Recently, in functional magnetic resonance imaging (fMRI), there has been an increased interest in quantifying changes in connectivity between brain regions over an experimental time course to provide a deeper insight into the fundamental properties of brain networks. The application of graphical modeling has been instrumental in these analyses and has enabled the examination of the brain as an integrated system. In this work, we propose a new statistical method, called Network Change Point Detection (NCPD), which is a data-driven technique for detecting changes in the functional connectivity (FC) structure over time where the location and number of changes are unknown a priori. The method provides important insights into the time varying nature of the FC of brain regions while subjects are at rest. NCPD uses spectral clustering to study the network structure between brain regions and uses a non-parametric metric to detect the change in the structures across the time course. NCPD promises to offer important insights into the inner workings of the whole brain. We apply NCPD to simulated data and to a resting-state fMRI data set. The temporal features of this novel connectivity method will provide a more accurate understanding of the large-scale characterizations of disorders such as Alzheimer's disease and may lead to better diagnostic and prognostic indicators.In this work, the objective is to understand the FC dynamics of the entire brain, the FCdynamics in the situations where the number of brain regions exceeds the number of time points in the experiment. The idea originates from community detection in an undirected network G = (V,E), where V and E are the collections of vertices and edges, respectively. In this framework, the community detection problem can be formulated as finding the true disjoint partition of the community. One of the most widely used community detection techniques is spectral clustering¹. In a brain imaging context, detecting FC between brain regions can be seen as a community detection problem. Here, vertices and edges represent brain regions and FC, respectively. Moreover, our target is not only to find the underlying clustering structure, but also to detect the structural change along the experimental time course. We use a non-parametric metric, the cosine of the principal angles between the two partitions, to detect the change in the structures across the time course. Finally, at each change point, we carry out inference on the cosine of the principal angles using the stationary bootstrap². We apply NCPD to a resting state fMRI data set, as described in ³. Participants are instructed to rest in the scanner for 9.5 min, with the instruction to keep their eyes open for the duration of the scan. The individual time series data are bandpass-filtered and motion corrected. The voxel time courses at white-matter and CSF locations are submitted to a Principal Components Analysis and, together with the motion parameters, we use all components with an eigenvalue > 1 as independent variables in a subsequent nuisance regression. Each voxel’s time series is residualized with respect to those independent variables. The residual time series images are then smoothed with an isotropic Gaussian kernel (FWHM=6mm). We apply the Automated Anatomic Atlas Labeling⁴ to the adjusted voxel-wise time series and produce time series for 116 ROIs for each subject. NCPD detects evidence of state changes in the resting-state networks for all subjects bar one (Figures 1 and 2). In addition, NCPD allows us to identify common functional states across subjects (Figure 3). Hence, NCPD not only allows for estimation of whole brain characterizations of the functional organization of the brain but it also can be used to characterize differences in the functional architecture for subjects with/out neurological disorders.

NCPD is very flexible as there is no a priori assumption on where the changes in the FCoccur. The new method has a major advantage over moving window-type methods as we do not have to choose the window length.

NCPD will lead to the robust identification of cognitive states at rest for both controls and subjects with brain disorders. It is hoped that the large-scale temporal features resulting from the accurate description of FC from our novel method will lead to better diagnostic and prognostic indicators of these disorders. More specifically, by comparing the change points and the community network structures of FC of healthy controls to patients with these disorders, we can understand the key differences in functional brain processes. In particular, NCPD allows us to find common cognitive states that recur in time, across subjects, and across groups in a study.