bioRxiv : the preprint server for biology
October 23, 2024
Rui Dai, Hyunwoo Jang, Anthony G Hudetz et al.
1 citation
preprint
Consciousness appears to depend on global interactions across multiple brain regions rather than on localized neural activity. Using fMRI data across psychedelic, sleep, and deep sedation states, the study found a mirror-image pattern: psychedelic states increased global functional connectivity and decreased local neural synchrony, while non-REM sleep and deep sedation showed the opposite pattern. This pattern was observed in anterior-posterior and posterior-posterior brain regions but not within the anterior brain alone. Anterior transmodal regions were key for anterior-posterior connectivity, while posterior transmodal and unimodal regions were critical for posterior-posterior connectivity. The findings support global theories of consciousness and bridge the Global Neuronal Workspace hypothesis and Integrated Information Theory by showing shared neural mechanisms.
bioRxiv : the preprint server for biology
June 1, 2026
Panagiotis Fotiadis, Hyunwoo Jang, Rui Dai et al.
Brain waves coordinate neural communication and shape conscious perception. Analyzing blood oxygen level-dependent activity from the Human Connectome Project and other datasets across sleep, propofol anesthesia, and psychedelic states (LSD, DMT, psilocybin, nitrous oxide, ketamine), four dominant wave propagation motifs were identified: a global synchronized wave, an anti-correlated unimodal-transmodal wave, an anti-correlated task-positive/task-negative wave, and an anti-correlated visual-somatomotor wave.
Nature communications
October 24, 2024
Hyunwoo Jang, George A Mashour, Anthony G Hudetz et al.
A metric called the integration-segregation difference (ISD), derived from fMRI data, captures two key brain network properties: efficiency (integration) and clustering (segregation). During anesthesia with propofol, brain networks shift profoundly toward segregation as consciousness is lost. A common sequence of disintegration and reintegration occurs in unimodal and transmodal networks during loss and return of responsiveness. Machine learning models using these measures accurately identify awake versus unresponsive states. Metastability is more closely linked to integration, while complexity is linked to segregation. Similar patterns appear in sleep. The ISD reliably indexes states of consciousness.
Brain sciences
August 30, 2024
Hyunwoo Jang, Rui Dai, George A Mashour et al.
A machine learning model that combines functional connectivity, graph-theoretic metrics, and cortical gradient features can classify brain states—including unconsciousness (NREM2 sleep, propofol sedation and anesthesia), psychedelic states (ketamine, LSD, nitrous oxide), and neuropsychiatric disorders (ADHD, bipolar disorder, schizophrenia)—with an average balanced accuracy of 79% (range 62–98%). The ensemble model outperformed individual feature-based models (70–76%). Transferability across datasets varied, and feature importance analysis indicated that different brain states rely on distinct neural mechanisms, suggesting that tailored approaches are needed for accurate classification. The findings highlight the value of integrating multiple feature types for robust brain-state classification, though further work is needed for broader generalizability.
Rui Dai, Hyunwoo Jang, Anthony G Hudetz et al.
Across altered states of consciousness, psychedelics and sedatives produce opposite patterns of brain network organization. Psychedelics increase large-scale integration and reduce segregation of brain network interactions, while sleep and propofol sedation show the opposite pattern. These opposing integration-segregation patterns were consistently observed across multiple measures of functional connectivity, network topology, and interaction complexity, and reliably differentiated conscious states in an unbiased, data-driven manner. The findings demonstrate that psychedelic and sedated states are characterized by systematic and opposing shifts in large-scale brain organization.