Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

Intrinsic functional networks for distinct sources of error in visual working memory.

Visual working memory (VWM) is a core cognitive function wherein visual information is stored and manipulated over short periods. Response errors in VWM tasks arise from the imprecise memory of target items, swaps between targets and nontargets, and random guesses. However, it remains unclear whether these types of errors are underpinned by distinct neural networks. To answer this question, we recruited 80 healthy adults to perform delayed estimation tasks and acquired their resting-state functional magnetic resonance imaging scans. The tasks required participants to reproduce the memorized visual feature along continuous scales, which, combined with mixture distribution modeling, allowed us to estimate the measures of memory precision, swap errors, and random guesses. Intrinsic functional connectivity within and between different networks, identified using a hierarchical clustering approach, was estimated for each participant. Our analyses revealed that higher memory precision was associated with increased connectivity within a frontal-opercular network, as well as between the dorsal attention network and an angular-gyrus-cerebellar network. We also found that coupling between the frontoparietal control network and the cingulo-opercular network contributes to both memory precision and random guesses. Our findings demonstrate that distinct sources of variability in VWM performance are underpinned by different yet partially overlapping intrinsic functional networks.

White matter microstructure is associated with the precision of visual working memory.

Visual working memory is critical for goal-directed behavior as it maintains continuity between previous and current visual input. Functional neuroimaging studies have shown that visual working memory relies on communication between distributed brain regions, which implies an important role for long-range white matter connections in visual working memory performance. Here, we characterized the relationship between the microstructure of white matter association tracts and the precision of visual working memory representations. To that purpose, we devised a delayed estimation task which required participants to reproduce visual features along a continuous scale. A sample of 80 healthy adults performed the task and underwent diffusion-weighted MRI. We applied mixture distribution modelling to quantify the precision of working memory representations, swap errors, and guess rates, all of which contribute to observed responses. Latent components of microstructural properties in sets of anatomical tracts were identified by principal component analysis. We found an interdependency between fibre coherence in the bilateral superior longitudinal fasciculus (SLF) I, SLF II, and SLF III, on one hand, and the bilateral inferior fronto-occipital fasciculus (IFOF), on the other, in mediating the precision of visual working memory in a functionally specific manner. We also found that individual differences in axonal density in a network comprising the bilateral inferior longitudinal fasciculus (ILF) and SLF III and right SLF II, in combination with a supporting network located elsewhere in the brain, form a common system for visual working memory to modulate response precision, swap errors, and random guess rates.

Things you wanted to know (but might have been afraid to ask) about how and why to explore and modulate brain plasticity with non-invasive neurostimulation technologies.

Brain plasticity can be defined as the ability of local and extended neural systems to organize either the structure and/or the function of their connectivity patterns to better adapt to changes of our inner/outer environment and optimally respond to new challenging behavioral demands. Plasticity has been traditionally conceived as a spontaneous phenomenon naturally occurring during pre and postnatal development, tied to learning and memory processes, or enabled following neural damage and their rehabilitation. Such effects can be easily observed and measured but remain hard to harness or to tame 'at will'. Non-invasive brain stimulation (NIBS) technologies offer the possibility to engage plastic phenomena, and use this ability to characterize the relationship between brain regions, networks and their functional connectivity patterns with cognitive process or disease symptoms, to estimate cortical malleability, and ultimately contribute to neuropsychiatric therapy and rehabilitation. NIBS technologies are unique tools in the field of fundamental and clinical research in humans. Nonetheless, their abilities (and also limitations) remain rather unknown and in the hands of a small community of experts, compared to widely established methods such as functional neuroimaging (fMRI) or electrophysiology (EEG, MEG). In the current review, we first introduce the features, mechanisms of action and operational principles of the two most widely used NIBS methods, Transcranial Magnetic Stimulation (TMS) and Transcranial Current Stimulation (tCS), for exploratory or therapeutic purposes, emphasizing their bearings on neural plasticity mechanisms. In a second step, we walk the reader through two examples of recent domains explored by our team to further emphasize the potential and limitations of NIBS to either explore or improve brain function in healthy individuals and neuropsychiatric populations. A final outlook will identify a series of future topics of interest that can foster progress in the field and achieve more effective manipulation of brain plasticity and interventions to explore and improve cognition and treat the symptoms of neuropsychiatric diseases.

A 4-year longitudinal neuroimaging study of cognitive control using latent growth modeling: developmental changes and brain-behavior associations.

Despite theoretical models suggesting developmental changes in neural substrates of cognitive control in adolescence, empirical research has rarely examined intraindividual changes in cognitive control-related brain activation using multi-wave multivariate longitudinal data. We used longitudinal repeated measures of brain activation and behavioral performance during the multi-source interference task (MSIT) from 167 adolescents (53% male) who were assessed annually over four years from ages 13 to 17 years. We applied latent growth modeling to delineate the pattern of brain activation changes over time and to examine longitudinal associations between brain activation and behavioral performance. We identified brain regions that showed differential change patterns: (1) the fronto-parietal regions that involved bilateral insula, bilateral middle frontal gyrus, left pre-supplementary motor area, left inferior parietal lobule, and right precuneus; and (2) the rostral anterior cingulate cortex (rACC) region. Longitudinal confirmatory factor analyses of the fronto-parietal regions revealed strong measurement invariance across time implying that multivariate functional magnetic resonance imaging data during cognitive control can be measured reliably over time. Latent basis growth models indicated that fronto-parietal activation decreased over time, whereas rACC activation increased over time. In addition, behavioral performance data, age-related improvement was indicated by a decreasing trajectory of intraindividual variability in response time across four years. Testing longitudinal brain-behavior associations using multivariate growth models revealed that better behavioral cognitive control was associated with lower fronto-parietal activation, but the change in behavioral performance was not related to the change in brain activation. The current findings suggest that reduced effects of cognitive interference indicated by fronto-parietal recruitment may be a marker of a maturing brain that underlies better cognitive control performance during adolescence.

