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1.
庞庞 (2024-06-30 23:01):
#paper Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety DOI: 10.1038/s41591-024-03057-9 本文基于作者提出的静息态以及任务态功能网络的指标,对1000多个抑郁症患者进行了亚型的分类,分出来六个亚型,这六个亚型存在行为、临床量表以及对治疗反应的差异,可以更好的进行精准化医疗。
Abstract:
There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked … >>>
There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry. <<<
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2.
庞庞 (2024-05-31 21:55):
#paper doi:https://doi.org/10.1038/s41593-023-01259-x Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder 该论文基于ASD患者功能连接和症状的相关情况,将ASD患者分为了三个亚型,并进一步探究了亚型间脑和基因的差异情况。对我而言其在CCA时做的特征筛选、模型验证等比较充分,且基因的部分还用brainspan数据集做了验证,可以参考。
Abstract:
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that … >>>
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms. <<<
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3.
庞庞 (2024-04-30 21:44):
#paper doi:10.1038/s41591-023-02296-6 Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality 可能我们大家比较熟悉人脑的年龄,即使用机器学习模型,基于脑指标(如功能连接、灰质体积等)预测人的年龄,预测值如果比真实年龄高,说明这个人比同龄人的脑子更加老化,反之更加年轻。预测值与真实值的差值可以衡量一个人脑的老化程度。而在这里,作者则使用UKbiobank数据集,进一步收集了除脑子以外身体的数据,构建了各个器官系统的年龄,进一步探究了器官系统的年龄是如何互相影响的、以及是如何影响脑龄的,即构建了一个Multi-organ的网络。同时他们也探究了哪些生活因素与器官老化有关,还有各种慢性病的器官年龄是怎样的异常模式。这篇文章给我们提供了一个研究个体老化的新视角,很有创新性。
Abstract:
Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish … >>>
Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ's biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals. <<<
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4.
庞庞 (2024-03-31 16:23):
#paper doi:10.1109/msp.2022.3155951 Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging 一直比较迷惑在探索脑-行为关系时,如何解释特征权重、进一步寻找影像学生物标志物的含义。本文对大部分的使用脑指标预测行为分数的机器学习研究进行了综述,告诉我们处理特征权重的几种基本形式:1.根据权重大小解释特征重要程度2.根据权重的稳定性确定重要程度3.根据逐步去除特征,通过判断特征在模型中的贡献确定。这篇研究给了我比较系统的处理此类问题的方法,很有参考价值。
5.
庞庞 (2024-02-29 21:11):
#paper Machine learning in major depression: From classification to treatment outcome prediction doi 10.1111/cns.13048 这是篇综述机器学习在抑郁症脑影像数据中应用的文章,角度主要是分类和疗效预测。我们可以发现,大部分的此类研究用的都是小样本数据集,这就导致模型的泛化性有限。近年来,已经有越来越多的研究使用多中心大样本抑郁症数据集,但是这些研究的模型准确率相应的会降低。如何对抑郁症进行分亚型,进行特征筛选,选择合适的机器学习乃至深度学习的模型,保证泛化性的同时提高准确率,是抑郁症判别和疗效预测研究未来的重要方向。
Abstract:
AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers … >>>
AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders. DISCUSSIONS: In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression. CONCLUSIONS: We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care. <<<
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6.
庞庞 (2024-01-31 21:42):
#paper doi:10.1001/jamapsychiatry 2023.5083 A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive 本文使用了四百万个机器学习模型,基于结构、功能、扩散磁共振成像探索了区分抑郁症患者和正常人的生物标记物。研究发现,通过多次优化,也很难找到可靠的生物标记物,说明了寻找抑郁症个体生物标记物的困难性,进一步阐明了对抑郁症进行分亚型的意义。
Abstract:
Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no … >>>
Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. Objective: To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. Design, Setting, and Participants: This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Exposure: Patients with MDD and healthy controls. Main Outcome and Measure: Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Results: Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Conclusion and Relevance: Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified. <<<
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7.
庞庞 (2023-12-30 20:07):
#paper Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group doi: 10.1038/s41380-020-0754-0 研究者使用大样本数据集(ENIGMA)探究了抑郁症患者脑龄相对正常人的差异。具体而言,他们使用正常人的脑结构信息,构建了预测脑龄的模型,并将抑郁症患者作为测试集对他们的脑龄进行了预测。研究发现,抑郁症患者的脑龄相比正常人更高,并且和临床症状无关。
Laura K M Han, Richard Dinga, Tim Hahn, Christopher R K Ching, Lisa T Eyler, Lyubomir Aftanas, Moji Aghajani, André Aleman, Bernhard T Baune, Klaus Berger, Ivan Brak, Geraldo Busatto Filho, Angela Carballedo, Colm G Connolly, Baptiste Couvy-Duchesne, Kathryn R Cullen, Udo Dannlowski, Christopher G Davey, Danai Dima, Fabio L S Duran, Verena Enneking, Elena Filimonova, Stefan Frenzel, Thomas Frodl, Cynthia H Y Fu, Beata R Godlewska, Ian H Gotlib, Hans J Grabe, Nynke A Groenewold, Dominik Grotegerd, Oliver Gruber, Geoffrey B Hall, Ben J Harrison, Sean N Hatton, Marco Hermesdorf, Ian B Hickie, Tiffany C Ho, Norbert Hosten, Andreas Jansen, Claas Kähler, Tilo Kircher, Bonnie Klimes-Dougan, Bernd Krämer, Axel Krug, Jim Lagopoulos, Ramona Leenings, Frank P MacMaster, Glenda MacQueen, Andrew McIntosh, Quinn McLellan, Katie L McMahon, Sarah E Medland, Bryon A Mueller, Benson Mwangi, Evgeny Osipov, Maria J Portella, Elena Pozzi, Liesbeth Reneman, Jonathan Repple, Pedro G P Rosa, Matthew D Sacchet, Philipp G Sämann, Knut Schnell, Anouk Schrantee, Egle Simulionyte, Jair C Soares, Jens Sommer, Dan J Stein, Olaf Steinsträter, Lachlan T Strike, Sophia I Thomopoulos, Marie-José van Tol, Ilya M Veer, Robert R J M Vermeiren, Henrik Walter, Nic J A van der Wee, Steven J A van der Werff, Heather Whalley, Nils R Winter, Katharina Wittfeld, Margaret J Wright, Mon-Ju Wu, Henry Völzke, Tony T Yang, Vasileios Zannias, Greig I de Zubicaray, Giovana B Zunta-Soares, Christoph Abé, Martin Alda, Ole A Andreassen, Erlend Bøen, Caterina M Bonnin, Erick J Canales-Rodriguez, Dara Cannon, Xavier Caseras, Tiffany M Chaim-Avancini, Torbjørn Elvsåshagen, Pauline Favre, Sonya F Foley, Janice M Fullerton, Jose M Goikolea, Bartholomeus C M Haarman, Tomas Hajek, Chantal Henry, Josselin Houenou, Fleur M Howells, Martin Ingvar, Rayus Kuplicki, Beny Lafer, Mikael Landén, Rodrigo Machado-Vieira, Ulrik F Malt, Colm McDonald, Philip B Mitchell, Leila Nabulsi, Maria Concepcion Garcia Otaduy, Bronwyn J Overs, Mircea Polosan, Edith Pomarol-Clotet, Joaquim Radua, Maria M Rive, Gloria Roberts, Henricus G Ruhe, Raymond Salvador, Salvador Sarró, Theodore D Satterthwaite, Jonathan Savitz, Aart H Schene, Peter R Schofield, Mauricio H Serpa, Kang Sim, Marcio Gerhardt Soeiro-de-Souza, Ashley N Sutherland, Henk S Temmingh, Garrett M Timmons, Anne Uhlmann, Eduard Vieta, Daniel H Wolf, Marcus V Zanetti, Neda Jahanshad, Paul M Thompson, Dick J Veltman, Brenda W J H Penninx, Andre F Marquand, James H Cole, Lianne Schmaal <<<
Abstract:
Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this … >>>
Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates. <<<
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8.
