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The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among many existing methods and tools for analysis of brain anatomy based on structural magnetic resonance imaging (sMRI) data, data-driven source based morphometry (SBM) focuses on the exploratory detection of such patterns. Constrained source-based morphometry (constrained SBM) is a widely used semi-blind extension of SBM that enables extracting maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. In order to operate, constrained SBM needs the data to be locally accessible. However, there exist many reasons (e.g., the concerns of revealing identifiable rare disease information, or violating strict IRB policies) that may preclude access to data from different sites. In this scenario, constrained SBM fails to leverage the benefits of decentralized data. To mitigate this problem, we present a novel approach: decentralized constrained source-based morphometry (dcSBM). In dcSBM, the original data never leaves the local site. Each site operates constrained ICA on their private local data while using a common distributed computation platform. Then, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to find out the significant sources. In our approach, we first use UK Biobank sMRI data to investigate the reliability of our dcSBM algorithm. Finally, we utilize two additional multi-site patient datasets to validate our model by comparing the resulting group difference estimates from both centralized and decentralized constrained SBM.

Data fusion analyses of brain imaging and genomics enable the linking of genomic factors to brain patterns. Due to the small to modest effect sizes of common genetic variants, it is usually challenging to reliably identify relevant genetic factors from the rest of the genome with the typical sample size in neuroimaging studies. To alleviate this problem, we propose sparse parallel independent component analysis (spICA) to leverage the sparsity of individual genomic sources, building upon the existing parallel independent component analysis (pICA) algorithm. Sparsity is enforced by performing Hoyer projection on the estimated independent sources. Simulation results demonstrate that spICA yields improved recovery of imaging-genomic associations and sources compared to pICA. We applied spICA to whole-brain gray matter volume (GMV) and whole-genome single nucleotide polymorphisms (SNPs) data of five different sets of 24,985 discovery samples in the UK Biobank. We identified three GMV sources significantly and stably associated with one SNP source and replicated these associations. GMV sources highlighted frontal, parietal, and temporal regions. Their corresponding loadings on individuals were related to multiple cognitive measures, and the temporal region interacts with age influencing cognition. The SNP component underscored SNPs in chromosome 17 that were enriched in the inflammation response pathway and in regulation effect in the prefrontal cortex via gene expression, methylation, transcription expression, and isoform percentage.

Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. To leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at one site. In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting. Due to the high dimension and small sample size, conventional approaches to decentralized differentially private algorithms suffer in terms of utility. When centralizing the data is not possible, we investigate the benefit of enabling limited collaboration in the form of generating jointly distributed random noise. We show that such (anti) correlated noise improves the privacy-utility trade-off, and can reach the same level of utility as the corresponding non-private algorithm for certain parameter choices. We validate this benefit using synthetic and real neuroimaging datasets. We conclude that it is possible to achieve meaningful utility while preserving privacy, even in complex signal processing systems.

As neuroimaging data increase in complexity and related analytical problems follow suite, more researchers are drawn to collaborative frameworks that leverage data sets from multiple data‐collection sites to balance out the complexity with an increased sample size. Although centralized data‐collection approaches have dominated the collaborative scene, a number of decentralized approaches—those that avoid gathering data at a shared central store—have grown in popularity. We expect the prevalence of decentralized approaches to continue as privacy risks and communication overhead become increasingly important for researchers. In this article, we develop, implement and evaluate a decentralized version of one such widely used tool: dynamic functional network connectivity. Our resulting algorithm, decentralized dynamic functional network connectivity (ddFNC), synthesizes a new, decentralized group independent component analysis algorithm (dgICA) with algorithms for decentralized k‐means clustering. We compare both individual decentralized components and the full resulting decentralized analysis pipeline against centralized counterparts on the same data, and show that both provide comparable performance. Additionally, we perform several experiments which evaluate the communication overhead and convergence behavior of various decentralization strategies and decentralized clustering algorithms. Our analysis indicates that ddFNC is a fine candidate for facilitating decentralized collaboration between neuroimaging researchers, and stands ready for the inclusion of privacy‐enabling modifications, such as differential privacy.

With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal sources in multiple datasets. In this work we utilized the multimodal independent vector analysis model in MISA to directly identify meaningful linked features across three neuroimaging modalities — structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI — in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (the sources/components) that capture age-associated decline, schizophrenia-related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality-specific brain-age deltas were evaluated for association with non-imaging variables. In addition, each set of linked sources reveals a corresponding set of multi-tissue spatial patterns that can be studied jointly.

Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three-way parallel group independent component analysis (pGICA) fusion method that incorporates the first-level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject-wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three-way pGICA provides highly accurate cross-modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional-structural-diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual-subcortical and default mode-cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three-way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework.

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

Advances in brain imaging acquisition techniques allow data from multiple modalities to be collected from each subject offering different but limited views of the structural, functional or temporal properties of human brain. Multimodal fusion can provide a way to leverage different perspectives from multiple complementary modalities. However, most current fMRI-related multimodal fusion approaches are restricted to the second-level 3D features, rather than the original 4D fMRI data. Here we are motivated to propose a novel 3-way fusion approach that can incorporate temporal information from fMRI by parallelizing “group ICA” and “parallel ICA” under a global optimization. Simulations show that GICA+pICA provides more accurate inter-modality linkage detection under both strong and weak correlations. In real data application, one linked fMRI-sMRI-dMRI component was identified showing differences between schizophrenia and controls in all modalities, demonstrating the stability of GICA+pICA to identify inter-modality linkage among three modalities.

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independentspatial mapsto characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs’ predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network’s learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs’ predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network’s learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

Background Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. Methods A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS–associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈ 1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. Results Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. Conclusions Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS–associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.

Resting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008∼0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies.Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions.While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.

Most psychiatric disorders are highly heritable and associated with altered brain structural and functional patterns. Data fusion analyses on brain imaging and genetics, one of which is parallel independent component analysis (pICA), enable the link of genomic factors to brain patterns. Due to the small to modest effect sizes of common genetic variants in psychiatric disorders, it is usually challenging to reliably separate disorder-related genetic factors from the rest of the genome with the typical size of clinical samples. To alleviate this problem, we propose sparse parallel independent component analysis (spICA) to leverage the sparsity of individual genomic sources. The sparsity is enforced by performing Hoyer projection on the estimated independent sources. Simulation results demonstrate that the proposed spICA yields improved detection of independent sources and imaging-genomic associations compared to pICA. We applied spICA to gray matter volume (GMV) and single nucleotide polymorphism (SNP) data of 341 unrelated adults, including 127 controls, 167 attention-deficit/hyperactivity disorder (ADHD) cases, and 47 unaffected siblings. We identified one SNP source significantly and positively associated with a GMV source in superior/middle frontal regions. This association was replicated with a smaller effect size in 317 adolescents from ADHD families, including 188 individuals with ADHD and 129 unaffected siblings. The association was found to be more significant in ADHD families than controls, and stronger in adults and older adolescents than younger ones. The identified GMV source in superior/middle frontal regions was not correlated with head motion parameters and its loadings (expression levels) were reduced in adolescent (but not adult) individuals with ADHD. This GMV source was associated with working memory deficits in both adult and adolescent individuals with ADHD. The identified SNP component highlights SNPs in genes encoding long non-coding RNAs and SNPs in genes MEF2C, CADM2, and CADPS2, which have known functions relevant for modulating neuronal substrates underlying high-level cognition in ADHD.