| ObjectiveType 2 Diabetes Mellitus(T2DM)can accelerate cognitive decline,and in severe cases,it can lead to Alzheimer’s disease with cognitive impairment as a clinical feature.Therefore,early detection and prevention of cognitive impairment in T2DM are crucial.The potential neural mechanisms underlying T2DM-related cognitive impairment have not been fully elucidated.This study aims to use resting-state functional magnetic resonance imaging(rs-fMRI)from a static or dynamic perspective to investigate the early changes in the overall connectivity pattern of T2DM patients and explore the correlation between these changes and cognitive function,to reveal early imaging biomarkers of T2DM.Additionally,the syndrome differentiation of six meridians of Traditional Chinese Medicine(TCM)can comprehensively reflect the pattern of T2DM and its pathogenesis.We hypothesize that different TCM patterns may have varying effects on cognitive function,and there is currently a lack of effective methods for the early diagnosis and classification of cognitive impairment in different TCM patterns.Therefore,this study also conducted a TCM pattern analysis based on rs-fMRI studies of T2DM patients,to obtain differences in brain imaging and cognitive function among different TCM patterns of T2DM,evaluate the risk of cognitive impairment in different TCM patterns,and better realize the medical concept of"prevention before disease onset.Based on the above scientific questions,this study consists of three parts.Part I:Before the diagnosis of mild cognitive impairment(MCI),T2DM patients already showed a trend of cognitive decline.In this study,T2DM subjects without MCI and HC were included,and percent amplitude of fluctuation(PerAF)and functional connectivity(functional connectivity,FC)were used to explore the changes of neural spontaneous activity in the early stage of T2DM.This study also compared the differences between PerAF and the traditional ALFF method for evaluating neural spontaneous activity in detecting T2DM imaging markers and compared them from a methodological perspective to evaluate the feasibility of PerAF.Part Ⅱ:In the resting state,brain function is still changing dynamically.Functional stability can assist in the maintenance of brain function normally in dynamic changes and manifest from a dynamic viewpoint how functional organizations adapt to accomplish complicated natural tasks.At present,it is still unknown whether the dynamic functional connectivity(dFC)between various brain regions is in a stable state.At the same time,how information spreads and flows in the dynamically fluctuating brain functional structure,and whether information transmission is affected by changes in dynamic stability is still unknown.The combination of stability and dEC can not only show the stability of dynamic functional architecture in brain but also reflect the fluidity of brain information.Part Ⅲ:This study combined structural and functional MRI techniques to explore the differences in brain of T2DM patients with different syndrome types(Shaoyin and Shaoyang),aiming to provide objective evidence for their TCM syndrome types.MethodsPart 11.Participants:In this study,we recruited 30 cognitively normal T2DM subjects and 30 healthy controls(HCs)from the Endocrinology Department of the First Affiliated Hospital of Guangzhou University of Chinese Medicine.The HCs were selected from individuals who had undergone physical examination and screening.All subjects were rigorously screened according to predefined inclusion and exclusion criteria.2.Data acquisition:Detailed clinical data were collected,including age,sex,years of education,hemoglobin Alc(HbA1c),Fasting blood glucose(FBG),triglycerides(TG),total cholesterol(TC),low-density lipoprotein(LDL).Neuropsychological assessments were conducted on all subjects by experienced researchers,including the Montreal Cognitive Assessment(MoCA),Auditory Verbal Learning Test(AVLT),Numeral Span Test(DST),Connection Test(TMT),grooved pegboard test(GPT),clock drawing test(CDT),symbolic number test(SDT),etc.Rs-fMRI data were acquired on a 3-T GE SIGN A MRI scanner(SIGNA EXCITE GE Medical Systems,Milwaukee,WI,USA).Image data includes conventional sequences,3D-T1 structural images and BOLD functional images.3.Data processing:Rs-fMRI data were preprocessed using the MATLAB R2014a platform and analysis toolkit RESTplus V1.211.The preprocessing procedure included format conversion,removing the first 10 volumes,slice timing,realignment,normalization to Montreal Neurological Institute(MNI)space,smoothing,detrending and covariance regression.The ALFF,PerAF,and seed-based FC methods were conducted successively and corrected by Gaussian random field for multiple comparisons.Part 21.Participants:50 T2DM patients and 58 HCs were included in this study.The two groups were matched in age,sex,and education level.The source of the participates is the same as that of Part 1.2.Data acquisition:The data acquisition is the same as that of Part 1.3.Data processing:Imaging data were processed by using DPABI and SPM based on the MATLAB toolbox.The steps of preprocessing were as follows:1)conversion of the image data formats;2)initial 10 time points were removed;3)slice timing;4)head motion;5)covariate of regression;6)spatial normalization;7)spatial smoothing;8)band-pass filter.The calculation process of.the dynamic stability is as follows:For a given voxel j,the Pearson’s correlation coefficients between the BOLD signal of voxel j and that of other voxels within the gray matter were calculated.Then,the dFC maps across time windows were generated for voxel j.The stability was quantified with the Kendall’s coefficient of concordance(KCC)of these dFC maps.The calculation process of the dEC is as follows:Brain regions