Abstract：
Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.
摘要：
脑电图（EEG）数据的多变量模式分析（MVPA）代表了研究大脑如何编码信息的革命性方法。通过在个体层面考虑时空特征之间的复杂相互作用，MVPA克服了单变量技术的局限性，这往往无法解释显著的个体间和个体内神经变异性。这在研究临床人群时尤其重要，因此，脑电图数据的MVPA最近开始被用作研究脑部疾病认知的工具。这里,我们回顾了这种方法在自闭症等疾病中神经活动异常模式研究中提供的见解，多动症,精神分裂症,诵读困难,神经和神经退行性疾病，在不同的认知领域(感知，注意,记忆,意识)。尽管潜在的缺点应该得到认真解决，这些研究揭示了MVPA在揭示信息处理的功能失调和代偿性神经认知动力学方面的特殊敏感性，通常对传统的单变量方法视而不见。表征个体神经认知谱的这种更高的灵敏度可以提供优化评估和促进个性化干预的独特机会。
