Prediction complements explanation in understanding the developing brain

Characterizing childhood trauma in individuals based on patterns of intrinsic brain connectivity

Childhood maltreatment represents a strong psychological stressor that may lead to the development of later psychopathology as well as a heightened risk of health and social problems. Despite a surge of interest in examining behavioral, neurocognitive, and brain connectivity profiles sculpted by such early adversity over the past decades, little is known about the neurobiological substrates underpinning childhood maltreatment. Here, we aim to detect the effects of childhood maltreatment on whole-brain resting-state functional connectivity (RSFC) in a cohort of healthy adults and to explore whether such RSFC profiles can be used to predict the severity of childhood trauma in subjects based on a data-driven connectome-based predictive modeling (CPM). Resting-state functional MRI (rs-fMRI) data were acquired from 97 healthy adults, each of whom was assessed for childhood maltreatment levels using the Childhood Trauma Questionnaire-Short Form (CTQ-SF). CPM was used to examine the association between whole-brain RSFC and childhood maltreatment levels. The results showed that CPM was able to decode individual childhood maltreatment levels from RSFC across multiple neural systems including RSFC between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes contributing to the prediction model included the amygdala, prefrontal, and anterior cingulate regions that have been linked to childhood maltreatment. These results remained robust using different validation procedures. Our findings revealed that RSFC among multiple neural systems can be used to predict childhood maltreatment levels in individuals.

Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis

Understanding the evolving dynamics of the brain throughout life is pivotal for anticipating and evaluating individual health. While previous research has described age effects on spectral properties of neural signals, it remains unclear which ones are most indicative of age-related processes. This study addresses this gap by analyzing resting-state data obtained from magnetoencephalography (MEG) in 350 adults (18 to 88 y). We employed advanced time-series analysis at the brain region level and machine learning to predict age. While traditional spectral features achieved low to moderate accuracy, over a hundred time-series features proved superior. Notably, temporal autocorrelation (AC) emerged as the most robust predictor of age. Distinct patterns of AC within the visual and temporal cortex were most informative, offering a versatile measure of age-related signal changes for comprehensive health assessments based on brain activity.

Using effective connectivity-based predictive modeling to predict MDD scale scores from multisite rs-fMRI data

BACKGROUND

Major depressive disorder (MDD) is a severe mental illness, and the Hamilton Depression Rating Scale (HAMD) is commonly used to quantify its severity. Our aim is to develop a predictive model for MDD symptoms using machine learning techniques based on effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI).

NEW METHOD

We obtained large-scale rs-fMRI data and HAMD scores from the multi-site REST-meta-MDD dataset. Average time series were extracted using different atlases. Brain EC features were computed using Granger causality analysis based on symbolic path coefficients, and a machine learning model based on EC was constructed to predict HAMD scores. Finally, the most predictive features were identified and visualized.

RESULTS

Experimental results indicate that different brain atlases significantly impact predictive performance, with the Dosenbach atlas performing best. EC-based models outperformed functional connectivity, achieving the best predictive accuracy (r = 0.81, p < 0.001, Root Mean Squared Error=3.55). Among various machine learning methods, support vector regression demonstrated superior performance.

COMPARISON WITH EXISTING METHODS

Current phenotype score prediction primarily relies on FC, which cannot indicate the direction of information flow within brain networks. Our method is based on EC, which contains more comprehensive brain network information and has been validated on large-scale multi-site data.

CONCLUSIONS

Brain network connectivity features effectively predict HAMD scores in MDD patients. The identified EC feature network may serve as a biomarker for predicting symptom severity. Our work may provide clinically significant insights for the early diagnosis of MDD, thereby facilitating the development of personalized diagnostic tools and therapeutic interventions.

Stimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions

While the use of naturalistic stimuli such as movie clips for understanding individual differences and brain-behaviour relationships attracts increasing interest, the influence of stimulus selection remains largely unclear. By using machine learning to predict individual traits (phenotypes) from brain activity evoked during various movie clips, we show that different movie stimuli can result in distinct prediction performances. In brain regions related to lower-level processing of the stimulus, prediction to a certain degree benefits from stronger synchronisation of brain activity across subjects. By contrast, better predictions in frontoparietal brain regions are mainly associated with larger inter-subject variability. Furthermore, we demonstrate that while movie clips with rich social content in general achieve better predictions, the importance of specific movie features for prediction highly depends on the phenotype under investigation. Overall, our findings underscore the importance of careful stimulus selection and provide novel insights into stimulus selection for phenotype prediction in naturalistic conditions, opening new avenues for future research.

