The takeaway

Ripple oscillations are brief bursts of high-frequency brain activity associated with memory in the cortex. This study shows that when ripples occur simultaneously across different brain regions, neurons fire together more strongly in a context-specific manner, and this coordination strengthens with mental effort.

What’s the science?

Cognition and memory in the mammalian brain rely on effective communication between distinct brain regions. Still, the mechanisms that facilitate binding of neuronal activity across regions during a task remain unclear. High-frequency (~90-Hz) ripple oscillations were first identified as components of hippocampal sharp-wave ripples associated with memory consolidation, but subsequent research shows that they occur widely across the cortex, sometimes in the absence of sharp-wave activity, and that local firing coordinates with these ripples during behavior and memory retrieval. This week in Nature Neuroscience, Verzhbinsky and colleagues measure brain activity in humans during a complex memory task to determine whether ripple oscillations coordinate neuronal firing across brain regions that scales with cognitive demand.

How did they do it?

The authors analyzed an open-access dataset of 35 patients implanted with intracranial Behnke-Fried microwires from five brain regions: hippocampus, amygdala, ventromedial prefrontal cortex, anterior cingulate cortex, and pre-supplementary motor area. Electrodes recorded simultaneous individual neuron spikes and local field potential (LFP) readings while patients performed a modified Sternberg working memory (WM) test. The WM test had varying memory loads. First, in the encoding phase, they had to remember a set of images (1 for lighter load and 3 for greater load), then maintained the memory during a delay period in the maintenance phase (2.5–2.8 seconds), and identified whether a probe image was part of the original set during the retrieval phase.

The authors detected ripple oscillations from the LFP using standard amplitude and duration thresholds, then defined a “co-ripple” whenever two regions showed overlapping ripples for at least 25 milliseconds. They separately defined “co-firing” whenever two neurons in different regions spiked within that same 25-millisecond window. Using these metrics, they measured how often co-ripples and co-firing occurred across the five brain regions and how strongly individual neurons in different brain regions fired together within narrow time windows. They then compared these measurements across each phase of the task and between levels of task difficulty (lighter vs. greater load).

What did they find?

Through this analysis, the authors reached three main conclusions. First, whenever there was a co-ripple, even between distant brain regions (over 220 mm apart), neurons in those regions “co-fired” together roughly 30% more than usual. The cross-region coordination didn’t weaken with distance, and reflected precisely timed coordination between the brain regions, not just an increase in activity in both regions. This coordinated co-firing and co-ripple effect was observed in all three phases of the task. Second, they observed that when a participant was asked to remember three images, there were more cross-region ripples and neuronal co-firing compared to when they only had to remember one image. This shows that, at the level of individual neurons, ripple-driven co-firing between brain regions increases with mental load. Finally, they found that the pattern of cross-region co-firing evoked by a specific stimulus during encoding reappeared during retrieval of the same stimulus, and that during high-load trials, co-firing during co-ripples was significantly greater on trials with faster response times compared to slower ones. This demonstrates that co-ripple-mediated repetition (co-firing of the same neurons in each brain region) is associated with more efficient responses in a WM task, suggesting that ripples don’t just increase general neuronal activity, but carry context-specific content and coordinate better memory recall.

What’s the impact?

This study is the first to analyze simultaneous single-unit firing and LFP across the brain during memory formation. They showed that ripple oscillations synchronize neuronal firing, coordinating long-range, stimulus-specific activation that increases with mental effort. This work lends support to the “interactive” over “hierarchical” model of cognition, providing evidence and a new method of studying this framework. Beyond immediate memory, establishing ripples as a demand-sensitive, context-carrying signal allows for further investigation or therapeutic targeting in memory loss disorders such as dementia.

Access the original scientific publication here.

The takeaway

This study in fruit flies showed it is both possible to recover forgotten memories and to create false memories. However, true and false memories resulted from different processes in the brain.

What’s the science?

There has been considerable debate about the recovery of latent (or silent) memories in humans: events from the past that someone had previously forgotten. Along with that debate is the one surrounding false memories, which can be created through the power of suggestion. This week in Nature Neuroscience, Yang and colleagues investigated the neural mechanisms underlying the recovery of true and false memories in the humble fruit fly.

How did they do it?

The authors performed a conditioning task with flies, where they exposed them once to an odor paired with a mild shock, and once to another odor that was not paired with a shock. This is a common experiment, and we already know that after about 24 hours, the flies lose their memory of the pairing and return to normal behavior if they are not reminded about the experience. We can tell the flies have forgotten the association because if they are put in a container with two arms, one with the odor that was paired with the shock and the other which was not, they show no preference, spending equal amounts of time in each arm. This forgotten association is a latent memory that could potentially be re-activated if it is still stored somewhere in the brain of the fly. The authors tested different conditions for trying to re-activate the memory: reminding the flies of the odor throughout the day (without shock), and varying things like the texture of the enclosure or lighting along with those reminders. They also tested to see if there were ways for a fly to form a false memory, learning to avoid an odor that had never been paired with a shock.

The authors then used genetic techniques to selectively block the activity of certain brain cells in the fly (a fruit fly brain has around 150,000 neurons, which have been mapped and documented). With this, they hoped to understand which cells were involved in retrieving forgotten memories. Finally, they observed the brain of the fly (pinned to a microscope slide), as they presented it the same protocol (one smell paired with a shock and another smell not paired with a shock) and tested its responses later in the day. They used calcium imaging to observe where brain activity was happening in the fly.

What did they find?

If flies were reminded of the aversive odor in between the initial experience and testing, they were able to recover and retain the shock-odor association—but only if important parts of the original context were also replicated (same floor texture, lighting). On the other hand, if the flies were reminded of the unpaired odor in between the initial learning and testing, they created a false memory, associating the unpaired odor with the shock instead, and preferring the odor that had originally been paired with the shock!

Contextual cues were necessary to help the flies retrieve aversive memories. When reminded of the texture or lighting where they had first experienced the shock, the flies were more likely to avoid the odor. When the flies were placed in a completely different environment (different texture/lighting), they acted neutrally. This indicates that, in order to recover lost memories, it might be necessary to re-create the original context of the memory in a multi-sensory way.