庞庞 (2023-11-30 19:59):
#paper doi:https://doi.org/10.1016/j.neuroimage.2019.01.074 Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals 静息态功能磁共振成像研究表明,重度抑郁症患者的脑功能存在广泛的改变。 然而,由于大样本、多站点数据集的稀缺,关于 MDD 相关改变的可重复模式的清晰一致的结论仍然有限。 研究者过五个中心的 1434 名参与者(709 名 MDD 患者和 725 名健康对照)的大型 R-fMRI 数据集来解决这个问题。 我们观察到,与对照组相比,重度抑郁症患者的眶额皮层、感觉运动皮层和视觉皮层显著减退,额顶皮层显著过度活跃。 这些改变不受不同统计分析策略、全局信号回归和药物状态的影响,并且通常可以在各个中心重现。 然而,这些组间差异部分受到患者发病状态和发病年龄的影响,并且脑-临床变量关系表现出较差的跨中心再现性
Abstract:
Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of … >>>
Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P < .05 and a height threshold of P < .001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder. <<<
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9.
庞庞 (2023-10-31 11:57):
#paper doi:10.1093/schbul/sbaa155 Disrupted Intersubject Variability Architecture in Functional Connectomes in Schizophrenia 精神分裂症是一种高度异质性的疾病,临床表现存在个体差异。 先前在精分进行的神经影像学研究主要集中于识别患者和健康对照 (HC) 之间大脑连接组的组平均差异,忽略了被试间差异。 作者获取了 121 名 精分患者和 183 名 HC 的全脑静息态功能 MRI 数据,并检查了 患者和 HC 功能连接组的个体间差异。 然后,作者评估了 个体间差异 与 患者 临床变量之间的关系。 患者 组的全脑个体间差异模式与 HC 组基本相似。 与HC组相比,精分组在双侧感觉运动、视觉、听觉和皮质下区域表现出更高的个体间差异。 此外,个体间差异的改变与发病年龄、病程和简明精神病评定量表评分呈负相关,与临床异质性呈正相关。 精分中全脑个体间差异的改变对于理解 精分的高度临床异质性以及该疾病未来的个体化临床诊断和治疗具有潜在的意义。
Abstract:
Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome … >>>
Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome between patients and healthy controls (HCs), largely neglecting the intersubject differences among patients. We acquired whole-brain resting-state functional MRI data from 121 SCZ patients and 183 HCs and examined the intersubject variability of the functional connectome (IVFC) in SCZ patients and HCs. Between-group differences were determined using permutation analysis. Then, we evaluated the relationship between IVFC and clinical variables in SCZ. Finally, we used datasets of patients with bipolar disorder (BD) and major depressive disorder (MDD) to assess the specificity of IVFC alteration in SCZ. The whole-brain IVFC pattern in the SCZ group was generally similar to that in HCs. Compared with the HC group, the SCZ group exhibited higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions. Moreover, altered IVFC was negatively correlated with age of onset, illness duration, and Brief Psychiatric Rating Scale scores and positively correlated with clinical heterogeneity. Although the SCZ shared altered IVFC in the visual cortex with BD and MDD, the alterations of IVFC in the sensorimotor, auditory, and subcortical cortices were specific to SCZ. The alterations of whole-brain IVFC in SCZ have potential implications for the understanding of the high clinical heterogeneity of SCZ and the future individualized clinical diagnosis and treatment of this disease. <<<
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10.
庞庞 (2023-09-30 22:56):
#paper https://doi.org/10.1016/j.tics.2023.05.006   Functional connectomics in depression: insights into therapies  本文首先讨论了与抑郁症相关的功能连接组的最新进展。然后,讨论了抑郁症中特定治疗的大脑网络结果,并提出了一个假设模型,突出了每种治疗在调节特定大脑网络连通性和抑郁症症状方面的优势和独特性。最后,文章期待在临床实践中结合多种治疗类型的未来前景。
Abstract:
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for … >>>
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes. <<<
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11.
庞庞 (2023-08-31 23:18):
#Paper Disrupted intrinsic functional brain topology in patients with major depressive disorder DOI:10.1038/s41380-021-01247-2 作者解决了因为样本量过少抑郁症异常脑机制研究不一致的问题,发现,与NC相比,MDD患者的全局和局部效率降低。在节点水平上,MDD患者的特征是感觉运动网络(SMN)、背侧注意网络(DAN)和视觉网络(VN)的节点度降低,默认模式网络(DMN)、SMN、DAN和VN的节点效率降低。
Abstract:
<jats:title>Abstract</jats:title><jats:p>Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a … >>>
<jats:title>Abstract</jats:title><jats:p>Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.</jats:p> <<<
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12.
庞庞 (2023-07-31 19:14):
#paper doi:10.1038/s41380-021-01247-2, Disrupted intrinsic functional brain topology in patients with major depressive disorder 之前人们去比较抑郁症和正常人大脑功能的拓扑差异,结果多有不同。这可能是因为数据量不够的原因。严超赣课题组使用了16个站点的821名MDD患者和765名正常对照,发现与正常人相比,抑郁症患者的全局和局部效率降低。在节点水平上,患者感觉运动网络(SMN)、背侧注意网络(DAN)和视觉网络(VN)的节点度降低,默认模式网络(DMN)、SMN、DAN和VN的节点效率降低。
Abstract:
Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a … >>>
Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks. <<<
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13.
庞庞 (2023-06-30 13:59):
#paper Performing group-level functional image analyses based on homologous functional regions mapped in individuals https://doi.org/10.1371/journal.pbio.2007032 大脑功能区域的大小、形状、位置和连接模式在个体之间可能存在巨大差异。 虽然功能组织的个体间差异已得到广泛认可,但迄今为止,功能神经影像研究的标准程序仍然依赖于将不同受试者的数据与基于整体大脑形态的名义“平均”大脑进行对齐。 研究者开发了一种方法来可靠地识别每个个体的同源功能区域,并证明基于这些同源功能区域对齐数据可以显着改善静息状态功能连接、任务功能磁共振成像激活和大脑行为关联的研究。 此外,我们发现大脑功能区域的大小、位置和连接性方面的个体差异是可分离的。
Abstract:
Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional … >>>
Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations. <<<
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14.
庞庞 (2023-05-31 16:12):
#paper doi:https://doi.org/10.1038/s41591-023-02317-4 A shared neural basis underlying psychiatric comorbidity 之前对于精神疾病共病程度,通常使用p因子衡量。但是,这种指标通过临床评分得到,和共病的神经底物、基因都没有关联。对此,作者提出了一种新的神经生物学的跨疾病精神因子:NP因子。作者通过将与多种精神疾病得分显著相关的脑网络连接进行并集,并筛除掉在纵向数据上不稳定的连接,从而获得NP因子。他们同时证明了,NP因子与神经解剖位置、行为以及基因的关系。最后,NP因子可以泛化到其他类型的数据集上,这对以后的精神疾病的干预提供了帮助。
Abstract:
Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging … >>>
Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the Stratify Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities. <<<
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15.
庞庞 (2023-04-30 19:59):
#paper doi:  10.1002/hbm.25985 Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks 童年虐待 (CM) 对儿童的身心健康有着长期的影响。 然而,CM 的神经基础仍不清楚。本研究基于个体化的功能脑网络分区方法,计算功能网络连接 (FNC)和通过儿童创伤问卷 (CTQ) 评估的 CM 影响之间的关联。个体化的 FNC可以很好地预测 CM 总分和子量表分数,涉及到了默认模式网络、额顶叶网络、视觉网络、边缘网络、运动网络、背侧和腹侧注意网络,不同的网络对预测有不同的贡献。
Abstract:
Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish … >>>
Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity. <<<
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16.
