Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers

Integrating Multimodal Neuroimaging and Genetics: A Structurally-Linked Sparse Canonical Correlation Analysis Approach

Neuroimaging genetics represents a multivariate approach aimed at elucidating the intricate relationships between high-dimensional genetic variations and neuroimaging data. Predominantly, existing methodologies revolve around Sparse Canonical Correlation Analysis (SCCA), a framework we expand to 1) encompass multiple imaging modalities and 2) promote the simultaneous identification of structurally linked features across imaging modalities. The structurally linked brain regions were assessed using diffusion tensor imaging, which quantifies the presence of neuronal fibers, thereby grounding our approach in biologically well-founded prior knowledge within the SCCA model. In our proposed structurally linked SCCA framework, we leverage T1-weighted MRI and functional MRI (fMRI) time series data to delineate both the structural and functional characteristics of the brain. Genetic variations, specifically single nucleotide polymorphisms (SNPs), are also incorporated as a genetic modality. Validation of our methodology was conducted using a simulated dataset and large-scale normative data from the Human Connectome Project (HCP). Our approach demonstrated superior performance compared to existing methods on simulated data and revealed interpretable gene-imaging associations in the real dataset. Thus, our methodology lays the groundwork for elucidating the genetic underpinnings of brain structure and function, thereby providing novel insights into the field of neuroscience. Our code is available at https://github.com/mungegg.

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study

BACKGROUND

Alzheimer disease (AD) is a progressive neurodegenerative disease closely related to genes and characterized by the atrophy of the cerebral cortex. Correlations between imaging phenotypes and the susceptibility genes for AD, as demonstrated in the findings of genome-wide association studies (GWASs), still need to be addressed due to the complicated structure of the human cortex.

METHODS

In our study, an improved GWAS method, whole cortex characteristics GWAS (WCC-GWAS), was proposed. The WCC-GWAS uses multiple cortex characteristics of gray-matter volume (GMV), cortical thickness (CT), cortical surface area (CSA), and local gyrification index (LGI). A cohort of 496 participants was enrolled and divided into 4 groups: normal control (NC; n=122), early mild cognitive impairment (EMCI; n=196), late mild cognitive impairment (LMCI; n=62), and AD (n=116). Based on the Desikan-Killiany atlas, the brain was parcellated into 68 brain regions, and the WCC of each brain region was individually calculated. Four cortex characteristics of GMV, CT, CSA, and LGI across the 4 groups optimized with multiple comparisons and the ReliefF algorithm were taken as magnetic resonance imaging (MRI) brain phenotypes. Under the model of multiple linear additive genetic regression, the correlations between the MRI brain phenotypes and single-nucleotide polymorphisms (SNPs) were deduced.

RESULTS

The findings identified 2 prominent correlations. First, rs7309929 of neuron navigator 3 (NAV3) located on chromosome 12 correlated with the decreased GMV for the left middle temporal gyrus (P=0.0074). Second, rs11250992 of long intergenic non-protein-coding RNA 700 (LINC00700) located on chromosome 10 correlated with the decreased CT for the left supramarginal gyrus (P=0.0019).

CONCLUSIONS

The findings suggested that the correlations between phenotypes and genotypes could be effectively evaluated. The strategy of extracting MRI phenotypes as endophenotypes provided valuable indications in AD GWAS.

Identification of subjective cognitive decline due to Alzheimer's disease using multimodal MRI combining with machine learning

Subjective cognitive decline (SCD) is a preclinical asymptomatic stage of Alzheimer's disease (AD). Accurate diagnosis of SCD represents the greatest challenge for current clinical practice. The multimodal magnetic resonance imaging (MRI) features of 7 brain networks and 90 regions of interests from Chinese and ANDI cohorts were calculated. Machine learning (ML) methods based on support vector machine (SVM) were used to classify SCD plus and normal control. To assure the robustness of ML model, above analyses were repeated in amyloid β (Aβ) and apolipoprotein E (APOE) ɛ4 subgroups. We found that the accuracy of the proposed multimodal SVM method achieved 79.49% and 83.13%, respectively, in Chinese and ANDI cohorts for the diagnosis of the SCD plus individuals. Furthermore, adding Aβ pathology and ApoE ɛ4 genotype information can further improve the accuracy to 85.36% and 82.52%. More importantly, the classification model exhibited the robustness in the crossracial cohorts and different subgroups, which outperforms any single and 2 modalities. The study indicates that multimodal MRI imaging combining with ML classification method yields excellent and powerful performances at categorizing SCD due to AD, suggesting potential for clinical utility.