Representing brain-behavior associations by retaining high-motion minoritized youth

BACKGROUND

Population neuroscience datasets provide an opportunity for researchers to estimate reproducible effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals.

METHODS

We employ motion-ordering and motion-ordering+resampling (bagging) to test if these methods preserve functional MRI (fMRI) data in the Adolescent Brain Cognitive DevelopmentSM Study (N = 5,733). For the two methods, brain-behavior associations were computed as the partial Spearman's Rank correlations (Rs) between functional connectivity and cognitive performance (NIH Cognition Toolbox) as well as externalizing and internalizing psychopathology (Child Behavior Checklist [CBCL]) while adjusting for participant sex assigned at birth and head motion.

RESULTS

Black and Hispanic youth exhibited excess head motion relative to data collected from White youth, and were discarded disproportionately when using conventional approaches. Motion-ordering and bagging methods retained more than 99% of Black and Hispanic youth. Both methods produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data.

CONCLUSIONS

The motion-ordering and bagging methods are two feasible approaches that can enhance sample representation for testing brain-behavior associations and result in reproducible effect sizes in diverse populations.

Identification and external validation of a problem cannabis risk network

BACKGROUND

Cannabis use is common, particularly during emerging adulthood when brain development is ongoing, and its use is associated with harmful outcomes for a subset of people. An improved understanding of the neural mechanisms underlying risk for problem-level use is critical to facilitate the development of more effective prevention and treatment approaches.

METHODS

The current study applied a whole-brain, data-driven, machine-learning approach to identify neural features predictive of problem-level cannabis use in a non-clinical sample of college students (n=191, 58% female) based on reward task functional connectivity data. We further examined whether the network identified would generalize to predict cannabis use in an independent sample of European adolescents/emerging adults (n=1320, 53% female), whether it would predict clinical characteristics among adults seeking treatment for cannabis use disorder (n=33, 9% female), and whether it was specific for predicting cannabis versus alcohol use outcomes across datasets.

RESULTS

Results demonstrated (i) identification of a problem cannabis risk network, which (ii) generalized to predict cannabis use in an independent sample of adolescents, and (iii) linked to increased addiction severity and poorer treatment outcome in a third sample of treatment-seeking adults; further, (iv) the identified network was specific for predicting cannabis versus alcohol use outcomes across all three datasets.

CONCLUSIONS

Findings provide insight into neural mechanisms of risk for problem-level cannabis use among adolescents/emerging adults. Future work is needed to assess whether targeting this network can improve prevention and treatment outcomes.

Mapping the neurodevelopmental predictors of psychopathology

Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.

Using precision approaches to improve brain-behavior prediction

Predicting individual behavioral traits from brain idiosyncrasies has broad practical implications, yet predictions vary widely. This constraint may be driven by a combination of signal and noise in both brain and behavioral variables. Here, we expand on this idea, highlighting the potential of extended sampling 'precision' studies. First, we discuss their relevance to improving the reliability of individualized estimates by minimizing measurement noise. Second, we review how targeted within-subject experiments, when combined with individualized analysis or modeling frameworks, can maximize signal. These improvements in signal-to-noise facilitated by precision designs can help boost prediction studies. We close by discussing the integration of precision approaches with large-sample consortia studies to leverage the advantages of both.

Syntactic development and its interplay with word processing and working memory in preschoolers' brain: An fNIRS longitudinal study

Understanding how children acquire syntactic structures from a limited set of grammatical rules and use them creatively to convey meaning has been a longstanding interest for scientific communities. Previous studies on syntactic development have revealed its close correlation with the development of vocabulary and working memory. Our study sought to elucidate how the relations between syntactic processing, word processing, and working memory were instantiated in the brain, and how earlier neural patterns might predict language abilities one year later. We employed functional near-infrared spectroscopy to examine among preschool children (N=50, Mage=61.5 months) the neural activation associated with processing sentences of varying syntactic complexities, as well as tasks assessing word comprehension and working memory. The results revealed greater brain activation in the left inferior frontal gyrus (IFG) for syntactically complex as compared to simple sentences, and the activation magnitude was correlated with working memory. There was also a link between neural activity for sentence comprehension and word comprehension in bilateral superior temporal regions (STG). Moreover, the inter-regional and inter-hemispheric connectivity of IFG and STG/MTG could successfully predict children's future language comprehension one year later. The findings provide new insights into how the brain supports syntactic development and its interplay with word processing and working memory.

