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Efficacy and safety profile of oral creatine monohydrate in add-on to cognitive-behavioural therapy in depression: An 8-week pilot, double-blind, randomised, placebo-controlled feasibility and exploratory trial in an under-resourced area 口服一水肌酸对抑郁症认知行为疗法的疗效和安全性:在资源匮乏地区进行的为期 8 周的双盲、随机、安慰剂对照可行性和探索性试验。
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-11-01 DOI: 10.1016/j.euroneuro.2024.10.004
Nima Norbu Sherpa , Riccardo De Giorgi , Edoardo Giuseppe Ostinelli , Amrita Choudhury , Tenzin Dolma , Sangila Dorjee
Pre-clinical and clinical evidence proposes that creatine monohydrate, an affordable nutraceutical, could be a useful adjunct to conventional antidepressant treatments. In this pilot feasibility and exploratory study, we investigate the 8-week effects of creatine in addition to cognitive-behavioural therapy (CBT) versus placebo plus CBT in depression. For the primary efficacy outcome of change in Patient Health Questionnaire-9 depression score at study endpoint, we used mixed-model repeated measures analysis of covariance. Logistic regressions were employed to assess acceptability (any-cause dropouts), tolerability (dropouts for adverse events), and safety (patients experiencing one or more adverse events). We calculated effect sizes adjusted for age, sex, and baseline depression score. One-hundred participants (50 females, mean age= 30.4 ± 7.4 years) with depression (mean PHQ-9 = 17.6 ± 6.3) were randomised to either creatine+CBT (N = 50) or placebo+CBT (N = 50). At 8 weeks, PHQ-9 scores were lower in both study arms, but significantly more so in participants taking creatine (mean difference= -5.12). Treatment discontinuations due to any cause and to adverse events, and proportion of participants with at least one adverse event were comparable between study arms. This hypothesis-generating trial suggests that creatine could be a useful and safe supplement to CBT for depression. Longer and larger clinical trials are warranted.
临床前和临床证据表明,一水肌酸这种经济实惠的营养保健品可以作为传统抗抑郁治疗的有效辅助药物。在这项试验性可行性和探索性研究中,我们调查了肌酸在认知行为疗法(CBT)基础上与安慰剂加 CBT 相比对抑郁症患者的 8 周疗效。对于研究终点时患者健康问卷-9 抑郁症评分变化这一主要疗效结果,我们采用了混合模型重复测量协方差分析法。逻辑回归用于评估可接受性(任何原因的退出)、耐受性(因不良事件退出)和安全性(出现一次或多次不良事件的患者)。我们计算了根据年龄、性别和基线抑郁评分调整后的效应大小。100 名抑郁症患者(50 名女性,平均年龄= 30.4 ± 7.4 岁)(平均 PHQ-9 = 17.6 ± 6.3)被随机分配到肌酸+CBT(50 人)或安慰剂+CBT(50 人)治疗方案中。8周时,两个研究组的PHQ-9评分均有所下降,但服用肌酸的参与者的PHQ-9评分明显更高(平均差异=-5.12)。因任何原因和不良事件导致的治疗中断,以及出现至少一种不良事件的参与者比例,在各研究组之间不相上下。这项提出假设的试验表明,肌酸可以作为抑郁症 CBT 的一种有用且安全的补充。有必要进行更长时间和更大规模的临床试验。
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引用次数: 0
Does 18 Hz deep TMS benefit a different subgroup of depressed patients relative to 10 Hz rTMS? The role of the individual alpha frequency 与 10 赫兹经颅磁刺激相比,18 赫兹深部经颅磁刺激是否能使不同亚组的抑郁症患者受益?个体阿尔法频率的作用
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-12 DOI: 10.1016/j.euroneuro.2024.09.007
Helena Voetterl , Uri Alyagon , Victoria J. Middleton , Jonathan Downar , Abraham Zangen , Alexander T. Sack , Hanneke van Dijk , Aimee Halloran , Nancy Donachie , Martijn Arns
Both 10 Hz repetitive transcranial magnetic stimulation (rTMS) as well as 18 Hz deep TMS (dTMS) constitute effective, FDA-approved TMS treatment protocols for depression. However, not all patients experience sufficient symptom relief after either of these protocols. Biomarker-guided treatment stratification could aid in personalizing treatment and thereby enhancing improvement. An individual alpha frequency (iAF)-based EEG-biomarker, Brainmarker-I, can differentially stratify patients to depression treatments. For instance, an iAF close to 10 Hz was associated with better improvement to 10 Hz rTMS, possibly reflecting entrainment of endogenous oscillations to the stimulation frequency.
Accordingly, we examined whether 18 Hz dTMS would result in better improvement in individuals whose iAF lies around 9 Hz, a harmonic frequency of 18 Hz.
Curve fitting and regression analyses were conducted to assess the relation between iAF and improvement. For treatment stratification purposes, correlations with iAF-distance to 10 Hz compared 18 Hz dTMS (N = 114) to 10 Hz rTMS (N = 72).
We found a robust quadratic effect, indicating that patients with an iAF around 9 Hz exhibited least symptom improvement (r2=0.126, p<.001). Improvement correlated positively with iAF-distance to 10 Hz (p=.003). A secondary analysis in 20 Hz figure-of-eight data confirmed this direction. A significant interaction of iAF-distance and stimulation frequency between 10 and 18 Hz datasets emerged (p=.026).
These results question entrainment of endogenous oscillations by their harmonic frequency for 18 Hz, and suggest that 10 Hz and 18 Hz TMS target different subgroups of depression patients. This study adds to iAF stratification, augmenting Brainmarker-I with alternative TMS protocols (18 Hz/20 Hz) for patients with a slower iAF, thereby broadening clinical applicability and relevance of the biomarker.
10 赫兹重复经颅磁刺激(rTMS)和 18 赫兹深部经颅磁刺激(dTMS)都是经美国食品及药物管理局批准的有效抑郁症经颅磁刺激治疗方案。然而,并非所有患者在接受这两种治疗方案后症状都能得到充分缓解。以生物标志物为指导的治疗分层有助于个性化治疗,从而改善病情。基于个体α频率(iAF)的脑电图生物标志物--Brainmarker-I,可以对抑郁症患者进行分层治疗。例如,iAF接近10赫兹与10赫兹经颅磁刺激的改善效果更佳相关,这可能反映了内源性振荡对刺激频率的诱导作用。因此,我们研究了iAF位于9赫兹(18赫兹的谐波频率)附近的个体,18赫兹经颅磁刺激是否会带来更好的改善效果。出于治疗分层的目的,将 18 赫兹经颅磁刺激(N = 114)与 10 赫兹经颅磁刺激(N = 72)进行了比较。我们发现了一个强有力的二次效应,表明 iAF 在 9 赫兹左右的患者症状改善最少(r2=0.126,p<.001)。改善程度与 iAF 与 10 赫兹的距离呈正相关(p=.003)。对 20 赫兹八位数数据的二次分析证实了这一方向。在10赫兹和18赫兹数据集之间,iAF-距离与刺激频率之间出现了明显的交互作用(p=.026)。这些结果质疑了18赫兹谐波频率对内源性振荡的诱导作用,并表明10赫兹和18赫兹TMS针对的是不同的抑郁症患者亚群。这项研究为iAF分层增添了新的内容,为iAF较慢的患者提供了可供选择的TMS方案(18赫兹/20赫兹)来增强Brainmarker-I,从而扩大了生物标记的临床适用性和相关性。
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引用次数: 0
COMBINING MENDELIAN RANDOMISATION WITH DEPRESSION TRAJECTORIES TO IDENTIFY DEVELOPMENTALLY SPECIFIC PREDICTORS OF CHANGE IN DEPRESSIVE SYMPTOMS 将 "泯灭随机法 "与抑郁轨迹相结合,确定抑郁症状变化的发育特异性预测因素
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.092
Robyn Wootton , Richard Parker , Michael Lawton , Kate Tilling
Prevalence of depression is increasing, especially amongst adolescents and young adults, representing a key risk period where intervention is critical. When using Mendelian randomisation (MR) to identify causal risk factors for depression, estimates are limited to average lifetime effects, rather than being specific to developmental stages.
Methods. We have combined trajectories of depressive symptoms with MR to identify developmentally specific risk factors. We used repeated measures of depressive symptoms (short Moods and Feelings Questionnaire) in the ALSPAC cohort, with 11 repeated assessments covering ages 9 to 27 years. First, we used a repeated measures multi-level model (MLM) to describe the average trajectory of depressive symptoms. Linear splines split by knot points were used to explain the non-linear pattern of growth. Second, we used latent class analysis to explore heterogeneity in depression trajectories. Third, we combined both trajectory models with genetic instruments for depression (positive control) and with modifiable risk factors for depression.
Our models included 44,611 repeated assessments of sMFQ from 6,422 unique individuals. Our best fitting MLM trajectory had three linear splines corresponding to puberty (9-14.5 years), adolescence (14.5-21 years) and early adulthood (21-27 years). Latent classes were stable low, decreasing, transient, increasing and stable high. Positive control genetic instrument for MDD predicted trajectories, most strongly membership into the increasing and stable high class. Genetic instruments for BMI and educational attainment were not associated with change in population average depressive symptoms at any of the different developmental stages nor with class membership. This could suggest no causal effects of these risk factors at these developmental stages, or low power.
We are continuing to develop our methods, test power and incorporate additional risk factors. We believe that combining outcome trajectories with MR analyses has wide ranging application to improve specificity of causal effects and recommendations for intervention development.
抑郁症的发病率正在上升,尤其是在青少年和年轻成年人中,这是一个关键的风险期,干预至关重要。当使用孟德尔随机法(MR)来确定抑郁症的因果风险因素时,估计结果仅限于平均终生效应,而不是针对特定的发展阶段。我们将抑郁症状的轨迹与孟德尔随机化相结合,以确定发育阶段的特定风险因素。我们在ALSPAC队列中使用了抑郁症状的重复测量方法(情绪和感觉简易问卷),共进行了11次重复评估,年龄涵盖9至27岁。首先,我们使用重复测量多层次模型(MLM)来描述抑郁症状的平均轨迹。我们使用按结点分割的线性样条来解释非线性增长模式。其次,我们使用潜类分析来探索抑郁轨迹的异质性。第三,我们将两个轨迹模型与抑郁的遗传工具(阳性对照)和可改变的抑郁风险因素结合起来。我们的最佳拟合 MLM 轨迹有三个线性样条,分别对应青春期(9-14.5 岁)、青春期(14.5-21 岁)和成年早期(21-27 岁)。潜伏类别为稳定低、下降、短暂、上升和稳定高。MDD 的正对照基因工具预测了轨迹,其中最强烈的是上升和稳定高类别。体重指数(BMI)和教育程度的遗传工具与不同发育阶段人群平均抑郁症状的变化无关,也与类别成员资格无关。这可能表明这些风险因素在这些发展阶段没有产生因果效应,或者说这些因素的作用力较低。我们正在继续开发我们的方法、测试作用力并纳入更多的风险因素。我们相信,将结果轨迹与 MR 分析相结合,可以提高因果效应的特异性,并为干预措施的制定提供建议,具有广泛的应用前景。
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引用次数: 0
DRUG REPOSITIONING ANALYSIS IDENTIFIED HUNDREDS OF NOVEL COMPOUNDS ASSOCIATED WITH TOBACCO USE DISORDER 药物重新定位分析发现了数百种与烟草使用障碍有关的新型化合物
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.097
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引用次数: 0
TRACKING THE LONGITUDINAL COURSE IN YOUNG PEOPLE AT HIGH RISK OF BIPOLAR DISORDER 追踪躁郁症高危青少年的纵向病程
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.019
Philip Mitchell , Gloria Roberts , Janice Fullerton , Michael Breakspear , Neve Thompson
Young people with a first-degree relative with bipolar disorder (BD) have a 10-15% risk of developing this illness, but currently we have little understanding of the specific clinical and/or biological features which predict such an outcome. Our group in Sydney established the Bipolar Disorder Kids and Sibs high-risk cohort over a decade ago (with 170 high risk young people, 130 controls and 65 who had already developed BD) and have collaborated closely with the US multi-site consortium led by John Nurnberger. We have followed this Australian sample regularly over time, with particular interest in neuroimaging, genetic (including epigenetic), neuropsychological and clinical findings. We have scanned subjects on 3 occasions (baseline, 2 and 10 years), having already published on changes over the first two years. We reported significant weakening in the high-risk subjects of structural connectivity in a network encompassing the left inferior and middle frontal areas, left striatal and thalamic structures, the left fusiform, and right parietal and occipital regions. These findings were more pronounced in those who had developed a first episode of hypo(mania) over those two years. We have also explored for clinical predictors of hypo(mania) onset, finding particular depressive features to be a strong predictor of this outcome. We will present clinical and biological findings of our 10-year follow-up. More studies of long-term biological and clinical changes over time, and predictors of the onset of hypo(mania) are needed to enable rational development of early intervention studies in those at increased familial risk of BD.
有一级亲属患有躁郁症(BD)的年轻人有 10-15% 的患病风险,但目前我们对预测这种结果的具体临床和/或生物学特征知之甚少。我们在悉尼的研究小组十多年前建立了双相情感障碍儿童和兄弟姐妹高风险队列(包括170名高风险青少年、130名对照组和65名已经患上双相情感障碍的青少年),并与约翰-努恩伯格(John Nurnberger)领导的美国多站点研究小组密切合作。我们一直定期跟踪这一澳大利亚样本,尤其关注神经影像学、遗传学(包括表观遗传学)、神经心理学和临床研究结果。我们对受试者进行了 3 次扫描(基线、2 年和 10 年),并已发表了前两年的变化情况。我们发现,在高危人群中,包括左侧额叶下部和中部区域、左侧纹状体和丘脑结构、左侧纺锤体以及右侧顶叶和枕叶区域在内的网络结构连通性明显减弱。这些发现在两年内首次出现低(躁)狂症的患者中更为明显。我们还探索了低躁狂症发病的临床预测因素,发现特定的抑郁特征是预测这一结果的有力因素。我们将介绍 10 年随访的临床和生物学研究结果。我们需要对长期的生物学和临床变化以及低(躁)狂发病的预测因素进行更多的研究,以便对那些BD家族风险增加的人群进行合理的早期干预研究。
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引用次数: 0
YOUTH AT RISK OF BIPOLAR DISORDER: TRACKING TRAJECTORIES, OUTCOMES AND BIOMARKERS USING NEUROIMAGING, GENOMICS AND EPIGENOMICS 躁郁症风险青少年:利用神经影像学、基因组学和表观基因组学追踪轨迹、结果和生物标志物
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.021
Janice Fullerton , Bronwyn Overs , Gloria Roberts , Sonia Hesam-Shariati , Kate Ridgeway , Tayla Williams , Neve Thomson , Kerrie Pierce , Howard Edenberg , Holly Wilcox , Emma Stapp , Melvin McInnis , Leslie Hulvershorn , John Nurnberger , Philip Mitchell
<div><div>Longitudinal prospective studies in high-risk populations are key for identifying pre-morbid risk factors for the development of psychopathology. The Australia-US collaborative Bipolar high-risk study comprises 3 groups of participants aged 12-30 years: ‘high-risk’ (with a sibling or parent with bipolar-I or -II), controls with no family history, and an unrelated group of BD-probands; with clinical, demographic and biological data. Familial risk is sometimes considered a surrogate for genetic risk (that is indexed via inherited DNA variants), but we know that this is a simplification of the ‘heritable’ component, which might comprise both direct and indirect genetic effects as well as the impact of family environment. We have used multiple analytic approaches to define and characterize features of disease risk, using neuroimaging, genomics and epigenomics. Analysis of magnetic resonance imaging (MRI) data from 217 unrelated Australian ‘Bipolar Kids and Sibs study’ participants (baseline n=217, follow-up n=152) finds accelerated cortical thinning over time (two scans, 2 years apart) in high-risk subjects (n=105) compared to controls (n=112), suggesting an early brain over-growth followed by normalisation towards the typical age of BD onset. Accelerated thickness and volume reductions over time were observed in ‘high-risk’ individuals across multiple cortical regions, relative to controls, including right lateral orbitofrontal thickness (β=.033, p < .001) and inferior frontal volume (β=.021, p < .001). We also find that bipolar polygenic risk (PsychArray) interacts with stress to increase suicide risk. We examined polygenic risk for both suicide attempt and risky behaviour on structural variance in cortical parcellations that have previously shown replicable associations with suicide attempts, finding that structural differences in the anterior cingulate, parahippocampal, and cuneus warrant further investigation as potential biomarkers for suicide attempts, particularly within the context of BD. Examination of epigenetic markers (450k/EPIC array) shows that genome-wide methylation patterns are broadly impacted by polygenic risk; highlighting an important interplay between genomically inherited risk and the potential biological encoding of environmental exposures. We are now collecting a 3rd MRI scan to capture nonlinear cortical developmental trajectories, and a 2nd blood sample to extend our baseline epigenetic work, derive serum measures and examine mRNA transcription patterns as potential biomarkers of emergent psychopathology. Brain regions associated with both genetic and clinical measures of psychopathology may serve as viable biomarkers, with clinical utility for the identification of individuals who are at greatest risk of developing psychopathology or suicidal intent. Future work will enable integration of these features into a prediction model of disease, to identify biological subgroups on the trajectory towards mental il
对高危人群进行纵向前瞻性研究,是确定精神病理学发展的病前风险因素的关键。澳大利亚和美国合作开展的躁郁症高危人群研究包括三组 12-30 岁的参与者:"高危人群"(兄弟姐妹或父母中有一人患有躁郁症 I 或 II)、无家族史的对照组和无亲属关系的躁郁症患者组;这些参与者都有临床、人口统计学和生物学数据。家族风险有时被认为是遗传风险的替代物(通过遗传的 DNA 变异来表示),但我们知道,这只是对 "遗传 "成分的简化,"遗传 "成分可能包括直接和间接的遗传效应以及家庭环境的影响。我们采用多种分析方法,利用神经影像学、基因组学和表观基因组学来定义和描述疾病风险特征。通过分析217名无亲属关系的澳大利亚 "双相儿童和兄弟姐妹研究 "参与者的磁共振成像(MRI)数据(基线人数为217人,随访人数为152人)发现,与对照组(人数为112人)相比,高风险受试者(人数为105人)的大脑皮层随着时间的推移(两次扫描,间隔2年)加速变薄,这表明大脑在早期过度生长,随后在双相情感障碍的典型发病年龄趋于正常。与对照组相比,"高危 "人群的多个皮质区域的厚度和体积随着时间的推移加速减少,包括右侧眶额叶厚度(β=.033,p <.001)和下额叶体积(β=.021,p <.001)。我们还发现,躁郁症多基因风险(PsychArray)与压力相互作用,增加了自杀风险。我们对自杀未遂和危险行为的多基因风险进行了研究,研究结果表明,前扣带回、海马旁和楔叶的结构差异作为自杀未遂的潜在生物标志物值得进一步研究,尤其是在双相情感障碍的背景下。对表观遗传标记物(450k/EPIC 阵列)的研究表明,全基因组的甲基化模式受到多基因风险的广泛影响;这凸显了基因遗传风险与环境暴露的潜在生物编码之间的重要相互作用。目前,我们正在收集第三次核磁共振成像扫描结果,以捕捉大脑皮层的非线性发育轨迹,并收集第二次血液样本,以扩展我们的基线表观遗传学工作,得出血清测量结果,并研究 mRNA 转录模式,作为新出现的精神病理学的潜在生物标志物。与精神病理学遗传和临床测量相关的脑区可作为可行的生物标志物,在临床上用于识别最有可能发展成精神病理学或自杀意图的个体。未来的工作将能够把这些特征整合到疾病预测模型中,以确定精神疾病轨迹上的生物亚群。
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引用次数: 0
LEVERAGING CONTEXT-SPECIFIC EPIGENOMIC REGULATORY NETWORKS (EPINETS) TO DISSECT THE GENETICS OF NEUROPSYCHIATRIC DISORDERS 利用特定情境表观基因组调控网络(epinets)剖析神经精神疾病的遗传学问题
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.024
Ting-Ting Fu, Bhavana Kapalli, Jawahar Mahendran, Neha Rao, Lei Hou
Expression quantitative locus (eQTL) mapping provides deep insights into the function of disease-associated variants from Genome-wide association studies (GWAS). However, previous studies and our research reported misalignment between eQTLs and GWAS signals, likely due to bulk eQTLs mapped in non-pathology-relevant contexts and lack of cell-type resolution. Alternatively, unraveling the links between cis-regulatory elements (CREs) and genes in various cellular contexts offers an independent strategy to associate GWAS variants with their target genes beyond eQTL mapping. The state-of-the-art approaches, such as experimental assays (e.g., Promoter Capture Hi-C and macro-C) and computational models (e.g., ABC and EpiMap), provide linking resources based on different pieces of evidence, however, are each confined to limited brain cell types or cellular states.
Addressing this challenge, our study proposes a machine-learning approach to predict CRE-gene associations by combining protein-protein interactions and transcription factor (TF) binding predictions based on ATAC-seq, an assay measuring genomic accessibility. This computational approach facilitates the discovery of CRE-gene connections across different contexts (combinations of cell types and various conditions) whenever ATAC-seq data are available, enriching our understanding of the cis-regulatory networks between TF-CRE-gene.
We have amassed over 130 cell-sorted and single-cell ATAC-seq datasets encompassing a variety of brain cell types—excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte progenitor cells (OPCs), astrocytes, microglia, immune cells, and brain vascular cells—under a range of conditions including chemical perturbations, genetic modifications, infections, and disease status. Utilizing this extensive data collection and our integrative pipeline, we have constructed an atlas of TF-CRE-gene linking, namely cEpiNets. We finally employ the atlas to evaluate the enrichment of GWAS signals in CRE modules under various cellular contexts and to prioritize target genes and key drivers across a spectrum of neuropsychiatric disorders.
表达定量基因座(eQTL)图谱能让人从全基因组关联研究(GWAS)中深入了解疾病相关变异的功能。然而,先前的研究和我们的研究都报告了 eQTL 与 GWAS 信号之间的不一致,这可能是由于大量 eQTL 映射在非病理学相关的环境中以及缺乏细胞类型分辨率。另外,揭示顺式调控元件(CRE)与各种细胞环境中基因之间的联系也是一种独立的策略,可将 GWAS 变异与其 eQTL 图谱之外的靶基因联系起来。最先进的方法,如实验检测(如 Promoter Capture Hi-C 和 macro-C)和计算模型(如 ABC 和 EpiMap),提供了基于不同证据的关联资源,但每种方法都局限于有限的脑细胞类型或细胞状态。为了应对这一挑战,我们的研究提出了一种机器学习方法,通过结合基于 ATAC-seq 的蛋白质-蛋白质相互作用和转录因子(TF)结合预测来预测 CRE 与基因的关联。只要有ATAC-seq数据,这种计算方法就能帮助发现不同情况下(细胞类型和各种条件的组合)的CRE-基因关联,从而丰富我们对TF-CRE-基因之间顺式调控网络的理解。我们已经积累了 130 多个细胞分选和单细胞 ATAC-seq 数据集,涵盖了各种脑细胞类型--兴奋性神经元、抑制性神经元、少突胶质细胞、少突胶质祖细胞 (OPC)、星形胶质细胞、小胶质细胞、免疫细胞和脑血管细胞--在一系列条件下,包括化学扰动、基因修饰、感染和疾病状态。利用这些广泛的数据收集和我们的整合管道,我们构建了一个 TF-CRE 基因连接图谱,即 cEpiNets。最后,我们利用该图集评估了各种细胞环境下 CRE 模块中 GWAS 信号的富集情况,并对一系列神经精神疾病的目标基因和关键驱动因素进行了优先排序。
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引用次数: 0
LEVERAGING COMORBIDITY TO REFINE PTSD ASSOCIATIONS AND DISSECT GENETIC RISK 利用合并症细化 ptsd 关联并分析遗传风险
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.014
Leveraging comorbidity to refine PTSD associations and dissect genetic risk
利用合并症来完善创伤后应激障碍的关联性并剖析遗传风险
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引用次数: 0
INNATE AND ADAPTIVE IMMUNITY IN PSYCHIATRY: INSIGHTS FROM GENETIC ASSOCIATION STUDIES AND PERIPHERAL BLOOD IMMUNOPHENOTYPING 精神病学中的先天免疫和适应性免疫:遗传关联研究和外周血免疫分型的启示
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.027
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引用次数: 0
CURRENT CHALLENGES AND OPPORTUNITIES IN DATA-DRIVEN APPROACHES IN NEUROPSYCHIATRIC DISORDERS 当前神经精神疾病数据驱动方法面临的挑战和机遇
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.053
Chunyu Liu (Chair) , Zhongming Zhao (Co-chair) , Eric Gamazon (Discussant)
<div><div>As the technological landscape evolves, neuropsychiatric research increasingly relies on data-driven methodologies to uncover the mysteries of the human brain malfunction and genetic risk in neuropsychiatric disorders. This symposium brings together experts and stakeholders for a focused dialogue on the pressing challenges and innovative solutions within this rapidly progressing domain. Through a series of thought-provoking presentations, we will delve into the nuances of data integration, the intricacies of handling big data, and the critical aspects of ensuring research reproducibility and clinical applicability.</div><div>Dr. Chunyu Liu from the State University of New York (SUNY) Upstate Medical University will present a compelling discussion on the paramount importance of quality control in single-cell RNA-seq data. His presentation will underscore the necessity of stringent data evaluation and filtering protocols to bolster the precision of genomic analyses. The talk will provide participants with a solid foundation on the principles of data quality, which is pivotal for the reproducibility and credibility of research findings.</div><div>Dr. Zhongming Zhao from the University of Texas Health Science Center at Houston (UTHealth Houston) will present an in-depth exploration of cutting-edge machine-learning algorithms for the integration of omics, genetic, neuroimaging, and phenotypic data. His session will be centered on the elucidation of Alzheimer's Disease's molecular mechanisms and the identification of potential therapeutic targets. This presentation will demonstrate the transformative power of data analytics in shaping the future of neuropsychiatric therapeutics.</div><div>Dr. Peter Kochunov from the UTHealth Houston, will tackle the multifaceted challenges of data integration, scalability, and reproducibility within the scope of large-scale population imaging genetic studies. His expertise will offer a strategic guide for navigating the complexities of big data, ensuring the robustness of research outcomes, and bridging the gap between scientific discoveries and their clinical translation.</div><div>Dr. Xing-Ming Zhao from Fudan University will conclude the symposium with an insightful examination of the challenges and strategies related to data integration in psychiatric disorder research. His work will emphasize the significance of synthesizing diverse data streams, such as clinical assessments, neuroimaging, and genetic information, to achieve a comprehensive view of neuropsychiatric conditions. This session will highlight the potential of integrated data approaches to surmount the obstacles faced in the field.</div><div>Dr. Eric Gamazon from Vanderbilt University will serve as the discussant to summarize the talks and offer his own insight into harnessing large biobank data of diverse ancestries.</div><div>The symposium will weave these presentations into a coherent narrative, progressively building upon each topic to pro
随着技术的发展,神经精神疾病研究越来越依赖于数据驱动的方法来揭示人脑功能失常和神经精神疾病遗传风险的奥秘。本次研讨会汇聚了专家和利益相关者,就这一快速发展领域中的紧迫挑战和创新解决方案展开集中对话。通过一系列发人深省的演讲,我们将深入探讨数据整合的微妙之处、处理大数据的复杂性以及确保研究可重复性和临床适用性的关键方面。他的演讲将强调严格的数据评估和过滤协议对提高基因组分析精度的必要性。来自德克萨斯大学休斯顿健康科学中心(UTHealth Houston)的赵忠明博士将深入探讨用于整合 omics、遗传、神经影像和表型数据的前沿机器学习算法。他的演讲将围绕阐明阿尔茨海默病的分子机制和确定潜在治疗靶点展开。来自休斯顿UTHealth 的 Peter Kochunov 博士将探讨大规模群体成像基因研究中数据整合、可扩展性和可重复性等多方面的挑战。他的专业知识将为驾驭复杂的大数据、确保研究成果的稳健性以及弥合科学发现与临床转化之间的差距提供战略指导。来自复旦大学的赵兴明博士将以对精神疾病研究中与数据整合相关的挑战和策略的深入探讨结束本次研讨会。他的工作将强调综合不同数据流(如临床评估、神经影像学和遗传信息)以全面了解神经精神疾病的重要性。来自范德比尔特大学的 Eric Gamazon 博士将作为讨论者对发言进行总结,并就如何利用不同血统的大型生物库数据提出自己的见解。研讨会将把这些发言编织成一个连贯的叙事,在每个主题的基础上逐步展开,全面概述数据驱动的神经精神疾病研究目前面临的挑战和未来前景。本次研讨会将通过营造合作环境和激发创新思维来推动这一领域的发展。与会者将深刻了解神经精神疾病数据驱动研究中的关键问题。此外,他们还将获得多功能工具包,以提高研究水平,为推动神经精神科学的发展做出贡献。
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European Neuropsychopharmacology
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