As for the cell-level interactions that occur to recover these memories, the authors found specific neurons that were necessary for the re-activation of memories, even though they weren’t involved in the initial learning. These neurons developed their connections even as the original memory was being forgotten. Stimulating these neurons allowed the flies to recover the aversive memory even in the absence of reminders, as well as during changes in the context (lighting, texture). However, stimulating these neurons did not lead to false memories, which meant that false memories are developed through an entirely different pathway.

It was a different set of neurons that turned out to be responsible for creating false memories (a similar class of neurons, but still distinct). Still, false memories were fragile and could be interfered with by changing the context. This means that even though it’s possible to form false memories, these can only form in contexts very similar to the true memory.

What’s the impact?

This work shows that there are different brain mechanisms for retrieving silent memories versus creating false memories. It’s an important distinction, but of course this study was performed in fruit flies, so more work would be needed to understand these kinds of memories in other animals or humans.

Access the original scientific publication here.

The takeaway

Neuronal hyperactivity in the adult brain can trigger local synapse loss by recruiting the immune protein complement component 1q (C1q), which initiates a cascade that flags and removes synapses at specific terminals. In mouse hippocampal circuits, C1q-dependent pruning requires nearby antibody-producing B cells that secrete immunoglobulins, especially IgM, to mark vulnerable synapses for removal.

What’s the science?

Synapse loss is tightly linked to cognitive decline in diseases such as Alzheimer’s disease (AD). Prior work showed that microglia and C1q mediate synaptic pruning; however, the mechanisms by which specific synapses are tagged and whether adaptive immune cells play a role in this process have not been characterized. This week in Science, Crowley and colleagues asked whether changing neuronal activity is sufficient to drive C1q-dependent synapse loss in adult hippocampal circuits and how immune proteins contribute to activity-dependent pruning.

How did they do it?

The authors used chemogenetic tools called DREADDs (Designer Receptor Exclusively Activated by Designer Drugs) to bidirectionally control activity in defined hippocampal pathways. They injected an excitatory DREADD virus into the medial entorhinal cortex to selectively activate perforant pathway neurons that project to the dentate gyrus, and an inhibitory DREADD virus into the same pathway in an Alzheimer’s mouse model (J20 amyloid model) to dampen its hyperactivity. For five days, animals received daily injections of the designer drug clozapine-N-oxide (CNO) to specifically activate these engineered receptors, and the authors confirmed efficacy through in vivo neural recording (Neuropixels) and immediate early genes (e.g. c-FOS) related to neuronal activity. To further validate their findings, they used other models of hyperexcitability (kainic acid and subthreshold pentylenetetrazole) that induce seizure-like network activity and then measured C1q deposition and synapse density.

To uncover molecular changes, they applied multiple forms of spatial transcriptomics (Visium and Xenium) to hippocampal sections, focusing on differentially expressed genes and cell types around the dentate gyrus in activated versus control hemispheres. These analyses unexpectedly highlighted immunoglobulin genes, so the authors used RNAscope, immunohistochemistry, and high-resolution microscopy to visualize B-lineage cells and different antibody isotypes in cells adjacent to hyperactive circuits. Finally, they used several transgenic and pharmacological tools targeting B cells and antibodies (e.g. IgM knockout mice) to test whether secreted, antigen-specific immunoglobulins are required for activity-dependent C1q deposition and synapse loss.

What did they find?

Inducing hyperactivity in the perforant pathway in healthy mice produced a focal increase in C1q deposition in the activated projection zone and targeted loss of specific presynaptic terminals (VGLUT2-positive). This pattern suggests that local circuit activity can spatially direct where C1q accumulates and which synapses are selectively removed, rather than causing global synaptic damage. Inhibiting perforant pathway hyperactivity in J20 amyloid mice reduced C1q accumulation, decreased amyloid-β immunoreactivity, and partially restored postsynaptic markers, supporting the relationship between hyperactivity and C1q deposition in disease. In mice lacking a functional C1q protein (C1qa knockout), chemogenetic activation increased c-Fos expression but no longer produced VGLUT2 terminal loss, demonstrating that C1q itself is required for this form of activity-dependent presynaptic pruning.

Spatial transcriptomics in activated wild-type brains revealed strongly upregulated immunoglobulin genes, indicating local antibody-secreting cells near the hyperactive pathway. RNAscope, spatial transcriptomics, and immunostaining identified B-lineage cells adjacent to activated hippocampal regions. Imaging revealed that IgM localized near C1q-positive VGLUT2 terminals, suggesting that locally produced IgM antibodies help recruit C1q to specific synapses during periods of increased activity. Functionally, mice lacking B cells or IgM, or with restricted B-cell receptor specificity (B cells all recognize the same kind of antigen), failed to show the typical activity-dependent C1q deposition and VGLUT2 loss after hyperactivity. This indicates that antigen-specific, secreted IgM from B-lineage cells is required to enable C1q-mediated synapse elimination in the perforant pathway.

What’s the impact?

This study is the first to demonstrate that neuronal hyperactivity is sufficient to drive C1q-dependent synapse loss in the adult brain and that local B-lineage cells and their IgM antibodies are key enablers of this process. By revealing that adaptive immune cells can respond to circuit activity and tag synapses for C1q-mediated pruning, it broadens our understanding of how innate and adaptive immunity collaborate to sculpt adult neural circuits. Further, the work suggests that in disorders with early hippocampal hyperexcitability such as Alzheimer’s disease, modulating neuronal activity or interrupting IgM-C1q interactions at synapses could be promising strategies to limit synapse loss and preserve cognitive function.

Access the original scientific publication here.

The takeaway

The bilingual brain must compute different grammar rules for different languages at millisecond resolution. This computation relies on a shared brain network spanning the left frontal and temporal lobes, using flexible, reusable rules rather than a separate network for each language.

What’s the science?