A data integration method for new advances in development cognitive neuroscience

Combining existing datasets to investigate key questions in developmental cognitive neuroscience brings exciting opportunities and unique challenges. However, many data pooling methods require identical or harmonized methodologies that are often not feasible. We propose Integrative Data Analysis (IDA) as a promising framework to advance developmental cognitive neuroscience with secondary data analysis. IDA serves to test hypotheses by combining data of the same construct from commensurate (but not identical) measures. To overcome idiosyncrasies of neuroimaging data, IDA explicitly evaluates if measures across studies assess the same construct. Moreover, IDA allows investigators to examine meaningful individual variability by de-confounding source-specific differences. To demonstrate IDA's potential, we explain foundational concepts, outline necessary steps, and apply IDA to volumetric measures of hippocampal subfields from 443 4- to 17-year-olds across three independent studies. We identified commensurate measures of Cornu Ammonis (CA) 1, dentate gyrus (DG)/CA3, and Subiculum (Sub). Model testing supported use of IDA to create IDA factor scores. We found age-related differences in DG/CA3, not but CA1 and Sub volume in the integrated dataset. By successfully demonstrating IDA, our hope is that future innovations come from the combination of existing neuroimaging data to create representative integrated samples when testing critical developmental questions.

Development of Self-Concept in Childhood and Adolescence: How Neuroscience Can Inform Theory and Vice Versa

How do we develop a stable and coherent self-concept in contemporary times? Susan Harter's original work, The Construction of Self (1999; 2012), argues that cognitive and social processes are building blocks for developing a coherent sense of self, resulting in self-concept clarity across various domains in life (e.g., [pro-]social, academic, and physical). Here, we show how this framework guides and can benefit from recent findings on (1) the prolonged and nonlinear structural brain development during childhood and adolescence, (2) insights from developmental neuroimaging studies using self-concept appraisal paradigms, (3) genetic and environmental influences on behavioral and neural correlates of self-concept development, and (4) youth's perspectives on self-concept development in the context of 21st century global challenges. We examine how neuroscience can inform theory by testing several compelling questions related to stability versus change of neural, behavioral, and self-report measures and we reflect on the meaning of variability and change/growth.

Machine-learning based prediction of future outcome using multimodal MRI during early childhood

The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays the foundation for later cognitive and behavioral development. Multimodal magnetic resonance imaging (MRI) techniques, especially structural MRI (sMRI), diffusion MRI (dMRI), functional MRI (fMRI), and perfusion MRI (pMRI), provide unprecedented opportunities to non-invasively quantify these early brain changes at whole brain and regional levels. Each modality offers unique insights into the complex processes of both typical neurodevelopment and the pathological mechanisms underlying psychiatric and neurological disorders. Compared to a single modality, multimodal MRI enhances discriminative power and provides more comprehensive insights for understanding and improving neurodevelopmental and mental health outcomes, particularly in high-risk populations. Machine learning- and deep learning-based methods have demonstrated significant potential for predicting future outcomes using multimodal brain MRI acquired during early childhood. Here, we review the unique characteristics of various MRI techniques for imaging early brain development and describe the common approaches to analyze these modalities. We then discuss machine learning approaches in predicting future neurodevelopmental and clinical outcomes using multimodal MRI information during early childhood, highlighting the potential of identifying biomarkers for early detection and personalized interventions in atypical development.

Identifying developmental changes in functional brain connectivity associated with cognitive functioning in children and adolescents with ADHD

Youth diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD) often show deficits in various measures of higher-level cognition, such as, executive functioning. Poorer cognitive functioning in children with ADHD has been associated with differences in functional connectivity across the brain. However, little is known about the developmental changes to the brain's functional properties linked to different cognitive abilities in this cohort. To characterize these changes, we analyzed fMRI data (ADHD = 373, NT = 106) collected while youth between the ages of 6 and 16 watched a short movie-clip. We applied machine learning models to identify patterns of network connectivity in response to movie-watching that differentially predict cognitive abilities in our cohort. Using out-of-sample cross validation, our models successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children (ages 6 - 11), but not in adolescents with ADHD (ages 12-16). Connections with the default mode, memory retrieval, and dorsal attention were driving prediction during early and middle childhood, but connections with the somatomotor, cingulo-opercular, and frontoparietal networks were more important in middle childhood. This work demonstrated that machine learning approaches can identify distinct functional connectivity profiles associated with cognitive abilities at different developmental stages in children and adolescents with ADHD.

Power and reproducibility in the external validation of brain-phenotype predictions

Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson's r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.

