The development of human functional brain networks

Networks Underlie Temporal Onset of Dysplasia-Related Epilepsy: A MELD Study

OBJECTIVE

The purpose of this study was to evaluate if focal cortical dysplasia (FCD) co-localization to cortical functional networks is associated with the temporal distribution of epilepsy onset in FCD.

METHODS

International (20 center), retrospective cohort from the Multi-Centre Epilepsy Lesion Detection (MELD) project. Patients included if >3 years old, had 3D pre-operative T1 magnetic resonance imaging (MRI; 1.5 or 3 T) with radiologic or histopathologic FCD after surgery. Images processed using the MELD protocol, masked with 3D regions-of-interest (ROI), and co-registered to fsaverage_sym (symmetric template). FCDs were then co-localized to 1 of 7 distributed functional cortical networks. Negative binomial regression evaluated effect of FCD size, network, histology, and sulcal depth on age of epilepsy onset. From this model, predictive age of epilepsy onset was calculated for each network.

RESULTS

Three hundred eighty-eight patients had median age seizure onset 5 years (interquartile range [IQR] = 3-11 years), median age at pre-operative scan 18 years (IQR = 11-28 years). FCDs co-localized to the following networks: limbic (90), default mode (87), somatomotor (65), front parietal control (52), ventral attention (32), dorsal attention (31), and visual (31). Larger lesions were associated with younger age of onset (p = 0.01); age of epilepsy onset was associated with dominant network (p = 0.04) but not sulcal depth or histology. Sensorimotor networks had youngest onset; the limbic network had oldest age of onset (p values <0.05).

INTERPRETATION

FCD co-localization to distributed functional cortical networks is associated with age of epilepsy onset: sensory neural networks (somatomotor and visual) with earlier onset, and limbic latest onset. These variations may reflect developmental differences in synaptic/white matter maturation or network activation and may provide a biological basis for age-dependent epilepsy onset expression. ANN NEUROL 2022;92:503-511.

Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity

Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17-30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy-i.e., their "footprint distinctiveness". We found that statistical patterns of smartphone-based mobility features represented unique "footprints" that allow individual identification (p < 0.001). Critically, mobility footprints exhibited varying levels of person-specific distinctiveness (4-99%), which was associated with age and sex. Furthermore, reduced individual footprint distinctiveness was associated with instability in affect (p < 0.05) and circadian patterns (p < 0.05) as measured by environmental momentary assessment. Finally, brain functional connectivity, especially those in the somatomotor network, was linked to individual differences in mobility patterns (p < 0.05). Together, these results suggest that real-world mobility patterns may provide individual-specific signatures relevant for studies of development, sleep, and psychopathology.

EEG functional connectivity analysis in the source space

There is a growing interest in using electroencephalography (EEG) and source modeling to investigate functional interactions among cortical processes, particularly when dealing with pediatric populations. This paper introduces two pipelines that have been recently used to conduct EEG FC analysis in the cortical source space. The analytic streams of these pipelines can be summarized into the following steps: 1) cortical source reconstruction of high-density EEG data using realistic magnetic resonance imaging (MRI) models created with age-appropriate MRI templates; 2) segmentation of reconstructed source activities into brain regions of interest; and 3) estimation of FC in age-related frequency bands using robust EEG FC measures, such as weighted phase lag index and orthogonalized power envelope correlation. In this paper we demonstrate the two pipelines with resting-state EEG data collected from children at 12 and 36 months of age. We also discuss the advantages and limitations of the methods/techniques integrated into the pipelines. Given there is a need in the research community for open-access analytic toolkits that can be used for pediatric EEG data, programs and codes used for the current analysis are made available to the public.

Neuroimaging of structural and functional connectivity in preterm infants with intraventricular hemorrhage

Preterm infants with intraventricular hemorrhage (IVH) are known to have some of the worst neurodevelopmental outcomes in all of neonatal medicine, with a growing body of evidence relating these outcomes to underlying disruptions in brain structure and function. This review begins by summarizing state-of-the-art neuroimaging techniques delineating structural and functional connectivity (diffusion and resting state functional MRI) and their application in infants with IVH, including unique technical challenges and emerging methods. We then review studies of altered structural and functional connectivity, highlighting the role of IVH severity and location. We subsequently detail investigations linking structural and functional findings in infancy to later outcomes in early childhood. We conclude with future directions including methodologic considerations for prospective and potentially interventional studies designed to mitigate disruptions to underlying structural and functional connections and improve neurodevelopmental outcomes in this high-risk population.

A Hierarchical Graph Learning Model for Brain Network Regression Analysis

Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.

Adolescent development of multiscale structural wiring and functional interactions in the human connectome

Adolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14 to 25 y (n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of corticocortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple corticocortical structural networks in youth. We then assessed structure-function coupling using resting-state functional MRI measures in the same participants both via cross-sectional analysis at baseline and by studying longitudinal change between baseline and follow-up scans. At baseline, regions with more similar structural wiring were more likely to be functionally coupled. Moreover, correlating longitudinal structural wiring changes with longitudinal functional connectivity reconfigurations, we found that increased structural differentiation, particularly between sensory/unimodal and default mode networks, was reflected by reduced functional interactions. These findings provide insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.

NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline

Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.

Interacting brains revisited: A cross-brain network neuroscience perspective

Elucidating the neural basis of social behavior is a long‐standing challenge in neuroscience. Such endeavors are driven by attempts to extend the isolated perspective on the human brain by considering interacting persons' brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current hyperscanning analyses mostly rely on global metrics, we encode the regions' roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.

Development of functional connectome gradients during childhood and adolescence

Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum that ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (aged 6-14 years), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis and synaptic transmission-related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders.

