LIVIVO - Search results -

Search results

Result 1 - 9 of total 9

Search options

Article ; Online: A longitudinal cohort study on benefit finding evolution in Chinese women breast cancer survivals

Weiyun Bi / Huaning Wang / Guitao Yang / Cailin Zhu

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 7

Abstract: Abstract Even though the prevalence of benefit finding (BF) has been empirically shown to exist among breast cancer (BC) survivals, how does benefit finding evolve over time remains inadequately investigated. The objective of this cohort study is to ... ...

Abstract	Abstract Even though the prevalence of benefit finding (BF) has been empirically shown to exist among breast cancer (BC) survivals, how does benefit finding evolve over time remains inadequately investigated. The objective of this cohort study is to examine how BF evolves over time among Chinese breast cancer survivals and determine the demographic, medical and psychosocial factors that can sustain BF increase over time. Participants were 486 women with different stages of breast cancer (stages I, II and III) followed from completion of primary treatment. Analysis were performed on the data collected during 2014–2019. During the assessment, each participant completed self-report questionnaires of characteristics and benefit finding at six time points with the interval of 6 months since BC diagnosis. The relationships between demographic, medical and psychosocial characteristics and benefit finding evolution over time were examined using mixed models. Participants reported mixed results on the evolving patterns of benefit finding: 28% reported an upward trend in BF scoring over time, 49% instead reported an downward trend, and the remaining 23% reported no obvious change. Our study has shown that some well-known covariates of benefit finding, e.g. education, income, and social support, are not associated with BF trends. In comparison, levels of spirituality and disease coping at diagnosis can more reliably predict BF evolution over time. Identifying the sustaining factors of benefit finding in the experience of breast cancer is the key to design effective psycho clinical solutions for patients’ long-term post-traumatic growth. As time goes by, breast cancer patients may experience less benefit finding. Our results strongly indicate that benefit finding can be sustained and increased by encouraging attempts at meaning-making and active disease coping during breast cancer treatment. To the best of our knowledge, this study is among the first to examine the evolution trends of benefit finding over time on breast ...
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Large-scale effective connectivity analysis reveals the existence of two mutual inhibitory systems in patients with major depression

Jia Wang / Baojuan Li / Jian Liu / Jiaming Li / Adeel Razi / Kaizhong Zheng / Baoyu Yan / Huaning Wang / Hongbing Lu / Karl Friston

NeuroImage: Clinical, Vol 41, Iss , Pp 103556- (2024)

1481

Abstract: It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal ... ...

Abstract	It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal propagation (effective connectivity) among distributed large-scale brain networks with 43 MDD patients and 56 healthy controls. The results revealed the existence of two mutual inhibitory systems: the anterior default mode network, auditory network, sensorimotor network, salience network and visual networks formed an “emotional” brain, while the posterior default mode network, central executive networks, cerebellum and dorsal attention network formed a “rational brain”. These two networks exhibited excitatory intra-system connectivity and inhibitory inter-system connectivity. Patients were characterized by potentiated intra-system connections within the “emotional/sensory brain”, as well as over-inhibition of the “rational brain” by the “emotional/sensory brain”. The hierarchical architecture of the large-scale effective connectivity networks was then analyzed using a PageRank algorithm which revealed a shift of the controlling role of the “rational brain” to the “emotional/sensory brain” in the patients. These findings inform basic organization of distributed large-scale brain networks and furnish a better characterization of the neural mechanisms of depression, which may facilitate effective treatment.
Keywords	Major depression ; Effective connectivity ; fMRI ; Brain networks ; Rational brain ; Emotional/sensory brain ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
Subject code	000
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Active versus sham DLPFC-NAc rTMS for depressed adolescents with anhedonia using resting-state functional magnetic resonance imaging (fMRI)

Runxin Lv / Min Cai / Nailong Tang / Yifan Shi / Yuyu Zhang / Nian Liu / Tianle Han / Yaochi Zhang / Huaning Wang

Trials, Vol 25, Iss 1, Pp 1-

a study protocol for a randomized placebo-controlled trial

2024 Volume 12

Abstract: Abstract Background Anhedonia, which is defined as the inability to feel pleasure, is considered a core symptom of major depressive disorder (MDD). It can lead to several adverse outcomes in adolescents, including heightened disease severity, resistance ... ...