D-MHGCN: An End-to-End Individual Behavioral Prediction Model Using Dual Multi-Hop Graph Convolutional Network

Predicting individual behavior is a crucial area of research in neuroscience. Graph Neural Networks (GNNs), as powerful tools for extracting graph-structured features, are increasingly being utilized in various functional connectivity (FC) based behavioral prediction tasks. However, current predictive models primarily focus on enhancing GNNs' ability to extract features from FC networks while neglecting the importance of upstream individual network construction quality. This oversight results in constructed functional networks that fail to adequately represent individual behavioral capacity, thereby affecting the subsequent prediction accuracy. To address this issue, we proposed a new GNN-based behavioral prediction framework, named Dual Multi-Hop Graph Convolutional Network (D-MHGCN). Through the joint training of two GCNs, this framework integrates individual functional network construction and behavioral prediction into a unified optimization model. It allows the model to dynamically adjust the individual functional cortical parcellation according to the downstream tasks, thus creating task-aware, individual-specific FCNs that largely enhance its ability to predict behavior scores. Additionally, we employed multi-hop graph convolution layers instead of traditional single-hop methods in GCN to capture complex hierarchical connectivity patterns in brain networks. Our experimental evaluations, conducted on the large, public Human Connectome Project dataset, demonstrate that our proposed method outperforms existing methods in various behavioral prediction tasks. Moreover, it produces more functionally homogeneous cortical parcellation, showcasing its practical utility and effectiveness. Our work not only enhances the accuracy of individual behavioral prediction but also provides deeper insights into the neural mechanisms underlying individual differences in behavior.

Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework

The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.

Growing Up Together in Society (GUTS): A team science effort to predict societal trajectories in adolescence and young adulthood

Our society faces a great diversity of opportunities for youth. The 10-year Growing Up Together in Society (GUTS) program has the long-term goal to understand which combination of measures best predict societal trajectories, such as school success, mental health, well-being, and developing a sense of belonging in society. Our leading hypothesis is that self-regulation is key to how adolescents successfully navigate the demands of contemporary society. We aim to test these questions using socio-economic, questionnaire (including experience sampling methods), behavioral, brain (fMRI, sMRI, EEG), hormonal, and genetic measures in four large cohorts including adolescents and young adults. Two cohorts are designed as test and replication cohorts to test the developmental trajectory of self-regulation, including adolescents of different socioeconomic status thereby bridging individual, family, and societal perspectives. The third cohort consists of an entire social network to examine how neural and self-regulatory development influences and is influenced by whom adolescents and young adults choose to interact with. The fourth cohort includes youth with early signs of antisocial and delinquent behavior to understand patterns of societal development in individuals at the extreme ends of self-regulation and societal participation, and examines pathways into and out of delinquency. We will complement the newly collected cohorts with data from existing large-scale population-based and case-control cohorts. The study is embedded in a transdisciplinary approach that engages stakeholders throughout the design stage, with a strong focus on citizen science and youth participation in study design, data collection, and interpretation of results, to ensure optimal translation to youth in society.

Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization

Adolescence is a timed process with an onset, tempo, and duration. Nevertheless, the temporal dimension, especially the pace of maturation, remains an insufficiently studied aspect of developmental progression. The primary objective is to estimate the precise influence of pubertal maturational tempo on the configuration of associative brain regions. To this end, the connection between maturational stages and the level of hierarchical organization of large-scale brain networks in 12-13-year-old females is analyzed. Skeletal maturity is used as a proxy for pubertal progress. The degree of maturity is defined by the difference between bone age and chronological age. To assess the level of hierarchical organization in the brain, the temporal dynamic of closed eye resting state high-density electroencephalography (EEG) in the alpha frequency range is analyzed. Different levels of hierarchical order are captured by the measured asymmetry in the directionality of information flow between different regions. The calculated EEG-based entropy production of participant groups is then compared with accelerated, average, and decelerated maturity. Results indicate that an average maturational trajectory optimally aligns with cerebral hierarchical order, and both accelerated and decelerated timelines result in diminished cortical organization. This suggests that a "Goldilocks rule" of brain development is favoring a particular maturational tempo.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder

BACKGROUND

Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder.

METHODS

We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.

RESULTS

Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD.

CONCLUSIONS

Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

Macronutrient intake is associated with intelligence and neural development in adolescents

Introduction

Macronutrient intake can be one of the most influential factors in cognitive and neural development in adolescents. Adolescence is a specific period of cognitive and neural development, and nutritional effects during this period could be life-long. Therefore, understanding the effects of macronutrient intake on cognitive and neural development in adolescents is crucially important. We thus examined the association across macronutrient intake, intelligence, and neural development using population-based cohort data.

