Thiele Jonas A, Faskowitz Joshua, Sporns Olaf, Hilger Kirsten
Department of Psychology I - Clinical Psychology and Psychotherapy, Würzburg University, Marcusstr. 9-11, 97070 Würzburg, Germany.
Department of Psychological and Brain Sciences, Indiana University, 1101 E 10th Street, Bloomington, IN 47405, USA.
PNAS Nexus. 2024 Dec 10;3(12):pgae519. doi: 10.1093/pnasnexus/pgae519. eCollection 2024 Dec.
A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves statistically significant prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive brain characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modeling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication: = 322). The choice of the predicted intelligence component as well as the task during which connectivity was measured proved crucial for better understanding intelligence at the neural level. Further, intelligence could be predicted not solely from one specific set of brain connections, but from various combinations of connections with system-wide locations. Such partially redundant, brain-wide functional connectivity characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future prediction studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive brain characteristics over maximizing prediction performance.
越来越多的研究从包括功能性脑连接在内的大脑特征来预测个体的认知能力水平。这项研究中的大多数都取得了具有统计学意义的预测性能，但对预测概念背后的神经生物学过程的洞察有限。对预测性大脑特征的识别不足可能是造成这一限制的一个关键重要因素。在此，我们鼓励设计强调可解释性的预测模型研究，以增强我们对人类认知的概念理解。例如，我们在一项预先注册的研究中调查了哪些功能性脑连接能够成功预测806名健康成年人样本中的一般智力、晶体智力和流体智力（重复实验：n = 322）。事实证明，预测智力成分的选择以及测量连接性时所采用的任务对于在神经层面更好地理解智力至关重要。此外，智力不仅可以从一组特定的脑连接来预测，还可以从与全脑区域的各种连接组合来预测。这种部分冗余的全脑功能连接特征补充了既定智力理论所提出的与智力相关的脑区连接。总之，我们的研究展示了未来关于人类认知的预测研究如何通过优先对预测性大脑特征进行系统评估而非最大化预测性能来提高解释价值。