Structural and functional variations in the prefrontal cortex are associated with learning in pre-adolescent common marmosets (Callithrix jacchus)

There is substantial evidence linking the prefrontal cortex (PFC) to a variety of cognitive abilities, with adolescence being a critical period in its development. In the current study, we investigated the neural basis of differences in learning in pre-adolescent common marmosets. At 8 months old, marmosets were given anatomical and resting state MRI scans (n=24). At 9 months old, association learning and inhibitory control was tested using a 'go/no go' visual discrimination (VD) task. Marmosets were grouped into 'learners' (n=12) and 'non-learners' (n=12), and associations between cognitive performance and sub-regional PFC volumes, as well as PFC connectivity patterns, were investigated. 'Learners' had significantly (p<0.05) larger volumes of areas 11, 25, 47 and 32 than 'non-learners', although 'non-learners' had significantly larger volumes of areas 24a and 8v than 'learners'. There was also a significant correlation between average % correct responses to the 'punished' stimulus and volume of area 47. Further, 'non-learners' had significantly greater global PFC connections, as well as significantly greater numbers of connections between the PFC and basal ganglia, cerebellum and hippocampus, compared to 'non-learners'. These results suggest that larger sub-regions of the orbitofrontal cortex and ventromedial PFC, as well more refined PFC connectivity patterns to other brain regions associated with learning, may be important in successful response inhibition. This study therefore offers new information on the neurodevelopment of individual differences in cognition during pre-adolescence in non-human primates.

Dissociable multi-scale patterns of development in personalized brain networks

The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

GAT-FD: An integrated MATLAB toolbox for graph theoretical analysis of task-related functional dynamics

Functional connectivity has been demonstrated to be varying over time during sensory and cognitive processes. Quantitative examinations of such variations can significantly advance our understanding on large-scale functional organizations and their topological dynamics that support normal brain functional connectome and can be altered in individuals with brain disorders. However, toolboxes that integrate the complete functions for analyzing task-related brain functional connectivity, functional network topological properties, and their dynamics, are still lacking. The current study has developed a MATLAB toolbox, the Graph Theoretical Analysis of Task-Related Functional Dynamics (GAT-FD), which consists of four modules for sliding-window analyses, temporal mask generation, estimations of network properties and dynamics, and result display, respectively. All the involved functions have been tested and validated using functional magnetic resonance imaging data collected from human subjects when performing a block-designed task. The results demonstrated that the GAT-FD allows for effective and quantitative evaluations of the functional network properties and their dynamics during the task period. As an open-source and user-friendly package, the GAT-FD and its detailed user manual are freely available at https://www.nitrc.org/projects/gat_fd and https://centers.njit.edu/cnnl/gat_fd/.

Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data

Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal. Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI. We performed an extensive parameter study to investigate the effectiveness of motion estimation and present in this work benchmark metrics to quantify the effect of motion correction and regularised volumetric reconstruction approaches on functional connectivity computations. We demonstrate the proposed framework's ability to improve functional connectivity estimates, reproducibility and signal interpretability, which is clinically highly desirable for the establishment of prognostic noninvasive imaging biomarkers. The motion correction and volumetric reconstruction framework is made available as an open-source package of NiftyMIC.

Systematic review of structural and functional neuroimaging studies of cannabis use in adolescence and emerging adulthood: evidence from 90 studies and 9441 participants

Cannabis use peaks in adolescence, and adolescents may be more vulnerable to the neural effects of cannabis and cannabis-related harms due to ongoing brain development during this period. In light of ongoing cannabis policy changes, increased availability, reduced perceptions of harm, heightened interest in medicinal applications of cannabis, and drastic increases in cannabis potency, it is essential to establish an understanding of cannabis effects on the developing adolescent brain. This systematic review aims to: (1) synthesize extant literature on functional and structural neural alterations associated with cannabis use during adolescence and emerging adulthood; (2) identify gaps in the literature that critically impede our ability to accurately assess the effect of cannabis on adolescent brain function and development; and (3) provide recommendations for future research to bridge these gaps and elucidate the mechanisms underlying cannabis-related harms in adolescence and emerging adulthood, with the long-term goal of facilitating the development of improved prevention, early intervention, and treatment approaches targeting adolescent cannabis users (CU). Based on a systematic search of Medline and PsycInfo and other non-systematic sources, we identified 90 studies including 9441 adolescents and emerging adults (n = 3924 CU, n = 5517 non-CU), which provide preliminary evidence for functional and structural alterations in frontoparietal, frontolimbic, frontostriatal, and cerebellar regions among adolescent cannabis users. Larger, more rigorous studies are essential to reconcile divergent results, assess potential moderators of cannabis effects on the developing brain, disentangle risk factors for use from consequences of exposure, and elucidate the extent to which cannabis effects are reversible with abstinence. Guidelines for conducting this work are provided.

In sync with your child: The potential of parent-child electroencephalography in developmental research

Healthy interaction between parent and child is foundational for the child's socioemotional development. Recently, an innovative paradigm shift in electroencephalography (EEG) research has enabled the simultaneous measurement of neural activity in caregiver and child. This dual-EEG or hyperscanning approach, termed parent-child dual-EEG, combines the strength of both behavioral observations and EEG methods. In this review, we aim to inform on the potential of dual-EEG in parents and children (0-6 years) for developmental researchers. We first provide a general overview of the dual-EEG technique and continue by reviewing the first empirical work on the emerging field of parent-child dual-EEG, discussing the limited but fascinating findings on parent-child brain-to-behavior and brain-to-brain synchrony. We then continue by providing an overview of dual-EEG analysis techniques, including the technical challenges and solutions one may encounter. We finish by discussing the potential of parent-child dual-EEG for the future of developmental research. The analysis of multiple EEG data is technical and challenging, but when performed well, parent-child EEG may transform the way we understand how caregiver and child connect on a neurobiological level. Importantly, studying objective physiological measures of parent-child interactions could lead to the identification of novel brain-to-brain synchrony markers of interaction quality.