with changed stability in T2DM were identified through the above steps.The regions which were obtained by group comparison of stability were extracted as region of interest(ROI)for further analysis.The dynamic BC toolbox was used to perform the GCA and calculate the dEC(30).The time series of each ROI was defined as x,and the time series of voxels in the whole brain was defined as y.The Granger causality effect between each ROI and each voxel in the whole brain was studied using bivariate coefficient GCA.Similarly,the sliding window method was used to estimate the dynamic GCA.The dEC variability of each ROI was calculated by variance over the average time course of the GCA coefficients.DPABI software was used to conduct an independent sample T test on the stability and dEC variability between the two groups,taking age,gender and education level as covariates,and performing GRF correction.Part 31.Participants:22 Shaoyin T2DM patients and 21 Shaoyang T2DM patients were included.In this study,T2DM subjects were grouped according to syndrome type by endocrinologists.The two groups were matched in age,sex,and education level.The source of the subjects is the same as that of Part 1.2.Data acquisition:The data acquisition is the same as that of Part 1.3.Data processing:The SPM 12 software was used to process and analyze the 3D-T1 structural data.The steps are as follows:preprocessing the original MRI images,including denoising,correcting distortion,removal of skull,spatial normalization,dividing the standardized brain structural images into gray matter,White matter,and cerebrospinal fluid;spatial smoothing.The gray matter volume was calculated.FWE correction was performed.The functional image processing was the same as that of Part 1 and Part 2.ResultsPart 1Intergroup comparison showed that compared with HCs,T2DM subjects had significantly increased ALFF in the right orbital inferior frontal gyrus,decreased ALFF in the right calcarine,and decreased PerAF in the bilateral middle occipital gyrus and the right calcarine.Seed-based FC analysis showed that compared with HCs,T2DM subjects exhibited significantly decreased FC between the right middle occipital gyrus and the right basal ganglia,which contains the right caudate nucleus and right putamen.According to the partial correlation analyses,HbAlc and AVLT immediate memory scores were negatively correlated in the T2DM group(P=0.045,r=-0.316).However,we found that TC was positively correlated with SDT in the T2DM group(P=0.047,r=0.366).Part 2Compared with HCs,T2DM subjects showed decreased stability in the right supra-marginal gyrus(SMG)and the right medial cingulate gyrus(MCG)with window length of 64 s and a step size of 4 s(Figure 2 and Table 3).To further verify the validity and reliability of the results,validation tests were also processed by using a window length of 60 s and a step size of 2 s and a window length of 96 s and step size of 8 s,respectively.The location of the peak MNI point and the cluster were consistent,but the size of the cluster was slightly altered.Compared with HCs,T2DM subjects showed that the dEC variability increased significantly between the left inferior frontal gyrus(IFG)and the right MCG.The direction of causal flow is from the right MCG to the left IFG.Partial correlation analysis found that AVLT immediate memory scores were negatively correlated with HbAlc(P=0.035,r=-0.309)and LDL(P=0.004,r=-0.399).Part 3The results of the comparison of structural images between groups showed that compared with Shaoyin T2DM patients,the right thalamus volume was higher in Shaoyang T2DM patients(P<0.001,FWE correction).The results of the comparison of structural images between groups showed that after multiple comparisons,no significant difference was found between the two groups.ConclusionPart 1Our study shows that PerAF and ALFF have different sensitivities in detecting abnormalities in spontaneous neuronal activity in T2DM subjects.Combining PerAF and ALFF may provide complementary information to better elucidate potential changes in brain function resulting from T2DM.Taken together,this study combined PerAF and FC analysis revealed that spontaneous activity and synchrony in the right occipital lobe and right basal ganglia were reduced before the onset of overt cognitive impairment in T2DM subjects.These findings may provide new insights that improve the current understanding of the mechanisms of T2DM-related cognitive dysfunction from a neuroimaging perspective.Part 2There are persistent endogenous fluctuations in human brain regions that represent the underlying functional brain architecture.The combination of stability and dEC can not only show the stability of the dynamic functional architecture of the brain,but also reflect the fluidity and directionality of brain information,which is an innovative and interesting attempt in the field of neuroimaging.The altered brain dynamic architecture of T2DM patients may provide an innovative perspective and explanation for their cognitive decline.Part 3This study combined structural and functional magnetic resonance techniques for the first time to study the differences in brain structure and function of T2DM patients with different syndromes(Shaoyin and Shaoyang).Compared with Shaoyang T2DM patients,the volume of the right thalamus decreased significantly in the Shaoyin T2DM patients,which may represent cognitive early signs of change.Therefore,we infer that the decreased volume of the right thalamus in Shaoyin patients is consistent with the law of disease transmission of its syndrome type.Longitudinal follow-up studies will be conducted in the future to verify the cognitive development trend of T2DM patients with different syndromes. |
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