Neural Responses to Internal and External Signals Predict Coma Recovery

Post by Shireen Parimoo

The takeaway

During wakefulness, the brain simultaneously processes both internally generated signals, such as the heartbeat, and external sensory stimuli, like sounds. Patients in a comatose state who later recover from the coma show preserved regularity of neural responses to cardiac and auditory signals. 

What's the science?

Interoception is the ability to sense signals generated by the body, such as the heartbeat or the sensation of goosebumps. The heartbeat evoked potential (HEP) is a specific neural response to heartbeats and is indicative of the brain’s processing of cardiac signals. The brain also tracks sensory input from the external world, like sounds, showing altered patterns of activity when there is deviation from an expected pattern or regularity. This change in response to the deviation is called a prediction error. Interestingly, during both wakefulness and sleep, neural responses to internally generated signals like the heartbeat track neural responses to externally generated signals like sounds in the environment. However, it is unclear whether neural responses to cardiac signals influence sensory processing during a deeply unconscious state. This week in PNAS, Pelentritou and colleagues used electrophysiological techniques to investigate whether the brain uses cardiac signals to track auditory input in a comatose state, and its relationship to patient outcomes. 

How did they do it?

The authors recorded brain and cardiac activity from 48 patients who had suffered cardiac arrest and entered a comatose state. In an auditory paradigm, patients were exposed to sounds and silences with varying levels of regularity relative to the heartbeat. There were four conditions: (1) baseline or control condition with no sound; (2) synchronous condition in which a sound occurred at a fixed interval after a heartbeat was detected; (3) isosynchronous condition in which a sound occurred at a fixed interval relative to the previous sound, but was not synchronized to the heartbeat; and (4) asynchronous condition in which sounds were presented irregularly relative to other sounds and to the heartbeat. Importantly, sounds were omitted on 20% of the trials, which allowed the authors to determine if the patients’ neural and cardiac responses showed evidence of prediction error (i.e., deviation from regularity) in any of the conditions. 

Electroencephalography was used to record neural responses, which included auditory evoked potentials (AEPs) in response to sound onset and omission-evoked potentials (OEPs) on omission trials, recorded relative to when the sound would have occurred. Cardiac activity was recorded using electrocardiography, including HEPs and omission HEPs during sound-on and omission trials, respectively. Here, OEPs and OHEPs were used as indicators of a prediction error response. The authors compared the regularity of neural and cardiac responses across conditions and for patients with a favorable outcome (i.e., recovery from coma) and an unfavorable outcome. Next, they used a support vector machine classifier (a machine learning technique) on neural data from each trial to predict which condition the brain activity belonged to and whether it could be used to predict patient outcome. Finally, they measured cardiac deceleration – or the amount of slowing between heartbeats – in response to sound omissions in the synchronous condition to predict whether a patient would recover from the coma. 

What did they find?

Patients with favorable outcomes showed a significant difference in OEPs during omissions in the synchronous condition, as compared to the asynchronous and baseline conditions. In patients with unfavorable outcomes, however, there was no difference in OEPs across the four conditions. Moreover, there was no difference in OEPs between the isosynchronous and baseline conditions in either patient group. This means that deviation from the regularity of external sounds relative to the heartbeat, but not to sounds, disrupts cardiac-auditory regularity of neural responses, but only in patients who later recover from the coma. 

Single-trial neural activity was predictive of patient outcomes. Specifically, patients with a favorable outcome showed greater cardiac-auditory regularity in the synchronous condition compared to the baseline condition. Relatedly, sound omissions in the synchronous condition influenced cardiac deceleration, but this effect was only observed in patients with a favorable outcome. The same effect was observed in the isosynchronous condition as well, but to a smaller extent. Thus, deviation from the regularity of auditory input – both in relation to the heartbeat and to previous sensory input – led to temporary slowing in between heartbeats of patients who went on to recover from the coma. 

What's the impact?

These results demonstrate that the brain uses internally generated signals to monitor sensory processing, even in deep unconsciousness, like a coma. Notably, the degree of neural synchronization in response to these signals predicts patient outcomes, offering a promising prognostic marker for coma recovery. 

Why Is Our Memory Gist-Like?

Post by Lila Metko

The takeaway

Engram cells are neurons that activate when a memory is formed and reactivate when a memory is recalled. Recollection is not always perfect, and sometimes these cells are activated under similar, but not identical, contexts to those in which the memory was formed. Formation of new cells in the hippocampus is necessary for this gist-like type of memory processing.  

What's the science?

The hippocampus (HC) is a brain region that consolidates and retrieves memories. For our survival, our memories must be more gist-like rather than very precise, so we can flexibly adapt to changing circumstances. Previous theory suggests that interactions between the prefrontal cortex and HC drive gist memory formation. This week in Nature, Ko and colleagues used optogenetic silencing, eGRASP visualization, and other methods to eliminate and accelerate neurogenesis to understand how gist memory formation can occur within the HC. 

How did they do it?

Experiment 1: The authors used a contextual fear conditioning paradigm to test memory in mice at 1 day (recent), 14 days (intermediate), and 28 days (remote). After mice are placed in a new environment (‘context A’), they will typically freeze if placed in an environment they associate with the stimulus again. One benefit of this paradigm is the ability to make a second environment (‘context B’) similar to context A so that they could test for gist memory. During the fear learning session, they labeled active neurons (engram neurons) with a fluorescent protein, and then quantified them for activity at each time point. Engram neurons were also silenced at the timepoints to determine effects on memory.

Experiment 2: The authors visualized engram cell synapses using the eGRASP technique to gain a better understanding of which subparts of the HC were involved in the engram reactivation and which neuron types played a role.  

Experiment 3: The authors then did a tracing experiment to label newborn neurons in the dentate gyrus region of the HC, to examine if they synapsed on a nearby engram cell. Finally, they used gamma irradiation and voluntary wheel running, respectively, to eliminate and boost neurogenesis in different cohorts of mice and examined memory in the contextual fear conditioning paradigm in each group. 

What did they find?

Experiment 1: The authors found that initially, the mice froze mostly in response to context A (the context in which they received the aversive stimulus), but by the 28th day, froze equally to the two similar contexts, indicating a decrease in precise memory. In some areas of the HC, engram cell activity mirrored the freezing patterns, showing high activity for A at timepoint one and equal activity for A and B at timepoint three. Silencing engram cells that project from one specific area of the HC to another suppresses freezing behavior in both context A and B at the 28-day timepoint, which indicates that these specific cells are responsible for gist memory. 

Experiment 2: In the experiment that labeled the engram cells, they found that over time, outputs from the dentate gyrus region of the HC to inhibitory neurons in the CA3 region decreased, and inputs to the CA1 region of the hippocampus from the CA3 increased. This indicates that there may be complementary feed-forward inhibition and excitation processes at play to facilitate gist memory. 

Experiment 3: The tracing experiment showed that newborn neurons from the dentate gyrus do synapse onto CA3 engrams. Importantly, CA3 engram cells that received inputs from newborn neurons were around three times more likely to be activated in context B (a similar but not identical context). When newborn neurons were eliminated, precise memory (more freezing to context A) increased at later timepoints, and the hippocampal connectivity patterns associated with later timepoints in experiment 2 were not seen at 28 days. Conversely, when neurogenesis was promoted with voluntary wheel running, precise memory went away at earlier timepoints (14 days), and the gist memory hippocampal connectivity changes that typically need 28 days to develop were seen as early as 14 days. This demonstrates that hippocampal neurogenesis likely facilitates gist memory. 

What's the impact?

This research shows that hippocampal neurogenesis can actively reshape memory circuits, shifting detailed event memories into flexible gist representations. It suggests that forgetting may not always be a bad thing, but more of an adaptive generalization of past experiences to new situations. This insight could influence strategies for education, mental health therapies, and age-related memory care by targeting neurogenesis or circuit remodeling to fine-tune the balance between precision and generalization in memory.

Access the original scientific publication here.

The Global fMRI Signal Tracks Changes in Arousal

Post by Natalia Ladyka-Wojcik

The takeaway

A global brain signal closely correlates with changes in arousal across the brain and body, suggesting this signal may be shaped by the autonomic nervous system that modulates arousal. 

What's the science?

In functional magnetic resonance imaging (fMRI), the global signal is the average signal intensity across the whole brain. The global signal is one of the strongest and most consistent signals that neuroscientists detect, but it is often regressed out of neuroimaging analyses of functional connectivity because it is believed to represent physiological noise (such as heart rate or breathing) that could confound experimental results. Recently, however, some scientists have started to consider whether the global fMRI signal might reflect valuable information — it could also be tied to changes in arousal, like how awake or restful a person is. These slow brain signals (within a low-frequency, 0.01–0.1 Hz range) seem to line up with changes in both brain electrical activity captured with electroencephalography (EEG) and bodily responses related to arousal, such as pupil size. This week in Nature Neuroscience, Bolt and colleagues explored how closely the global fMRI signal is linked to activity in the autonomic nervous system, a part of the peripheral nervous system that regulates arousal-related processes in the body, including blood pressure and breathing. 

How did they do it?

The authors used several datasets, including fMRI, EEG, and recordings of physiological signals, to study what happens during rest and sleep with the global signal. They focused on things like heart rate changes, breathing patterns, sweating (measured through skin conductance), blood vessel pulsation, and changes in pupil size (with pupillometry) to see if these body signals fluctuated with the global fMRI signal. Importantly, they also examined whether moments when arousal levels spontaneously changed, like during brief events in sleep called “K-complexes” in EEG, were linked with coordinated changes in both the brain and body. Furthermore, their analysis included a dataset that measured how much carbon dioxide (CO₂) people exhaled (using a method called PETCO₂), which changes with arousal. By looking at how PETCO₂ was related to the global fMRI signal specifically, they could test whether breathing itself might simply be driving some of these global fluctuations between fMRI signal and physiological signals during rest, something that neuroimaging analyses typically aim to remove. 

What did they find?

The authors found that a single, global pattern could explain a lot of the shared activity between the brain’s global signal and various arousal systems. They observed this same brain–body pattern not only during spontaneous arousal changes in sleep (like K-complexes), but also when arousal was intentionally changed, for example, during deep breathing or sensory stimulation. Critically, they found that CO₂ levels alone could not explain the global brain fluctuations during rest. Instead, the authors suggest that their results demonstrate that the autonomic nervous system and brain signals that regulate arousal are likely driving these widespread patterns seen in fMRI. 

What's the impact?

This study is the first to show comprehensive evidence across many datasets to challenge the prevailing idea that global signals should be removed from fMRI simply because they are dominated by physiological noise. This research also highlights the potential importance of the global signal in understanding how the brain and body coordinate during rest and sleep.  

Access the original scientific publication here.