Structured Brain “Chronnectome” Reveals New Brain Dynamic Patterns for Early Detection of Alzheimer’s Disease
Han Zhang1, Xiaobo Chen1, Lichi Zhang1, and Dinggang Shen1,2

1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea, Republic of


To understand dynamics of human brain connectome, we introduce a novel method named “structured brain chronnectome (SBC)”, which measures spatiotemporal architecture of dynamic functional connectivities, a pivotal mechanism for human to adapt to the outside world. From dynamic view angle, with graph theoretic analysis and blind-source separation, we detect meaningful SBCs with typical and atypical configurations compared with traditional networks. They reflect high-order brain functional organization. By applying SBC to an Alzheimer’s disease progression data, we revealed pre-symptomatic brain high-level function alterations from early mild cognitive impairment subjects which are difficult to detect using traditional methods.


Recently evidences have convergently indicated that our brain is not only a complex but also dynamic system. Brain functional connectivity (FC) dynamics has become to be a sensitive mean of imaging biomarker detection for brain diseases at the time point much earlier than traditional static metrics is able to detect. However, the emerging field is still in lack of effective methods for characterizing and quantifying such dynamic connectome due to the complexity caused by introducing a new temporal dimension into the already complicated brain spatiotemporal weaves. Previous methods, although successfully demonstrate the dynamic characteristic of our brain, are not quite suitable for clinical investigation, because they are either oversimplified by abstracting limited “states” from time-varying FC [1] or inevitably induce dimension disaster while calculating high-order FC [2]. A method characterizing voxel-wise spatiotemporal dynamics is highly needed for biomarker detection research.


We propose a dedicated, data-driven, multivariate, voxel-wise method for exploring time-varying brain connectome that we called “chronnectome” after a previous leading study [3]. It will detect underlying spatiotemporal patterns from complex and time-evolving brain FC organization that thus gain its name “structured brain chronnectome (SBC)”. Our method consists of three major components (Fig. 1).

First, we divide resting-state functional MRI (rs-fMRI) data into multiple overlapping sub-series using sliding window. In each sub-series, we calculate FC density in each grey matter voxel by summarizing FC values between this voxel and other grey matter voxels. By these means, we can transform raw rs-fMRI signals into a new dataset with time-varying FC density maps, representing how this inter-connected biological system changes its interweaving pattern (Fig. 2).

Second, the dynamic FC density maps of all subjects are concatenated and fed into group independent component analysis (ICA) to decompose them according to their inherent structure into the SBCs of each subject. Each SBC contain a spatial map and an associated time course of it. The former represents how such a high-order FC spatially organized across different regions. The latter characterizes how this structured high-order FC evolve or fluctuate along time.

Third, group comparisons can be conducted to the spatial maps and time courses of the same SBC, to the interaction among different SBCs, and to many others via matured statistical methods.

Therefore, there is not going to be the comparison of the occurrence of a specific brain status, or that of a particular high-level hyper connectivity link with elusive meaning, as have done so in previous studies. The major difference between the SBC and traditional low-order FC network is that the input to ICA is time-varying FC density data rather than the original rs-fMRI. The resultant SBCs indicate a higher level of brain functional organization that can be quite different from its low-order counterpart. We then apply our method on the Alzheimer’s Disease Neuroimaging Initiative phase-2 (ADNI2) data [4] aiming to find out how AD imaging pathology evolves and where initial functional abnormalities take place in the early or prodromal stages.


We include 7-min rs-fMRI data from each of the 67 cognitive normal (CN), 48 early mild cognitive impairment (EMCI), 45 late mild cognitive impairment (LMCI), and 33 Alzheimer’s disease (AD) subjects. We detected 29 SBCs with our method, among which 17 have matched patterns with traditional low-order brain FC networks (Fig. 3A) and 12 have interesting, novel patterns (Fig. 3B).

