Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.

Brain function can be modeled as dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed "telescopic independent component analysis (TICA)," designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive independent component analysis (ICA) strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on the DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of the DMN, VN, and RFPN. In addition, the TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources.

Background: Time-resolved functional network connectivity (trFNC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFNC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchrony (PS), a phase-based technique. Methods: To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project with 827 subjects [repetition time (TR): 0.7 sec] and the Function Biomedical Informatics Research Network with 311 subjects (TR: 2 sec), which included 151 schizophrenia (SZ) patients and 160 controls. Results: Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, whereas PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (∼30 sec), but larger windows (∼88 sec) sacrifice clinically relevant information. Both methods identify a SZ-associated brain network state but show different patterns: SWPC highlights low anticorrelations between visual, subcortical, auditory, and sensory-motor networks, whereas PS shows reduced positive synchronization among these networks. Conclusion: In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

Understanding interactions in complex systems requires capturing the directionality of coupling, not only its strength. Phase synchronization captures this timing, yet most methods either reduce phase to its cosine or collapse it into scalar indices such as the phase-locking value, discarding directionality. We propose a complex-valued phase synchrony (CVPS) framework that estimates phase with an adaptive Gabor wavelet and preserves both cosine and sine components. Simulations confirm that CVPS recovers true phase offsets and tracks non-stationary dynamics more faithfully than Hilbert-based methods. Because antipsychotics are known to modulate the timing of cortical interactions, they provide a rigorous context to evaluate whether CVPS can capture such pharmacological effects. CVPS further reveals cortical neuro-hemodynamic drivers, with occipital-to-parietal and prefrontal-to-striatal lead-lag flows consistent with known receptor targets, confirming its ability to capture pharmacological timing. CVPS, therefore, offers a robust and generalizable framework for detecting directional coupling in complex systems such as the brain.

Representing data using time-resolved networks is valuable for analyzing functional data of the human brain. One commonly used method for constructing time-resolved networks from data is sliding window Pearson correlation (SWPC). One major limitation of SWPC is that it applies a high-pass filter to the activity time series. Therefore, if we select a short window (desirable to estimate rapid changes in connectivity), we will remove important low-frequency information. Here, we propose an approach based on single sideband modulation (SSB) in communication theory. This allows us to select shorter windows to capture rapid changes in the time-resolved functional network connectivity (trFNC). We use simulation and real resting-state functional magnetic resonance imaging (fMRI) data to demonstrate the superior performance of SSB+SWPC compared to SWPC. We also compare the recurring trFNC patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEPs stay more in states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TCs stay more in a state with negative connectivity between sub-cortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.

In neuroimaging research, understanding the intricate dynamics of brain networks over time is paramount for unraveling the complexities of brain function. One approach commonly used to explore the dynamic nature of brain networks is functional connectivity analysis. However, while functional connectivity offers valuable insights, it fails to consider the diverse timescales of coupling between different brain regions. This gap in understanding leaves a significant aspect of brain dynamics unexplored in neuroimaging research. We propose an innovative approach that delves into the dynamic coupling/connectivity timescales of brain regions relative to one another, focusing on how brain region couplings stretch or shrink over time, rather than relying solely on functional connectivity measures. Our method introduces a novel metric called “warping elasticity,” which utilizes dynamic time warping (DTW) to capture the temporal nuances of connectivity. Unlike traditional methods, our approach allows for (potentially nonlinear) dynamic compression and expansion of the time series, offering a more intricate understanding of how coupling between brain regions evolves. Through the adaptive windows employed by the DTW method, we can effectively capture transient couplings within varying connectivity timescales of brain network pairs. In extensive evaluations, our method exhibits high replicability across subjects and diverse datasets, showcasing robustness against noise. More importantly, it uncovers statistically significant distinctions between healthy control (HC) and schizophrenia (SZ) groups through the identification of warp elasticity states. These states are cluster centroids, representing the warp elasticity across subjects and time, offering a novel perspective on the dynamic nature of brain connectivity, distinct from conventional metrics focused solely on functional connectivity. For instance, controls spend more time in a warp elasticity state characterized by timescale stretching of the visual domain relative to other domains, suggesting disruptions in the visual cortex. Conversely, patients show increased time spent in a warp elasticity state with stretching timescales in higher cognitive areas relative to sensory regions, indicative of prolonged cognitive processing of sensory input. Overall, our approach presents a promising avenue for investigating the temporal dynamics of brain network interactions in functional magnetic resonance imaging (fMRI) data. By focusing on the elasticity of connectivity timescales, rather than adhering to functional connectivity metrics, we pave the way for a deeper understanding of neuropsychiatric disorders in neuroscience research.

To capture different aspects of a complex system, the modeling approach should be able to take these effectively into consideration. Two aspects of the human brain we are quite interested in are its interconnected nature and its dynamism. One modeling approach that can capture these two aspects is based on networks that change with time and go beyond pairwise interactions. Partly because of the size of these temporal high-order networks, analyzing and visualizing them is quite a challenge. In this work, we propose a pipeline based on canonical polyadic (CP) decomposition to analyze high-order networks that are resolved in both time and frequency estimated from resting-state functional magnetic resonance imaging (fMRI) data. We show that we can combine different subjects' information into a common frame of reference for comparison. We also show that different factors provide different patterns that are easy to visualize and interpret. To the best of our knowledge, this is the first work that has proposed a pipeline for analyzing different subjects' brain networks while also incorporating temporal and spectral information about high-order interactions.

Intrinsic Connectivity Networks (ICNs) reflect functional brain organization responsible for various cognitive processes, including sensory perception, motor control, memory, and attention. In this study, we used the Multivariate-Objective Optimization Independent Component Analysis with Reference (MOO-ICAR) and the NeuroMark 2.1 (adult) template to estimate subject-specific ICNs in resting-state functional magnetic resonance imaging (rsfMRI) data of infants. The NeuroMark 2.1 template contains 105 multi-scale canonical ICNs derived from 100k+ adults across multiple datasets. The multi-scale ICNs capture functional segregation across various levels of granularity across brain, revealing functional sources and their interactions. The results showed that the 105 ICNs in infants were spatially aligned with those in the template and revealed age-related distinctive patterns in static Functional Network Connectivity (sFNC), particularly in the sub-cortical and high-level cognitive domains. This study is the first to investigate the presence and development of these multi-scale ICNs in infant rsfMRI data. Our findings confirmed the presence of identifiable canonical ICNs in infants as young as six months, showcasing a strong association between these networks and age and suggesting potential biomarkers for early identification of neurodevelopmental disability.

Background:Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. Methods:We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. Results:Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. Conclusions:Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.

The past 10 years have seen an explosion of approaches that focus on the study of time-resolved change in functional connectivity (FC). FC characterization among networks at a whole-brain level is frequently termed functional network connectivity (FNC). Time-resolved or dynamic functional network connectivity (dFNC) focuses on the estimation of transient, recurring, whole-brain patterns of FNC. While most approaches in this area have attempted to capture dynamic linear correlation, we are particularly interested in whether explicitly nonlinear relationships, above and beyond linear, are present and contain unique information. This study thus proposes an approach to assess explicitly nonlinear dynamic functional network connectivity (EN dFNC) derived from the relationship among independent component analysis time courses. Linear relationships were removed at each time point to evaluate, typically ignored, explicitly nonlinear dFNC using normalized mutual information (NMI). Simulations showed the proposed method estimated explicitly nonlinearity over time, even within relatively short windows of data. We then, applied our approach on 151 schizophrenia patients, and 163 healthy controls fMRI data and found three unique, highly structured, mostly long-range, functional states that also showed significant group differences. In particular, explicitly nonlinear relationships tend to be more widespread than linear ones. Results also highlighted a state with long range connections to the visual domain, which were significantly reduced in schizophrenia. Overall, this work suggests that quantifying EN dFNC may provide a complementary and potentially valuable tool for studying brain function by exposing relevant variation that is typically ignored.

The dynamics of the human brain can be captured by estimating time-resolved functional network connectivity (trFNC). The most used method for estimating trFNC is sliding window Pearson correlation (SWPC). Methods based on instantaneous phase synchrony, which uses phase information for estimating trFNC are being increasingly used. These two approaches are similar under specific assumptions. Prior works have focused on which of these approaches is the best. Some works argue that SWPC can capture amplitude information and therefore we believe that instantaneous phase synchrony methods and SWPC capture different aspects of connectivity since phase synchrony methods work with the phase of the signal. Here we show that these two approaches result in different time-resolved information and therefore should be viewed as complimentary views of connectivity.

Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.

We recently showed that the brain is spatially dynamic, i.e., the spatial patterns of brain networks evolve over time. Yet, most studies, even those studying brain dynamics, omit this information, leading to incorrect inferences. Moreover, spatial dynamics carry unique information about networks hidden from existing spatially static approaches. However, estimating networks in a time-resolved manner is challenging because (1) resting-state fMRI (rsfMRI) has a low signal-to-noise ratio (SNR), (2) relatively short time segments have insufficient information to characterize the spatiotemporal patterns stand-alone, and (3) identification of correspondence within and between-subject is suboptimal and uncertain. Inspired by the group-inference framework, we addressed these limitations by adopting a referenced-informed estimation technique in a time-resolved manner to capture time-varying brain networks and their spatial functional network connectivity (spFNC). Our approach detects schizophrenia alterations in dynamic spFNC. Interestingly these changes in low-dimensional global brain dynamics are also manifested in high-dimensional (voxel-level) space.

Functional connectivity is a widely used measure for finding the relationships between functional entities of the brain. Recently, more focus has been put on the methods that aim to estimate these relationships in a time-resolved fashion. However, the similarities and differences between these methods are not always clear and can result in unfair and incorrect comparisons. Here, we present a framework that provides a unified, systematic view for some of the more well-known methods. Using the proposed unified framework, we explain different methodologies using a unified language and show how they are similar and different conceptually. We give examples how this framework exposes important assumptions made by various methods, which can help clarify differences in results and facilitate reproducibility. We also show how such a framework will enable us to develop methods that improve upon previous methods.

Statistical moments provide us with quantitative measures of the data and estimating these moments in a time-resolved fashion is challenging but important, especially for analyzing human brain data. In this work, we present our attempt at providing a general pipeline for finding different moments (of different orders), which are also resolved in both time and frequency. We showcase the application of our method by analyzing a resting-state functional magnetic resonance imaging dataset. Here we find that individuals with first-episode psychosis (FEP) stay significantly more (compared to controls) in a state which has a very strong pair-wise (two-way) connectivity between salience and default mode networks. That same state also shows strong tri-wise (three-way) connectivity with networks other than the default mode, which points to the fact that different interaction orders have different and complementary information. Our proposed approach can extract meaningful and structured information by calculating (typically ignored) different order moments that are resolved in both time and frequency of the moment space as opposed to sample space.

Multimodal neuroimaging fusion studies of complex disorders like schizophrenia (SZ) have revealed disease-specific links between brain structure and function that unimodal analyses alone could not produce. Here, we utilize a multi-set CCA+ joint ICA fusion framework to study the connection between structural MRI and functional network connectivity (FNC) states derived via filter-banked connectivity (FBC), a unified method for estimating both static and dynamic FNC across the frequency spectrum. This joint analysis showed functional connections in both low-and high-frequency SZ-dominant states were significantly related to alterations in grey matter volume in several areas in the frontal and temporal cortices, which have formerly been implicated in SZ.

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.

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.

Background: Drug-related cue-reactivity, dysfunctional negative emotion processing, and response-disinhibition constitute three core aspects of methamphetamine use disorder (MUD). These phenomena have been studied independently, but the neuroscientific literature on their interaction in addictive disorders remains scant.Methods: 62 individuals with MUD were scanned when responding to the geometric Go or No-Go cues superimposed over blank, neutral, negative-emotional and drug-related background images. Neural correlates of drug and negative-emotional cue-reactivity, response-inhibition and their interactions were estimated, and methamphetamine cue-reactivity was compared between individuals with MUD and 23 healthy controls. Relationships between behavioral characteristics and observed activations were investigated.Results: Individuals with MUD had longer reaction times and more errors in drug and negative-emotional compared to blank blocks, and more omission errors in drug compared to neutral blocks. They showed higher drug cue-reactivity than controls across prefrontal, fusiform, and visual regions (Z > 3.1, p-corrected<0.05). Response-inhibition was associated with precuneal, inferior parietal, anterior cingulate, temporal, and inferior frontal activations (Z > 3.1, p-corrected<0.05). Response-inhibition in drug cue blocks coincided with higher activations in the visual cortex and lower activations in the paracentral lobule and superior and inferior frontal gyri, while inhibition during negative-emotional blocks led to higher superior parietal, fusiform, and lateral occipital activations (Z > 3.1, p-corrected<0.05).Conclusion: Drug cue-reactivity may impair response inhibition partly through activating dis-inhibitory regions, while temporal and parietal activations associated with response-inhibition in negative blocks suggest compensatory activity. Results suggest that drug and negative-emotional cue-reactivity influence responseinhibition, and the study of these interactions may aid mechanistic understanding of methamphetamine use disorder.

Time-resolved functional network connectivity (trFNC) provides a useful tool for representing functional magnetic resonance imaging (fMRI) data with functional networks that change with time. Partly due to its simplicity, sliding window Pearson correlation (SWPC) is the most widely used method for trFNC estimation. In SWPC, the window size should be selected long enough to avoid spurious estimates of connectivity values, and short enough to capture meaningful fast variations in connectivity estimates. To solve this issue, we propose a method inspired by single sideband (SSB) modulation that allows us to select small window sizes for SWPC without filtering out important low-frequency activity information. We use simulation to show the improvement offered by the proposed method. Additionally, we use fMRI data to show that SSB-SWPC estimates have reduced spurious variation compared with typical SWPC estimators.