Methods

We conducted two studies. In study 1, we included a total of 1,734 participants (boys, 907, age [mean ± standard deviation] 171.9 ± 3.44 months; range 163.0-186.0 months) from the Tokyo TEEN Cohort (TTC) to examine the association between macronutrient intake and intelligence quotient (IQ). In study 2, we included a total of 63 participants (boys, 38, age 174.4 ± 7.7 months; range 160.7-191.6 months) to investigate the effect of nutrition intake on neural development using graph theory analysis for resting-state functional magnetic resonance imaging (rs-fMRI) derived from a subset of the TTC.

Results

TTC data revealed that a higher IQ was associated in boys with increased protein intake (β = 0.068, p = 0.031), and in girls, with reduced carbohydrate intake (β = -0.076, p = 0.024). Graph theory analysis for rs-fMRI at approximately age 12 has shown that impaired local efficiency in the left inferior frontal gyrus was associated with higher carbohydrate and fat intake ([x, y, z] = [-51, 23, 8], pFDR-corrected = 0.00018 and 0.02290, respectively), whereas increased betweenness centrality in the left middle temporal gyrus was associated with higher carbohydrate, fat, and protein intake ([x, y, z] = [-61, -43, -13], pFDR-corrected = 0.0027, 0.0029, and 0.00075, respectively). Moreover, we identified a significant moderating effect of fat and protein intake on the relationship between change in betweenness centrality over a 2-year measurement gap in the left middle temporal gyrus and intelligence (β = 12.41, p = 0.0457; β = 12.12, p = 0.0401, respectively).

Conclusion

Our study showed the association between macronutrient intake and neural development related to intelligence in early adolescents. Appropriate nutritional intake would be a key factor for healthy cognitive and neural development.

Using a deep generation network reveals neuroanatomical specificity in hemispheres

Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.

A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents

Interpreting the neural response elicited during task functional magnetic resonance imaging (fMRI) remains a challenge in neurodevelopmental research. The monetary incentive delay (MID) task is an fMRI reward processing task that is extensively used in the literature. However, modern psychometric tools have not been used to evaluate measurement properties of the MID task fMRI data. The current study uses data for a similar task design across three adolescent samples (N = 346 [Agemean 12.0; 44 % Female]; N = 97 [19.3; 58 %]; N = 112 [20.2; 38 %]) to evaluate multiple measurement properties of fMRI responses on the MID task. Confirmatory factor analysis (CFA) is used to evaluate an a priori theoretical model for the task and its measurement invariance across three samples. Exploratory factor analysis (EFA) is used to identify the data-driven measurement structure across the samples. CFA results suggest that the a priori model is a poor representation of these MID task fMRI data. Across the samples, the data-driven EFA models consistently identify a six-to-seven factor structure with run and bilateral brain region factors. This factor structure is moderately-to-highly congruent across the samples. Altogether, these findings demonstrate a need to evaluate theoretical frameworks for popular fMRI task designs to improve our understanding and interpretation of brain-behavior associations.

Multi-Scale FC-Based Multi-Order GCN: A Novel Model for Predicting Individual Behavior From fMRI

Predicting individual behavior from brain imaging data using machine learning is a rapidly growing field in neuroscience. Functional connectivity (FC), which captures interactions between different brain regions, contains valuable information about the organization of the brain and is considered a crucial feature for modeling human behavior. Graph convolutional networks (GCN) have proven to be a powerful tool for extracting graph structure features and have shown promising results in various FC-based classification tasks, such as disease classification and prognosis prediction. Despite this success, few behavior prediction models currently exist based on GCN, and their performance is not satisfactory. To address this gap, a new model called the Multi-Scale FC-based Multi-Order GCN (MSFC-MO-GCN) was proposed in this paper. The model considers the hierarchical structure of the brain system and utilizes FCs inferred from multiple spatial scales as input to comprehensively characterize individual brain organization. To enhance the feature learning ability of GCN, a multi-order graph convolutional layer is incorporated, which uses multi-order neighbors to guide message passing and learns high-order graph information of nodal connections. Additionally, an inter-subject contrast constraint is designed to control the potential information redundancy of FCs among different spatial scales during the feature learning process. Experimental evaluation were conducted on the publicly available dataset from human connectome project. A total of 805 healthy subjects were included and 5 representative behavior metrics were used. The experimental results show that our proposed method outperforms the existing behavior prediction models in all behavior prediction tasks.

Functional brain connectivity predicts sleep duration in youth and adults

Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includes n-back task-based and resting-state fMRI data from adults aged 22-35 years (task n = 896; rest n = 898). We applied connectome-based predictive modeling (CPM) to predict participants' mean sleep duration from their functional connectivity patterns. Models trained and tested using 10-fold cross-validation predicted self-reported average sleep duration for the past month from n-back task and resting-state connectivity patterns. We replicated this finding in data from the 2-year follow-up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includes n-back task and resting-state fMRI for adolescents aged 11-12 years (task n = 786; rest n = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10-fold cross-validation again predicted sleep duration from n-back task and resting-state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting-state functional brain connectivity patterns reflect sleep duration in youth and young adults.

