Functional connectivity of resting-stated fMRI networks reveals much about the normal aging.^1,2 However the functional connectivity is verified to be time-varying over even minutes.³ This paper is to investigate the age related changes on the variation and fluctuation energy of dynamic function connectivity networks. 68 healthy subjects rsfMRI data, from the NKI-Rockland Sample which is provided by the Nathan Kline Institute (NKI, Orangeburg, NY, USA), were divided into two groups: 36 young subjects are assigned into one group (aged from 20 to 25, 24 males); 32 old subjects are assigned into the other group (aged from 60 to 85, 15 males). All approvals and procedures for collection and sharing of data were approved by the NKI institutional review board. According to the demographic characteristics information provided by NKI-RS data set, there is remarkable difference merely in age and no significant difference in gender and hand dominance. The experiments were acquired on 3.0 T SIMENS scanner with following setting: TR/TE = 2500/30ms, Flip angle(FA) = 80, FOV=216×216mm², voxel size = 3×3×3mm³, slicer = 38, scan time=650s, time points =260. High-resolution T1-weighted images were also acquired with TR/TE=2500/3.5ms, FA=8, FOV=256×256mm², voxel size = 1×1×1mm³, slice = 192. All the rsfMRI data were preprocessed using the Connectome Computation System^4,5. We selected Montreal Neurological Institute (MNI) coordinates that have been identified as 142 nodes in cingulo-opercular network(CON), default mode network(DMN), fronto-parietal network(FP), occipital network(OCC), and Sensorimotor network(SM), republished by Dosenbach. Firstly, variou length of sliding window were used to obtain and show how the FC fluctuation including FC variation and fluctuation frequency spectrum varied with window length on one young induvidual. Secondly, A window length of 24TR(60 sec) was used to obtain the FC fluctuation. Then FC variation and frequency energy of the fluctuation were calculated respectively. In the frequency analysis, a five-odere wavelet packet method was ultilized the seprate the fluctuation into detailed frequency bands which had 0.00625Hz, and energy in each band was normalized by the total. In the end, FCV and normalized energy in all frequency bands were compared respectively between two groups on the within- and inter- networks. FDR corrected p<0.05 was used. From the Figure 1, as the window length increasing, the FC changed in three stages which were divided by the 20TR(50sec) and 120TR(300sec). In the first when window was short, the FC showed high variation as well as the mean FC. In the third stage when window was long, the meang FC was steady and closed to the static FC,and the variaion was small. In the second stage, the mean FC as wel as the variation were both relatively stable. As the increasing of window length, the low frequency was always stable but the high frequency. The white line was the 0.0125Hz . With the length of 24TR(60sec) in Figure 2, the dynamic FC showed higher FC variation bewteen networks in young group, especially networks related with OCC and SM. Within the networks, only FP shoed decreased in old group. In the frequency analysis, only the lowest two band showed significant sifference (E1:<0.00625Hz and E2:>0.00625Hz). The low frequency E1 indecated to be increased in old group and the high frequency E2 indecated to be decreased in old group. All these differences were locaed between the networks, especially the OCC and CON, OCC and FP. In the Figure 2, T statistics was color encoded under the significnace threshold of FDR corrected p<0.05.The sliding window length is always an important issue in dynamic FC. The short window is inadvisable as that may lead high variation and instability.⁶ When the window is long enough, it closes to or is static FC, since the static FC is reliable only when the resting time of duration is longer than about 4 min.^5,6 So we think that the length of 24TR(60sec) in this paper is receivable. Then, although there is a low-pass effect on the fluctuation as the rectangular window, we have calculated and confirmed that this cannot impact the results as the cut-off frequency is higher than the E2 frequency. The dynamic FC is mainly associated with the inter-networks which reflect the functional connectivity coding and the networks cooperation. When the brain is aging, these networks cooperation may be decreased and difficult in some complicated tasks. The information processing and encoding need the brain connectivity varies anytime to fulfill the task demands. The FCV and the fluctuation energy are two important measurements and have been demonstrated to be effective to reveal the age related brain change. FCV can measure the tasks related functional variation^7,8 and also the capability during rest. The low frequency fluctuation of dynamic FC, which includes E1 and E2 in this paper, has also been studied to predict the functional maturity in teenagers.⁹ These changes in the frequency energy showed a lowing fluctuation in aging group and slow wave is popular in various brain diseases. This may represent the speed capability of encoding, handling or adopting different information.^9,10 The consistent inter-networks results from FCV and frequency may also confirm the reliability of the two dynamic features. What can be concluded is that resting stated dynamic FC reflects the age related functional declines as aging, which can represent the ability of transforming or adapting various and complex tasks. FCV and low frequency are two important features of dynamic FC to reveal the aging mechanism. This is a promising view of resting-stated based human brain development explore.