On the detection of high frequency correlations in resting state fMRI

The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches, and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled data set of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation, and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p < .05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high‐dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA‐based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC.

Section: Discussionsupporting

confidence: 90%

Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination

Sala‐Llonch

Smith

Woolrich

et al. 2018

“…While the proposed approach can capture the spatial fluidity of networks' couplings, new indices are also needed to quantify the spa- in future studies. Furthermore, while the sliding-window approach Damaraju et al, 2014;Sakoglu et al, 2010) is the most commonly used approach to study time-varying properties of brain networks, we highlight the importance of capturing the dynamic information of BOLD signals to its full potential, that is, up to the maximum frequency that exists in the data (Trapp et al, 2018;Vidaurre et al, 2017;Yaesoubi et al, 2018). Therefore, the approach should be improved to capture the full amount of time-varying information in the data.…”

Section: Discussionmentioning

confidence: 99%

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration

Iraji

DeRamus

Lewis

et al. 2019

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra‐ and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi‐site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel‐wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within‐ and between‐subject spatial variability.

“…The most common approach for this category is the sliding-window technique (Allen et al, 2014; Sakoglu et al, 2010). The second category extracts the moment-to-moment dominant spatial co-activation or connectivity pattern without capturing the spatiotemporal variations within and between functional organizations (Karahanoglu and Van De Ville, 2015; Liu et al, 2013; Liu and Duyn, 2013; Preti and Van De Ville, 2017; Tagliazucchi et al, 2012; Trapp et al, 2018; Vidaurre et al, 2017). The co-activation patterns (CAPs) approach and its derivatives are used most frequently within this category (Karahanoglu and Van De Ville, 2015; Liu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time‐varying brain function

Iraji

Damaraju

et al. 2018

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FD), as well as temporal dynamics within and between FD. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g. the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variation provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.