Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence. Our GitHub implementation is publicly available on https://github.com/bishalth01/BrainRGIN/.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. While deep learning has gained substantial popularity for modeling complex relational data, its application to uncovering the spatiotemporal dynamics of the brain is still limited. In this study we propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification. Our model, DSAM , leverages temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations, a temporal attention unit to identify important time points, a self-attention unit to construct the goal-specific connectivity matrix, and a novel variant of graph neural network to capture the spatial dynamics for downstream classification. To validate our approach, we conducted experiments on the Human Connectome Project dataset with 1075 samples to build and interpret the model for the classification of sex group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples for independent testing. Compared our proposed framework with other state-of-art models, results suggested this novel approach goes beyond the assumption of a fixed connectivity matrix, and provides evidence of goal-specific brain connectivity patterns, which opens up potential to gain deeper insights into how the human brain adapts its functional connectivity specific to the task at hand. Our implementation can be found on https://github.com/bishalth01/DSAM . • Multilevel Temporal Feature Extraction: • Utilizes a novel multilevel Temporal Convolutional Network (TCN) adaptation to directly extract temporal features from raw brain activity data. • Shared Temporal Attention for Key Time Points: • Implements shared temporal attention mechanisms to selectively focus on the most informative time points, enhancing model efficiency. • Node-Node Self-Attention for Dynamic Brain Connectivity: • Leverages self-attention mechanisms to dynamically construct a goal-specific brain connectivity matrix, capturing complex inter-node interactions. • ROI-Aware Graph Neural Networks (GNNs): • Introduces ROI-aware GNNs to model spatial brain dynamics, ensuring region-specific contextual learning and improving interpretability.

With neuroimaging data scientists have gained substantial information of the neuronal underpinning of intelligence. Yet how to integrate multimodal neuronal features effectively in relation to intelligence remains elusive. In this paper, we have developed a reference Canonical Correlation Analysis (RCCA) model that extracts latent, correlated multimodal features while enhancing correlation to a reference of interest. We applied RCCA to gray matter and white matter images from 7874 participants, and compared the derived features with those from Principle Components Analysis (PCA) and sparse CCA (SCCA), in terms of association with intelligence and prediction effectiveness using LASSO regression models. Eight RCCA features explained 10%, 16% and 17% variance of fluid intelligence, crystallized intelligence, and total composite score, respectively, which are similar to the percentage of variance explained by over 100 principle components. SCCA features presented the least variance of intelligence. Our results indicate RCCA model can successfully extract features of interest. The top brain regions that contribute to intelligence include the frontal regions and cingulate gyrus.

The reliability of functional network connectivity (FNC) measured using independent component analysis (ICA) has frequently been explored within the literature, with results displaying varying levels of reliability and demonstrating that minor changes in data preprocessing procedures can significantly alter FC results and reliability. However, one important avenue of research that has not been explored within the current literature is the effect of spatial normalization techniques on FNC reliability. Spatially constrained independent component analysis techniques such as multi-objective optimization with reference (MOO-ICAR) is one of many methods used to study brain functional connectivity (FC) using fMRI that is theoretically robust to variations which may arise in data as a result of normalization procedures. In this work, we deploy MOO-ICAR across 30 different spatial normalization pipelines varying across participant template, normalization modality (anatomical vs functional), and one vs. two-stage warps to MNI space. Most components display relatively high consistency intraclass-correlation coefficients (ICCs), with the vast majoritv (~80%) ereater than 0.5.

The general psychopathology factor (p factor), derived from a wide range of psychological symptoms, is proposed to approximate an individual’s tendency to develop a broad range of psychiatric disorders. The aim of this study was to extract the general p factor in a bifactor model from confirmatory factor analysis (CFA), as well as internalizing and externalizing factors, and test the stability of the factors derived from different behavioral measures. Using data from the Adolescent Brain and Cognitive Development (ABCD) study, we compared the latent factors derived from Child Behavior Checklist measure (a standard approach) with those from broader measures. Multiple linear regression was used to assess the relationship of the p factor with other behavioral or cognition measures and clinical diagnoses. The results showed that the p factor had greater stability and a higher correlation between models (r=0.87) than internalizing and externalizing factors (r=0.32,0.02, respectively). The p factor explained significant variances in all dimensional behavioral variables as well as neurocognition and was significantly related to all mental disorders available. A mixed-effects model was constructed to measure the association of the p factor with screen time activity, sleep duration, physical activity, household income, and gender (altogether, these explained 8.02% of the variance in the p factor). The findings from this study also show that the general p factor defined in the bifactor model has sufficient determinacy.

Anxiety and Depression are currently among the most common mental disorders in children and adolescents. Both genetics and environments play an important role in the development and progress of disorders. This study aimed to understand the effect of multiscale environmental factors on anxiety and depression in school-age children, to refine the identification of genetic variants contributing to susceptibility to anxiety and depression, and to evaluate the genetic heritability. We analyzed data from the Adolescent Brain and Cognitive Development study and computed one principal factor to present the overall anxiety and depression scale in 11,875 participants with ages between 9 and 10 years old. Linear mixed-effect models along with the recursive feature elimination regression and LASSO regression models were used to determine the environmental effects from the macro scale (population density, air pollution), meso scale (neighborhood, school), to micro scale (family and individual experience). Genome-wide association analyses were then performed controlling for environmental factors and sample relatedness to determine the susceptible genetic variants. Furthermore, heritability was calculated for the white population. The results showed that six environmental factors (early life stress, household income, population density, area crime, neighborhood safety, and school risk) and sex had significant effect on anxiety/depression score. Genome-wide association tests showed no SNPs reached a genome-wide significance (p=5e-08), but some genetic mutations including SNPs in SCN1A showed promising effects, and the heritability was estimated close to 15% in the white population.

