Relations of gray matter volume to dimensional measures of cognition and affect in mood disorders.
Relations of gray matter volume to dimensional measures of cognition and affect in mood disorders.
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DOI：
10.1016/j.cortex.2022.06.019
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发表时间：
2022-11
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了解大脑测量和行为表现之间的关系是开发早期识别任何精神病困难和干预措施以改变这些挑战的方法的重要一步。识别区域脑容量和行为测量之间的关联的常规方法在规模、范围或特异性方面都没有优化。为了以更高的灵敏度和精确度找到大脑与行为之间有意义的关联，我们应用数据驱动的因子分析模型来识别和提取嵌入在几个计算机化认知任务中的潜在认知功能的个体差异。此外，我们同时利用了基于关键词的神经成像元分析工具（即，NeuroSynth）、限制性地图集-包裹匹配和因子分析模型，以缩小搜索范围，并进一步将灰质体积（GMV）数据聚合到经验聚类中。我们招募了一个早期成人社区横断面样本（总n=177，年龄18-30），包括无任何心境障碍病史的个体（HC，n=44），缓解型重性抑郁障碍（rMDD，n=104）和目前处于正常心境状态的双相情感障碍（eBP，n=29）。研究参与者接受了结构MRI扫描，并使用计算机测量分别完成了行为测试。因素分析五个计算机任务，用于评估认知和情感处理方面的七个潜在维度：（a）情绪记忆，（B）干扰分辨率，（c）奖励敏感性，（d）复杂抑制控制，（e）面部情绪敏感性，（f）持续注意力，（g）简单冲动/反应风格。然后用特定的关键词标记这七个维度，这些关键词用于使用NeuroSynth创建神经解剖图。这些掩码被进一步细分为GMV簇。使用回归，我们确定了GMV聚类，这些聚类预测了上述七个认知维度中每个维度的个体差异。我们证明，与RDoC的核心原则相一致的维度方法可以用来识别预测人类行为关键维度的结构变异性。
Understanding the relationship between brain measurements and behavioral performance is an important step in developing approaches for early identification of any psychiatric difficulties and interventions to modify these challenges. Conventional methods to identify associations between regional brain volume and behavioral measures are not optimized, either in scale, scope, or specificity. To find meaningful associations between brain and behavior with greater sensitivity and precision, we applied data-driven factor analytic models to identify and extract individual differences in latent cognitive functions embedded across several computerized cognitive tasks. Furthermore, we simultaneously utilized a keyword-based neuroimaging meta-analytic tool (i.e., NeuroSynth), restricted atlas-parcel matching, and factor-analytic models to narrow down the scope of search and to further aggregate gray-matter volume (GMV) data into empirical clusters. We recruited an early adult community cross-sectional sample (Total n=177, age 18–30) that consisted of individuals with no history of any mood disorder (HC, n=44), those with remitted major depressive disorder (rMDD, n=104), and those with a diagnosis of bipolar disorder currently in euthymic state (eBP, n=29). Study participants underwent structural MRI scans and separately completed behavioral testing using computerized measures. Factor-analyzing five computerized tasks used to assess aspects of cognitive and affective processing resulted in seven latent dimensions: (a) Emotional Memory, (b) Interference Resolution, (c) Reward Sensitivity, (d) Complex Inhibitory Control, (e) Facial Emotion Sensitivity, (f) Sustained attention, and (g)Simple Impulsivity/Response Style. These seven dimensions were then labeled with specific keywords which were used to create neuroanatomical maps using NeuroSynth. These masks were further subdivided into GMV clusters. Using regression, we identified GMV clusters that were predictive of individual differences across each of the aforementioned seven cognitive dimensions. We demonstrate that a dimensional approach consistent with core principles of RDoC can be utilized to identify structural variability predictive of critical dimensions of human behavior.