BACKGROUND:The role of abnormal communications among large-scale brain networks have been given increasing attentions in the pathophysiology of major depressive disorder (MDD). However, few studies have investigated the effect of antidepressant medication treatment on the information communication of structural brain networks, especially converged from the individual analysis.METHODS:Nineteen unipolar MDD patients completed two diffusion tensor imaging (DTI) scans before and after 8-week treatment with selective serotonin reuptake inhibitor. DTI data of 37 matched healthy controls were acquired. We focused on a hub-level community structure network, and investigated whether it had differences on the whole structure and which regions drove these differences in terms of modular affiliation and hub role shift. Data were analyzed by the novel permutation network framework, which appraised the topological consistency of hubs and reserved an individual information.RESULTS:Compared to the pre-treatment state, post-treatment patients exhibited increasing number of modular members in the modules that included the right medial superior frontal gyrus (SFGmed) or the thalamus. Moreover, the result suggested a hub role shift of the left insula from a provincial-hub before treatment to a connector-hub after treatment. Additionally, reduced inter-module degree in the right SFGmed was positively correlated with the reduced sum score of 17-item Hamilton depression rating scale at the follow-up.CONCLUSIONS:Antidepressant medication treatment might be associated with modular reconfigurations of hubs within the fronto-limbic circuit. Moreover, increased inter-module connections of the left insula might improve its integration ability, promoting the remission of MDD. The correlation results of the right SFGmed suggested it might be a valuable indicator for treatment response.

Purpose To detect the consecutive variations of the internetwork interactions over time, which helps to discover the underlying dysfunction of depressive disorders. Abnormal interactions of resting‐state functional networks have been reported in depression. However, little is known regarding the dynamics of how these crucial networks interact and the disease‐related dysfunction. Materials and Methods Functional magnetic resonance imaging data at 3.0T in the resting state were acquired from 20 depressed patients and 20 healthy controls. Twelve resting‐state networks were extracted by group‐independent component analysis, and their interactions were calculated through a sliding windowed Granger causality model analysis. The acquired effective connectivity matrices were used to construct multislice networks with modular structures that were detected via a multislice community detection method. Results No significant differences were observed in the modularity and total module numbers between the depressed patients and the healthy controls. The P values were 0.133 with a confidence interval (–0.0001 0.0093) and 0.136 with a confidence interval (–0.30 0.90), respectively. However, the depressed patients exhibited decreased flexibility of the salience network (SN) compared with the controls ( P = 0.048, corrected, with a confidence interval 0.0068 0.066). Conclusion SN was inclined to participate less in the multiple brain functional modules across the resting time in depression, and infrequently changed its modular allegiance. These findings support the potential importance of the SN in the neuropathological mechanism of depression. Level of Evidence: 1 J. Magn. Reson. Imaging 2017;45:1135–1143

This study investigates the abnormal brain structural connectivity in patients with major depressive disorder (MDD) through a modularization approach.Diffusion tensor imaging (DTI) data were collected from 20 MDD patients and 20 age-matched controls.The structural modularity characteristics and their relationships with clinical variables were examined.Results indicated that MDD patients exhibited reorganized modules with widely damaged global inter-module connectivity in limbic system and altered intra-module connectivity in left hemisphere.Furthermore, the module degree in right rolandic operculum, left posterior cingulate gyrus, right cuneus and right fusiform gyrus were negatively correlated with the depression severity.Additionally, a positive relationship was also found between the duration of disease and participant coefficient in left median cingulate.The current findings highlight altered modular organization in MDD patients, which might contribute to the pathogenesis of the disease.

Purposes: To probe abnormality that may lead to anxiety in depressive patients. Procedures: This study investigated the graph theory features ahead of machine learning feature selection procedure. Classification methods were applied afterwards. Methods: Graph theory, statistical analysis and forward sequential feature selection were combined to find features. SVM classifier was also involved. Results: 1 global and 22 local features were found correlated with clinical anxiety factor. Conclusions: Anxiety is correlated with emotion and cognitive loop and other regions.

