Decreased functional connectivity in schizophrenia: The relationship between social functioning, social cognition and graph theoretical network measures

DeRamus

Lewis

et al. 2019

Human Brain Mapping

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.

Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 93%

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

DeRamus

Lewis

et al. 2019

Human Brain Mapping

“…In our analysis, we observed overall higher coupling variability and lower network coupling strengths among patients with SZ, which is aligned with the dysconnectivity or disconnection hypothesis of schizophrenia (Friston, 1998). Reduced functional connectivity has been constantly reported in patients with SZ Erdeniz et al, 2017;Gavrilescu et al, 2010;Skudlarski et al, 2010;Vercammen et al, 2010), and higher fluctuations of brain connectivity within brain networks in SZ could be related to the brain's effort to compensate for dysconnectivity and/or unbalancing of brain circuitries (Cazorla et al, 2015).…”

Section: Discussionsupporting

confidence: 84%

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

DeRamus

Lewis

et al. 2018

Preprint

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 are changing in fundamental and recurring ways through time. Most previous work has assumed fixed spatial nodes/networks, ignoring the possibility that spatial nodes or networks may vary in time, particularly at the level of the voxel. Here, we introduce an approach allowing for a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variation in spatiotemporal coupling at the voxel level within each network. We identify a novel set of spatially dynamic features which can be obtained and evaluated under different conditions. Results reveal transient spatially fluid interactions between intra-and inter-network relationships in which brain networks transiently merge and then separate again, emphasizing the dynamic interplay between segregation and integration. We also show that brain networks exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies which assumed static networks. Moreover, we show for the first time that anticorrelative connections to the default mode network, are transient as opposed to constant across the entire scan.Preliminary assessments of the approach using a multi-site dataset collected from 160 healthy subjects and 149 patients with schizophrenia (SZ) revealed the ability of the approach to obtain new information and nuanced alterations of brain networks that remain undetected during static analysis. For example, patients with SZ display transient decreases in voxel-wise network coupling including within visual and auditory networks that are not detectable in a spatially static analysis. Our approach also enabled calculation of a novel parameter, the intra-domain coupling variability which was higher within patients with SZ. The significant association between spatiotemporal uniformity and attention/vigilance cognitive domain highlights the cognitive relevance of the spatial chronnectome. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks that are transient at the voxel level and identification of mechanisms for within and between-subject spatial variability to study functional brain homeostasis. KeywordsBrain spatial dynamics, spatial chronnectome, dynamic segregation and integration, spatial coupling, spatial states, spatiotemporal transition matrix, large-scale networks, resting state fMRI (rsfMRI), schizophrenia (SZ) ( Figure 1(A) Step 2). This results in one dynamic coupling map (dCM) per window for each brain network. This approach, unlike its predecessors such as whole brain dynamic functional network connectivity (dFNC) and coactivation patterns (CAP), provides nuanced information regarding temporal variations of spatial patterns of ...

“…Damaraju et al, also reported decreased static connectivity in sensory domains when comparing SZ to TC. Decrease connectivity in SZ compared to TC has been reported in other studies too (Dong, et al, 2018;Erdeniz, et al, 2017;Friston and Frith, 1995;Lynall, et al, 2010;Skudlarski, et al, 2010).…”

Section: Schizophrenia-related Findingssupporting

confidence: 70%

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Faghiri

Damaraju

et al. 2019

Preprint

Studying dynamic functional connectivity (dFC) has been the focus of many studies in recent years. The most commonly used estimator for dFC uses a sliding window in combination with a connectivity estimator such as Pearson correlation. Here, we propose a new approach to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. This approach which we call filter banked connectivity (FBC), implements frequency tiling directly in the connectivity domain contrary to other studies where frequency tiling is done in the activity domain. This leads to more accurate modeling, and a unified approach to capture connectivity ranging from static to highly dynamic, avoiding the need to pick a specific band as in a sliding window approach.First, we demonstrated that our proposed approach, can estimate connectivity at frequencies that sliding window approach fails. Next we evaluated the ability of the approach to identify group differences by using the FBC approach to estimate dFNC in a resting fMRI data set including schizophrenia patients (SZ, n=151) and typical controls (TC, n=163). To summarize the results, we used k-means to cluster the FBC values into different clusters. Some states showed very weak low frequency strength and as such SWPC was not well suited to capture them. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies and engaging the default mode network and its anticorrelations with other networks compared to TCs which spent more time in lower frequency states which primarily includes strong intercorrelations within the sensorimotor domains. In summary, the proposed approach offers a novel way to estimate connectivity while unifying static and dynamic connectivity analyses and can provide additional otherwise missed information about the frequency profile of connectivity patterns.