With the development of neuroimaging techniques, a growing amount of multi-modal brain imaging data are collected, facilitating comprehensive study of the brain. In this paper, we jointly analyzed functional magnetic resonance imaging (fMRI) collected under different paradigms in order to understand cognitive behaviors of an individual. To this end, we proposed a novel multi-view learning algorithm called structure-enforced collaborative regression (SCoRe) to extract co-expressed discriminative brain regions under the guidance of anatomical structure of the brain. An advantage of SCoRe over its predecessor collaborative regression (CoRe) lies in its incorporation of group structures in the brain imaging data, which makes the model biologically more meaningful. Results from real data analysis has confirmed that by incorporating prior knowledge of brain structure, SCoRe can deliver better prediction performance and is less sensitive to hyper-parameters than CoRe. After validation with simulation experiments, we applied SCoRe to fMRI data collected from the Philadelphia Neurodevelopmental Cohort and adopted the scores from the wide range achievement test (WRAT) to evaluate an individual's cognitive skills. We located 14 relevant brain regions that can efficiently predict WRAT scores and these brain regions were further confirmed by other independent studies.

The birth of wireless communication systems nearly a century ago has transformed and redefined the way humans communicate and interact. This transformation has evolved over many years and has brought along not only seamless connectivity for human interactions but also communication between machines and devices. While these communication systems are manmade artifacts, the research community has more recently turned its attention to other communication strategies that have spontaneously evolved in nature.

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.

Multiple types of signals or images are often collected from the same participants in biomedical research. Multimodal analyses have been shown to better capture the joint information. We propose a new method named parallel group independent component analysis (para-GICA) to address a special need for parallel processing of multimodal brain images or signals where it is desirable to partition into groups, for example to stratify by age. Para-GICA is designed to identify associated components between two modalities based on their loading variations in participants, while allowing components to show group specificity. Simulation using synthetic MRI and genetic data demonstrates that para-GICA is able to recover group specific brain networks and the connection between brain networks and genetic factors. A real data application on brain gray matter concentration and whiter matter fractional anisotropy images extracts associated gray matter and white matter components, and ageing induced spatial differences of the components.

Fusion of functional magnetic resonance imaging (fMRI) and genetic information is becoming increasingly important in biomarker discovery. These studies can contain vastly different types of information occupying different measurement spaces and in order to draw significant inferences and make meaningful predictions about genetic influence on brain activity; methodologies need to be developed that can accommodate the acute differences in data structures. One powerful, and occasionally overlooked, method of data fusion is canonical correlation analysis (CCA). Since the data modalities in question potentially contain millions of variables in each measurement, conventional CCA is not suitable for this task. This paper explores applying a sparse CCA algorithm to fMRI and genetic data fusion.

This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component's discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies.

Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of signals. We propose an improved ICA framework for group data analysis by adding an adaptive constraint to the mixing coefficients, namely, constrained coefficients ICA (CCICA). The method is dedicated to identification and increasing the accuracy of components that show significant group differences reflected in the mixing coefficients. Performance of CCICA is assessed by simulations under different signal to noise ratios. An application to multitask functional magnetic resonance imaging analysis is conducted to illustrate the advantages of CCICA. It is shown that CCICA provides stable results and can estimate both the components and the mixing coefficients with a relatively high accuracy compared to Infomax, hence is a promising tool for the identification of biomarkers from brain imaging data.

We present a novel multivariate approach called source based morphometry (SBM) to study the novel structural MRI phase images and get sources of relative gray matter and white matter differences between patients and healthy controls. SBM considers the information cross brain voxels and provides spatially maximal independent sources about localization of changes. The structural MRI phase images efficiently summarize the relationship that exists between the gray and white matter without having to increase the dimensionality of the problem. SBM was then applied to the phase images. Results identified patient versus control differences in gray matter and white matter for visual-motor cortex as well as other areas. These interesting findings show that SBM is a useful multivariate approach for studying the brain. Moreover, the use of structural MRI phase images to joint gray and white matter together provides a significant advantage.

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.

Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with event-related information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a laboratory setting and classifying those ERPs is trivial, such a task presents major challenges in a "real world" setting where the EEG signals are recorded when subjects freely move their eyes and the sensory inputs are continuously, as opposed to discretely presented. Here we demonstrate that with the aid of second-order blind identification (SOBI), a blind source separation (BSS) algorithm: (1) we can extract ERPs from such challenging data sets; (2) we were able to obtain meaningful single-trial ERPs in addition to averaged ERPs; and (3) we were able to estimate the spatial origins of these ERPs. Finally, using back-propagation neural networks as classifiers, we show that these single-trial ERPs from specific brain regions can be used to determine moment-to-moment changes in cognitive processing load during a complex "real world" task.