Effect of Anesthetics on Functional Connectivity of Developing Brain

The potential anesthetic neurotoxicity on the neonate is an important focus of research investigation in the field of pediatric anesthesiology. It is essential to understand how these anesthetics may affect the development and growth of neonatal immature and vulnerable brains. Functional magnetic resonance imaging (fMRI) has suggested that using anesthetics result in reduced functional connectivity may consider as core sequence for the neurotoxicity and neurodegenerative changes in the developed brain. Anesthetics either directly impact the primary structures and functions of the brain or indirectly alter the hemodynamic parameters that contribute to cerebral blood flow (CBF) in neonatal patients. We hypothesis that anesthetic agents may either decrease the brain functional connectivity in neonatal patients or animals, which was observed by fMRI. This review will summarize the effect and mechanism of anesthesia on the rapid growth and development infant and neonate brain with fMRI through functional connectivity. It is possible to provide the new mechanism of neuronal injury induced by anesthetics and objective imaging evidence in animal developing brain.

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy.

Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia

OBJECTIVE

Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ.

METHODS

We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level.

RESULTS

A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (p_FDR = 0.005). The same measure correlated positively with both social and occupational functioning (both p_FDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC.

CONCLUSIONS

Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.

Cerebellar Coordination of Neuronal Communication in Cerebral Cortex

Cognitive processes involve precisely coordinated neuronal communications between multiple cerebral cortical structures in a task specific manner. Rich new evidence now implicates the cerebellum in cognitive functions. There is general agreement that cerebellar cognitive function involves interactions between the cerebellum and cerebral cortical association areas. Traditional views assume reciprocal interactions between one cerebellar and one cerebral cortical site, via closed-loop connections. We offer evidence supporting a new perspective that assigns the cerebellum the role of a coordinator of communication. We propose that the cerebellum participates in cognitive function by modulating the coherence of neuronal oscillations to optimize communications between multiple cortical structures in a task specific manner.

Dynamic Functional Connectivity Alterations and Their Associated Gene Expression Pattern in Autism Spectrum Disorders

Autism spectrum disorders (ASDs) are a group of heterogeneous neurodevelopmental disorders that are highly heritable and are associated with impaired dynamic functional connectivity (DFC). However, the molecular mechanisms behind DFC alterations remain largely unknown. Eighty-eight patients with ASDs and 87 demographically matched typical controls (TCs) from the Autism Brain Imaging Data Exchange II database were included in this study. A seed-based sliding window approach was then performed to investigate the DFC changes in each of the 29 seeds in 10 classic resting-state functional networks and the whole brain. Subsequently, the relationships between DFC alterations in patients with ASDs and their symptom severity were assessed. Finally, transcription-neuroimaging association analyses were conducted to explore the molecular mechanisms of DFC disruptions in patients with ASDs. Compared with TCs, patients with ASDs showed significantly increased DFC between the right dorsolateral prefrontal cortex (DLPFC) and left fusiform/lingual gyrus, between the DLPFC and the superior temporal gyrus, between the right frontal eye field (FEF) and left middle frontal gyrus, between the FEF and the right angular gyrus, and between the left intraparietal sulcus and the right middle temporal gyrus. Moreover, significant relationships between DFC alterations and symptom severity were observed. Furthermore, the genes associated with DFC changes in ASDs were identified by performing gene-wise across-sample spatial correlation analysis between gene expression extracted from six donors’ brain of the Allen Human Brain Atlas and case-control DFC difference. In enrichment analysis, these genes were enriched for processes associated with synaptic signaling and voltage-gated ion channels and calcium pathways; also, these genes were highly expressed in autistic disorder, chronic alcoholic intoxication and several disorders related to depression. These results not only demonstrated higher DFC in patients with ASDs but also provided novel insight into the molecular mechanisms underlying these alterations.

A comparative study of machine learning methods for predicting the evolution of brain connectivity from a baseline timepoint

BACKGROUND

Predicting the evolution of the brain network, also called connectome, by foreseeing changes in the connectivity weights linking pairs of anatomical regions makes it possible to spot connectivity-related neurological disorders in earlier stages and detect the development of potential connectomic anomalies. Remarkably, such a challenging prediction problem remains least explored in the predictive connectomics literature. It is a known fact that machine learning (ML) methods have proven their predictive abilities in a wide variety of computer vision problems. However, ML techniques specifically tailored for the prediction of brain connectivity evolution trajectory from a single timepoint are almost absent.

NEW METHOD

To fill this gap, we organized a Kaggle competition where 20 competing teams designed advanced machine learning pipelines for predicting the brain connectivity evolution from a single timepoint. The teams developed their ML pipelines with combination of data pre-processing, dimensionality reduction and learning methods. Each ML framework inputs a baseline brain connectivity matrix observed at baseline timepoint t₀ and outputs the brain connectivity map at a follow-up timepoint t₁. The longitudinal OASIS-2 dataset was used for model training and evaluation. Both random data split and 5-fold cross-validation strategies were used for ranking and evaluating the generalizability and scalability of each competing ML pipeline.

RESULTS

Utilizing an inclusive approach, we ranked the methods based on two complementary evaluation metrics (mean absolute error (MAE) and Pearson Correlation Coefficient (PCC)) and their performances using different training and testing data perturbation strategies (single random split and cross-validation). The final rank was calculated using the rank product for each competing team across all evaluation measures and validation strategies. Furthermore, we added statistical significance values to each proposed pipeline.

CONCLUSION

In support of open science, the developed 20 ML pipelines along with the connectomic dataset are made available on GitHub (https://github.com/basiralab/Kaggle-BrainNetPrediction-Toolbox). The outcomes of this competition are anticipated to lead the further development of predictive models that can foresee the evolution of the brain connectivity over time, as well as other types of networks (e.g., genetic networks).

Correlations between facial emotion processing and biochemical abnormalities in untreated adolescent patients with major depressive disorder: A proton magnetic resonance spectroscopy study

BACKGROUND

Studies show that disturbances of the fronto-striato-thalamic-cerebellar circuit could be correlated to facial emotion processing (FEP) biases in major depressive disorder (MDD). Nevertheless, the underlying mechanism of natural metabolism-emotion relationships in adolescent MDD remains unclear.

METHODS

Thirty-seven adolescent patients with MDD and 30 healthy controls completed FEP tasks using the Chinese Facial Affective Picture System (CAFPS). Proton magnetic resonance spectroscopy (¹H-MRS) was also used to obtain ratios of N-acetylaspartate (NAA) /creatine (Cr) and choline (Cho) /Cr ratios in the prefrontal cortex (PFC), anterior cingulate cortex (ACC), putamen, thalamus and cerebellum. Correlations between abnormal neurometabolic ratios and FEP were also computed.

RESULTS

Compared with the control group, the MDD group had significantly lower accuracy and perception intensity of happiness, and significantly higher accuracy of disgust and perception intensity of sad and fearful faces in FEP tasks. Compared to healthy controls, adolescent patients with MDD showed significantly lower NAA/Cr ratios in the left PFC, higher NAA/Cr ratios in the right thalamus, and higher Cho/Cr ratios in the right putamen, although there were no significant differences in metabolites in the ACC and cerebellum between two groups. In the MDD group, NAA/Cr ratios of the right thalamus were negatively correlated with happy reaction time and positively correlated with sad, anger, and fear intensity; Cho/Cr ratios in the right putamen were positively correlated with fear reaction time.

CONCLUSIONS

Our findings suggest that FEP bias may exist in adolescents with MDD, while the impairment of FEP may be associated with abnormal metabolites in the fronto-striato-thalamic circuit.

Analyzing Complex Problem Solving by Dynamic Brain Networks

Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested method models the brain network as an Artificial Neural Network, where the weights correspond to the relationships among the brain anatomic regions. The first step of the model is preprocessing that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using the preprocessed fMRI signal to train the Artificial Neural Network. The properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The representation power of the suggested brain network is shown by decoding the planning and execution subtasks of complex problem solving. Our findings are consistent with the previous results of experimental psychology. Furthermore, it is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution.

Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms

OBJECTIVE

Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health.

METHODS

We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance.

RESULTS

Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN.

DISCUSSION

Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.

Brain network coupling associated with cognitive performance varies as a function of a child's environment in the ABCD study

Prior research indicates that lower resting-state functional coupling between two brain networks, lateral frontoparietal network (LFPN) and default mode network (DMN), relates to cognitive test performance, for children and adults. However, most of the research that led to this conclusion has been conducted with non-representative samples of individuals from higher-income backgrounds, and so further studies including participants from a broader range of socioeconomic backgrounds are required. Here, in a pre-registered study, we analyzed resting-state fMRI from 6839 children ages 9-10 years from the ABCD dataset. For children from households defined as being above poverty (family of 4 with income > $25,000, or family of 5+ with income > $35,000), we replicated prior findings; that is, we found that better performance on cognitive tests correlated with weaker LFPN-DMN coupling. For children from households defined as being in poverty, the direction of association was reversed, on average: better performance was instead directionally related to stronger LFPN-DMN connectivity, though there was considerable variability. Among children in households below poverty, the direction of this association was predicted in part by features of their environments, such as school type and parent-reported neighborhood safety. These results highlight the importance of including representative samples in studies of child cognitive development.

Adolescent Brain Development and Contextual Influences: A Decade in Review

Adolescence is a developmental period characterized by substantial psychological, biological, and neurobiological changes. This review discusses the past decade of research on the adolescent brain, as based on the overarching framework that development is a dynamic process both within the individual and between the individual and external inputs. As such, this review focuses on research showing that the development of the brain is influenced by multiple ongoing and dynamic elements. It highlights the implications this body of work on behavioral development and offers areas of opportunity for future research in the coming decade.

Prospective study on resting state functional connectivity in adolescents with major depressive disorder after antidepressant treatment

Recent advances in functional magnetic resonance imaging (fMRI) have resulted in many studies on resting-state functional connectivity (rsFC) in depressed patients. Previous studies have shown alterations between multiple brain areas, such as the prefrontal cortex, anterior cingulate cortex, and basal ganglia, but there are very few prospective studies with a longitudinal design on adolescent depression patients. We therefore investigated the change in positive rsFC in a homogeneous drug-naïve adolescent group after 12 weeks of antidepressant treatment. Functional neuroimaging data were collected and analyzed from 32 patients and 27 healthy controls. Based on previous literature, the amygdala, anterior cingulate cortex (ACC), insula, hippocampus, and dorsolateral prefrontal cortex (DLPFC) were selected as seed regions. Seed-to-voxel analyses were performed between pre- and post-treatment states as well as between the patients and controls at baseline. The positive rsFC between the right DLPFC and the left putamen/right frontal operculum were shown to be higher in patients than in the controls. The positive rsFC between the left DLPFC and left putamen/left lingual gyrus was also higher in the patients than in the controls. The positive rsFC between the right dorsal ACC and the left precentral gyrus had reduced after the 12-week antidepressant treatment. Regions involved in the frontolimbic circuit showed changes in the positive rsFC in the depressed adolescents as compared to in the healthy controls. There were also significant changes in the positive rsFC after 12-weeks of antidepressant treatment. The involved regions were associated with emotional regulation, cognitive functioning, impulse control, and visual processing.

Resting state functional connectivity in adolescent synthetic cannabinoid users with and without attention-deficit/hyperactivity disorder

OBJECTIVE

Synthetic cannabinoids (SCs) have become increasingly popular in recent years, especially among adolescents. The first aim of the current study was to examine resting-state functional connectivity (rsFC) in SC users compared to controls. Our second aim was to examine the influence of comorbid attention-deficit/hyperactivity disorder (ADHD) symptomatology on rsFC changes in SC users compared to controls.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) analysis included 25 SC users (14 without ADHD and 11 with ADHD combined type) and 12 control subjects.

RESULTS

We found (i) higher rsFC between the default mode network (DMN) and salience network, dorsal attention network and cingulo-opercular network, and (ii) lower rsFC within the DMN and between the DMN and visual network in SC users compared to controls. There were no significant differences between SC users with ADHD and controls, nor were there any significant differences between SC users with and without ADHD.

CONCLUSIONS

We found the first evidence of abnormalities within and between resting state networks in adolescent SC users without ADHD. In contrast, SC users with ADHD showed no differences compared to controls. These results suggest that comorbidity of ADHD and substance dependence may show different rsFC alterations than substance use alone.

Whole-Brain Resting-State Functional Connectivity Patterns Associated With Pediatric Anxiety and Involuntary Attention Capture

BACKGROUND

Pediatric anxiety disorders are linked to dysfunction in multiple functional brain networks, as well as to alterations in the allocation of spatial attention. We used network-level analyses to characterize resting-state functional connectivity (rs-fc) alterations associated with 1) symptoms of anxiety and 2) alterations in stimulus-driven attention associated with pediatric anxiety disorders. We hypothesized that anxiety was related to altered connectivity of the frontoparietal, default mode, cingulo-opercular, and ventral attention networks and that anxiety-related connectivity alterations that include the ventral attention network would simultaneously be related to deviations in stimulus-driven attention.

METHODS

A sample of children (n = 61; mean = 10.6 years of age), approximately half of whom met criteria for a current anxiety disorder, completed a clinical assay, an attention task, and rs-fc magnetic resonance imaging scans. Network-level analyses examined whole-brain rs-fc patterns associated with clinician-rated anxiety and with involuntary capture of attention. Post hoc analyses controlled for comorbid symptoms.

RESULTS

Elevated clinician-rated anxiety was associated with altered connectivity within the cingulo-opercular network, as well as between the cingulo-opercular network and the ventral attention, default mode, and visual networks. Connectivity between the ventral attention and cingulo-opercular networks was associated with variation in both anxiety and stimulus-driven attention.

CONCLUSIONS

Pediatric anxiety is related to aberrant connectivity patterns among several networks, most of which include the cingulo-opercular network. These results help clarify the within- and between-network interactions associated with pediatric anxiety and its association with altered attention, suggesting that specific network connections could be targeted to improve specific altered processes associated with anxiety.

Atypicalities in the developmental trajectory of cortico-striatal functional connectivity in autism spectrum disorder

LAY ABSTRACT

Autism spectrum disorder has long been conceptualized as a disorder of "atypical development of functional brain connectivity (which refers to correlations in activity levels of distant brain regions)." However, most of the research has focused on the connectivity between cortical regions, and much remains unknown about the developmental changes of functional connectivity between subcortical and cortical areas in autism spectrum disorder. We used the technique of resting-state functional magnetic resonance imaging to explore the developmental characteristics of intrinsic functional connectivity (functional brain connectivity when people are asked not to do anything) between subcortical and cortical regions in individuals with and without autism spectrum disorder aged 6-30 years. We focused on one important subcortical structure called striatum, which has roles in motor, cognitive, and affective processes. We found that cortico-striatal intrinsic functional connectivities showed opposite developmental trajectories in autism spectrum disorder and typically developing individuals, with connectivity increasing with age in autism spectrum disorder and decreasing or constant in typically developing individuals. We also found significant negative behavioral correlations between those atypical cortico-striatal intrinsic functional connectivities and autistic symptoms, such as social-communication deficits, and restricted/repetitive behaviors and interests. Taken together, this work highlights that the atypical development of cortico-subcortical functional connectivity might be largely involved in the neuropathological mechanisms of autism spectrum disorder.

Development of functional network architecture explains changes in children's altruistically motivated helping

Childhood is marked by profound changes in prosocial behaviour. The underlying motivational mechanisms remain poorly understood. We investigated the development of altruistically motivated helping in middle childhood and the neurocognitive and -affective mechanisms driving this development. One-hundred and twenty seven 6-12 year-old children performed a novel gustatory costly helping task designed to measure altruistic motivations of helping behaviour. Neurocognitive and -affective mechanisms including emotion regulation, emotional clarity and attentional reorienting were assessed experimentally through an extensive task-battery while functional brain activity and connectivity were measured during an empathy for taste paradigm and during rest. Altruistically motivated helping increased with age. Out of all mechanisms probed for, only emotional clarity increased with age and accounted for altruistically motivated helping. This was associated with greater functional integration of the empathy-related network with fronto-parietal brain regions at rest. We isolate a highly specific neuroaffective mechanism as the crucial driver of altruistically motivated helping during child development.

Spontaneous transient brain states in EEG source space in disorders of consciousness

Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment.

Adolescent health series: Adolescent neurocognitive development in Western and Sub-Saharan African contexts

The transitional period of adolescence has long been associated with physical, social and behavioural change. During this time, adolescents start to develop their own self-identity, make important life decisions and acquire the necessary skills to successfully transition to adulthood. More recently, advances in brain imaging technology have enabled increased understanding of structural and functional changes in the human brain during this developmental period, and how they relate to social, emotional, motivational and cognitive development. The ability to integrate these developing cognitive processes in increasingly complex social contexts is a key aspect of mature decision-making, which has implications for adolescent health, educational, economic and social outcomes. Insights from the field of developmental cognitive neuroscience could increase our understanding of this influential stage of life and thus inform potential interventions to promote adolescent health, a critical goal for global health research. Many social changes occur during adolescence and the social environment shapes both brain and cognitive development and the decisions adolescents make. Thus, it is important to study adolescent neurocognitive development in socio-cultural context. Yet, despite evidence from Western studies that socio-cultural and economic factors impact on adolescent neurocognitive development, existing studies of adolescent neurocognitive development in sub-Saharan Africa are relatively scarce. We summarise research findings from Western and sub-Saharan African contexts and highlight areas where research is lacking. Longitudinal studies from more diverse global samples will be needed to build a comprehensive model of adolescent development, that characterises both commonalities in developmental trajectories, as well as the way these can meaningfully differ between both individuals and contexts.

Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence

The transition from childhood to adolescence is marked by pronounced shifts in brain structure and function that coincide with the development of physical, cognitive, and social abilities. Prior work in adult populations has characterized the topographical organization of the cortex, revealing macroscale functional gradients that extend from unimodal (somatosensory/motor and visual) regions through the cortical association areas that underpin complex cognition in humans. However, the presence of these core functional gradients across development as well as their maturational course have yet to be established. Here, leveraging 378 resting-state functional MRI scans from 190 healthy individuals aged 6 to 17 y old, we demonstrate that the transition from childhood to adolescence is reflected in the gradual maturation of gradient patterns across the cortical sheet. In children, the overarching organizational gradient is anchored within the unimodal cortex, between somatosensory/motor and visual territories. Conversely, in adolescence, the principal gradient of connectivity transitions into an adult-like spatial framework, with the default network at the opposite end of a spectrum from primary sensory and motor regions. The observed gradient transitions are gradually refined with age, reaching a sharp inflection point in 13 and 14 y olds. Functional maturation was nonuniformly distributed across cortical networks. Unimodal networks reached their mature positions early in development, while association regions, in particular the medial prefrontal cortex, reached a later peak during adolescence. These data reveal age-dependent changes in the macroscale organization of the cortex and suggest the scheduled maturation of functional gradient patterns may be critically important for understanding how cognitive and behavioral capabilities are refined across development.

Age-related differences in resting state functional connectivity in pediatric migraine

BACKGROUND

Migraine affects roughly 10% of youth aged 5-15 years, however the underlying mechanisms of migraine in youth are poorly understood. Multiple structural and functional alterations have been shown in the brains of adult migraine sufferers. This study aims to investigate the effects of migraine on resting-state functional connectivity during the period of transition from childhood to adolescence, a critical period of brain development and the time when rates of pediatric chronic pain spikes.

METHODS

Using independent component analysis, we compared resting state network spatial maps and power spectra between youth with migraine aged 7-15 and age-matched controls. Statistical comparisons were conducted using a MANCOVA analysis.

RESULTS

We show (1) group by age interaction effects on connectivity in the visual and salience networks, group by sex interaction effects on connectivity in the default mode network and group by pubertal status interaction effects on connectivity in visual and frontal parietal networks, and (2) relationships between connectivity in the visual networks and the migraine cycle, and age by cycle interaction effects on connectivity in the visual, default mode and sensorimotor networks.

CONCLUSIONS

We demonstrate that brain alterations begin early in youth with migraine and are modulated by development. This highlights the need for further study into the neural mechanisms of migraine in youth specifically, to aid in the development of more effective treatments.

A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics

Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of mental disorders. Understanding complex interactions among these heterogeneous data may give rise to a new perspective, while at the same time demand statistical models for their integration. Various graphical models have been proposed for the study of interaction or association networks with continuous, binary, and count data as well as the mixture of them. However, limited efforts have been made for the multinomial case, for instance, SNP data. Our goal is therefore to fill the void by developing a graphical model for the integration of fMRI image and SNP data, which can provide deeper understanding of the unknown neurogenetic mechanism. In this article, we propose a latent Gaussian copula model for mixed data containing multinomial components. We assume that the discrete variable is obtained by discretizing a latent (unobserved) continuous variable and then create a semi-rank based estimator of the graph structure. The simulation results demonstrate that the proposed latent correlation has more steady and accurate performance than several existing methods in detecting graph structure. When applying to a real schizophrenia data consisting of SNP array and fMRI image collected by the Mind Clinical Imaging Consortium (MCIC), the proposed method reveals a set of distinct SNP-brain associations, which are verified to be biologically significant. The proposed model is statistically promising in handling mixed types of data including multinomial components, which can find widespread applications. To promote reproducible research, the R code is available at https://github.com/Aiying0512/LGCM.

QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder

Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.

Development of the bodily self: Effects of visuomotor synchrony and visual appearance on virtual embodiment in children and adults

The sense of a bodily self is thought to depend on adaptive weighting and integration of bodily afferents and prior beliefs. Evidence from studies using paradigms such as the rubber hand illusion and full body illusion suggests changes in the integration of visuotactile bodily signals throughout childhood. Here, we extended this line of research by assessing how bottom-up visuomotor synchrony and expectancy, modulated by visual appearance of virtual avatars, contribute to embodiment in children. We compared responses to a first-person perspective virtual full body illusion from 8- to 12-year-old children and adults while manipulating synchrony of the avatar's movements (synchronous, 0.5-s delay, or 1-s delay compared with the participant's movements) and appearance of the avatar (human or skeleton). We measured embodiment with both subjective questionnaires and objective skin conductance responses to virtual threat. Results showed that children experienced ownership for the virtual avatar in a similar way as adults, which was reduced with increasing asynchrony, and for the skeleton avatar as compared with the human avatar. This modulation of ownership was not reflected in the skin conductance responses, which were equally high in all experimental conditions and only showed a modulation of repetition by age. In contrast, in children the subjective experience of agency was less affected by the dampening effects of visuomotor asynchrony or reduced human likeness and was overall higher. These findings suggest that children can easily embody a virtual avatar but that different aspects of embodiment develop at different rates, which could have important implications for applications of embodied virtual reality.

Neurobiological substrates underlying corpus callosum hypoconnectivity and brain metabolic patterns in the valproic acid rat model of autism spectrum disorder

Atypical connectivity between brain regions and altered structure of the corpus callosum (CC) in imaging studies supports the long-distance hypoconnectivity hypothesis proposed for autism spectrum disorder (ASD). The aim of this study was to unveil the CC ultrastructural and cellular changes employing the valproic acid (VPA) rat model of ASD. Male Wistar rats were exposed to VPA (450 mg/kg i.p.) or saline (control) during gestation (embryonic day 10.5), and maturation, exploration, and social behavior were subsequently tested. Myelin content, ultrastructure, and oligodendroglial lineage were studied in the CC at post-natal days 15 (infant) and 36 (juvenile). As a functional outcome, brain metabolic activity was determined by positron emission tomography. Concomitantly with behavioral deficits in juvenile VPA rats, the CC showed reduced myelin basic protein, conserved total number of axons, reduced percentage of myelinated axons, and aberrant and less compact arrangements of myelin sheath ultrastructure. Mature oligodendrocytes decreased and oligodendrocyte precursors increased in the absence of astrogliosis or microgliosis. In medial prefrontal and somatosensory cortices of juvenile VPA rats, myelin ultrastructure and oligodendroglial lineage were preserved. VPA animals exhibited global brain hypometabolism and local hypermetabolism in brain regions relevant for ASD. In turn, the CC of infant VPA rats showed reduced myelin content but preserved oligodendroglial lineage. Our findings indicate that CC hypomyelination is established during infancy and prior to oligodendroglial pattern alterations, which suggests that axon-oligodendroglia communication could be compromised in VPA animals. Thus, CC hypomyelination may underlie white matter alterations and contribute to atypical patterns of connectivity and metabolism found in ASD.

Brain functional network modeling and analysis based on fMRI: a systematic review

In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.

Different Functional Network Connectivity Patterns in Epilepsy: A Rest-State fMRI Study on Mesial Temporal Lobe Epilepsy and Benign Epilepsy With Centrotemporal Spike

The stark discrepancy in the prognosis of epilepsy is closely related to brain damage features and underlying mechanisms, which have not yet been unraveled. In this study, differences in the epileptic brain functional connectivity states were explored through a network-based connectivity analysis between intractable mesial temporal lobe epilepsy (MTLE) patients and benign epilepsy with centrotemporal spikes (BECT). Resting state fMRI imaging data were collected for 14 MTLE patients, 12 BECT patients and 16 healthy controls (HCs). Independent component analysis (ICA) was performed to identify the cortical functional networks. Subcortical nuclei of interest were extracted from the Harvard-Oxford probability atlas. Network-based statistics were used to detect functional connectivity (FC) alterations across intranetworks and internetworks, including the connectivity between cortical networks and subcortical nuclei. Compared with HCs, MTLE patients showed significant lower activity between the connectivity of cortical networks and subcortical nuclei (especially hippocampus) and lower internetwork FC involving the lateral temporal lobe; BECT patients showed normal cortical-subcortical FC with hyperconnectivity between cortical networks. Together, cortical-subcortical hypoconnectivity in MTLE suggested a low efficiency and collaborative network pattern, and this might be relevant to the final decompensatory state and the intractable prognosis. Conversely, cortical-subcortical region with normal connectivity remained well in global cooperativity, and compensatory internetwork hyperconnectivity caused by widespread cortical abnormal discharge, which might account for the self-limited clinical outcome in BECT. Based on the fMRI functional network study, different brain network patterns might provide a better explanation of mechanisms in different types of epilepsy.

Multi-scale structural rich-club organization of the brain in full-term newborns: a combined DWI and fMRI study

Objective.Our understanding of early brain development is limited due to rapid changes in white matter pathways after birth. In this study, we introduced a multi-scale cross-modal approach to investigate the rich club (RC) organization and topology of the structural brain networks in 40 healthy neonates using diffusion-weighted imaging and resting-state fMRI data.Approach.A group independent component analysis was first performed to identify eight resting state networks (RSNs) used as functional modules. A groupwise whole-brain functional parcellation was also performed at five scales comprising 100-900 parcels. The distribution of RC nodes was then investigated within and between the RSNs. We further assessed the distribution of short and long-range RC, feeder and local connections across different parcellation scales.Main results.Sharing the scale-free characteristic of small-worldness, the neonatal structural brain networks exhibited an RC organization at different nodal scales (NSs). The subcortical, sensory-motor and default mode networks were found to be strongly involved in the RC organization of the structural brain networks, especially in the zones where the RSNs overlapped, with an average cross-scale proportion of 45.9%, 28.5% and 10.5%, respectively. A large proportion of the connector hubs were found to be RC members for the coarsest (73%) to finest (92%) NSs. Our results revealed a prominent involvement of cortico-subcortical and cortico-cerebellar white matter pathways in the RC organization of the neonatal brain. Regardless of the NS, the majority (more than 65.2%) of the inter-RSN connections were long distance RC or feeder with an average physical connection of 105.5 and 97.4 mm, respectively. Several key RC regions were identified, including the insula and cingulate gyri, middle and superior temporal gyri, hippocampus and parahippocampus, fusiform gyrus, precuneus, superior frontal and precentral gyri, calcarine fissure and lingual gyrus.Significance.Our results emphasize the importance of the multi-scale connectivity analysis in assessing the cross-scale reproducibility of the connectivity results concerning the global and local topological properties of the brain networks. Our findings may improve our understanding of the early brain development.

Resting state functional connectivity in the default mode network: Relationships between cannabis use, gender, and cognition in adolescents and young adults

INTRODUCTION

Cannabis is the most commonly used illicit substance in the United States, and nearly 1 in 4 young adults are current cannabis users. Chronic cannabis use is associated with changes in resting state functional connectivity (RSFC) in the default mode network (DMN) in adolescents and young adults; results are somewhat inconsistent across studies, potentially due to methodological differences. The aims of the present study were to examine potential differences in DMN RSFC between cannabis users and controls, and to examine, as an exploratory analysis, if gender moderated any findings. We further examined whether differences in RSFC related to differences in performance on selected neuropsychological measures.

MATERIALS AND METHODS

Seventy-seven 16-26-year-old participants underwent an MRI scan (including resting state scan), neuropsychological battery, toxicology screening, and drug use interview. Differences in DMN connectivity were examined between groups (cannabis vs. control) and with an exploratory group by gender interaction, using a left posterior cingulate cortex (PCC) seed-based analysis conducted in AFNI.

RESULTS

Cannabis users demonstrated weaker connectivity than controls between the left PCC and various DMN nodes, and the right Rolandic operculum/Heschl's gyrus. Cannabis users demonstrated stronger connectivity between the left PCC and the cerebellum and left supramarginal gyrus. The group by gender interaction was not significantly associated with connectivity differences. Stronger left PCC-cerebellum connectivity was associated with poorer performance on cognitive measures in cannabis users. In controls, intra-DMN connectivity was positively correlated with performance on a speeded selective/sustained attention measure.

DISCUSSION

Consistent with our hypotheses and other studies, cannabis users demonstrated weaker connectivity between the left PCC and DMN nodes. Chronic THC exposure may alter GABA and glutamate concentrations, which may alter brain communication. Future studies should be conducted with a larger sample size and examine gender differences and the mechanism by which these differences may arise.

The NIMH Intramural Longitudinal Study of the Endocrine and Neurobiological Events Accompanying Puberty: Protocol and rationale for methods and measures

Delineating the relationship between human neurodevelopment and the maturation of the hypothalamic-pituitary-gonadal (HPG) axis during puberty is critical for investigating the increase in vulnerability to neuropsychiatric disorders that is well documented during this period. Preclinical research demonstrates a clear association between gonadal production of sex steroids and neurodevelopment; however, identifying similar associations in humans has been complicated by confounding variables (such as age) and the coactivation of two additional endocrine systems (the adrenal androgenic system and the somatotropic growth axis) and requires further elucidation. In this paper, we present the design of, and preliminary observations from, the ongoing NIMH Intramural Longitudinal Study of the Endocrine and Neurobiological Events Accompanying Puberty. The aim of this study is to directly examine how the increase in sex steroid hormone production following activation of the HPG-axis (i.e., gonadarche) impacts neurodevelopment, and, additionally, to determine how gonadal development and maturation is associated with longitudinal changes in brain structure and function in boys and girls. To disentangle the effects of sex steroids from those of age and other endocrine events on brain development, our study design includes 1) selection criteria that establish a well-characterized baseline cohort of healthy 8-year-old children prior to the onset of puberty (e.g., prior to puberty-related sex steroid hormone production); 2) temporally dense longitudinal, repeated-measures sampling of typically developing children at 8-10 month intervals over a 10-year period between the ages of eight and 18; 3) contemporaneous collection of endocrine and other measures of gonadal, adrenal, and growth axis function at each timepoint; and 4) collection of multimodal neuroimaging measures at these same timepoints, including brain structure (gray and white matter volume, cortical thickness and area, white matter integrity, myelination) and function (reward processing, emotional processing, inhibition/impulsivity, working memory, resting-state network connectivity, regional cerebral blood flow). This report of our ongoing longitudinal study 1) provides a comprehensive review of the endocrine events of puberty; 2) details our overall study design; 3) presents our selection criteria for study entry (e.g., well-characterized prepubertal baseline) along with the endocrinological considerations and guiding principles that underlie these criteria; 4) describes our longitudinal outcome measures and how they specifically relate to investigating the effects of gonadal development on brain development; and 5) documents patterns of fMRI activation and resting-state networks from an early, representative subsample of our cohort of prepubertal 8-year-old children.

Greater functional connectivity between sensory networks is related to symptom severity in toddlers with autism spectrum disorder

BACKGROUND

Symptoms of autism spectrum disorder (ASD) emerge in the first years of life. Yet, little is known about the organization and development of functional brain networks in ASD proximally to the symptom onset. Further, the relationship between brain network connectivity and emerging ASD symptoms and overall functioning in early childhood is not well understood.

METHODS

Resting-state fMRI data were acquired during natural sleep from 24 young children with ASD and 23 typically developing (TD) children, aged 17-45 months. Intrinsic functional connectivity (iFC) within and between resting-state functional networks was derived with independent component analysis (ICA).

RESULTS

Increased iFC between visual and sensorimotor networks was found in young children with ASD compared to TD participants. Within the ASD group, the degree of overconnectivity between visual and sensorimotor networks was associated with greater autism symptoms. Age-related weakening of the visual-auditory between-network connectivity was observed in the ASD but not the TD group.

CONCLUSIONS

Taken together, these results provide evidence for disrupted functional network maturation and differentiation, particularly involving visual and sensorimotor networks, during the first years of life in ASD. The observed pattern of greater visual-sensorimotor between-network connectivity associated with poorer clinical outcomes suggests that disruptions in multisensory brain circuitry may play a critical role for early development of behavioral skills and autism symptomatology in young children with ASD.

Higher Levels of Pro-inflammatory Cytokines Are Associated With Higher Levels of Glutamate in the Anterior Cingulate Cortex in Depressed Adolescents

Animal models of stress and related conditions, including depression, have shown that elevated peripheral levels of inflammatory cytokines have downstream consequences on glutamate (Glu) in the brain. Although studies in human adults with depression have reported evidence of higher inflammation but lower Glu in the anterior cingulate cortex (ACC), the extent to which peripheral inflammation contributes to glutamatergic abnormalities in adolescents with depression is not well-understood. It is also unclear whether antioxidants, such as ascorbate (Asc), may buffer against the effects of inflammation on Glu metabolism. Fifty-five depressed adolescents were recruited in the present cross-sectional study and provided blood samples, from which we assayed pro-inflammatory cytokines, and underwent a short-TE proton magnetic spectroscopy scan at 3T, from which we estimated Glu and Asc in the dorsal ACC. In the 31 adolescents with usable cytokine and Glu data, we found that IL-6 was significantly positively associated with dorsal ACC Glu (β = 0.466 ± 0.199, p = 0.029). Of the 16 participants who had usable Asc data, we found that at higher levels of dorsal ACC Asc, there was a negative association between IL-6 and Glu (interaction effect: β = -0.906 ± 0.433, p = 0.034). Importantly, these results remained significant when controlling for age, gender, percentage of gray matter in the dorsal ACC voxel, BMI, and medication (antidepressant and anti-inflammatory) usage. While preliminary, our results underscore the importance of examining both immune and neural contributors to depression and highlight the potential role of anti-inflammatory compounds in mitigating the adverse effects of inflammation (e.g., glutamatergic neuroexcitotoxicity). Future studies that experimentally manipulate levels of inflammation, and of ascorbate, and that characterize these effects on cortical glutamate concentrations and subsequent behavior in animals and in humans are needed.