LARGE-SCALE MULTIVARIATE SPARSE REGRESSION WITH APPLICATIONS TO UK BIOBANK

In high-dimensional regression problems, often a relatively small subset of the features are relevant for predicting the outcome, and methods that impose sparsity on the solution are popular. When multiple correlated outcomes are available (multitask), reduced rank regression is an effective way to borrow strength and capture latent structures that underlie the data. Our proposal is motivated by the UK Biobank population-based cohort study, where we are faced with large-scale, ultrahigh-dimensional features, and have access to a large number of outcomes (phenotypes)-lifestyle measures, biomarkers, and disease outcomes. We are hence led to fit sparse reduced-rank regression models, using computational strategies that allow us to scale to problems of this size. We use a scheme that alternates between solving the sparse regression problem and solving the reduced rank decomposition. For the sparse regression component we propose a scalable iterative algorithm based on adaptive screening that leverages the sparsity assumption and enables us to focus on solving much smaller subproblems. The full solution is reconstructed and tested via an optimality condition to make sure it is a valid solution for the original problem. We further extend the method to cope with practical issues, such as the inclusion of confounding variables and imputation of missing values among the phenotypes. Experiments on both synthetic data and the UK Biobank data demonstrate the effectiveness of the method and the algorithm. We present multiSnpnet package, available at http://github.com/junyangq/multiSnpnet that works on top of PLINK2 files, which we anticipate to be a valuable tool for generating polygenic risk scores from human genetic studies.

Fusing Multimodal and Anatomical Volumes of Interest Features Using Convolutional Auto-Encoder and Convolutional Neural Networks for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is an age-related disease that affects a large proportion of the elderly. Currently, the neuroimaging techniques [e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)] are promising modalities for AD diagnosis. Since not all brain regions are affected by AD, a common technique is to study some region-of-interests (ROIs) that are believed to be closely related to AD. Conventional methods used ROIs, identified by the handcrafted features through Automated Anatomical Labeling (AAL) atlas rather than utilizing the original images which may induce missing informative features. In addition, they learned their framework based on the discriminative patches instead of full images for AD diagnosis in multistage learning scheme. In this paper, we integrate the original image features from MRI and PET with their ROIs features in one learning process. Furthermore, we use the ROIs features for forcing the network to focus on the regions that is highly related to AD and hence, the performance of the AD diagnosis can be improved. Specifically, we first obtain the ROIs features from the AAL, then we register every ROI with its corresponding region of the original image to get a synthetic image for each modality of every subject. Then, we employ the convolutional auto-encoder network for learning the synthetic image features and the convolutional neural network (CNN) for learning the original image features. Meanwhile, we concatenate the features from both networks after each convolution layer. Finally, the highly learned features from the MRI and PET are concatenated for brain disease classification. Experiments are carried out on the ADNI datasets including ADNI-1 and ADNI-2 to evaluate our method performance. Our method demonstrates a higher performance in brain disease classification than the recent studies.

A review ofimaging genetics in Alzheimer's disease

Determining the association between genetic variation and phenotype is a key step to study the mechanism of Alzheimer's disease (AD), laying the foundation for studying drug therapies and biomarkers. AD is the most common type of dementia in the aged population. At present, three early-onset AD genes (APP, PSEN1, PSEN2) and one late-onset AD susceptibility gene apolipoprotein E (APOE) have been determined. However, the pathogenesis of AD remains unknown. Imaging genetics, an emerging interdisciplinary field, is able to reveal the complex mechanisms from the genetic level to human cognition and mental disorders via macroscopic intermediates. This paper reviews methods of establishing genotype-phenotype to explore correlations, including sparse canonical correlation analysis, sparse reduced rank regression, sparse partial least squares and so on. We found that most research work did poorly in supervised learning and exploring the nonlinear relationship between SNP-QT.

New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms

In order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a large matrix can be processed in its factorized form, advantages arise in terms of computation, implementation, and data-compression. In this work, we propose two new parallel iterative algorithms as extensions of the Gauss–Seidel algorithm (GSA) to solve regression problems involving many variables. The convergence study in terms of error-bounds of the proposed iterative algorithms is also performed, and the required computation resources, namely time- and memory-complexities, are evaluated to benchmark the efficiency of the proposed new algorithms. Finally, the numerical results from both Monte Carlo simulations and real-world datasets are presented to demonstrate the striking effectiveness of our proposed new methods.

Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease

Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.