Background: Dynamic functional-structural connectivity (FC-SC) coupling might reflect the flexibility by which SC relates to functional connectivity (FC). However, during the dynamic acute state change phases of FC, the relationship between FC and SC may be distinctive and embody the abnormality inherent in depression. This study investigated the depression-related inter-network FC-SC coupling within particular dynamic acute state change phases of FC.Methods: Magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data were collected from 26 depressive patients (13 women) and 26 age-matched controls (13 women). We constructed functional brain networks based on MEG data and structural networks from DTI data. The dynamic connectivity regression algorithm was used to identify the state change points of a time series of inter-network FC. The time period of FC that contained change points were partitioned into types of dynamic phases (acute rising phase, acute falling phase,acute rising and falling phase and abrupt FC variation phase) to explore the inter-network FC-SC coupling. The selected FC-SC couplings were then fed into the support vector machine (SVM) for depression recognition.Results: The best discrimination accuracy was 82.7% (P=0.0069) with FC-SC couplings, particularly in the acute rising phase of FC. Within the FC phases of interest, the significant discriminative network pair was related to the salience network vs ventral attention network (SN-VAN) (P=0.0126) during the early rising phase (70-170ms).Limitations: This study suffers from a small sample size, and the individual acute length of the state change phases was not considered.Conclusions: The increased values of significant discriminative vectors of FC-SC coupling in depression suggested that the capacity to process negative emotion might be more directly related to the SC abnormally and be indicative of more stringent and less dynamic brain function in SN-VAN, especially in the acute rising phase of FC. We demonstrated that depressive brain dysfunctions could be better characterized by reduced FC-SC coupling flexibility in this particular phase. (c) 2015 Elsevier B.V. All rights reserved.

Background: Network-level brain analysis on resting state has demonstrated that depression is not only associated with intra-network dysfunction, but relates to the disturbed interplay between the networks. However, the underlying associations between the intra-network dysfunction and the disturbed inter-network interactions remain unexplored. This study was aimed to explore the association of resting-state networks dysfunction with their dynamics of inter-network interactions in depression.Methods: Resting-state functional magnetic resonance imaging (fMRl) data were collected from 20 depressed patients and 20 matched healthy controls. We evaluated he Hurst exponents of the time series from resting-state networks, and employed multivariate pattern analysis to capture depression-associated networks with increased or decreased Hurst values. Granger causalities between these networks were explored to undertake an intensive study of the dynamic inter-network interactions.Results: The default mode network (DMN) exhibited decreased Hurst value, indicative of more irregular oscillation within the DMN implicated in depressive symptoms. The ventromedial prefrontal network (vmPFN) and salience network (SN) with increased Hurst values, as compensatory mechanisms, continually enhanced the interactions to the DMN for trying hard to impel the DMN to function synchronously. On the other side, the DMN exerted frequently enhanced causality on the left frontoparietal network with elevated Hurst exponent, accompanied by imbalance between the fronto-parietal network and DMN circuits in depression.Limitations: This study suffers from small sample size and is confined to large-scale networks.Conclusions: Our preliminary Findings mainly revealed the DMN-related dynamic interactions with the vmPFN, SN and the fronto-parietal network in depression, which might offer useful information for discovering the neuropathological mechanisms underlying the depressive symptoms. (C) 2014 Elsevier B.V. All rights reserved.

