PET scans show activity of the brain network that is linked to changes in connectedness.
When it comes to losing consciousness, it turns out that there is not much difference between falling asleep like normal and undergoing general anesthesia for surgery. In fact, PET scans show brain activity in both instances is quite similar.
In a new study published in JNeurosci, Finnish investigators from the University of Turku discovered the fading and regaining of consciousness is associated with the same network of brain regions for both sleep and anesthesia.
To date, understanding and unearthing a good explanation for the biological basis of human consciousness has been a conundrum for researchers. The culprit, said Harry Scheinin, principal investigator of the study, has largely been experiment design.
“One major challenge has been to design a set-up, where brain data in different states differ only in respect to consciousness,” said the Docent of Pharmacology and anesthesiologist. “Our study overcomes many previous confounders, and for the first time, reveals the neural mechanisms underlying connected consciousness.”
For more coverage based on industry expert insights and research, subscribe to the Diagnostic Imaging e-Newsletter here.
Behavior itself can be an obstacle because it does not always reflect an individual’s state of consciousness. A non-responsive person could still be aware of his or her surroundings, or they could be disconnected, experiencing only their internal world, he said.
Differences in brain activity between connected and disconnected states of consciousness studied with positron emission tomography (PET) imaging. Activity of the thalamus, anterior (ACC) and posterior cingulate cortices (PCC), and bilateral angular gyri (AG) show the most consistent associations with the state of consciousness (A = general anesthesia, B = sleep). The same brain structures, which are deactivated when the state of consciousness changes to disconnected in general anesthesia or natural sleep (cool colors in the left columns), are reactivated when regaining a connected state upon emergence from anesthesia (warm colors in the right columns). CREDIT Scheinin et al., JNeurosci 2020
Harry Scheinin and his team used PET imaging to measure brain activity in an attempt to identify the networks associated with human consciousness. They scanned 76 adult men both as they fell asleep (37 males) and when they were under two anesthetic agents – propofol or dexmedetomidine (39 males). Investigators captured measurements during wakefulness, escalating and constant levels of two anesthetic agents, and during sleep-deprived wakefulness and non-rapid eye movement (NREM) sleep.
After imaging, the team woke the study participants mid-experiment for interviews that confirmed their state of connectedness.
“This unique experimental design was the key idea of our study and enabled us to distinguish the changes that were specific to the state of consciousness from the overall effects of anesthesia,” said first author Annalotta Scheinin, anesthesiologist and doctoral candidate. “Because of the minimal delay between the awakenings and the interviews, the current results add significantly to our understanding of the nature of the anesthetic state.”
Based on their image analysis, they determined changes in connectedness were linked to activity in the thalamus, anterior and posterior cingulate cortex, and the angular gyri. These areas were affected independently of which anesthetic was used, the drug concentration, or the direction of any change in the state of consciousness.
As an individual lost consciousness, these regions saw less blood flow, and the flow returned as they regained consciousness. It is a pattern the team saw both in patients who fell asleep and those who were put under general anesthesia. Not only did those results indicate that changes corresponded to connectedness and not sleep or drugs, but it also highlighted that the network could be necessary for human consciousness.
Ultimately, the team said, these findings challenges conventional wisdom surrounding general anesthesia and provides new information about brain function and human consciousness. According to Harry Scheinin, general anesthesia more closely resembles normal sleep than has been previously thought as these results fall in line with some of the team’s previous electrophysiological findings.
Annalotta Scheinin echoed his conclusions.
“Against a common belief, full loss of consciousness is not needed for successful general anesthesia, as it is sufficient to just disconnect the patient’s experiences from what is going on in the operating room,” she said.
Photon-Counting Computed Tomography: Eleven Takeaways from a New Literature Review
May 27th 2025In a review of 155 studies, researchers examined the capabilities of photon-counting computed tomography (PCCT) for enhanced accuracy, tissue characterization, artifact reduction and reduced radiation dosing across thoracic, abdominal, and cardiothoracic imaging applications.
Can AI Predict Future Lung Cancer Risk from a Single CT Scan?
May 19th 2025In never-smokers, deep learning assessment of single baseline low-dose computed tomography (CT) scans demonstrated a 79 percent AUC for predicting lung cancer up to six years later, according to new research presented today at the American Thoracic Society (ATS) 2025 International Conference.
Can Emerging AI Software Offer Detection of CAD on CCTA on Par with Radiologists?
May 14th 2025In a study involving over 1,000 patients who had coronary computed tomography angiography (CCTA) exams, AI software demonstrated a 90 percent AUC for assessments of cases > CAD-RADS 3 and 4A and had a 98 percent NPV for obstructive coronary artery disease.
Photon-Counting Computed Tomography: Eleven Takeaways from a New Literature Review
May 27th 2025In a review of 155 studies, researchers examined the capabilities of photon-counting computed tomography (PCCT) for enhanced accuracy, tissue characterization, artifact reduction and reduced radiation dosing across thoracic, abdominal, and cardiothoracic imaging applications.
Can AI Predict Future Lung Cancer Risk from a Single CT Scan?
May 19th 2025In never-smokers, deep learning assessment of single baseline low-dose computed tomography (CT) scans demonstrated a 79 percent AUC for predicting lung cancer up to six years later, according to new research presented today at the American Thoracic Society (ATS) 2025 International Conference.
Can Emerging AI Software Offer Detection of CAD on CCTA on Par with Radiologists?
May 14th 2025In a study involving over 1,000 patients who had coronary computed tomography angiography (CCTA) exams, AI software demonstrated a 90 percent AUC for assessments of cases > CAD-RADS 3 and 4A and had a 98 percent NPV for obstructive coronary artery disease.
2 Commerce Drive
Cranbury, NJ 08512