Biology subjects

Iraji, A.

Publications and source records attributed to Iraji, A..

59 records · Page 4Linked to original sources

bioRxiv2019

Connectivity domain analysis: characterization of connectivity frequency profile using filter banks

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.

neuroscience↗

bioRxiv2019

Parallel Group ICA + ICA: Joint Estimation of Linked Functional Network Variability and Structural Covariation with Application to Schizophrenia

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called \"parallel group ICA+ICA\" that incorporates temporal fMRI information from GICA into a parallel ICA framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g. structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate inter-modality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

bioinformatics↗

bioRxiv2019

Static and Dynamic Functional Connectivity Analysis of Cerebrovascular Reactivity: An fMRI Study

The human brain, as a finely-tuned system, needs a constant flow of oxygen to function properly. To accomplish this, the cerebrovascular system ensures a steady stream of oxygenation to brain cells. One tool that the cerebrovascular system uses is cerebrovascular reactivity (CVR), which is the systems ability to react to vasoactive stimuli. Understanding CVR can provide unique information about cerebrovascular diseases and general brain function. CVR can be evaluated by scanning subjects with blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) while they periodically inhale room air and CO2-enriched gas, a powerful and widely-used vasodilator. Our goal is to understand the effect of vasodilation on individual intrinsic connectivity networks (ICNs), as well as how functional network connectivity (FNC) adapts to the same vasodilation. To achieve this goal, we first developed an innovative metric to measure the effect of CVR on ICNs, which contrasts to the commonly used voxel-wise CVR. Furthermore, for the first time, we studied static (sFNC) and dynamic (dFNC) FNC in the context of CVR. Our results show that network connectivity is generally weaker during vascular dilation, and these results are more pronounced in dFNC analysis. dFNC analysis reveals that participants did not return to the pre-CO2 inhalation state, suggesting that the one-minute period of room-air inhalation is not enough for the CO2 effect to fully dissipate in humans. Overall, we see new relationships between CVR and ICNs, as well as how FNC adapts to vascular system changes.

neuroscience↗

bioRxiv2018

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

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.

neuroscience↗

bioRxiv2018

Spatial dynamics of brain functional domains: A new avenue to study time-varying brain function

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.

neuroscience↗