庞庞 (2023-03-31 15:06):
#paper https://doi.org/10.1038/s41380-023-01958-8 Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression 由于个体的异质性,不同个体对抗抑郁药物的缓释程度各有不同。因此,理解抗抑郁药物的作用机制对个性化医疗至关重要。本文采用去除组成分的COBE算法,获得个体化的功能连接矩阵,作为特征对抗抑郁药物舍曲林和安慰剂的疗效进行预测。研究发现,个体化的功能连接比起组水平的功能连接显著提高了预测准确率;对预测舍曲林贡献高的脑区主要位于左侧颞中皮层和右侧脑岛;对安慰剂贡献高的主要位于双侧扣带皮层和左侧颞上皮层。这位抗抑郁的疗效预测标志物提供了新视角。
Abstract:
Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the … >>>
Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment. <<<
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17.
庞庞 (2023-02-28 21:02):
#paper doi:https://doi.org/10.1038/nn.4164 Parcellating cortical functional networks in individuals 大脑网络的位置、大小等属性具有个体差异。之前的研究忽略了这些差异,运用组水平的分区模板计算功能连接矩阵,导致功能连接个体间差异的混淆。本研究是第一个提出个体化功能网络的研究,基于组水平网络分区模板迭代的思路,获得了具有可重复性、可以捕捉个体间差异、可通过电刺激验证的功能网络个体化分区,为我们进行个体化功能网络的研究提供了新思路。
Abstract:
The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and … >>>
The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications. <<<
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18.
庞庞 (2023-01-31 12:30):
#paper doi:https://doi.org/10.1371/journal.pbio.2007032 Performing group-level functional image analyses based on homologous functional regions mapped in individuals 大脑功能区域的大小、形状、位置和连接模式在个体之间可能存在巨大差异。 作者提出了新的个体化功能分区方法,并证明该方法可以显着改进静息状态功能连接、任务-fMRI 激活和大脑-行为关联的研究。 此外,作者还表明大脑功能区域在大小、位置和连通性方面的个体差异可以提供解释人类行为的信息。
Abstract:
Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional … >>>
Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations. <<<
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19.
庞庞 (2022-12-31 18:17):
#paper Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes https://doi.org/10.1016/j.biopsych.2020.01.012 抑郁症是一种高度异质性的综合症。 该综述文章回顾了抑郁症诊断异质性的主要原因。 并讨论使用数据驱动策略根据功能性神经影像学测量发现新的抑郁症亚型的前景,包括维度、分类和混合方法来解析诊断异质性和理解其生物学基础。 最后,文章考虑了使用静息态功能磁共振成像功能连接技术进行子类型化的优点以及一系列技术挑战和潜在解决方案。
Abstract:
Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and … >>>
Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making. <<<
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20.
庞庞 (2022-11-17 18:14):
​#paper https://doi.org/10.1073/pnas.2110416119 Sex differences in the functional topography of association networks in youth 之前的工作已经证实,大脑皮层功能网络的空间分布存在个体间差异。然而,至今仍不知道年轻人大脑功能网络形态是否存在性别差异。该研究基于693个年轻人被试,使用NMF定义个体的功能网络,并使用多变量(LSVM)模型,用个体功能形态指标对性别进行分类,准确率达到82.9%,对预测做出显著贡献的脑区分布在联合网络:包括额顶网络,默认模式网络,背外侧注意网络。同时,作者也使用单变量(GAM)模型探究形态指标和性别的关系,发现了一致的结果。最后,作者使用艾伦脑图谱的基因表达数据,揭示了功能形态存在差异的脑区与X染色体的基因表达相关。综上,该研究表明性别是塑性功能形态的重要生物因素。
Abstract:
Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there … >>>
Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy ( < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography. <<<
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