Impaired Interactions Among White-Matter Functional Networks in Antipsychotic-Naive First-Episode Schizophrenia

Chen, Huafu; Li, Zehan; Xiao, Jinming; Guo, Xiaonan; Han, Shaoqiang; Guo, Jing; Yang, Siqi; Li, Jiao; Duan, Xujun; Cui, Qian; Liao, Wei; Chen, Huafu

doi:10.2139/ssrn.3391378

Cited by 4 publications

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“…Consequently, we postulate that the EC may have direct functional links with the GM regions listed above and could be an important neural substrate for MDD. Our findings are similar to earlier studies, 18,19 which provided the evidence that signals in WM correlated with signals from functional GM networks. Meanwhile, we observed negative correlations between WM‐GM connectivity and depressive symptoms, which suggested that the more severe the depression, the lower the connectivity is.…”

Section: Discussionsupporting

confidence: 93%

“…A growing number of reports have provided convincing evidence that BOLD signals in WM encode neural activities as well as GM activities and are robustly measureable with conventional fMRI, under both functional loading and resting conditions 14‐16 . As such, resting‐state fMRI has been employed to explore FC in WM 17 or between WM and GM 18 and the manner in which it was modulated by the underpinnings of a psychiatric disorder 19 …”

Section: Discussionmentioning

confidence: 99%

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Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder

Zhang

Kong

Liu

et al. 2020

Magnetic Resonance Imaging

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Background Alterations in gray matter (GM) have been recognized as playing an important role in the neurobiological mechanism underlying major depressive disorder (MDD) and antidepressant responses. However, little is known about white matter (WM) connectivity in MDD, leaving an incomplete understanding of the pathophysiology of the disorder. Purpose To examine the functional connectivity (FC) of WM, GM, and WM‐GM in MDD patients and explore the relationship between FC and antidepressant response. Study Type Longitudinal study. Subjects In all, 129 MDD patients and 89 healthy controls (HC). Field Strength/Sequence Whole‐brain blood oxygen level‐dependent (BOLD) single‐shot echo planar imaging was acquired at 3.0T. Assessment At baseline, all participants received Hamilton depression rating scale (HAMD) assessment and an fMRI scan. After 2‐ and 8‐week antidepressant treatment, patients completed the HAMD again. The HAMD reductive rate of 2‐ and 8‐weeks were calculated. Statistical Tests The comparisons of age, education, HAMD scores, and FC values (false discovery rate correction) between patients and controls were calculated with a two‐sample t‐test. The chi‐square test was employed to compare the differences of gender between these two groups. Correlations between FC and HAMD, as well as the reductive rate of HAMD, were analyzed with Pearson or Spearman correlation. Receiver operator curve analysis was performed to predict the antidepressant response. Results Compared to HC, MDD patients exhibited widespread decreases in FC of WM‐GM. Furthermore, 28 GM regions and 11 WM bundles had lower connectivity in MDD patients. At baseline, four FC of WM‐GM showed negative correlations with the HAMD scores. Six FC of WM‐GM correlated with the 2‐week reductive rate of HAMD. Moreover, FC in GM, WM, and WM‐GM also exhibited significantly positive correlations with an 8‐week reductive rate of HAMD. Data Conclusion The FC of WM‐GM was decreased in MDD and may play a role in its pathophysiology and antidepressant responses. Level of Evidence 2. Technical Efficacy Stage 2.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder

Zhang

Kong

Liu

et al. 2020

Magnetic Resonance Imaging

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“…Resting‐state functional images were preprocessed as previously described (Yeo et al, 2011 ) . Briefly, the first four volumes were discarded; slice‐time and head motion (cut‐off <2 mm) were corrected with the FSL package; global mean signal intensity was normalized; a 0.01–0.08 Hz band‐pass temporal filter was applied; and head motion, ventricular, white matter, and cerebrospinal fluid signals were regressed out along with whole brain signal to improve the correction of motion‐related artifacts (Fan et al, 2019; Han et al, 2019; Yan et al, 2013). The mean FD was calculated for each participant.…”

Section: Methodsmentioning

confidence: 99%

Individual‐specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation

Chen

Peng

et al. 2020

Human Brain Mapping

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Even with an overarching functional dysconnectivity model of adolescent‐onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group‐level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual‐specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual‐level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension‐specific psychotic symptoms in 30 antipsychotic‐naïve first‐episode AOS patients (recruited sample of 39). Age‐related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic‐naïve first‐episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual‐specific FC biomarkers could significantly and better predict AOS positive‐dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN–SN connection exhibited aberrant age‐associated alteration in AOS. The individual‐level findings reveal reproducible FPN‐based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent‐onset disorders.

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“…(superficial perception-motor white-matter networks)ALFF 降低而功能连接增强 [1] 。Fan 等人利用格兰杰因果分析发现，精神分裂症患者的上放射冠网络到表层眶额网络和深层网络的信息输入发生紊乱，且对表层网络(如额顶、额颞网络等)的抑制作用被破坏 [42] 。Jiang 等人指出，罗兰迪克区(rolandic area)功能连接的增强以及额叶区功能连接的减弱可能是导致癫痫患者认知功能障碍的原因 [39] 。值得注意的是，他们还发现上述功能连接的改变表现出频率特异性，主要集中于 0.027～0.198Hz 频段，表明额叶白质以及罗兰迪克区在该频段的 BOLD 信号和癫痫疾病存在紧密的联系。Zhao 等人发现重度抑郁症患者白质网络之间的功能连接相比健康对照组普遍减弱 [41] 。此外，研究也发现精神分裂症患者白质网络的局部效率、聚类系数和小世界属性等拓扑学属性均遭到破坏；胼胝体、放射冠和视辐射等白质结构的节点度和节点效率(nodal degree and nodal efficiency)也较正常人降低 [43] 。Ji 等人发现，相比灰质网络，正常人和帕金森症患者的白质功能网络都表现出更弱的小世界属性，意味着白质网络的随机化转变，其更偏向于全局整合 [40] 。此外，大部分神经精神类疾病(包括：精神分裂症、抑郁症、阿尔兹海默症、多发性硬化症等)患者静息态下全脑白质-灰质的功能连接均普遍降低 [36,41,[44][45][46] 。此外，研究者们在部分临床研究中观察到，白质功能性的现象学变化往往也与疾病的临床症状和个体认知功能等表现密切相关。例如 Zhao 等人发现重度抑郁症患者感觉运动网络的 BOLD 信号与病程呈负相关 [41] 。此外，重度抑郁症患者也有多处白质 -灰质功能连接与 Hamilton 抑郁量表呈负相关，包括扣带-梭状回、外囊-梭状回、内囊前肢-听觉皮层等 [45] 。精神分裂症患者的白质网络的拓扑学属性破坏与患者的阴性症状呈负相关 [43] 。多发性硬化症患者双侧穹隆与灰质的功能连接变化与病灶体积呈负相关，全脑白质-灰质功能连接与病变率呈负相关 [46] 。此外，一些研究显示，健康被试白质网络的小世界属性与个体的智力呈正相关，而与年龄呈负相关 [47,48] (inferior corticospinal network)等白质网络与胼胝体之间结构连接与功能连接的重合度较低 [32] 。此外，相比于 DTI，fMRI 能够检测不同任务刺激下白质内功能的动态变化以及任务特异性 [21,[25][26][27]29] [18] 。但由于技术限制，磁共振机器的场强有一定的上限，并且高场机器造价十分昂贵，不利于研究的普及。因此还需要寻求其他方法来提高对白质信号的探测能力，例如优化 MRI 的扫描参数 [19] 。其次，白质的 HRF 与灰质有着些许差别，甚至位于不同深度的白质的 HRF 之间也存在差异 [22] 。若采用传统的灰质 HRF 对白质信号进行反卷积，可能会产生假阴性的结果 [23,24] 。因此，未来的研究需要对白质的 HRF 特征进行更深入的探究。最后，目前大多数白质功能性研究仍采用低频段 0.01～0.1Hz 滤波，但白质在较高频段 (0.073～0.25Hz)也有着较强的信号功率 [49] 。此外，Jiang 等人对白质信号的分频段研究显示，在较高频段中能够发现更多与疾病相关的改变 [39] 。具体来说，他们在高频段( 0.027～…”

Section: 低；该通路以及对侧丘脑-背外侧前额叶通路的功能相关张量(Functional Correlation Tensors)unclassified

Research progress of brain white matter based on functional magnetic resonance imaging

Sun¹,

Chen

Hong

et al. 2021

Chin. Sci. Bull.

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Functional magnetic resonance imaging (fMRI), a noninvasive imaging technology, measures metabolism and hemodynamics during brain activity based on blood oxygen leveldependent (BOLD) signals that indirectly reflect neural activity. Since the technique was proposed, it has been widely used in studies of cognitive neuroscience and clinical neuropsychiatry. The finding that BOLD signals between the bilateral sensorimotor cortices and other brain regions in the resting state had high temporal correlations led to the concept of functional connectivity being proposed. The functional connectivity method measures the degree of connection between different brain regions, revealing the functional integration of the brain, and has vigorously promoted the development of fMRI research. To date, however, fMRI applications usually focus on the cortical gray matter of the brain, in which blood vessels are relatively dense, rather than the deep white matter regions. The reasons are mainly attributed to the following two points. First, the vascular density, blood flow and blood volume are lower in white matter than in gray matter; thus, BOLD signal changes are much weaker. Second, white matter contains fewer postsynaptic potentials, which is the main source of BOLD signals. As a result, the reliability and biophysical origin of the white matter BOLD signal have always been controversial, and the BOLD signals from white matter have often been treated as noise in previous fMRI studies. In recent years, with the revelation of the physiological significance of BOLD signals in white matter, researchers have gradually turned their attention to fMRI studies of white matter. Through analysis, these studies have found that BOLD signals in white matter are generated by neural activity rather than by noise or artifacts and that white matter and gray matter have similar physiological origins and significance. It was demonstrated that external stimulation could activate white matter regions and that the white matter had functional networks similar to the gray matter cortex. Therefore, future studies should explore more activation patterns of white matter under tasks to further reveal the potential relationship between white matter function and individual's behavior and cognition. In addition, the study of functional connectivity between white matter and gray matter further revealed functional interactions between them. Subsequent clinical studies found that patients generally showed abnormal white matter activity in the brain and pathological changes in its functional networks and that the dysfunctions in patients may also be related to abnormal communication between white matter and gray matter. Although white matter fMRI provides a direct method to investigate and evaluate white matter function, this research is still at a preliminary exploratory stage. At present, the function of deep brain white matter is rarely studied due to the limitations of analytic methods. Hence, future studies on white matter fMRI need to provide a set of normative...

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Impaired Interactions Among White-Matter Functional Networks in Antipsychotic-Naive First-Episode Schizophrenia

Cited by 4 publications

References 0 publications

Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder

Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder

Individual‐specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation

Research progress of brain white matter based on functional magnetic resonance imaging

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