Huntington disease (HD) is most widely known for its selective degeneration of striatal neurons but there is also growing evidence for white matter (WM) deterioration. The primary objective of this research was to conduct a large-scale analysis using multisite diffusion-weighted imaging (DWI) tractography data to quantify diffusivity properties along major prefrontal cortex WM tracts in prodromal HD. Fifteen international sites participating in the PREDICT-HD study collected imaging and neuropsychological data on gene-positive HD participants without a clinical diagnosis (i.e., prodromal) and gene-negative control participants. The anatomical prefrontal WM tracts of the corpus callosum (PFCC), anterior thalamic radiations (ATRs), inferior fronto-occipital fasciculi (IFO), and uncinate fasciculi (UNC) were identified using streamline tractography of DWI. Within each of these tracts, tensor scalars for fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity coefficients were calculated. We divided prodromal HD subjects into three CAG-age product (CAP) groups having Low, Medium, or High probabilities of onset indexed by genetic exposure. We observed significant differences in WM properties for each of the four anatomical tracts for the High CAP group in comparison to controls. Additionally, the Medium CAP group presented differences in the ATR and IFO in comparison to controls. Furthermore, WM alterations in the PFCC, ATR, and IFO showed robust associations with neuropsychological measures of executive functioning. These results suggest long-range tracts essential for cross-region information transfer show early vulnerability in HD and may explain cognitive problems often present in the prodromal stage. Hum Brain Mapp 36:3717-3732, 2015. © 2015 Wiley Periodicals, Inc.

Background: Brain 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). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-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. Results: Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Conclusions: Outcomes 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 studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run k-means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the k-means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in "spurious" correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies.

Background

A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.

Methods

We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.

Results

We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.

Conclusion

Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.

It has been widely shown that genomic factors influence both risk for schizophrenia and variation in functional brain connectivity. Moreover, schizophrenia is characterized by disrupted brain connectivity. In this work, we proposed a genome-connectome bipartite graph model to perform imaging genomic analysis. Functional network connectivity (FNC) was estimated after decomposing resting state functional magnetic resonance imaging data from both healthy controls (HC) and patients with schizophrenia (SZ) into spatial brain components using group independent component analysis (G-ICA). Then 83 FNC connections showing a group difference (HC vs SZ) were selected as fMRI nodes, and eighty-one schizophrenia-related single nucleotide polymorphisms (SNPs) were selected as genetic nodes respectively in the bipartite graph. Edges connecting pairs of genetic and fMRI nodes were defined based on the SNP-FNC associations across subjects evaluated by a general linear model. Results show that some SNP nodes in the bipartite graph have a high degree implying they are influential in modulating brain connectivity and may be more strongly associated with the risk of schizophrenia than other SNPs. A bi-clustering analysis detected a cluster with 15 SNPs interacting with 38 FNC connections, most of which were within or between somato-motor and visual brain areas. This suggests that the activity of these brain regions may be related to common SNPs and provides insights into the pathology of schizophrenia. The findings suggest that the SNP-FNC bipartite graph approach is a novel model to investigate genetic influences on functional brain connectivity in mental illness.

Background

Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC.

New method

Here we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC.

Results

Using WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls.

Comparison with existing methods

We compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower.

Conclusions

As large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.

Functional connectivity is one of the most widely used tools for investigating brain changes due to schizophrenia. Previous studies have identified abnormal functional connectivity in schizophrenia patients at the resting state brain network level. This study tests the existence of functional connectivity effects at whole brain and domain levels. Domain level refers to the integration of data from several brain networks grouped by their functional relationship. Data integration provides more consistent and accurate information compared to an individual brain network. This work considers two domain level measures: functional connectivity strength and randomness. The first measure is simply an average of connectivities within the domain. The second measure assesses the unpredictability and lack of pattern of functional connectivity within the domain. Domains with less random connectivity have higher chance of exhibiting a biologically meaningful connectivity pattern. Consistent with prior observations, individuals with schizophrenia showed aberrant domain connectivity strength between subcortical, cerebellar, and sensorial brain areas. Compared to healthy volunteers, functional connectivity between cognitive and default mode domains showed less randomness, while connectivity between default mode-sensorial areas showed more randomness in schizophrenia patients. These differences in connectivity patterns suggest deleterious rewiring trade-offs among important brain networks.

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.

Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.