Department of Movement Sciences, Research Center for Motor Control and Neuroplasticity, KU, Leuven, Belgium; IRCCS San Camillo Hospital, Venice, Italy.
Department of General Psychology, University of Padova, Italy.
J Psychiatr Res. 2021 Dec;144:59-65. doi: 10.1016/j.jpsychires.2021.09.051. Epub 2021 Sep 25.
Bipolar disorder (BD) is a psychiatric condition causing acute dysfunctional mood states and emotion regulation. Specific neuropsychological features are often present also among patients in euthymic phase, who do not show clear psychotic symptoms, and for whom the characterization from functional magnetic resonance imaging (fMRI) is very limited. This study aims at identifying the neural and behavioral correlates of the default mode network (DMN) using the fractional amplitude of low frequency fluctuations (fALFF). Eighteen euthymic BD patients (10 females; age = 54.50 ± 11.38 years) and sixteen healthy controls (HC) (8 females; age = 51.16 ± 11.44 years) underwent a 1.5T fMRI scan at rest. The DMN was extracted through independent component analysis. Then, DMN time series was used to compute the fALFF, which was correlated with clinical scales. From the between-group comparison, no significant differences emerged in correspondence to regions belonging to the DMN. For fALFF analysis, we reported significant increase of low-frequency fluctuations for lower frequencies, and decreases for higher frequencies compared to HC. Correlations with clinical scales showed that an increase in higher frequency spectral content was associated with lower levels of mania and higher levels of anxious symptoms, while an increase in lower frequencies was linked to lower depressive symptoms. Starting from our findings on the DMN in euthymic BD patients, we suggest that the fALFF derived from network time series represents a viable approach to investigate the behavioral correlates of resting state networks, and the pathophysiological mechanisms of different psychiatric conditions.
双相情感障碍(BD)是一种导致急性功能失调情绪状态和情绪调节障碍的精神疾病。即使在没有明显精神病症状且功能磁共振成像(fMRI)特征非常有限的病情稳定期患者中,也经常存在特定的神经认知特征。本研究旨在使用低频振幅(fALFF)识别默认模式网络(DMN)的神经和行为相关性。18 名病情稳定的 BD 患者(10 名女性;年龄=54.50±11.38 岁)和 16 名健康对照者(HC)(8 名女性;年龄=51.16±11.44 岁)在休息时接受了 1.5T fMRI 扫描。通过独立成分分析提取 DMN。然后,使用 DMN 时间序列计算 fALFF,并将其与临床量表相关联。在组间比较中,DMN 所属区域没有出现显著差异。对于 fALFF 分析,与 HC 相比,我们报告了低频下低频波动增加,高频下低频波动减少。与临床量表的相关性表明,较高频率谱内容的增加与较低的躁狂水平和较高的焦虑症状水平相关,而较低频率的增加与较低的抑郁症状相关。从我们对病情稳定期 BD 患者 DMN 的发现出发,我们认为从网络时间序列得出的 fALFF 代表了一种可行的方法,可以研究静息态网络的行为相关性,以及不同精神疾病的病理生理机制。