Human language requires the speaker to alter the pronunciation of words to account for changing information such as the plural form of an object, but the phonological rules for this are different across languages. This raises the question as to whether the brain utilizes language-specific neural systems or if it relies on a shared abstract mechanism that generalizes across linguistic contexts. Prior brain imaging work on this question has produced mixed results and has mostly examined language comprehension, leaving open whether the same holds true during the split-second demands of planning and producing speech. This week in Journal of Neuroscience, Chen and colleagues aimed to resolve this by using magnetoencephalography (MEG) to track millisecond-scale brain activity in Spanish/English bilinguals, using a task designed to separate the mental act of changing a word’s grammatical form from other factors like the word’s meaning or how it sounds, allowing them to pinpoint when and where this computation occurs in the brain as bilinguals produced singular and plural nouns in both languages.

How did they do it?

The authors recruited 23 highly proficient Spanish-English bilinguals to complete a phrase-completion task: participants saw a written noun (e.g., “boat”), then heard a spoken cue (”one,” “two,” or “say”) prompting them to produce the singular form, plural form, or simply repeat the word aloud. The task varied language, whether grammatical change was needed, and whether that change altered the word’s sound, all independently of one another.

While participants performed the task, the authors recorded brain activity using MEG, a non-invasive technique that detects magnetic fields from active neurons at a millisecond timescale, critical for capturing the split-second planning between hearing a cue and speaking. They also collected structural MRI scans and combined them with MEG data through source localization, which estimates where in the brain each signal originated using each participant’s own anatomy. First, they compared brain activity when transforming versus simply repeating a word. Second, they trained a computer algorithm to recognize that activity pattern in one language (e.g., English) and then tested whether it could detect the same pattern in the other (Spanish). Successful detection across languages (”cross-decoding”) would show that English and Spanish rely on a common neural code, rather than separate systems.

What did they find?

The authors found that transforming a word’s grammatical form (e.g., “boat” to “boats”) activated the left frontal and temporal regions much more strongly than simply repeating a word, even for words that don’t change sound when pluralized (like “tuna/tuna”). This suggests that these brain regions carry out grammatical transformation as an abstract computation, rather than one tied to a specific sound pattern. Comparing English and Spanish separately, both languages activated nearly the same brain regions, with Spanish showing activity about 110 milliseconds earlier than English. A decoding algorithm trained to detect grammatical transformation in one language successfully detected it in the other, suggesting that English and Spanish rely on a shared brain mechanism for this computation rather than two separate systems, with the timing difference simply reflecting how quickly each language can be processed. Finally, this same pattern held even for words that don’t exist in either language, and cognates (like “chocolate/chocolate”) showed no added advantage over other real words. This suggests that the brain’s grammar system is highly generalizable: it can apply grammatical rules to entirely novel words and doesn’t depend on having a matching or overlapping vocabulary entry to work with.

What’s the impact?

This study found that Spanish-English bilinguals engage in a shared left frontal-temporal network to process the grammatical transformation of pluralizing a word in both languages, suggesting the bilingual brain uses flexible, reusable rules rather than separate networks per language. These findings support a growing body of work showing that the brain favors efficient, generalizable abstract computations that can be reused across different contexts. Further, this study demonstrates how bilingualism can be used to interrogate general principles of neural organization.

Access the original scientific publication here

The takeaway

In a preliminary study designed to understand how people with obsessive-compulsive disorder may differ in time processing, the authors found that while objective time perception and movement ability were intact, subjective time perception (including time management, time knowledge, and time experience) was significantly impacted.

What’s the science?

Obsessive Compulsive Disorder (OCD) is a condition in which a person experiences recurring worries (obsessions) and enacts certain behaviors (compulsions) to try and manage these worries. OCD shares some features with depression (for example, some depression medications that act on serotonin can also be used to treat OCD). It additionally shares features with movement disorders such as Tourette’s, where differences in brain function cause involuntary actions that are difficult for the person to control.

People with OCD can take longer to complete certain tasks and experience difficulties with time management. However, it is currently unclear what contributes to this. One possibility is that people with OCD have more difficulty controlling their movements, which means that doing things takes more time. Another possibility is that their objective time perception is altered—they might be less able to estimate time passing due to differences in time-keeping mechanisms in the brain. Finally, a third possibility is that people with OCD differ in their subjective experience of time, which could include difficulty making plans or realizing how much time they have spent on a task.

Recently, in an exploratory study published in European Archives of Psychiatry and Clinical Neuroscience, Mavrogiorgou and colleagues tested both objective and subjective time perception, along with movement ability and depression/anxiety symptoms, to better understand whether OCD uniquely contributes to altered time processing.

How did they do it?

In this exploratory study, the authors recruited 20 participants with OCD and 20 healthy participants of similar ages and genders. They ran them through a battery of tests, including questionnaires to establish the severity of OCD symptoms, questionnaires for depression and anxiety symptoms, measures of movement ability, experimental tests to measure more concrete/objective aspects of time perception, and questionnaires about subjective aspects of time perception.

The objective time perception tasks included flicker fusion frequency, which presents a blinking light that flickers at faster and faster frequency until the participant indicates that they perceive it to be stable and not flickering anymore—this measures the timing resolution of their visual perception. Another was the Chronotest, where participants are presented with a light stimulus and asked to estimate how long it was on, in seconds. They are also asked to reproduce different time intervals by turning a light on for that amount of time.

What did they find?

Participants with OCD were not significantly different from the healthy controls in their objective time perception, and there was only a slight trend for a difference in movement ability. In contrast, subjective time measures such as time knowledge, time experience, and time management were significantly impaired in OCD, and were correlated with OCD symptoms—in other words, those with more severe OCD had poorer subjective time perception. Subjective time perception was also correlated with depression scores, both in people with OCD and in the healthy control group, but some influence of OCD symptoms remained even after controlling for depression, anxiety, and movement issues, specifically on time management. It’s worth noting that subjective time perception was measured using a questionnaire, while the objective time measures were done using experiments, so self-perception had more of a chance to influence the results.

What’s the impact?

Since this is an exploratory study, it should be treated as preliminary, but it has the potential to increase our knowledge of a lesser-known aspect of OCD. If these results hold, it means that there are time perception differences in OCD that are not related to movement difficulties or depression/anxiety symptoms.

Access the original scientific publication here.

The takeaway

This study shows that grammatical features are encoded by individual neurons in the brain, distinct from other characteristics of the speech signal. Grammar-sensitive neurons are distributed throughout the left hemisphere in a way that is hard to detect when averaging regional activity.

What’s the science?

Since the time of Broca, scientists have developed more and more detailed maps of the language-processing areas of the human brain. They have used a variety of brain imaging techniques, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and others. All of these techniques have been helpful, but still lack the precise detail of single-neuron recording that is done routinely in animals. The only time that electrodes are implanted in human brains is when they are already undergoing an operation, such as for epilepsy. During those times when the brain is exposed, researchers can gather precious single-neuron data.

One challenge about studying human language processing is the complexity of language itself. In everyday conversation, we are free to put together words in a myriad of forms and sentence structures. Most of the time, studies of language involve presenting pre-designed words, phrases, or sentences that have a feature we are interested in (for example, sentences with different relationships between the subject and the object). However, this eliminates the natural spontaneity of language production, and so we can’t be sure if the brain activity we observe is reflecting what our brains do daily.

Fortunately, the technology behind large language models (some of which are now commonly referred to as “AI,” though not all of them are on the same scale) has given us the ability to represent natural language production in a way that computers can digest, and we can identify interesting features without having to design awkward stimuli. This week in Nature, Cai and colleagues combined natural language processing and recordings from electrodes implanted in the brains of epilepsy patients to map human language production, including grammatical features.

How did they do it?

The authors had access to eight people who were undergoing surgery for epilepsy. At the time of their operations, the patients consented to have an array of electrodes temporarily implanted in their brains so that the researchers could gather data about the firing of individual neurons. Depending on the patient, these electrode arrays were located in different spots in the brain, so the researchers had a variety of brain regions represented.

After implantation, the researchers recorded brain activity through the electrodes while asking the participant questions and allowing them to respond freely. They recorded these conversations and applied natural language processing algorithms to classify each sample of speech. In addition to representing the individual words, this approach extracts both grammatical relationships and the hierarchical structure of phrases (see image below). By labeling these features, the authors could then look at which parts of the brain were selectively responding to parts of speech, hierarchical features, or specific conversation contexts.

What did they find?

About 9% of neurons responded specifically to a word’s part of speech (noun, adjective, verb, etc). About 16% of neurons responded to constituency depth (how deep the word was in the nested hierarchy of the sentence), and so on. These neurons had very little overlap with each other, or with neurons that tracked other features of the speech (like the meaning of the word itself, or whether the pitch of the spoken word was high or low). Interestingly, these neurons that responded to different grammatical features were scattered throughout various brain regions, which may explain why it has been hard to pinpoint grammatical processing in the brain by summing up large amounts of activity. In addition, these grammar-selective neurons appeared in both hemispheres of the brain, but the most strongly predictive ones were in the left hemisphere, which lines up with decades of research confirming that language is processed on the left side of the brain (for most people).

What’s the impact?

The results of this study inform debates about how the brain processes language. First, it supports the idea that certain high-level features of language can be coded by single neurons, rather than being only a result of multiple neurons firing in a pattern. These results also suggest that the popular but simplistic “modular” approach, where “region X is responsible for function Y,” doesn’t really work here: grammar-sensitive neurons are scattered throughout the left hemisphere.

Access the original scientific publication here.

The takeaway

Within the brain, facial recognition is lateralized to the right, and word processing is lateralized to the left. In individuals who suffer an early stroke in the left hemisphere, both word processing and facial recognition occur on the right without any impairment to facial recognition ability.

What’s the science?

In the neurotypical human brain, the fusiform face area (FFA), a region within the ventro-occipital temporal cortex (vOTC), responds preferentially to faces. Face processing is right-lateralized, meaning the right hemisphere of the FFA is more active and dominant than the left. Conversely, language and visual word form processing are left-lateralized in a region called the visual word form area (VWFA) within the left VOTC. It has been hypothesized that these two lateralization patterns are linked: as children learn to read, the VWFA establishes itself in the left vOTC, either displacing or blocking the expansion of facial processing there, causing face processing to rely more heavily on the right vOTC. While correlations between literacy and face lateralization support this idea, evidence of direct competition between word and face processing remains mixed. This week in the Journal of Neuroscience, Seydell-Greenwald and colleagues used functional MRI (fMRI) to test whether face processing is altered in individuals who suffered a left-hemisphere perinatal stroke (LHPS) and consequently developed right-lateralized language and word processing.

How did they do it?

The authors performed fMRI on 15 individuals with LHPS (mean age 19 years) who had previously established right-hemisphere language dominance and right-lateralized VWFA and compared them to 14 neurotypical individuals (mean age 16 years) with typical left-hemisphere language dominance. Both groups were fluent readers with comparable language comprehension scores. Participants completed a visual localizer task in which they viewed rapid sequences of images from three categories: faces, places, and word forms, allowing the authors to map and compare the lateralization of face and word form processing across groups. Participants then completed the Cambridge Face Memory Test, a standardized assessment of unfamiliar face recognition, requiring them to learn six faces and then identify them across different viewing angles and conditions. Together, these two tasks allowed the authors to assess both the neural lateralization of face processing and its behavioral consequences in individuals with LHPS relative to unaffected control participants.

What did they find?

Control participants showed the typical pattern of left-lateralization for word forms and right-lateralization for faces, while the LHPS group was right-lateralized for both. This indicates that when word processing reorganizes to the right hemisphere after an early stroke, face processing does not compensate by shifting to the left. Instead, both functions are supported by the same hemisphere. There were no significant differences in the strength, spatial extent, or peak activation of face responses between the two groups, and both groups showed a positive correlation between face lateralization and word form lateralization, meaning stronger right-lateralization for words was associated with stronger right-lateralization for faces. Lastly, LHPS participants performed slightly but not significantly worse than controls on the Cambridge Face Memory Test, and both groups scored within normal range. Together, these findings suggest that sharing the right vOTC with word form processing does not meaningfully compromise the neural representation of faces or the ability to recognize them.

What’s the impact?

