The review discusses the limitations of neural field continuum theories in understanding the structure-function partitioning of cognitive-emotional brain networks. Kevin B. Clark critiques the work of Luiz Pessoa, who argues that brain networks are resource-limited and require multichannel structure-function connectivity for effective cognitive-emotional processing. Clark highlights that Pessoa's reliance on graph theory overlooks the importance of neural field theories, which consider the dynamic interplay between structure and function in brain networks. He argues that neural field theories provide a more accurate framework for understanding how brain networks evolve and function, particularly in terms of their embeddedness and partitioning. Clark also points out that Pessoa's work fails to account for the role of weak-to-strong structure-function correlations in brain dynamics, which are influenced by factors such as Hebbian and anti-Hebbian neuronal plasticity. The review emphasizes the importance of considering both classical and quantum computational phases in understanding brain network behavior, as well as the role of local control parameters in shaping network partitioning and embeddedness. The review concludes that neural field theories offer a more comprehensive and accurate understanding of brain network dynamics than traditional graph-theoretic approaches.The review discusses the limitations of neural field continuum theories in understanding the structure-function partitioning of cognitive-emotional brain networks. Kevin B. Clark critiques the work of Luiz Pessoa, who argues that brain networks are resource-limited and require multichannel structure-function connectivity for effective cognitive-emotional processing. Clark highlights that Pessoa's reliance on graph theory overlooks the importance of neural field theories, which consider the dynamic interplay between structure and function in brain networks. He argues that neural field theories provide a more accurate framework for understanding how brain networks evolve and function, particularly in terms of their embeddedness and partitioning. Clark also points out that Pessoa's work fails to account for the role of weak-to-strong structure-function correlations in brain dynamics, which are influenced by factors such as Hebbian and anti-Hebbian neuronal plasticity. The review emphasizes the importance of considering both classical and quantum computational phases in understanding brain network behavior, as well as the role of local control parameters in shaping network partitioning and embeddedness. The review concludes that neural field theories offer a more comprehensive and accurate understanding of brain network dynamics than traditional graph-theoretic approaches.