Background: Accumulated evidence has illuminated the topological infrastructure of major depressive disorder (MDD). However, the changes of topological properties of anatomical brain networks in remitted major depressive disorder patients (rMDD) remain an open question. The present study provides an exploratory examination of pattern changes among current major depressive disorder patients (cMDD), EMDD patients and healthy controls (HC) by means of a pattern recognition analysis.Methods: Twenty-eight cMDD patients (age range: 22-54, mean age: 39.57), 15 EMDD patients (age range: 23-53, mean age: 3840) and 30 HC (23-54, mean age: 35.57) were enrolled. For each subject, we computed five kinds of weighted white matter (WM) networks via employing five physiological parameters (i.e. fractional anisotropy, mean diffusivity, lambda(1), lambda(2) and lambda(3)) and then calculated three network measures of these weighted networks. We treated these measures as features and fed into a feature selection mechanism to choose the most discriminative features for linear support vector machine (SVM) classifiers.Results: Linear SVM could excellently distinguish the three groups with the 100% classification accuracy of recognizing cMDD/rMDD from HC, and 97.67% classification accuracy of recognizing cMDD from rMDD. The further pattern analysis found two types of discriminative patterns among cMDD, rMDD and HC. (i) Compared with HC, both cMDD and EMDD exhibited the similar deficit patterns of node strength primarily involving the salience network (SN), default mode network (DMN) and frontoparietal network (FPN). (ii) Compared with cMDD and rMDD showed the altered pattern of intra-communicability within DMN and inter-communicability between DMN and the other sub-networks including the visual recognition network (VRN) and SN.Limitations: The present study had a limited sample size and a lack of larger independent data set to validate the methods and confirm the findings.Conclusions: These findings implied that the impairment of MOD was closely associated with the alterations of connections within SN, DMN and FPN, whereas the remission of MDD was benefitted from the network compensatory of intra-communication within DMN and inter-communication between DMN and the other sub-networks (i.e., VRN and SN). (C) 2015 Elsevier B.V. All rights reserved.

Objective To explore the characteristic differences of the fractional amplitude of low-frequency fluctuation (fALFF) feature of the spoutaneous neural activity between young male unipolar depression and bipolar depression patients,and determine the biological markers to distinguish the two disorders.Methods Twelve male unipolar depression,12 bipolar depression patients and 11 age and educated-matched healthy males underwent resting-state functional magnetic resonance imaging scanning at 3.0 Tesla.The whole brain' s fALFF were calculated and analyzed.Results The differences of the fALFF of the three groups had significant differences (P＜0.01,Alphasim) in the right orbital medial frontal gyrus (6,33,-9;K =29),the left medial frontal gyrus (-6,60,3;K =44) and the left paracentral lobule (-3,-27,5 1;K =20).The unipolar depression subjects had significantly higher fALFF compared with heahhy controls in the left anterior cingulate gyrus.The bipolar depression subjects had significantly higher fALFF compared with healthy controls in the bilateral medial frontal gyrus and the left middle cingulate gyrus.And the unipolar depression subjects had significantly lower fALFF compared with bipolar depression ones in the right orbital medial frontal gyrus,the right anterior cingulate gyrus and the bilateral medial frontal ~rus(all P＜0.05).Conclusions Abnormalities exist in the brain regions in male with unipolar or bipolar depression patients in the resting state,and the abnormal regions are different.

Objective To explore the regional homogeneity (ReHo) feature of the spontaneous neural activity within young male depressed patients,and conduct correlation analysis of the abnormal regions with the severity of depressive symptoms.Methods 19 male depressed patients and 19 educated age-matched male healthy controls were undergone resting-state functional magnetic resonance imaging scanning at 3.0 Tesla.The whole brain' s regional homogeneity was calculated.The ReHo values of abnormal brain regions after alphasim correction were conducted correlation analysis with the total score of Hamilton Rating Scale for Depression.Results As compared with the controls,the depressed patients exhibited significantly increased ReHo in the bilateral parahippocampal (-30,-42,-9:K=43:27,-33,-12:K=18) and the right posterior cingulate gyrus(3,15,33:K=54),while decreased ReHo in the right postcentral gyms (27,-36,54,K =21) (P＜0.05,Alphasim).The correlation analysis showed that the ReHo of the right parahippocampal gyrus was positively correlated with the HAMD17 total scores (r=0.535,P=0.018).Conclusions The male depressed patients exhibit abnormities in the limbicrelated brain regions during rest,and the extent of damage is correlated with the severity of depression.