This study was the first to show that although the neurotypical brain delineates facial processing and visual word processing to either the right or left hemisphere, respectively, patients who have experienced early stroke to the left hemisphere can process both faces and written words together in the right hemisphere without any meaningful cost to either. This challenges the long-standing idea that the typical opposite-hemisphere arrangement of face and word processing is driven by competition. These findings provide insight into the key drivers of lateralizing specific neuronal pathways and demonstrate the remarkable capacity of the developing brain to reorganize after injury.

Access the original scientific publication here.

The takeaway

Stimulating specific brain regions may help “jump-start” brain activity in patients who have limited awareness and disrupted brain communication. While the effects were modest, the study suggests this noninvasive approach could help support recovery of awareness and responsiveness.

What’s the science?

People with severe disorders of consciousness have limited awareness and responsiveness because communication between different parts of the brain can be disrupted. Currently, there are very few effective treatments to improve wakefulness or recovery in these patients. This week in NeuroImage, Sangare and colleagues aim to improve brain activity and awareness in patients with limited wakefulness through brain stimulation.

How did they do it?

The authors studied 18 patients with disorders of consciousness. The authors used brain stimulation on the frontal cortex, an area at the front of the brain involved in higher-level thinking and control. Before and during treatment, the researchers measured how patients responded to sounds, sights, movement, and attempts to communicate. They also used EEG to record the brain’s electrical activity through sensors placed on the scalp, to track changes in brain function. Patients received stimulation or no stimulation (control), and the authors compared their responses before and after treatment. Six months later, the researchers followed up again to see whether any changes lasted over time.

What did they find?

The researchers found some signs that the brain stimulation may have helped improve patients’ responsiveness and awareness, as the treatment group showed slightly greater behavioral improvement than the control group after stimulation. However, they did not observe changes in the overall level of consciousness. EEG results showed more active and complex brain activity in certain regions after treatment, which may suggest stronger communication between different parts of the brain. Overall, stimulation increased electrical activity across seven areas throughout the brain, although these changes did not consistently lead to major improvements in consciousness or behavior. Together, the findings suggest the stimulation may influence brain function, but larger studies are needed to determine whether it can produce meaningful recovery for patients with severe disorders of consciousness.

What’s the impact?

This study found that brain stimulation may help improve brain activity and responsiveness in patients with limited wakefulness by strengthening communication across different parts of the brain. If future studies confirm these findings, noninvasive brain stimulation could offer a safe way to improve awareness and brain communication in people with a wide array of serious neurological conditions.

Access the original scientific publication here.

The takeaway

Neural information spreads across the cortex through brain oscillation patterns called “traveling waves.” Altering the direction of these traveling waves using traveling-wave transcranial alternating current stimulation (twtACS) influences cognitive performance.

What’s the science?

Cortical “traveling waves” are rhythmic patterns of brain activity that move across the surface of the cortex, like ripples spreading across a pond, and they have been observed across species and during many different cognitive tasks. Cognitive dysfunction is a hallmark of many neurodegenerative diseases, psychiatric disorders, and aging populations, and previous research has shown that standard tACS can enhance certain aspects of human cognition. While tACS can synchronize brain rhythms, its effect is confined to one location and cannot generate dynamic patterns of neural activity. This week in PNAS, Lee and colleagues created and validated a stimulation method to impose a directionally controlled traveling electric field across the cortex and demonstrated how the wave’s directionality influences specific cognitive tasks.

How did they do it?

The authors used computer models of human head and brain anatomy to calculate how currents from multiple scalp electrodes would flow through the brain and then optimized the phases of the injected sinusoidal currents so that the location of the maximal electric field (the “peaks”) would sweep across the cortex over time (backward: from anterior to posterior), forming a traveling phase gradient. The team applied twtACS to two human patients with electrocorticography (ECoG) electrodes implanted for clinical reasons and showed that the measured phase at cortical electrodes closely matched the target pattern from the simulations and exhibited clear traveling phase gradients in the backward direction.

A nonhuman primate was implanted with a dense grid of intracortical microelectrodes spanning frontal to motor cortex, allowing measurement of multiunit activity (MUA; pooled local spiking) across space. The animal underwent four consecutive conditions at 10 Hz: baseline (no stimulation), standard tACS (same phase everywhere), forward twtACS (from posterior to anterior), and backward twtACS. For each condition, the authors computed the preferred phase of spiking relative to the stimulation cycle at each electrode. They confirmed that neural population activity aligned with the propagation of twtACS-induced electric fields.

Healthy participants received 10 Hz twtACS targeted to right frontal and parietal regions, with conditions optimized to produce either forward or backward traveling fields. During 20 minutes of stimulation, participants performed two tasks in fixed order: (1) A visual attention task where they covertly attended to flickering disks on the left or right, reporting whether a brief target appeared in the attended disk. (2) An episodic memory task where they encoded 60 images during stimulation, then, after a 10-minute rest, performed an old/new recognition test.

What did they find?

In ECoG patients, the phase of the recorded electric fields across electrodes showed robust gradients, confirming that twtACS can impose direction-specific electric fields across the human cortex that mimic observed cortical traveling waves.

Compared to tACS and baseline, twtACS produced systemic shifts in the preferred phase of spiking along the anterior-posterior axis. The spatial phase gradients in the MUA closely matched those of the electric field, indicating that population spike timing aligned with the propagating twtACS-induced field.

In the visual attention task, performance differed between forward and backward stimulation and depended on whether participants attended to the left or right visual field. Specifically, performance accuracy was higher with backward twtACS when participants attended to the right visual field. In the memory task, accuracy was higher under forward twtACS conditions. Together, these tasks indicate that the directionality of the imposed wave was critical to performance, and they are broadly consistent with previous work linking forward waves to feedforward sensory processing and memory encoding and backward waves to top-down control and spatial attention.

What’s the impact?

This study provides evidence that manipulating the direction of traveling cortical waves can actively shape cognition. These findings support the idea that the direction of traveling waves is closely related to task demands, with different directions favoring feedforward vs feedback processing. This approach could eventually provide new therapeutic strategies for cognitive dysfunction in aging and psychiatric or neurological disorders that are thought to involve disrupted large-scale brain coordination.