Voxelwise Multivariate Analysis of Brain-Psychosocial Associations in Adolescents Reveals 6 Latent Dimensions of Cognition and Psychopathology.

BACKGROUND: Adolescence heralds the onset of considerable psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxelwise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges. METHODS: We obtained voxelwise gray matter density and psychosocial variables from the baseline (ages 9-10 years) Adolescent Brain Cognitive Development (ABCD) Study cohort (N = 11,288) and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting. RESULTS: We found 6 latent dimensions, 4 of which pertain specifically to mental health. The mental health dimensions were related to overeating, anorexia/internalizing, oppositional symptoms (all ps < .002) and attention-deficit/hyperactivity disorder symptoms (p = .03). Attention-deficit/hyperactivity disorder was related to increased and internalizing symptoms related to decreased gray matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms were related to increased gray matter in a noradrenergic nucleus. Internalizing symptoms were related to increased and oppositional symptoms to reduced gray matter density in the insular, cingulate, and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus gray matter in attention-deficit/hyperactivity disorder and reduced putamen gray matter in oppositional/conduct problems. Voxelwise gray matter density generated stronger brain-psychosocial correlations than brain parcellations. CONCLUSIONS: Voxelwise brain data strengthen latent dimensions of brain-psychosocial covariation, and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing symptoms are associated with opposite gray matter changes in similar cortical and subcortical areas.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available.

Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).

Static and dynamic fMRI-derived functional connectomes represent largely similar information.

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Functional maturation in visual pathways predicts attention to the eyes in infant rhesus macaques: Effects of social status.

Differences in looking at the eyes of others are one of the earliest behavioral markers for social difficulties in neurodevelopmental disabilities, including autism. However, it is unknown how early visuo-social experiences relate to the maturation of infant brain networks that process visual social stimuli. We investigated functional connectivity (FC) within the ventral visual object pathway as a contributing neural system. Densely sampled, longitudinal eye-tracking and resting state fMRI (rs-fMRI) data were collected from infant rhesus macaques, an important model of human social development, from birth through 6 months of age. Mean trajectories were fit for both datasets and individual trajectories from subjects with both eye-tracking and rs-fMRI data were used to test for brain-behavior relationships. Exploratory findings showed infants with greater increases in FC between left V1 to V3 visual areas have an earlier increase in eye-looking before 2 months. This relationship was moderated by social status such that infants with low social status had a stronger association between left V1 to V3 connectivity and eye-looking than high status infants. Results indicated that maturation of the visual object pathway may provide an important neural substrate supporting adaptive transitions in social visual attention during infancy.

Atypical Associations between Functional Connectivity during Pragmatic and Semantic Language Processing and Cognitive Abilities in Children with Autism.

Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain-behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks.

Neuro-Behavioral Dynamic Prediction of Interpersonal Cooperation and Aggression.

How to quickly predict an individual's behavioral choices is an important issue in the field of human behavior research. Using noninvasive electroencephalography, we aimed to identify neural markers in the prior outcome-evaluation stage and the current option-assessment stage of the chicken game that predict an individual's behavioral choices in the subsequent decision-output stage. Hierarchical linear modeling-based brain-behavior association analyses revealed that midfrontal theta oscillation in the prior outcome-evaluation stage positively predicted subsequent aggressive choices; also, beta oscillation in the current option-assessment stage positively predicted subsequent cooperative choices. These findings provide electrophysiological evidence for the three-stage theory of decision-making and strengthen the feasibility of predicting an individual's behavioral choices using neural oscillations.

The Prediction of Brain Activity from Connectivity: Advances and Applications.

The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain-behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.

Empirical examination of the replicability of associations between brain structure and psychological variables.

Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the previously-reported 'structural brain behavior' (SBB)-associations has been questioned, recently. Here, we conducted an empirical investigation, assessing replicability of SBB among heathy adults. For a wide range of psychological measures, the replicability of associations with gray matter volume was assessed. Our results revealed that among healthy individuals 1) finding an association between performance at standard psychological tests and brain morphology is relatively unlikely 2) significant associations, found using an exploratory approach, have overestimated effect sizes and 3) can hardly be replicated in an independent sample. After considering factors such as sample size and comparing our findings with more replicable SBB-associations in a clinical cohort and replicable associations between brain structure and non-psychological phenotype, we discuss the potential causes and consequences of these findings.

Assessing reproducibility in association studies.

Research that links brain structure with behavior needs more data, better analyses, and more intelligent approaches.

Neuro-Behavioral Dynamic Prediction of Interpersonal Cooperation and Aggression / 神经科学通报·英文版

How to quickly predict an individual's behavioral choices is an important issue in the field of human behavior research. Using noninvasive electroencephalography, we aimed to identify neural markers in the prior outcome-evaluation stage and the current option-assessment stage of the chicken game that predict an individual's behavioral choices in the subsequent decision-output stage. Hierarchical linear modeling-based brain-behavior association analyses revealed that midfrontal theta oscillation in the prior outcome-evaluation stage positively predicted subsequent aggressive choices; also, beta oscillation in the current option-assessment stage positively predicted subsequent cooperative choices. These findings provide electrophysiological evidence for the three-stage theory of decision-making and strengthen the feasibility of predicting an individual's behavioral choices using neural oscillations.