a single-blinded design, 108 healthy participants received either anodal (N = 36) or cathodal (N = 36) tDCS at 4 mA total, or no stimulation (N = 36) while they practiced a 12-min sequence learning task. Anodal stimulation was delivered across four electrode pairs (1 mA each), with anodes above the right parietal lobe and cathodes above the right frontal lobe. Cathodal stimulation, with reversed polarities, served as an active control for sensation, while the no-stimulation condition established baseline performance. fMRI-localized targets on the primary motor cortex in 10 subjects were used in current flow models to optimize electrode placement for maximal field intensity. A single electrode montage was then selected for all participants. RESULTS: We found a significant difference in performance with anodal vs. cathodal stimulation (Cohen's d = 0.71) and vs. no stimulation (d = 0.56). This effect persisted for at least 1 h, and subsequent learning for a new sequence and the opposite hand also improved. Sensation ratings were comparable in the active groups and did not exceed moderate levels. Current flow models suggest the new electrode montage can achieve stronger motor cortex polarization than alternative montages. CONCLUSION: The present paradigm shows a medium to large effect size and is well-tolerated. It may serve as a go-to experiment for future studies on motor learning and tDCS.

A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement.

Low-intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation, applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional MRI (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. The objective of this work was to develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency and reproducibility (ContES checklist). A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists through the International Network of the tES-fMRI Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC on the basis of a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed by using the checklist. Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (i) technological factors, (ii) safety and noise tests and (iii) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article. In conclusion, use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies and increase methodological transparency and reproducibility.

Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery.

BACKGROUND: Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint. OBJECTIVE: We investigated the stability of functional connectivity across tasks and sessions in a cohort of individuals with chronic stroke using a supervised machine learning approach. METHODS: Twelve individuals with chronic stroke underwent functional magnetic resonance imaging (fMRI) seven times over 18 weeks. The middle 6 weeks consisted of intensive aphasia therapy. We collected fMRI data during rest and performance of 2 tasks. We calculated functional connectivity metrics for each imaging run, then applied a support vector machine to classify data on the basis of participant, task, and time point (pre- or posttherapy). Permutation testing established statistical significance. RESULTS: Whole brain functional connectivity matrices could be classified at levels significantly greater than chance on the basis of participant (87.1% accuracy; P < .0001), task (68.1% accuracy; P = .002), and time point (72.1% accuracy; P = .015). All significant effects were reproduced using only the contralesional right hemisphere; the left hemisphere revealed significant effects for participant and task, but not time point. Resting state data could also be used to classify task-based data according to subject (66.0%; P < .0001). While the strongest posttherapy changes occurred among regions outside putative language networks, connections with traditional language-associated regions were significantly more positively correlated with behavioral outcome measures, and other regions had more negative correlations and intrahemispheric connections. CONCLUSIONS: Findings suggest the profound importance of considering interindividual variability when interpreting mechanisms of recovery in studies of functional connectivity in stroke.

fMRI and transcranial electrical stimulation (tES): A systematic review of parameter space and outcomes.

The combination of non-invasive brain stimulation interventions with human brain mapping methods have supported research beyond correlational associations between brain activity and behavior. Functional MRI (fMRI) partnered with transcranial electrical stimulation (tES) methods, i.e., transcranial direct current (tDCS), transcranial alternating current (tACS), and transcranial random noise (tRNS) stimulation, explore the neuromodulatory effects of tES in the targeted brain regions and their interconnected networks and provide opportunities for individualized interventions. Advances in the field of tES-fMRI can be hampered by the methodological variability between studies that confounds comparability/replicability. In order to explore variability in the tES-fMRI methodological parameter space (MPS), we conducted a systematic review of 222 tES-fMRI experiments (181 tDCS, 39 tACS and 2 tRNS) published before February 1, 2019, and suggested a framework to systematically report main elements of MPS across studies. Publications dedicated to tRNS-fMRI were not considered in this systematic review. We have organized main findings in terms of fMRI modulation by tES. tES modulates activation and connectivity beyond the stimulated areas particularly with prefrontal stimulation. There were no two studies with the same MPS to replicate findings. We discuss how to harmonize the MPS to promote replication in future studies.

White Matter Plasticity in Anxiety: Disruption of Neural Network Synchronization During Threat-Safety Discrimination.

Recent evidence highlighted the importance of white matter tracts in typical and atypical behaviors. White matter dynamically changes in response to learning, stress, and social experiences. Several lines of evidence have reported white matter dysfunction in psychiatric conditions, including depression, stress- and anxiety-related disorders. The mechanistic underpinnings of these associations, however, remain poorly understood. Here, we outline an integrative perspective positing a link between aberrant myelin plasticity and anxiety. Drawing on extant literature and emerging new findings, we suggest that in anxiety, unique changes may occur in response to threat and to safety learning and the ability to discriminate between both types of stimuli. We propose that altered myelin plasticity in the neural circuits underlying these two forms of learning relates to the emergence of anxiety-related disorders, by compromising mechanisms of neural network synchronization. The clinical and translational implications of this model for anxiety-related disorders are discussed.

Methodology for tDCS integration with fMRI.

Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state-level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS-fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS-fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS-fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.