The exploration of the dynamics of intrinsic activity has been documented and some breakthrough reports emerge in healthy volunteer. However, the dynamic research on the depression remains unclear. This study was to investigate dynamic anomaly of the resting-state networks (RSNs) in depression. Forty-seven RSNs were extracted from Resting-state functional magnetic resonance imaging data of the 40 depressed patients and 40 matched healthy controls. The dynamic functional connectivities were calculated for constructing multislice networks, whose modular structures were detected by the multislice community detection method. The dwelling time in the dominant community with significant difference distributed in the posterior cingulated cortex (PCC), middle cingulated cortex (MCC), insula, thalamus, and middle temporal gyrus networks. The PCC network with increased dwelling time, together with the MCC and insula networks with decreased dwelling time, were associated with the imbalance between the internal-directed and external-directed behavior, indicating the switching deficits in the depressed patients.

Purpose Despite the increasing understanding of major depressive disorder (MDD) using neuroimaging techniques, the topological organization of anatomical networks underlying MDD remains unclear. Methods The topological organization of brain anatomical networks was explored using complex network approaches. Diffusion tensor image data of 29 MDD patients and 30 healthy controls were collected. Network metrics such as strength, efficiency, and centrality were computed after the construction of brain anatomical networks. Between‐group differences and correlations with clinical measurements were further explored. Results Compared with the healthy controls, MDD patients exhibited widely damaged information interactions in both cognitive‐emotional circuitry and frontoparietal circuitry. Moreover, the centralities in the right prefrontal cortex involving the right middle frontal gyrus and the right gyrus rectus were negatively correlated with the duration of disease. Additionally, the centrality in the right inferior frontal gyrus (triangular part) and the efficiency in the right superior frontal gyrus (orbital part) were both positively related to the depression severity. Conclusion These findings suggest that altered connectivity involved in affective and cognitive processing procedures might contribute to the pathogenesis of MDD. Magn Reson Med 72:1397–1407, 2014. © 2013 Wiley Periodicals, Inc.

Objective To explore the relationship between energy feature of the spontaneous neural activity and separate symptom clusters in first-episode depression.Methods 22 first-episode depression patients and 26 age-,gender-matched healthy controls were scanned with 3.0 T MRI Scanner.The t-test was employed to compare the difference of amplitude of low frequency fluctuation (ALFF) between the two groups,and the correlation analyses were conducted between ALFF of brain regions with significant difference and the severity of depressive symptoms clusters.Results Compared with healthy group,the depression group showed significantly increased ALFF in the right middle frontal gyrus (9,45,-6; K =18) and the bilateral fusiform gyrus (-34,-19,-12; K =37 and 30,-33,-18 ; K =31,respectively),and decreased ALFF in the left precuneus (0,-72,42; K =19) (P＜0.05,corrected by Alphasim).The ALFF of the the right middle frontal gyrus,the right fusiform gyrus,and the left precuneus were negative correlated with the scores of weight factor,retardation factor and sleep disturbance factor (r=-0.494,P=0.019; r=-0.486,P=0.022 and r=-0.484,P=0.023,respectively).Conclusion Abnormal energy feature of the spontaneous neural activity may be associated with severity of specific depressive symptoms clusters in first-episode depression patients during resting-state.

Previous studies had explored the diagnostic and prognostic value of the structural neuroimaging data of MDD and treated the whole brain voxels, the fractional anisotropy and the structural connectivity as classification features. To our best knowledge, no study examined the potential diagnostic value of the hubs of anatomical brain networks in MDD. The purpose of the current study was to provide an exploratory examination of the potential diagnostic and prognostic values of hubs of white matter brain networks in MDD discrimination and the corresponding impaired hub pattern via a multi-pattern analysis. We constructed white matter brain networks from 29 depressions and 30 healthy controls based on diffusion tensor imaging data, calculated nodal measures and identified hubs. Using these measures as features, two types of feature architectures were established, one only included hubs (HUB) and the other contained both hubs and non hubs. The support vector machine classifiers with Gaussian radial basis kernel were used after the feature selection. Moreover, the relative contribution of the features was estimated by means of the consensus features. Our results presented that the hubs (including the bilateral dorsolateral part of superior frontal gyrus, the left middle frontal gyrus, the bilateral middle temporal gyrus, and the bilateral inferior temporal gyrus) played an important role in distinguishing the depressions from healthy controls with the best accuracy of 83.05%. Moreover, most of the HUB consensus features located in the frontal-parieto circuit. These findings provided evidence that the hubs could be served as valuable potential diagnostic measure for MDD, and the hub-concentrated lesion distribution of MDD was primarily anchored within the frontal-parieto circuit.

Objective To explore energy feature of the spontaneous neural activity in young female depressive patients,and its correlation to the severity of depressive symptoms.Methods Fourteen female depressive patients and 18 healthy controls were scanned with 3.0 T MRI scanner.The difference with amplitude of low frequency fluctuation (ALFF) between both groups was compared with the t-test,and the correlation analysis between ALFF of brain regions with significant difference and the severity of depressive symptoms was conducted.Results Compared with healthy group,the depression group showed significantly increased ALFF in the right cerebellum anterior lobe (Montreal Neurological Institute(MNI) coordinates (x,y,z):39,-54,-36; k =20; t=3.678,P ＜ 0.05),and decreased ALFF in the left posterior cingulate (MNI coordinates (x,y,z):-6,-45,15; k =18) and the left superior parietal lobule (MNI coordinates (x,y,z):-21,-78,48; k =20;t =-3.967,-3.669; both P ＜0.05; corrected by Alphasim).The ALFF in the right cerebellum anterior lobe was positively correlated to the number of depressive episodes (r=0.607,P =0.021),and negatively correlated to the HAMD17 total score and cognitive disturbance score (r =-0.595,P =0.025 ; r =-0.542,P =0.045,respectively).Conclusion Abnormal brain activity could emerge in female depressive patients during resting-state,the level of ALFF may be associated with severity of depressive symptoms and cognitive disturbance.

Objective: We examined the gender-difference effect on abnormal spontaneous neuronal activity of male and female major depressive disorder (MDD) patients using the amplitude of low-frequency fluctuation (ALFF) and the further clarified the relationship between the abnormal ALFF and differences in MDD prevalence rates between male and female patients.Methods: Fourteen male MDD patients, 13 female MDD patients and 15 male and 15 female well matched healthy controls (HCs) completed this study. The ALFF approach was used, and Pearson correlation was conducted to observe a possible clinical relevance.Results: There were widespread differences in ALFF values between female and male MDD patients, including some important parts of the frontoparietal network, auditory network, attention network and cerebellum network. In female MDD patients, there was a positive correlation between average ALFF values of the left postcentral gyrus and the severity of weight loss symptom.Conclusions: The gender-difference effect leading to abnormal brain activity is an important underlying pathomechanism for different somatic symptoms in MDD patients of different genders and is likely suggestive of higher MDD prevalence rates in females.Significance: The abnormal ALFF resulting from the gender-difference effect might improve our understanding of the differences in prevalence rates between male and female MDD patients from another perspective. (C) 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Resting-state functional magnetic resonance imaging (fMRI) studies of major depressive disorder (MDD) have revealed abnormalities of functional connectivity within or among the resting-state networks. They provide valuable insight into the pathological mechanisms of depression. However, few reports were involved in the "long-term memory" of fMRI signals. This study was to investigate the "long-term memory" of resting-state networks by calculating their Hurst exponents for identifying depressed patients from healthy controls. Resting-state networks were extracted from fMRI data of 20 MDD and 20 matched healthy control subjects. The Hurst exponent of each network was estimated by Range Scale analysis for further discriminant analysis. 95% of depressed patients and 85% of healthy controls were correctly classified by Support Vector Machine with an accuracy of 90%. The right fronto-parietal and default mode network constructed a deficit network (lower memory and more irregularity in MDD), while the left fronto-parietal, ventromedial prefrontal and salience network belonged to an excess network (longer memory in MDD), suggesting these dysfunctional networks may be related to a portion of the complex of emotional and cognitive disturbances. The abnormal "long-term memory" of resting-state networks associated with depression may provide a new possibility towards the exploration of the pathophysiological mechanisms of MDD.