Deep-gated recurrent unit and diet network-based genome-wide association analysis for detecting the biomarkers of Alzheimer's disease

Genome-wide association analysis (GWAS) is a commonly used method to detect the potential biomarkers of Alzheimer's disease (AD). Most existing GWAS methods entail a high computational cost, disregard correlations among imaging data and correlations among genetic data, and ignore various associations between longitudinal imaging and genetic data. A novel GWAS method was proposed to identify potential AD biomarkers and address these problems. A network based on a gated recurrent unit was applied without imputing incomplete longitudinal imaging data to integrate the longitudinal data of variable lengths and extract an image representation. In this study, a modified diet network that can considerably reduce the number of parameters in the genetic network was proposed to perform GWAS between image representation and genetic data. Genetic representation can be extracted in this way. A link between genetic representation and AD was established to detect potential AD biomarkers. The proposed method was tested on a set of simulated data and a real AD dataset. Results of the simulated data showed that the proposed method can accurately detect relevant biomarkers. Moreover, the results of real AD dataset showed that the proposed method can detect some new risk-related genes of AD. Based on previous reports, no research has incorporated a deep-learning model into a GWAS framework to investigate the potential information on super-high-dimensional genetic data and longitudinal imaging data and create a link between imaging genetics and AD for detecting potential AD biomarkers. Therefore, the proposed method may provide new insights into the underlying pathological mechanism of AD.

Alzheimer's disease diagnosis framework from incomplete multimodal data using convolutional neural networks

Alzheimer's disease (AD) is a severe irreversible neurodegenerative disease that has great sufferings on patients and eventually leads to death. Early detection of AD and its prodromal stage, mild cognitive impairment (MCI) which can be either stable (sMCI) or progressive (pMCI), is highly desirable for effective treatment planning and tailoring therapy. Recent studies recommended using multimodal data fusion of genetic (single nucleotide polymorphisms, SNPs) and neuroimaging data (magnetic resonance imaging (MRI) and positron emission tomography (PET)) to discriminate AD/MCI from normal control (NC) subjects. However, missing multimodal data in the cohort under study is inevitable. In addition, data heterogeneity between phenotypes and genotypes biomarkers makes learning capability of the models more challenging. Also, the current studies mainly focus on identifying brain disease classification and ignoring the regression task. Furthermore, they utilize multistage for predicting the brain disease progression. To address these issues, we propose a novel multimodal neuroimaging and genetic data fusion for joint classification and clinical score regression tasks using the maximum number of available samples in one unified framework using convolutional neural network (CNN). Specifically, we initially perform a technique based on linear interpolation to fill the missing features for each incomplete sample. Then, we learn the neuroimaging features from MRI, PET, and SNPs using CNN to alleviate the heterogeneity among genotype and phenotype data. Meanwhile, the high learned features from each modality are combined for jointly identifying brain diseases and predicting clinical scores. To validate the performance of the proposed method, we test our method on 805 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Also, we verify the similarity between the synthetic and real data using statistical analysis. Moreover, the experimental results demonstrate that the proposed method can yield better performance in both classification and regression tasks. Specifically, our proposed method achieves accuracy of 98.22%, 93.11%, and 97.35% for NC vs. AD, NC vs. sMCI, and NC vs. pMCI, respectively. On the other hand, our method attains the lowest root mean square error and the highest correlation coefficient for different clinical scores regression tasks compared with the state-of-the-art methods.

DeFusion: a denoised network regularization framework for multi-omics integration

With diverse types of omics data widely available, many computational methods have been recently developed to integrate these heterogeneous data, providing a comprehensive understanding of diseases and biological mechanisms. But most of them hardly take noise effects into account. Data-specific patterns unique to data types also make it challenging to uncover the consistent patterns and learn a compact representation of multi-omics data. Here we present a multi-omics integration method considering these issues. We explicitly model the error term in data reconstruction and simultaneously consider noise effects and data-specific patterns. We utilize a denoised network regularization in which we build a fused network using a denoising procedure to suppress noise effects and data-specific patterns. The error term collaborates with the denoised network regularization to capture data-specific patterns. We solve the optimization problem via an inexact alternating minimization algorithm. A comparative simulation study shows the method's superiority at discovering common patterns among data types at three noise levels. Transcriptomics-and-epigenomics integration, in seven cancer cohorts from The Cancer Genome Atlas, demonstrates that the learned integrative representation extracted in an unsupervised manner can depict survival information. Specially in liver hepatocellular carcinoma, the learned integrative representation attains average Harrell's C-index of 0.78 in 10 times 3-fold cross-validation for survival prediction, which far exceeds competing methods, and we discover an aggressive subtype in liver hepatocellular carcinoma with this latent representation, which is validated by an external dataset GSE14520. We also show that DeFusion is applicable to the integration of other omics types.

Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification

Histopathological image contains rich pathological information that is valued for the aided diagnosis of many diseases such as cancer. An important issue in histopathological image classification is how to learn a high-quality discriminative dictionary due to diverse tissue pattern, a variety of texture, and different morphologies structure. In this paper, we propose a discriminative dictionary learning algorithm with pairwise local constraints (PLCDDL) for histopathological image classification. Inspired by the one-to-one mapping between dictionary atom and profile, we learn a pair of discriminative graph Laplacian matrices that are less sensitive to noise or outliers to capture the locality and discriminating information of data manifold by utilizing the local geometry information of category-specific dictionaries rather than input data. Furthermore, graph-based pairwise local constraints are designed and incorporated into the original dictionary learning model to effectively encode the locality consistency with intra-class samples and the locality inconsistency with inter-class samples. Specifically, we learn the discriminative localities for representations by jointly optimizing both the intra-class locality and inter-class locality, which can significantly improve the discriminability and robustness of dictionary. Extensive experiments on the challenging datasets verify that the proposed PLCDDL algorithm can achieve a better classification accuracy and powerful robustness compared with the state-of-the-art dictionary learning methods. Graphical abstract The proposed PLCDDL algorithm. 1) A pair of graph Laplacian matrices are first learned based on the class-specific dictionaries. 2) Graph-based pairwise local constraints are designed to transfer the locality for coding coefficients. 3) Class-specific dictionaries can be further updated.

DEEP NETWORK-BASED FEATURE SELECTION FOR IMAGING GENETICS: APPLICATION TO IDENTIFYING BIOMARKERS FOR PARKINSON'S DISEASE

Imaging genetics is a methodology for discovering associations between imaging and genetic variables. Many studies adopted sparse models such as sparse canonical correlation analysis (SCCA) for imaging genetics. These methods are limited to modeling the linear imaging genetics relationship and cannot capture the non-linear high-level relationship between the explored variables. Deep learning approaches are underexplored in imaging genetics, compared to their great successes in many other biomedical domains such as image segmentation and disease classification. In this work, we proposed a deep learning model to select genetic features that can explain the imaging features well. Our empirical study on simulated and real datasets demonstrated that our method outperformed the widely used SCCA method and was able to select important genetic features in a robust fashion. These promising results indicate our deep learning model has the potential to reveal new biomarkers to improve mechanistic understanding of the studied brain disorders.

Joint-Connectivity-Based Sparse Canonical Correlation Analysis of Imaging Genetics for Detecting Biomarkers of Parkinson's Disease

Imaging genetics is a method used to detect associations between imaging and genetic variables. Some researchers have used sparse canonical correlation analysis (SCCA) for imaging genetics. This study was conducted to improve the efficiency and interpretability of SCCA. We propose a connectivity-based penalty for incorporating biological prior information. Our proposed approach, named joint connectivity-based SCCA (JCB-SCCA), includes the proposed penalty and can handle multi-modal neuroimaging datasets. Different neuroimaging techniques provide distinct information on the brain and have been used to investigate various neurological disorders, including Parkinson's disease (PD). We applied our algorithm to simulated and real imaging genetics datasets for performance evaluation. Our algorithm was able to select important features in a more robust manner compared with other multivariate methods. The algorithm revealed promising features of single-nucleotide polymorphisms and brain regions related to PD by using a real imaging genetic dataset. The proposed imaging genetics model can be used to improve clinical diagnosis in the form of novel potential biomarkers. We hope to apply our algorithm to cohorts such as Alzheimer's patients or healthy subjects to determine the generalizability of our algorithm.

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

Leveraging Coupled Interaction for Multimodal Alzheimer's Disease Diagnosis

As the population becomes older worldwide, accurate computer-aided diagnosis for Alzheimer's disease (AD) in the early stage has been regarded as a crucial step for neurodegeneration care in recent years. Since it extracts the low-level features from the neuroimaging data, previous methods regarded this computer-aided diagnosis as a classification problem that ignored latent featurewise relation. However, it is known that multiple brain regions in the human brain are anatomically and functionally interlinked according to the current neuroscience perspective. Thus, it is reasonable to assume that the extracted features from different brain regions are related to each other to some extent. Also, the complementary information between different neuroimaging modalities could benefit multimodal fusion. To this end, we consider leveraging the coupled interactions in the feature level and modality level for diagnosis in this paper. First, we propose capturing the feature-level coupled interaction using a coupled feature representation. Then, to model the modality-level coupled interaction, we present two novel methods: 1) the coupled boosting (CB) that models the correlation of pairwise coupled-diversity on both inconsistently and incorrectly classified samples between different modalities and 2) the coupled metric ensemble (CME) that learns an informative feature projection from different modalities by integrating the intrarelation and interrelation of training samples. We systematically evaluated our methods with the AD neuroimaging initiative data set. By comparison with the baseline learning-based methods and the state-of-the-art methods that are specially developed for AD/MCI (mild cognitive impairment) diagnosis, our methods achieved the best performance with accuracy of 95.0% and 80.7% (CB), 94.9% and 79.9% (CME) for AD/NC (normal control), and MCI/NC identification, respectively.

