Decision-Tree-Based Approaches to Brain Mapping and Consciousness Research: Neural Interactions, Emergence and Level Assessment

Abstract Mapping consciousness across the brain requires more than locating isolated activations: it requires explaining how distributed, hierarchical networks integrate information and why awareness varies in strength across contexts. Against this backdrop, decision-tree systems offer an interpretable bridge between complex neuroimaging, electrophysiological, behavioral, and physiological data and clinically meaningful states. Their conditional branches can expose how regional activity, connectivity, oscillations, and network measures combine to distinguish neural configurations, while ensemble extensions improve predictive flexibility. As evidence accumulated, however, the explanatory value of these models became inseparable from broader mechanisms of consciousness. Predictive-brain accounts, recurrent exchange, excitation–inhibition balance, and population-level coordination suggest that trees can identify observable conditions associated with emergent awareness but cannot themselves produce or establish its underlying causal dynamics. Their greatest promise therefore lies in grading consciousness probabilistically, including covert capacities that behavior may miss, rather than enforcing binary classifications. Yet tensions persist between readability and mechanistic truth, regional interpretability and distributed physiology, and static prediction and changing brain–environment relationships. Model explanations remain target- and context-dependent, while incomplete data, distributional shifts, and concept drift limit generalization. Future progress will require temporally sensitive, uncertainty-aware, multimodal, and clinically validated frameworks that connect interpretable decision paths to physiological mechanisms, enabling decision-tree systems to support—not replace—neuroscientific inference about the emergence and modulation of conscious states.