Acid-base balance is the very important part of human homeostasis. Alteration in tissue pH can be an indicator of many diseases, such as tumor. Amide proton transfer imaging, a novel technique based on magnetization transfer and chemical exchange, was proved to be relying on pH to a great extent, in which proton transfer between endogenous proteins and peptides and tissue water. To the present APT imaging technology, a steady state of chemical exchange is needed, hence the specific absorption rate limit precludes its use. In order to minimize the SAR and extend the pH-weighted imaging in clinical practice, we establish a new algorithm in the unsteady state. Our earlier experiments with pH phantom prove that we can detect the pH value at 1.5 Tesla. And now we are carrying out pioneering work at 1.5 Tesla. We use a new sequence with 3.5 ppm. and -3.5 ppm. radiofrequency offset to obtain pH-weighted magnetic resonance imaging of volunteers with brain tumor at 1.5 Tesla. We find that this imaging technology can delineate the heterogeneous areas of brain tumor at 1.5 Tesla, such as separating the mass of solid tumor and infiltration from the surrounding edema without gadolinium enhanced. Artifact caused by field inhomogeneity is the weakness of this technology, which need to be optimized in post processing. This finding may provide metabolic imaging marker as a complement for conventional imaging, and has its potential advantages in predicting the nature of tumor.

Objective:To investigate the feasibility of magnetization transfer(MT) technology in application of magnetic resonance pH imaging on 1.5 T clinical MR scanner.Materials and Methods:Two groups of agarose-creatine pH phantoms were prepared and imaged by improved spin-echo-magnetization transfer pulse sequence.Results:At radiofrequency offset of 224 Hz,the signal intensity magnetization transfer image of same pH phantoms showed no difference.The signal intensity of different pH phantoms depended on pH values.The higher pH was,the stronger signal intensity was.Conclusions:pH-sensitive magnetization transfer image can be acquired by improving the parameters and choosing appropriate radiofrequency offset of spin-echo-magnetization transfer pulse sequence on clinical 1.5 T MR scanner.This experimental study laid a solid foundation for further clinical applications.

吴仁华,1959年出生于江西省大余县现为汕头大学医学院教授,博士生导师,汕头大学医学院学术委员会成员,第二附属医院医学影像学教研室主任,放射科主任,医学影像学学科带头人,中华医学会放射学会神经组全国委员,中华医学会医学工程学会数字医学影像工程与技术学组全国委员,《中华放射学杂志》编委,《磁共振成像》常务编委,American Journal of Roentgenology,IEEE Transactions on Medical Imaging,Neuroradiology,Experimental Brain Research,Psychiatry Research:Neuroimaging,Computer Methods and Programs in Biomedicine,AJR Integrative Imaging,Chinese Medical Journal等国际刊物审稿员.他是美国电气电子工程师协会( IEEE)和生物医学工程学会(EMBS)会员,国际脑功能成像学会(OHBM)会员,The Scientific Advisory Board for HBM2006成员.

Magnetization transfer (MT) is an important source of contrast in magnetic resonance imaging (MRI), and is the basis of chemical exchange saturation transfer (CEST) imaging, which does not exist in our clinical MR scanner at 1.5 Tesla (T). Amide proton transfer (APT) imaging, a variant of CEST imaging, has been shown capable of detecting tissue acidosis during stroke at above 3.0 T. According to the theory of CEST and APT imaging developed by MT, our study is to make full use of MT technology in the clinic by modifying MT parameters to acquire clinical pH-weighted MT imaging. We modified the source codes of the MT sequence to enable the frequency offset to reach 121 Hz and 224 Hz for saturating the amine protons of creatine. The results showed that MT imaging at a frequency offset of 121 Hz could not be acquired clearly for free water saturation, and MT imaging at 224 Hz could reflect slightly different pH values. We analyzed MT imaging of the same and different pH phantoms, and gave reasonable explanation for the results. We conclude that MT imaging at clinical 1.5 T could differentiate pH phantoms by choosing appropriate parameters.