Power and reproducibility in the external validation of brain-phenotype predictions

Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.

Similarity in functional connectome architecture predicts teenage grit

Grit is a personality trait that encapsulates the tendency to persevere and maintain consistent interest for long-term goals. While prior studies found that grit predicts positive behavioral outcomes, there is a paucity of work providing explanatory evidence from a neurodevelopmental perspective. Based on previous research suggesting the utility of the functional connectome (FC) as a developmental measure, we tested the idea that individual differences in grit might be, in part, rooted in brain development in adolescence and emerging adulthood (N = 64, 11-19 years of age). Our analysis showed that grit was associated with connectome stability across conditions and connectome similarity across individuals. Notably, inter-subject representational similarity analysis revealed that teenagers who were grittier shared similar FC architecture with each other, more so than those with lower grit. Our findings suggest that individuals with high levels of grit are more likely to exhibit a converging pattern of whole-brain functional connectivity, which may underpin subsequent beneficial behavioral outcomes.

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), the first ten-year stage of the lifespan CCNP (2013-2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.

Connectome-based machine learning models are vulnerable to subtle data manipulations

Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (r = 0.99) and did not affect other downstream analysis. Essentially, connectome data could be inconspicuously modified to achieve any desired prediction performance. Overall, our enhancement attacks and evaluation of existing adversarial noise attacks in connectome-based models highlight the need for counter-measures that improve the trustworthiness to preserve the integrity of academic research and any potential translational applications.

A topography-based predictive framework for naturalistic viewing fMRI

Functional magnetic resonance imaging (fMRI) during naturalistic viewing (NV) provides exciting opportunities for studying brain functions in more ecologically valid settings. Understanding individual differences in brain functions during NV and their behavioural relevance has recently become an important goal. However, methods specifically designed for this purpose remain limited. Here, we propose a topography-based predictive framework (TOPF) to fill this methodological gap. TOPF identifies individual-specific evoked activity topographies in a data-driven manner and examines their behavioural relevance using a machine learning-based predictive framework. We validate TOPF on both NV and task-based fMRI data from multiple conditions. Our results show that TOPF effectively and stably captures individual differences in evoked brain activity and successfully predicts phenotypes across cognition, emotion and personality on unseen subjects from their activity topographies. Moreover, TOPF compares favourably with functional connectivity-based approaches in prediction performance, with the identified predictive brain regions being neurobiologically interpretable. Crucially, we highlight the importance of examining individual evoked brain activity topographies in advancing our understanding of the brain-behaviour relationship. We believe that the TOPF approach provides a simple but powerful tool for understanding brain-behaviour relationships on an individual level with a strong potential for clinical applications.

Characterizing the dimensional structure of early-life adversity in the Adolescent Brain Cognitive Development (ABCD) Study

Early-life adversity has profound consequences for youth neurodevelopment and adjustment; however, experiences of adversity are heterogeneous and interrelated in complex ways that can be difficult to operationalize and organize in developmental research. We sought to characterize the underlying dimensional structure of co-occurring adverse experiences among a subset of youth (ages 9-10) from the Adolescent Brain Cognitive Development (ABCD) Study (N = 7115), a community sample of youth in the United States. We identified 60 environmental and experiential variables that reflect adverse experiences. Exploratory factor analysis identified 10 robust dimensions of early-life adversity co-occurrence, corresponding to conceptual domains such as caregiver substance use and biological caregiver separation, caregiver psychopathology, caregiver lack of support, and socioeconomic disadvantage / neighborhood lack of safety. These dimensions demonstrated distinct associations with internalizing problems, externalizing problems, cognitive flexibility, and inhibitory control. Non-metric multidimensional scaling characterized qualitative similarity among the 10 identified dimensions. Results supported a nonlinear three-dimensional structure representing early-life adversity, including continuous gradients of "perspective", "environmental uncertainty", and "acts of omission/commission". Our findings suggest that there are distinct dimensions of early-life adversity co-occurrence in the ABCD sample at baseline, and the resulting dimensions may have unique implications for neurodevelopment and youth behavior.

A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth

Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.

One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry

Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.

Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance

Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n = 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.

An fMRI-Based Brain Marker of Individual Differences in Delay Discounting

Individual differences in delay discounting-how much we discount future compared to immediate rewards-are associated with general life outcomes, psychopathology, and obesity. Here, we use machine learning on fMRI activity during an intertemporal choice task to develop a functional brain marker of these individual differences in human adults. Training and cross-validating the marker in one dataset (Study 1, N = 110 male adults) resulted in a significant prediction-outcome correlation (r = 0.49), generalized to predict individual differences in a completely independent dataset (Study 2: N = 145 male and female adults, r = 0.45), and predicted discounting several weeks later. Out-of-sample responses of the functional brain marker, but not discounting behavior itself, differed significantly between overweight and lean individuals in both studies, and predicted fasting-state blood levels of insulin, c-peptide, and leptin in Study 1. Significant predictive weights of the marker were found in cingulate, insula, and frontoparietal areas, among others, suggesting an interplay among regions associated with valuation, conflict processing, and cognitive control. This new functional brain marker is a step toward a generalizable brain model of individual differences in delay discounting. Future studies can evaluate it as a potential transdiagnostic marker of altered decision-making in different clinical and developmental populations.SIGNIFICANCE STATEMENT People differ substantially in how much they prefer smaller sooner rewards or larger later rewards such as spending money now versus saving it for retirement. These individual differences are generally stable over time and have been related to differences in mental and bodily health. What is their neurobiological basis? We applied machine learning to brain-imaging data to identify a novel brain activity pattern that accurately predicts how much people prefer sooner versus later rewards, and which can be used as a new brain-based measure of intertemporal decision-making in future studies. The resulting functional brain marker also predicts overweight and metabolism-related blood markers, providing new insight into the possible links between metabolism and the cognitive and brain processes involved in intertemporal decision-making.

Systematic Review and Meta-analysis: Task-based fMRI Studies in Youths With Irritability

OBJECTIVE

Childhood irritability, operationalized as disproportionate and frequent temper tantrums and low frustration tolerance relative to peers, is a transdiagnostic symptom across many pediatric disorders. Studies using task-dependent functional magnetic resonance imaging (fMRI) to probe neural dysfunction in irritability have increased. However, an integrated review summarizing the published methods and synthesized fMRI results remains lacking.

METHOD

We conducted a systematic search using irritability terms and task functional neuroimaging in key databases in March 2021, and identified 30 studies for our systematic review. Sample characteristics and fMRI methods were summarized. A subset of 28 studies met the criteria for extracting coordinate-based data for quantitative meta-analysis. Ten activation-likelihood estimations were performed to examine neural convergence across irritability measures and fMRI task domains.

RESULTS

Systematic review revealed small sample sizes (median = 58, mean age range = 8-16 years) with heterogeneous sample characteristics, irritability measures, tasks, and analytical procedures. Meta-analyses found no evidence for neural activation convergence of irritability across neurocognitive functions related to emotional reactivity, cognitive control, and reward processing, or within each domain. Sensitivity analyses partialing out variances driven by heterogeneous tasks, irritability measures, stimulus types, and developmental ages all yielded null findings. Results were compared with a review on irritability-related structural anomalies from 11 studies.

CONCLUSION

The lack of neural convergence suggests a need for common, standardized irritability assessments and more homogeneous fMRI tasks. Thoughtfully designed fMRI studies probing commonly defined neurocognitive functions may be more fruitful to elucidate the neural mechanisms of irritability. Open science practices, data mining in large neuroscience databases, and standardized analytical methods promote meaningful collaboration in irritability research.

Socioeconomic resources are associated with distributed alterations of the brain's intrinsic functional architecture in youth

Little is known about how exposure to limited socioeconomic resources (SER) in childhood gets "under the skin" to shape brain development, especially using rigorous whole-brain multivariate methods in large, adequately powered samples. The present study examined resting state functional connectivity patterns from 5821 youth in the Adolescent Brain Cognitive Development (ABCD) study, employing multivariate methods across three levels: whole-brain, network-wise, and connection-wise. Across all three levels, SER was associated with widespread alterations across the connectome. However, critically, we found that parental education was the primary driver of neural associations with SER. These parental education associations with the developing connectome exhibited notable concentrations in somatosensory and subcortical regions, and they were partially accounted for by home enrichment activities, child's cognitive abilities, and child's grades, indicating interwoven links between parental education, child stimulation, and child cognitive performance. These results add a new data-driven, multivariate perspective on links between household SER and the child's developing functional connectome.

Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development

Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

A hybrid learning framework for fine-grained interpretation of brain spatiotemporal patterns during naturalistic functional magnetic resonance imaging

Naturalistic stimuli, including movie, music, and speech, have been increasingly applied in the research of neuroimaging. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test–retest reliability, suggesting greater potential to study adaptive human brain function. In the current research, naturalistic functional magnetic resonance imaging (N-fMRI) has been a powerful tool to record brain states under naturalistic stimuli, and many efforts have been devoted to study the high-level semantic features from spatial or temporal representations via N-fMRI. However, integrating both spatial and temporal characteristics of brain activities for better interpreting the patterns under naturalistic stimuli is still underexplored. In this work, a novel hybrid learning framework that comprehensively investigates both the spatial (via Predictive Model) and the temporal [via convolutional neural network (CNN) model] characteristics of the brain is proposed. Specifically, to focus on certain relevant regions from the whole brain, regions of significance (ROS), which contain common spatial activation characteristics across individuals, are selected via the Predictive Model. Further, voxels of significance (VOS), whose signals contain significant temporal characteristics under naturalistic stimuli, are interpreted via one-dimensional CNN (1D-CNN) model. In this article, our proposed framework is applied onto the N-fMRI data during naturalistic classical/pop/speech audios stimuli. The promising performance is achieved via the Predictive Model to differentiate the different audio categories. Especially for distinguishing the classic and speech audios, the accuracy of classification is up to 92%. Moreover, spatial ROS and VOS are effectively obtained. Besides, temporal characteristics of the high-level semantic features are investigated on the frequency domain via convolution kernels of 1D-CNN model, and we effectively bridge the “semantic gap” between high-level semantic features of N-fMRI and low-level acoustic features of naturalistic audios in the frequency domain. Our results provide novel insights on characterizing spatiotemporal patterns of brain activities via N-fMRI and effectively explore the high-level semantic features under naturalistic stimuli, which will further benefit the understanding of the brain working mechanism and the advance of naturalistic stimuli clinical application.

Predicting children's math skills from task-based and resting-state functional brain connectivity

A critical goal of cognitive neuroscience is to predict behavior from neural structure and function, thereby providing crucial insights into who might benefit from clinical and/or educational interventions. Across development, the strength of functional connectivity among a distributed set of brain regions is associated with children's math skills. Therefore, in the present study we use connectome-based predictive modeling to investigate whether functional connectivity during numerical processing and at rest "predicts" children's math skills (N = 31, Mage = 9.21 years, 14 Female). Overall, we found that functional connectivity during symbolic number comparison and rest, but not during nonsymbolic number comparison, predicts children's math skills. Each task revealed a largely distinct set of predictive connections distributed across canonical brain networks and major brain lobes. Most of these predictive connections were negatively correlated with children's math skills so that weaker connectivity predicted better math skills. Notably, these predictive connections were largely nonoverlapping across task states, suggesting children's math abilities may depend on state-dependent patterns of network segregation and/or regional specialization. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

Understanding the nature of face processing in early autism: A prospective study

Dimensional approaches to psychopathology interrogate the core neurocognitive domains interacting at the individual level to shape diagnostic symptoms. Embedding this approach in prospective longitudinal studies could transform our understanding of the mechanisms underlying neurodevelopmental disorders. Such designs require us to move beyond traditional group comparisons and determine which domain-specific alterations apply at the level of the individual, and whether they vary across distinct phenotypic subgroups. As a proof of principle, this study examines how the domain of face processing contributes to the emergence of autism spectrum disorder (ASD). We used an event-related potentials (ERPs) task in a cohort of 8-month-old infants with (n = 148) and without (n = 68) an older sibling with ASD, and combined traditional case-control comparisons with machine-learning techniques for prediction of social traits and ASD diagnosis at 36 months, and Bayesian hierarchical clustering for stratification into subgroups. A broad profile of alterations in the time-course of neural processing of faces in infancy was predictive of later ASD, with a strong convergence in ERP features predicting social traits and diagnosis. We identified two main subgroups in ASD, defined by distinct patterns of neural responses to faces, which differed on later sensory sensitivity. Taken together, our findings suggest that individual differences between infants contribute to the diffuse pattern of alterations predictive of ASD in the first year of life. Moving from group-level comparisons to pattern recognition and stratification can help to understand and reduce heterogeneity in clinical cohorts, and improve our understanding of the mechanisms that lead to later neurodevelopmental outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders

It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.

COVID-19 vicarious traumatization links functional connectome to general distress

As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q2 = -0.18, MSE = 617, r [predicted, actual] = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.

Robust Brain Age Estimation based on sMRI via Nonlinear Age-Adaptive Ensemble Learning

Precise prediction on brain age is urgently needed by many biomedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients' brains are healthy or not. Such age prediction is often challenging for single model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four different machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithm-based independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doctors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.

Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging

Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.

A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

We propose a simple framework-meta-matching-to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = -0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching.