N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia

Saha

et al. 2020

Preprint

Self Cite

Dynamic functional network connectivity (dFNC) analysis has been attracting interest over the past years by elucidating crucial details of brain activation patterns in severe neurological or psychiatric disorders. Consequently, a considerable amount of work has been conducted to analyze dFNC and leverage it for an improved understanding of neuro-dynamics and cognition. However, state-of-art methods mostly focus on fixed patterns of connectivity that reoccur. Such approaches do not capture the more transient feature space of the brain's neural system and its dynamic properties. Likewise, the dynamical system approaches to model the system continuously and do not well capture unmodelled heterogeneity across subjects and time. Here, we seek an improved understanding of these connectivity states and the mechanism through which their dynamics vary across individuals. Relying on the concept of shapelets to model the temporal dynamics of functional associations between intrinsic brain networks, we develop the 'statelet' - a high dimensional state-shape representation of dynamics. We handle scale differences using the earth mover distance as our similarity metric and utilizing kernel density estimation to build a probability density profile for each shapelet. Unlike clustering the time series, we approximate short, variable-length connectivity patterns using the probability density profile of the shapelets from lower dimensions. We apply the proposed approach to study patients with schizophrenia (SZ) exhibit and identify reduced modularity increased statelet recurrence in their brain network organization relative to healthy controls (HC). An analysis of the connections' consistency across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced statelet-approach enables the handling of dynamic information in cross-modal applications to study healthy and disordered brains and multi-modal fusion within a single dataset.

Section: Definitions and Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statelets: High dimensional predominant shapes in dynamic functional network connectivity

Saha

et al. 2020

Preprint

Self Cite

“…So, these tri-clusters can provide imperative results on connectivity signatures and group differences. The parameters selection and use cases are partially explained in (Rahaman et al, 2020). For creating the T a b l e 1 .…”

Section: Mdfs Subroutinementioning

confidence: 99%

A novel method for tri-clustering dynamic functional network connectivity (dFNC) identifies significant schizophrenia effects across multiple states in distinct subgroups of individuals

Turner

et al. 2020

Preprint

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BackgroundBrain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). Though, such an approach does not factor in the homogeneity of underlying data and may end up with a less meaningful subgrouping of the dataset.MethodsDynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more apples-to-apples comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state.ResultsResulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared to HC, SZ in most tri-clusters show hypoconnectivity (low positive) among subcortical, default mode, cognitive control but hyper-connectivity (high positive) between sensory networks. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks compared to SZ. Results also provide statistically significant difference in reoccurrence time between SZ and HC subjects for two distinct dFNC states.ConclusionsOutcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in the study of a heterogeneous disorder like schizophrenia and in unconstrained experimental conditions such as resting fMRI.

“…Schizophrenia (SZ) is a neuropsychiatric disorder characterized by diverse cognitive impairments and a decline in personal and social functioning. The decomposition of brain images into meaningful independent components (ICs) and generating biomarkers helps analyze SZ to a greater extent (Calhoun, Liu, & Adalı, 2009; Du et al, 2019; Erhardt et al, 2011; Liang et al, 2006; Rahaman et al, 2019; Zhou et al, 2008). Nevertheless, to reason about the neuropsychiatric disorders and studying the brain remains challenging because of the heterogeneous nature of these diseases (Alnæs et al, 2019; Tsuang, Lyons, & Faraone, 1990).…”

Section: Introductionmentioning

confidence: 99%

Statelets: Capturing recurrent transient variations in dynamic functional network connectivity

Saha

et al. 2022

Human Brain Mapping

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Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approach for analyzing dFNC models the system through a fixed sequence of connectivity states. SWC assumes connectivity patterns span throughout the brain, but they are relatively spatially constrained and temporally short‐lived in practice. Thus, SWC is neither designed to capture transient dynamic changes nor heterogeneity across subjects/time. We propose a state‐space time series summarization framework called “statelets” to address these shortcomings. It models functional connectivity dynamics at fine‐grained timescales, adapting time series motifs to changes in connectivity strength, and constructs a concise yet informative representation of the original data that conveys easily comprehensible information about the phenotypes. We leverage the earth mover distance in a nonstandard way to handle scale differences and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC of patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. Statelets in the HC group show an increased recurrence across the dFNC time‐course compared to the SZ. Analyzing the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced approach also enables handling dynamic information in cross‐modal and multimodal applications to study healthy and disordered brains.