Group sparse reduced rank regression for neuroimaging genetic study

The neuroimaging genetic study usually needs to deal with high dimensionality of both brain imaging data and genetic data, so that often resulting in the issue of curse of dimensionality. In this paper, we propose a group sparse reduced rank regression model to take the relations of both the phenotypes and the genotypes for the neuroimaging genetic study. Specifically, we propose designing a graph sparsity constraint as well as a reduced rank constraint to simultaneously conduct subspace learning and feature selection. The group sparsity constraint conducts feature selection to identify genotypes highly related to neuroimaging data, while the reduced rank constraint considers the relations among neuroimaging data to conduct subspace learning in the feature selection model. Furthermore, an alternative optimization algorithm is proposed to solve the resulting objective function and is proved to achieve fast convergence. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method has superiority on predicting the phenotype data by the genotype data, than the alternative methods under comparison.

A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis

To characterize associations between genetic and neuroimaging data, a variety of analytic methods have been proposed in neuroimaging genetic studies. These methods have achieved promising performance by taking into account inherent correlation in either the neuroimaging data or the genetic data alone. In this study, we propose a novel robust reduced rank graph regression based method in a linear regression framework by considering correlations inherent in neuroimaging data and genetic data jointly. Particularly, we model the association analysis problem in a reduced rank regression framework with the genetic data as a feature matrix and the neuroimaging data as a response matrix by jointly considering correlations among the neuroimaging data as well as correlations between the genetic data and the neuroimaging data. A new graph representation of genetic data is adopted to exploit their inherent correlations, in addition to robust loss functions for both the regression and the data representation tasks, and a square-root-operator applied to the robust loss functions for achieving adaptive sample weighting. The resulting optimization problem is solved using an iterative optimization method whose convergence has been theoretically proved. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our method could achieve competitive performance in terms of regression performance between brain structural measures and the Single Nucleotide Polymorphisms (SNPs), compared with state-of-the-art alternative methods.

Sparse Group Representation Model for Motor Imagery EEG Classification

A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust classifier training. The calibration burden during data acquisition phase will most probably cause a subject to be reluctant to use a BCI system. To alleviate this issue, we propose a novel sparse group representation model (SGRM) for improving the efficiency of MI-based BCI by exploiting the intersubject information. Specifically, preceded by feature extraction using common spatial pattern, a composite dictionary matrix is constructed with training samples from both the target subject and other subjects. By explicitly exploiting within-group sparse and group-wise sparse constraints, the most compact representation of a test sample of the target subject is then estimated as a linear combination of columns in the dictionary matrix. Classification is implemented by calculating the class-specific representation residual based on the significant training samples corresponding to the nonzero representation coefficients. Accordingly, the proposed SGRM method effectively reduces the required training samples from the target subject due to auxiliary data available from other subjects. With two public EEG data sets, extensive experimental comparisons are carried out between SGRM and other state-of-the-art approaches. Superior classification performance of our method using 40 trials of the target subject for model calibration (Averaged accuracy = 78.2%, Kappa = 0.57 and Averaged accuracy = 77.7%, Kappa = 0.55 for the two data sets, respectively) indicates its promising potential for improving the practicality of MI-based BCI.

Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity

In contrast to most existing studies that typically characterize the developmental sex differences using analysis of variance or equivalently multiple linear regression, we present a parameter-free centralized multi-task learning method to identify sex specific and common resting state functional connectivity (RSFC) patterns underlying the brain development based on resting state functional MRI (rs-fMRI) data. Specifically, we design a novel multi-task learning model to characterize sex specific and common RSFC patterns in an age prediction framework by regarding the age prediction for males and females as separate tasks. Moreover, the importance of each task and the balance of these two patterns, respectively, are automatically learned in order to make the multi-task learning robust as well as free of tunable parameters, i.e., parameter-free for short. Our experimental results on synthetic datasets verified the effectiveness of our method with respect to prediction performance, and experimental results on rs-fMRI scans of 1041 subjects (651 males) of the Philadelphia Neurodevelopmental Cohort (PNC) showed that our method could improve the age prediction on average by 5.82% with statistical significance than the best alternative methods under comparison, in addition to characterizing the developmental sex differences in RSFC patterns.