Division of Psychological and Social Medicine and Developmental Neuroscience, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.
Psychol Med. 2020 Jan;50(1):107-115. doi: 10.1017/S0033291718004002. Epub 2019 Jan 9.
Resting state functional magnetic resonance imaging studies have identified functional connectivity patterns associated with acute undernutrition in anorexia nervosa (AN), but few have investigated recovered patients. Thus, a trait connectivity profile characteristic of the disorder remains elusive. Using state-of-the-art graph-theoretic methods in acute AN, the authors previously found abnormal global brain network architecture, possibly driven by local network alterations. To disentangle trait from starvation effects, the present study examines network organization in recovered patients.
Graph-theoretic metrics were used to assess resting-state network properties in a large sample of female patients recovered from AN (recAN, n = 55) compared with pairwise age-matched healthy controls (HC, n = 55).
Indicative of an altered global network structure, recAN showed increased assortativity and reduced global clustering as well as small-worldness compared with HC, while no group differences at an intermediate or local network level were evident. However, using support-vector classifier on local metrics, recAN and HC could be separated with an accuracy of 70.4%.
This pattern of results suggests that long-term recovered patients have an aberrant global brain network configuration, similar to acutely underweight patients. While the finding of increased assortativity may represent a trait marker of AN, the remaining findings could be seen as a scar following prolonged undernutrition.
静息态功能磁共振成像研究已经确定了与神经性厌食症(AN)急性营养不良相关的功能连接模式,但很少有研究调查过已康复的患者。因此,与该疾病相关的特征性连通性特征仍然难以捉摸。在急性 AN 中,作者先前使用最先进的图论方法发现了异常的全脑网络结构,这可能是由局部网络改变驱动的。为了区分特征和饥饿效应,本研究检查了已康复患者的网络组织。
使用图论指标评估了一组从 AN 中恢复的女性患者(recAN,n=55)与配对年龄匹配的健康对照组(HC,n=55)的静息状态网络特性。
与 HC 相比,recAN 表现出增加的聚类系数和降低的全局聚类以及小世界特性,表明其整体网络结构发生改变,而在中间或局部网络水平上没有组间差异。然而,使用支持向量机分类器对局部指标进行分析,recAN 和 HC 可以以 70.4%的准确率区分开。
该结果模式表明,长期康复的患者具有异常的全脑网络配置,类似于急性体重不足的患者。虽然增加的聚类系数可能代表 AN 的特征标志物,但其余发现可能被视为长期营养不良后的疤痕。