Abstract	Abstract Background Anhedonia, which is defined as the inability to feel pleasure, is considered a core symptom of major depressive disorder (MDD). It can lead to several adverse outcomes in adolescents, including heightened disease severity, resistance to antidepressants, recurrence of MDD, and even suicide. Specifically, patients who suffer from anhedonia may exhibit a limited response to selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioral therapy (CBT). Previous researches have revealed a link between anhedonia and abnormalities within the reward circuitry, making the nucleus accumbens (NAc) a potential target for treatment. However, since the NAc is deep within the brain, repetitive transcranial magnetic stimulation (rTMS) has the potential to modulate this specific region. Recent advances have enabled treatment technology to precisely target the left dorsolateral prefrontal cortex (DLPFC) and modify the functional connectivity (FC) between DLPFC and NAc in adolescent patients with anhedonia. Therefore, we plan to conduct a study to explore the safety and effectiveness of using resting-state functional connectivity magnetic resonance imaging (fcMRI)-guided rTMS to alleviate anhedonia in adolescents diagnosed with MDD. Methods The aim of this article is to provide a study protocol for a parallel-group randomized, double-blind, placebo-controlled experiment. The study will involve 88 participants who will be randomly assigned to receive either active rTMS or sham rTMS. The primary object is to measure the percentage change in the severity of anhedonia, using the Snaith-Hamilton Pleasure Scale (SHAPS). The assessment will be conducted from the baseline to 8-week post-treatment period. The secondary outcome includes encompassing fMRI measurements, scores on the 17-item Hamilton Rating Scale for Depression (HAMD-17), the Montgomery Asberg Depression Rating Scale (MADRS), the Chinese Version of Temporal Experience of Pleasure Scale (CV-TEPS), and the Chinese Version of Beck Scale for Suicide ...
Keywords	Transcranial magnetic stimulation ; Anhedonia ; Depressive disorder ; Major ; Neuromodulation ; Nucleus accumbens ; Medicine (General) ; R5-920
Subject code	150
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Bioactive Components and Potential Mechanism Prediction of Kui Jie Kang against Ulcerative Colitis via Systematic Pharmacology and UPLC-QE-MS Analysis

Jinbiao He / Chunping Wan / Xiaosi Li / Zishu Zhang / Yu Yang / Huaning Wang / Yan Qi

Evidence-Based Complementary and Alternative Medicine, Vol

2022 Volume 2022

Abstract: Kui Jie Kang (KJK)—a traditional Chinese medicine—has demonstrated clinical therapeutic efficacy against ulcerative colitis (UC). However, the active compounds and their underlying mechanisms have not yet been fully characterized. Therefore, the current ... ...

Abstract	Kui Jie Kang (KJK)—a traditional Chinese medicine—has demonstrated clinical therapeutic efficacy against ulcerative colitis (UC). However, the active compounds and their underlying mechanisms have not yet been fully characterized. Therefore, the current study sought to identify the volatile compounds in KJK responsible for eliciting the therapeutic effect against UC, while also analyzing key targets and potential mechanisms. To this end, systematic network pharmacology analysis was employed to obtain UC targets by using GeneCards, DisGeNET, OMIM, among others. A total of 145 candidate ingredients, 412 potential targets of KJK (12 herbs), and 1605 UC targets were identified. Of these KJK and UC targets, 205 intersected and further identified AKT1, JUN, MAPK, ESR, and TNF as the core targets and the PI3K/AKT signaling pathway as the top enriched pathway. Moreover, molecular docking and ultra-performance liquid chromatography Q Exactive-mass spectrometry analysis identified quercetin, kaempferol, luteolin, wogonin, and nobiletin as the core effective compounds of KJK. In vivo murine studies revealed that KJK exposure increases the body weight and colon length, while reducing colonic epithelial injury, and the expression of inflammatory factors in colitis tissues such as TNF-α, IL-6, and IL-1β. Furthermore, KJK treatment downregulates the expression of pi3k and akt genes, as well as p-PI3K/PI3K and p-AKT/AKT proteins. Collectively, these findings describe the therapeutic effects and mechanisms of KJK in UC and highlight KJK as a potentially valuable therapeutic option for UC via modulation of the PI3K/AKT signaling pathway, thus providing a theoretical reference for the broader application of KJK in the clinical management of UC.
Keywords	Other systems of medicine ; RZ201-999
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score

Ke Hu / Meng Wang / Yong Liu / Hao Yan / Ming Song / Jun Chen / Yunchun Chen / Huaning Wang / Hua Guo / Ping Wan / Luxian Lv / Yongfeng Yang / Peng Li / Lin Lu / Jun Yan / Huiling Wang / Hongxing Zhang / Dai Zhang / Huawang Wu /

Yuping Ning / Tianzi Jiang / Bing Liu

NeuroImage: Clinical, Vol 32, Iss , Pp 102860- (2021)

2021

Abstract: Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies ... ...

Abstract	Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.
Keywords	Schizophrenia ; Classification ; Structural magnetic resonance imaging ; Gray matter volume ; Polygenic risk score ; Machine learning ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
Subject code	616
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Identifying first-episode drug naïve patients with schizophrenia with or without auditory verbal hallucinations using whole-brain functional connectivity

Peng Huang / Long-Biao Cui / Xiangrui Li / Zhong-Lin Lu / Xia Zhu / Yibin Xi / Huaning Wang / Baojuan Li / Fang Hou / Danmin Miao / Hong Yin

NeuroImage: Clinical, Vol 19, Iss , Pp 351-

A pattern analysis study

2018 Volume 359

Abstract: Many studies have focused on patients with schizophrenia with or without auditory verbal hallucinations (AVHs), but due to the complexity of schizophrenia, biologically based diagnosis of patients with schizophrenia remains unsolved. The objectives of ... ...

Abstract	Many studies have focused on patients with schizophrenia with or without auditory verbal hallucinations (AVHs), but due to the complexity of schizophrenia, biologically based diagnosis of patients with schizophrenia remains unsolved. The objectives of this study are to classify between first-episode drug-naïve patients with schizophrenia and healthy controls, and to classify between patients with and without AVHs. Resting state fMRI data from 41 patients with schizophrenia (22 with and 19 without AVHs) and 23 normal controls (NC) were included to compute functional connectivity between brain regions. Classifiers based on support vector machine (SVM) were developed to classify patients with schizophrenia from NC, as well as between the two subgroups of patients. The classification accuracy was evaluated with a leave-one-out cross-validation (LOOCV) strategy. The accuracy in discriminating both subgroups of patients from NC was 81.3%, with 92.0% (sensitivity) and 65.2% (specificity) for the patients and NC, respectively. The classification accuracy in discriminating patients with and without AVHs was 75.6%, with 77.3% (sensitivity) and 73.9% (specificity) for patients with and without AVHs, respectively. The results suggest that functional connectivity provided good discriminative power not only for identifying patients with schizophrenia among NC, but also in discriminating patients with schizophrenia with and without AVHs. Keywords: First episode schizophrenia, Auditory verbal hallucinations, Functional connectivity, Support vector machine
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
Subject code	150
Language	English
Publishing date	2018-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI

Ling-Li Zeng / Huaning Wang / Panpan Hu / Bo Yang / Weidan Pu / Hui Shen / Xingui Chen / Zhening Liu / Hong Yin / Qingrong Tan / Kai Wang / Dewen Hu

EBioMedicine, Vol 30, Iss , Pp 74-

2018 Volume 85

Abstract: Background: A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods ...

Abstract	Background: A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. Methods: A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Findings: Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. Interpretation: The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the “disconnectivity” model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Keywords: Schizophrenia, Deep learning, Connectome, fMRI, Striatum, Cerebellum
Keywords	Medicine ; R ; Medicine (General) ; R5-920
Subject code	006
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI dataResearch in context

Weizheng Yan / Vince Calhoun / Ming Song / Yue Cui / Hao Yan / Shengfeng Liu / Lingzhong Fan / Nianming Zuo / Zhengyi Yang / Kaibin Xu / Jun Yan / Luxian Lv / Jun Chen / Yunchun Chen / Hua Guo / Peng Li / Lin Lu / Ping Wan / Huaning Wang /

Huiling Wang / Yongfeng Yang / Hongxing Zhang / Dai Zhang / Tianzi Jiang / Jing Sui

EBioMedicine, Vol 47, Iss , Pp 543-

2019 Volume 552

Abstract: Background: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. Methods: Motivated by the ...

Abstract	Background: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. Methods: Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs. Findings: Accuracies of 83·2% and 80·2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series. Interpretation: This is the first attempt to apply a multi-scale RNN model directly on fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. Fund: Natural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation. Keywords: Recurrent neural network (RNN), Schizophrenia, Multi-site classification, fMRI, Striatum, Cerebellum, Deep learning
Keywords	Medicine ; R ; Medicine (General) ; R5-920
Subject code	006
Language	English
Publishing date	2019-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: A Schizophrenia-Related Genetic-Brain-Cognition Pathway Revealed in a Large Chinese PopulationResearch in context

Na Luo / Jing Sui / Jiayu Chen / Fuquan Zhang / Lin Tian / Dongdong Lin / Ming Song / Vince D. Calhoun / Yue Cui / Victor M. Vergara / Fanfan Zheng / Jingyu Liu / Zhenyi Yang / Nianming Zuo / Lingzhong Fan / Kaibin Xu / Shengfeng Liu / Jian Li / Yong Xu /

Sha Liu / Luxian Lv / Jun Chen / Yunchun Chen / Hua Guo / Peng Li / Lin Lu / Ping Wan / Huaning Wang / Huiling Wang / Hao Yan / Jun Yan / Yongfeng Yang / Hongxing Zhang / Dai Zhang / Tianzi Jiang

EBioMedicine, Vol 37, Iss , Pp 471-

2018 Volume 482

Abstract: Background: In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to ... ...

Abstract	Background: In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset. Methods: Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways. Findings: One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance. Interpretation: This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia. Keywords: Schizophrenia, Multimodal ...
Keywords	Medicine ; R ; Medicine (General) ; R5-920
Subject code	616
Language	English
Publishing date	2018-11-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED