Cavadini Riccardo, Casartelli Luca, Pedrocchi Alessandra, Antonietti Alberto
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy.
APL Bioeng. 2025 Apr 24;9(2):026109. doi: 10.1063/5.0251429. eCollection 2025 Jun.
The remarkable ability of the human brain to create a coherent perception of reality relies heavily on multisensory integration-the complex process of combining inputs from different senses. While this mechanism is fundamental to our understanding of the world, its underlying neural architecture remains partially unknown. This study investigates the role of the cerebellum in multisensory integration through a novel computational approach inspired by clinical observations of a patient with cerebellar agenesis. With reference to the clinical data comparing an acerebellar patient with age-matched control subjects, we exploited biologically realistic spiking neural networks to model both conditions. Our computational framework enables testing multiple network configurations and parameters, effectively replicating and extending the clinical experiments . To enhance accessibility and promote broader adoption among researchers, we complemented this framework with a user-friendly web-based interface, eliminating the need for programming expertise. The computational results closely mirror the clinical findings, providing support for the critical contribution of the cerebellum in multisensory integration. Beyond being a consistent proof of concept for the previous clinical observations, this study introduces a versatile platform for testing brain models through our newly developed framework and interface. Thus, this work not only advances our understanding of the cerebellar role in sensory processing but also establishes a robust methodology for future computational investigations of neural mechanisms.
人类大脑创造连贯现实感知的非凡能力在很大程度上依赖于多感官整合——将来自不同感官的输入进行组合的复杂过程。虽然这种机制是我们理解世界的基础,但其潜在的神经结构仍部分未知。本研究通过一种受小脑发育不全患者临床观察启发的新型计算方法,研究小脑在多感官整合中的作用。参照将无脑小脑患者与年龄匹配的对照受试者进行比较的临床数据,我们利用具有生物现实性的脉冲神经网络对两种情况进行建模。我们的计算框架能够测试多种网络配置和参数,有效地复制和扩展临床实验。为了提高可及性并促进研究人员更广泛地采用,我们用一个用户友好的基于网络的界面补充了这个框架,无需编程专业知识。计算结果与临床发现密切相符,为小脑在多感官整合中的关键作用提供了支持。除了为先前的临床观察提供一致的概念验证外,本研究还通过我们新开发的框架和界面引入了一个用于测试脑模型的通用平台。因此,这项工作不仅推进了我们对小脑在感觉处理中作用的理解,还为未来神经机制的计算研究建立了一种强大的方法。