Mingze Xu1,2, Shiyang Chen2, Bing Ji2,3, Jiuquan Zhang4, Huaiqiu Zhu1, Yi Zhang5, Yonggui Yuan6, Jiahong Gao1, Yijun Liu1, and Xiaoping Hu2
1Biomedical Engineering, Peking University, Beijing, China, People's Republic of, 2Biomedical Engineering, Emory University & Georgia Institute of Technology, Atlanta, GA, United States, 3University of Shanghai for Science & Technology, Shanghai, China, People's Republic of, 4Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, China, People's Republic of, 5School of Life Science and Technology, Xidian University, Shaanxi, China, People's Republic of, 6Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China, People's Republic of
Synopsis
We conducted
dynamic whole-brain connectivity analysis in Late-onset
depression (LOD) to investigate alterations in brain networks. All subjects’
ROI-to-ROI dynamic FC were explored using a data-driven method to obtain the
most explanatory states. Each state indicate a particular ROI-to-ROI FC
pattern. The property of each state were determined based on its scores across
time. Besides decreased FC in normal state, we found LOD patients switch
between brain states more frequently and tend to enter LOD-risk states, due to
and its high states variance and dominating increased FC in LOD-risk states. These results suggest
neural mechanisms of disorder from dynamic perspective.PURPOSE
Late-onset
depression (LOD) is associated with an increased risk for developing Alzheimer’s
disease[1]. However, neurobiological features of LOD remain unclear. Dynamics
in resting state functional connectivity could provide additional insights regarding
brain networks [2]. Therefore,
we performed whole-brain dynamic connectivity analysis in LOD patients to
examine possible alterations in brain networks. We hypothesized that LOD patients
frequently switch into the “LOD” states from other states.
METHODS
Subjects
and Scans
This
study was conducted with approval from the medical ethics committee for
clinical research of ZhongDa Hospital affiliated with Southeast University of
China. Thirty four LOD patients and forty four normal controls (NCs), matched
for age, gender, cerebrovascular risk factor and education, were studied. All
subjects were assessed with a neuropsychological test battery, including overall
cognition, Hamilton depression rating scale (HAMD), and working memory, and
underwent resting-state functional MRI scans using a Siemens Verio 3.0
Tesla scanner (Siemens Medical Solutions, Erlangen, Germany). All imaging data
were manually checked by two experienced radiologists for quality controls
before processing.
Dynamic Functional
Connectivity
rfMRI data
were first preprocessed using functional realignment, functional slice timing,
structural segmentation & normalization, functional normalization and band
pass filter, detrending and regression out WM, CSF and other noise. A total 132
ROIs were defined as the combination of 91 cortical ROIs and 15 subcortical ROIs
based on the FSL Harvard-Oxford maximum likelihood atlas and 26 cerebellar ROIs from the AAL atlas (26 ROIs). Subsequently,
ROI-to-ROI connectivity matrices were calculated using CONN [3]. A
data-driven method (http://www.nitrc.org/projects/conn)
based on PsychoPhysiological Interaction model was applied to obtain the most explanatory
discrete functional connectivity states by exploring all subjects’ ROI-to-ROI
connectivity matrices across time. Each state is associated with a particular ROI-to-ROI
connectivity pattern. Also the property of each
state were determined based on its scores across time. Finally, state properties including temporal variability
and rate of state change in each state were determined and statistically
analyzed for differences and correlations. In addition, whole brain functional
connectivities in each state were statistically compared between LOD and NC.
Results
States
As shown
in Fig. 1, 6 states were derived reflecting the largest temporal modulation
effects observed in connectivity matrix cross all subjects. The time courses at
the bottom depict the scores of six states in subject NC01 over time.
States
Properties
Temporal
variability and rate of state change were calculated for each state. Results
are presented in Fig. 2. LOD patients showed significantly higher state
variance (P<0.05) than NCs in states 2 and 4. Rates of state change were
found significantly correlated with HAMD. Several other neuropsychological test
scores also exhibited similar correlation.
ROI-to-ROI connectivity in each States
Each
state is associated with an ROI-to-ROI connectivity pattern. Whole brain
functional connectivities were compared between NC and LOD groups in each
pattern. Results are shown in Fig. 3.The central ring illustrates the conventional
functional connectivity results. The surrounding rings depict the dynamic
functional connectivity results in each state (FDR
corrected P<0.05, red indicated NC>LOD and blue indicated LOD>NC).
DISCUSSION
In this study,
temporal variability and rate of state change were found significantly altered
in LOD and their neuropsychological test scores, suggesting that LOD patients switch
brain states more frequently. In addition, the functional connectivity in each
state was also changed. Decreased functional connectivities in LOD were mostly
seen in state 1 and in connections present in conventional functional
connectivity map. Some increased connectivities were seen in states 2-6 indicating
LOD might exhibit stronger connections in these states, especially in states
4-6, which might represent LOD-risk states. These results confirmed our
hypothesis, as illustrated in Fig. 4, that unlike healthy person, LOD patients switch between brain states
more frequently and tend to enter LOD-risk states. State 2 and 3 might indicate a middle state, from which LOD-risk state is entered.
CONCLUSION
This
study found that LOD patients exhibit altered brain network dynamics, with
probably more time spent on LOD-risk state. This result have revealed some neural
mechanisms of LOD and provided new insight for prevention and treatment in LOD
and other disorder disease.
Acknowledgements
We wish to thank all the participants
in Yijun Liu's Lab and Xiaoping Hu's Lab and the financial support of the National Natural Science
Foundation of China (81371488, Yonggui Yuan).References
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