Besides AD progression characterized by using the spatial maps of four default mode network-related SBCs (not shown due to limited space), we found that a ventral attention function-related SBC’s temporal evolution complexity (characterized by sample entropy of their associated time series) could be served as early biomarker for AD detection (Fig. 4A, C). This is a novel pattern found to be responsible for pathology-related functional disorder, suggesting high-order FC in attention system be out of function at very early stage of AD. From the SBCs with similar patterns to the traditional brain networks, we found executive control function-related SBC also have abnormally decreased temporal complexity in early stages (Fig. 4B, D) which was often reported in only AD group [5].

Not only within-SBC changes, we also found early abnormalities in inter-SBC connectivity in EMCI and LMCI groups, most of which can only be detected by using the novel-patterned SBCs (Fig. 5). This, for the first time, reveals that the system-level interactions among brain high-order networks could be broken-down in early stages of AD.


We introduce a highly applicable dynamic brain connectome characterization method named “structured brain chronnectome”, and demonstrate its promising value in future clinical research.


No acknowledgement found.


[1] Allen EA, Damaraju E, Plis SM, et al. Tracking Whole-Brain Connectivity Dynamics in the Resting State. Cereb. Cortex. 2014; 24: 663–676.

[2] Chen X, Zhang H, Gao Y, et al. High-order resting-state functional connectivity network for MCI classification. Hum. Brain Mapp. In press; doi:10.1002/hbm.23240.

[3] Calhoun VD, Miller R, Pearlson G, et al. The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery. Neuron; 2014; 84: 262–274.

[4] www.loni.ucla.edu/ADNI.

[5] Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 2011; 15: 483–506.


Fig. 1 Flowchart of our method. Rs-fMRI data is input to a sliding window-based dynamic functional connectivity density (FCD) calculation (A). The FCD at each voxel is calculated by counting all supra-threshold voxels. By going through each grey-matter voxel and each window, a new 4D dataset is generated (B). All subjects’ dynamic FCD data are concatenated (C). Group ICA (GICA) on this big data matrix results in each subject’s independent components (ICs) (D). Group comparisons are then conducted to compare the spatial map and temporal characteristics of an SBC of interest, or compare the functional network connectivity (FNC) among SBCs.

Fig. 2 Dynamic functional connectivity density maps from a sample subject. There is significant time varying pattern for FCD in a brief time (60 s, 20 time points). For demonstration purpose, the dynamic FCD was displayed at an interval of 15 s (5 time points). There are significant spatial pattern changes, either in a global manner (FCD is globally increased or decreased, see the second and the third rows) or localized manner (the pattern with large FCD changes from one functional network to another, see the last row).

Fig. 3 Group-level SBCs with typical spatial patterns (A) and atypical, novel patterns (B) compared to traditional low-order brain networks. The group-level SBC was derived by GICA on dFCD maps. These t maps resulted from one-sample t-test using all subjects from all groups. Most of them are thresholded with t > 5 (p < 1e-7); several of them are thresholded with t > 10 for better illustration. A total of 17 typical-patterned group-level SBCs are shown in left panel, while 12 atypical-patterned group-level SBCs are shown in right panel. Each type of the SBCs is further divided into four sub-types.

Fig. 4 Group difference in temporal activity complexity of two SBCs (VAN, in A; and lFPN, in B). Sample entropy was used to measure temporal activity complexity. One-way ANOVA reveals significant group difference among the CN, EMCI, LMCI and AD groups (p < 0.05). Post hoc pairwise comparisons with two-sample t tests further reveal group differences between CN and EMCI, indicating the importance of using SBC to early detect AD and to characterize AD progression. In (C) and (D) the SBC and its low-order counterpart derived from traditional ICA (TradIC) are compared for VAN and lFPN.

Fig. 5 Group differences in inter-SBC connectivity. Each element in matrix (A) shows group comparison results as revealed by ANOVA (p < 0.05). Light blue shaded area shows the inter-SBC connectivity between any two of the SBCs with typical spatial patterns. Light red shaded area shows the inter-SBC connectivity between any two of the SBCs with atypical spatial patterns. White areas show the inter-SBC connectivity between a typical-patterned SBC and an atypical-patterned SBC. Warm color indicates EMCI (or LMCI) > CN and cold color indicates EMCI (or LMCI) < CN.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)