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Resting-state functional magnetic resonance imaging (rsfMRI) has shown considerable promise for improving our understanding of brain function and characterizing various mental and cognitive states in the healthy and disordered brain. However, the lack of accurate and precise estimations of comparable functional patterns across datasets, individuals, and ever-changing brain states in a way that captures both individual variation and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses.We posit that using reliable network templates and advanced group-informed network estimation approaches to accurately and precisely obtain individualized (dynamic) networks that retain cross-subject correspondence while maintaining subject-specific information is one potential solution to overcome the aforementioned barrier when considering cross-study comparability, independence of subject-level estimates, the limited data available in single studies, and the low signal-to-noise ratio (SNR) of rsfMRI.Toward this goal, we first obtained a reliable and replicable network template. We combined rsfMRI data of over 100k individuals across private and public datasets and selected around 58k that meet quality control (QC) criteria. We then applied multi-model-order independent component analysis (ICA) and subsampling to obtain reliable canonical intrinsic connectivity networks (ICNs) across multiple spatial scales. The selected ICNs (i.e., network templates) were also successfully replicated by independently analyzing the data that did not pass the QC criteria, highlighting the robustness of our adaptive template to data quality.We next studied the feasibility of estimating the corresponding subject-specific ICNs using a multivariate-spatially constrained ICA as an example of group-informed network estimation approaches. The results highlight that several factors, including ICNs themselves, data length, and spatial resolution, play key roles in successfully estimating the ICNs at the subject level. Large-scale ICNs, in general, require less data to achieve a specific level of spatial similarity with their templates (as well as within- and between-subject spatial similarity). Moreover, increasing data length can reduce an ICN’s subject-level specificity, suggesting longer scans might not always be desirable. We also show spatial smoothing can alter results, and the positive linear relationship we observed between data length and spatial smoothness (we posit that it is at least partially due to averaging over intrinsic dynamics or individual variation) indicates the importance of considering this factor in studies such as those focused on optimizing data length. Finally, the consistency in the spatial similarity between ICNs estimated using the full-length of data and subset of it across different data lengths may suggest that the lower within-subject spatial similarity in shorter data lengths is not necessarily only defined by lower reliability in ICN estimates; rather, it can also be an indication of brain dynamics (i.e., different subsets of data may reflect different ICN dynamics), and as we increase the data length, the result approaches the average (also known as static) ICN pattern, and therefore loses its distinctiveness.

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (MI), that calculates the nonlinear correlation between different brain regions. We demonstrate our proposed approach using simulated data, then apply it to a dataset previously studied in (Damaraju et al., 2014). This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in nonlinear dependencies between schizophrenia patients and healthy controls particularly in visual cortex, with controls showing more nonlinearity in most cases. Certain domains, including cognitive control, and default mode, appeared much less nonlinear, whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored.Further, we propose a method that captures both linear and nonlinear effects in a ‘boosted’ approach. This method increases the sensitivity to group differences in comparison to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

We introduce an extension of independent component analysis (ICA), called multiscale ICA (msICA), and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. msICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and -common connectivity patterns in schizophrenia.Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (1) within the subcortical domain, (2) between the somatomotor and cerebellum domains, and (3) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.

The analysis of time-varying connectivity in functional magnetic resonance imaging (fMRI) has become an important part of ongoing neuroscience discussions. The majority of such studies have focused primarily on temporal variations of functional connectivity among fixed regions of interest (ROIs) or networks of the brain. However, the brain reorganizes its activity on both temporal and spatial scales. Thus an approach which captures time-varying characteristics of brain functional organizations is essential to improving our understanding of brain function. Here, we propose an approach that uses blood oxygenation-level dependent (BOLD) signal to capture spatiotemporal variations of the functional domains of the brain. The approach is based on the well-accepted assumption that the brain can be modeled as a hierarchical functional architecture with different levels of granularity, and as we move to lower levels of this architecture, the complexity associated with each element of this hierarchy decreases. In other words, lower levels have less dynamic behavior and higher functional homogeneity, and there is a level at which its elements can be approximated as "functional units". A functional unit is a pattern of regions with very similar functional activity over time. At the macro-scale, we suggest high-order intrinsic connectivity networks (hICNs) obtained from a high-order spatial independent component analysis (ICA) provide a way to approximate functional units. We consider hICNs to comprise functional domains, elements of a higher level of the brain functional hierarchy which are being investigated in this study. Focusing on time-varying characteristics of functional domains, our approach captures the unique information of each functional domain at every timepoint and has the capacity to examine spatiotemporal variations of the functional domains up to the maximum temporal and spatial resolutions that exist in the data. Here, k-means clustering was deployed to summarize time-varying characteristics of each functional domain into a set of representatives called spatial domain states. Furthermore, the functional connectivity between domains was examined to identify functional modules. Each functional module contains states of functional domains with higher connectivity with each other than with other states. Our proposed approach was further evaluated using a multi-site dataset of resting-state BOLD/fMRI data collected from 160 healthy controls and 149 patients with schizophrenia (SZ). The findings demonstrate that functional domains are spatially fluid over time. Results highlight a negative relationship between the default mode domain and the attention domain in functional module 3. The same pattern exists between the default mode domain and visual, motor, and auditory domains. This is consistent with previous findings showing a negative correlation between the default mode and these domains. The results also revealed distinct differences in the spatial domain states of SZ patients, primarily in the subcortical domain. Importantly, our findings also show that alterations in functional connectivity can occur in the absence of changes in functional activity and vice versa. In short, our approach demonstrates spatially fluid behavior of intra- and inter-domain relationships and highlights a hierarchy of spatial dynamics providing new insight into macro-level functional connectivity in the human brain.