Access the original scientific publication here.

The takeaway

In a re-analysis of data from 11 different MRI studies, researchers found that psychedelics increase connectivity between many different brain networks, but only slightly decrease connectivity within networks. This analysis synthesizes and harmonizes findings in a field where research to date has been inconsistent and contradictory.

What’s the science?

Psychedelics are a class of compounds that primarily target serotonin receptors in the brain, changing neuronal activity and connectivity (see a previous BrainPost for more information on how these drugs work on a molecular level). There are currently many clinical trials looking at their potential therapeutic benefits for neuropsychiatric disorders like depression. Alongside this, researchers are trying to understand how exactly psychedelics act in the brain.

While we are beginning to understand the effect of psychedelics on brain networks, the picture is not consistent from study to study. Each psychedelic compound is a little bit different, and sample sizes in this kind of research tend to be small, so each brain network study has somewhat different findings—in fact, some papers report opposite results. Recently, in Nature Medicine, Girn and colleagues performed a “mega-analysis” from 11 different magnetic resonance imaging (MRI) datasets from studies on psychedelics to try to settle some of these debates and uncover the general effects of psychedelics on brain function.

How did they do it?

One common way of studying brain function that has been applied to many research topics, including psychedelics, is called resting-state functional MRI (or rs-fMRI). In regular functional MRI (fMRI), researchers measure the blood flow in the brain while a person lies in the scanner doing some kind of task: seeing images, hearing sounds, or thinking about certain things. As neurons use glucose and oxygen, the blood vessels in the brain open and increase the flow to supply them with more, and this can be tracked with the fMRI signal. However, in rs-fMRI, this signal is measured while people lie in the scanner doing nothing in particular—in other words, they are “at rest.” The brain is still active at rest, and a lot can be learned from studying this kind of data.

The authors collected and re-analyzed data from multiple laboratories that had scanned people under the effects of psychedelics using rs-fMRI. Most of these studies were randomized controlled trials with some kind of psychedelic versus a placebo. They didn’t just compile the results: instead, they started from scratch with the raw data and re-analyzed it with their own pipeline. MRI analysis is complex, and there is room for a lot of variation in processing methods, so by re-analyzing the data themselves, they hoped to iron out some of those inconsistencies. The type of analysis they conducted looked at functional connectivity, which tracks the fMRI signal to see which parts are correlated in their activity over time (and are therefore probably working together). They were primarily interested in testing one finding from previous studies: that psychedelics decrease connectivity within regions that usually work together, and they increase connectivity between regions that are usually distinct.

What did they find?

The main claim the researchers were testing was partially confirmed: compared to participants with a placebo, people who were under the influence of psychedelics showed increased connectivity between networks, especially general networks having to do with attention, self-reflection, sensory processing, and executive function. On the other hand, while they did see somewhat decreased connectivity within brain networks, these effects were much less robust. This amounts to more “cross-talk” between regions and only slightly less “internal chatter.” However, the one study using ayahuasca showed a completely different pattern: there was an overall decrease in connectivity between most brain areas. Since there was only one study on ayahuasca that had only 9 participants and lacked a placebo condition, more research will be needed to understand its effects. Overall, these results align better with some theories about how psychedelics work (like the subcortical connectivity account) while providing less support for others (like the ‘network disintegration’ account).

What’s the impact?

As clinical trials progress and potential therapies are identified, this research helps us understand the mechanisms behind those therapies and the mental health conditions they could treat. Knowing about the brain mechanisms of psychedelics might help us to better predict who could benefit from their use (personalized medicine), identify potential side effects, and develop sensible regulations.

Access the original scientific publication here.

The takeaway

The brain constructs an internal map of social networks, even for relationships never directly observed. New research shows that the hippocampus and entorhinal cortex encode these maps, enabling us to infer and navigate complex real-world social connections.

What’s the science?

The brain’s ability to navigate complex social networks, like tracking how individuals are connected within a broader group, is fundamental to human social life. Neurons in the medial temporal lobe (MTL) are known to encode cognitive maps of physical space, and converging evidence suggests the MTL may similarly represent abstract relational structures; however, how the brain encodes large-scale, real-world social networks remains unknown. This week in PNAS, Teoh and colleagues combined computational modeling with fMRI and a longitudinal study of a real-world social network to investigate how activity in the hippocampus and entorhinal cortex scales to represent naturalistic social structures.

How did they do it?

To uncover how social network structure is encoded across individuals, the authors recruited undergraduates within a real-world social network and asked them to identify their friends within that network at multiple timepoints, generating a ground-truth friendship map (defined as mutual recognition between two individuals). Participants were asked to judge the relationships between other network members in a pairwise manner, including pairs they had never directly interacted with, to assess participants’ ability to infer unseen social connections. The authors then tested four computational models of network representation to determine which best explained participants’ behavioral patterns. These models varied in their relational complexity, the weight they gave to connections between individuals, the shortest path distance between two people, and the structural complexity of those paths.

To identify the brain regions supporting these representations, the authors used functional MRI (fMRI) to measure MTL activity while participants viewed photographs of community members and judged whether each person belonged to their network. Regions of interest included both the left and right hemispheres of the anterior hippocampus (aHC), posterior hippocampus (pHC), and the entorhinal cortex (EC). To link brain activity patterns to the computational models, they applied representational similarity analysis (RSA), a method that asks whether the pattern of similarity across brain responses mirrors the structure predicted by a given model of representation.

Finally, to assess whether these neural maps translate into functional social reasoning, the authors administered an Information Flow task where participants were asked to determine how information would travel from one participant across the network; a measure of how well participants could navigate their social network to trace indirect connections. Responses were linked to individual neural representations to determine how MTL activity patterns relate to real-world social inference.

What did they find?

Participants demonstrated structured, non-random judgments about unseen relationships, and their accuracy decreased with social distance: participants were most accurate when judging pairs close to their own friendships, with performance declining as the path distance between pairs increased. This suggests that participants do not simply recall direct ties but actively infer indirect connections using a structured internal representation of the network. Of the computational models tested, the Katz communicability model, which captures integration across multiple indirect paths between individuals, rather than just the shortest or most direct route, provided the best fit to participants’ behavioral data. This indicates that people represent their social networks as distributed, multi-path structures rather than simple maps of direct connections.

At the neural level, the RSA revealed that the right EC encoded an abstract map of multistep network connections, while the right aHC may encode a more veridical representation of directly observed social ties. This regional dissociation suggests the MTL supports social network representation through at least two complementary encoding mechanisms, paralleling its known role in spatial navigation.

Finally, participants again relied on a Katz communicability-based strategy when reasoning about how news would spread through the network. Critically, when the right EC strongly encodes Katz communicability, the more strongly the right EC predicts task performance.

What’s the impact?

This study is the first to demonstrate that the human brain encodes large-scale, real-world social networks as structured cognitive maps, within the MTL. These findings extend the MTL’s known role in spatial navigation into the domain of complex social cognition. This work lays the foundation for investigating how social cognitive maps are updated over time, and how their disruption may contribute to deficits seen across neurological and psychiatric conditions.

Access the original scientific publication here.

The takeaway

Placebo pain reduction is a phenomenon where prior experience or expectations suppress pain in response to the administration of an inactive treatment. Placebo reduction of pain involves input from multiple cortical regions to the brainstem, which gates brainstem endogenous opioid release, reducing the experience of pain.

What’s the science?

Placebo analgesia (reduction of pain) is well known for its ability to complicate experimental procedures. It can also be a useful phenomenon relevant to therapeutic development. For example, if systems involved in placebo analgesia are understood, clinicians may be able to provide treatments that deliberately engage them to provide pain relief. This week in Neuron, Livrizzi and colleagues reverse translate a human placebo conditioning paradigm to mice, and uncover cortex to brainstem connections that gate release of endogenous opioids to downstream pain-modulatory regions.

How did they do it?

The authors used a conditioning paradigm where contextual cues were paired with either morphine + pain stimulus or saline injection (placebo) + pain stimulus. For the morphine conditioning, the idea is that in the absence of morphine, these contextual cues would trigger placebo analgesia in the mice. After conditioning, the placebo test included placing these conditioned mice in similar contexts to the morphine conditioning, but with a saline (placebo) injection to induce placebo analgesia. They measured pain in animals by how long it took them to remove their paw from a pain-inducing apparatus (withdrawal latency). The tools they used to manipulate and record pathways involved in placebo analgesia were chemogenetics to activate pathways and fiber photometry to record opioid signaling. The authors used TRAP2 mice - mice that have genetic modifications that allow for labelling and then selectively analyzing neurons involved in a certain behavior or process. In this case, that behavior was placebo analgesia. They also used an interesting approach called in-vivo drug uncaging, which allowed the release of an opioid receptor antagonist over a specific temporal window, in this case, the placebo analgesia test window.

What did they find?

This study found that the vlPAG (ventrolateral periaqueductal grey), a brainstem region with glutamatergic neurons that activate to produce analgesia, is involved in placebo analgesia. They also found that neurons active in the vlPAC during placebo analgesia receive projections from neocortical and insular regions, while neurons in the rostroventral medulla (RVM) received projections from PAG analgesia neurons. After further experiments, they found that placebo analgesia was reduced when the neocortical regions, and not the anterior insular regions, were inhibited. Similar findings occurred when the PAG to RVM connections were inhibited, although both morphine and placebo nociception were altered in this case, not just placebo nociception. They additionally showed that placebo analgesia can be transferred between multiple pain modalities.

What’s the impact?

This is the first research study to provide causal evidence of circuits involved in placebo analgesia. Importantly, it moves from correlational human evidence of cortex to brainstem circuits being involved in placebo analgesia to causational data using animal models. Understanding this circuit, especially its role in lasting analgesia after injury, opens up possibilities for future therapeutics.

Access the original scientific publication here.

The takeaway

In this study, the authors show that different parts of the hippocampus, a memory formation structure in the brain, work together to bind environmental location and emotional information to form memories of reward or danger.

What’s the science?

The hippocampus is a brain structure involved in memory formation. When an animal is navigating its environment, cells in the dorsal (“upper”) hippocampus called “place cells” fire when the animal is in different locations. Then, when an animal is sleeping, these cells fire again, in special bursts called “sharp-wave ripples.” This process reactivates other parts of the brain that were involved in the experience, and this is one way scientists think memories are consolidated and strengthened.

However, the hippocampus is not just for remembering where we’ve been. The ventral (“lower”) hippocampus has strong connections to the limbic system, a collection of deep brain structures that helps us process emotion. The limbic system includes the amygdala, which is involved in fear and other strong emotions, and the ventral tegmental area, which is involved in reward and motivation. This week in Nature Neuroscience, Morici and colleagues proposed that the ventral hippocampus is a key linking structure that helps us associate specific emotions with specific environments, driving behavior.

How did they do it?

The authors recorded brain activity from both the dorsal and ventral hippocampus of rats as they completed a navigation task, either to avoid danger (in the form of mild shocks) or to get a reward (in the form of a drink). They recorded this activity both as the animal was learning the task, as well as when the animal was sleeping that night and consolidating their memories from the day.

During learning, both the dorsal and ventral hippocampus were active, but the authors were specifically interested in whether they were working together or not. They used independent component analysis to identify patterns of coherent neural activity, which the authors called “assemblies.” They then tracked these assemblies during learning and during sleep.

What did they find?

During learning, the authors found 446 neural assemblies with coordinated activity. About 62% of these reflected activity mainly in the dorsal hippocampus, 14% reflected activity mainly in the ventral hippocampus, and 24% reflected activity in both regions (which they called “joint assemblies”). These joint assemblies looked very different between the reward and danger conditions, potentially indicating that the hippocampus was changing its internal connectivity depending on the emotional content of the experience. About half of the assemblies were associated with rewarding experiences, and half with dangerous experiences.

During sleep, the authors continued to track neural activity in both regions. Many of the ripples, those bursts of activity that help reactivate memories, were coordinated between the dorsal and ventral hippocampus. Depending on the experience of the day, different assemblies would be activated: specifically, on the days the animal had been learning from shocks, the cells in the ventral hippocampus responding to danger were more coordinated with place cells in the dorsal hippocampus. Again, this shows that the hippocampus may have different internal connectivity when linking a memory to reward or to danger.

What’s the impact?

This work helps us understand the mechanism that links environmental location to emotional memories. It helps us tie what we know about the binding of memories to specific, anatomically connected parts of the brain.

Access the original scientific publication here.

The takeaway

Resilience enables us to navigate challenges and recover from stress. Notably, resilience is not immediate; key neurophysiological changes emerge in a distinct window approximately one hour after stress exposure, revealing a delayed but dynamic process of recovery.

What’s the science?

Stress is part of everyday life, whether it is a looming deadline or a never-ending list of chores. Humans are uniquely equipped to cope with these challenges, yet we vary widely in how well we adapt and recover from stress. This week in PNAS, Watanabe and colleagues set out to understand how the brain responds to acute stress by examining time-specific changes in neural activity and physiological responses following stress exposure.

How did they do it?

To explore how people respond to stress, the authors followed about 100 participants through a carefully timed experiment, beginning with self-reported assessments of their baseline stress levels. They measured both brain activity and physical stress responses, like heart rate, breathing, pupil size, and cortisol (a hormone released during stress). Brain activity was assessed using fMRI, which shows active brain areas, and EEG, which measures electrical brain signals. These measurements were taken before, immediately after, and up to 1.5 hours after the stress event to understand how responses change over time. The stress itself was induced using a cold pressor test, where participants placed their hand in ice-cold water for two minutes. By comparing responses across time and between individuals who were more stress-resilient vs. more stress-susceptible, the researchers were able to investigate when stress responses peak.

What did they find?

The researchers first found that self-reported stress levels did not clearly match immediate changes in brain and body activity, suggesting there is no simple biological pattern that separates more resilient from less resilient individuals right away. However, when they looked at brain activity over time, the most pronounced activity changes appeared about one hour after the stressor. At this point, two key brain networks showed opposite patterns, one becoming more active while the other became less active depending on individuals’ stress levels. This same one-hour window was confirmed by machine learning analyses as the most important time point for distinguishing resilience, highlighting that this time point after stress is especially dynamic in the context of stress response. Interestingly, EEG showed similar changes during this time in the same brain region, particularly in the prefrontal cortex (the part of the brain involved in decision-making and emotional regulation), suggesting this region may play a crucial role in stress recovery.

What’s the impact?

This study is the first to show that the biggest differences between people who cope well with stress and those who don’t appear about one hour after a stressful event. In other words, resilience isn’t immediate; the brain takes time to shift into a recovery mode. There may be a critical window where the brain is most actively adapting to stress. Understanding this timing could help us better support mental health and develop treatments that target the right moment to improve recovery from stress.

Access the original scientific publication here.

The takeaway

Recent studies have shown that humans can grow new neurons (neurogenesis) even as adults. Individuals with Alzheimer’s disease have much lower neurogenesis than normal, while older adults with high cognitive capacity have abnormally high neurogenesis.

What’s the science?

It was long thought that adults do not grow new brain cells (neurogenesis), but more recently, we have come to understand that neurogenesis happens throughout our lives. Most studies use rodents as a model for human brains, which doesn’t yield enough insight into how neurogenesis occurs in adult humans. This week in Nature, Disouky and colleagues studied the brains of humans at all ages, with and without Alzheimer’s disease, to better understand the link between neurogenesis and cognitive functioning.

How did they do it?

To study neurogenesis in humans, the authors used the brains of deceased donors. The donors were groups of individuals at different ages and cognitive abilities, including young and older adults with normal cognitive ability, individuals who had or were starting to show signs of Alzheimer’s disease, as well as adults they called “SuperAgers”, or individuals who had high memory test scores. The authors identified neurogenesis from the genetic sequence present in cells in the hippocampus, a part of the brain involved in memory, using a method called RNA-seq. Cells that have just grown, immature cells, have a specific genetic sequence that the authors analyzed to determine how much neurogenesis had occurred.

What did they find?

Both younger and older adults with healthy cognitive ability had high levels of neurogenesis in their hippocampi. In contrast, individuals with Alzheimer’s disease or symptoms of it had less neurogenesis and fewer immature neurons. This suggests that the slowing of neurogenesis may impact memory in diseases such as Alzheimer’s. One interesting result was that the “SuperAger” adults had significantly more neurogenesis and more immature neurons than any other group, including the healthy adults, suggesting that neurogenesis may help support healthy cognitive functioning as we age.

What’s the impact?

This study is the first to analyze the sequence of new immature neurons in humans and the first to link neurogenesis to cognitive function in individuals of different age groups and cognitive ability. Studies like these can help us better understand mechanisms of memory preservation or memory decline and how to develop better preventative care and treatment for those impacted.

Access the original scientific publication here.

The takeaway

Given the global loneliness epidemic, many scientists are looking to technology to find scalable solutions. A recent study shows that artificial intelligence (AI) may be a good solution when an immediate boost in affect is needed, but that over the long term, real human relationships can better foster a reduction in loneliness.

What’s the science?

Generative AI is used by a large number of people not just for information, but for a meaningful source of connection. In recent years, a growing number of scientific studies have examined the effects of chatbots on social connection and positive mood. While many studies have shown positive immediate effects of chatbots, few have assessed the benefits over time. Interestingly, one long-term study found that chatbots may negatively impact emotional state, with seeking chatbot social support predicting increased loneliness. This week in Journal of Experimental Social Psychology, Li and colleagues assessed the cumulative impact of interacting with a custom AI chatbot on emotional outcomes in a population vulnerable to loneliness.

How did they do it?

The authors conducted this study using two experimental groups (chatbot and human) and one control group (daily journaling) to compare the impact of a supportive chatbot on loneliness with that of conversing with a random peer. They conducted this two-week study using a custom chatbot named Sam, designed to have the qualities of an ideal supportive friend, and a subject pool of first-semester college students at a Canadian university. Conversations for each group, including the journaling group, were held on the social media platform Discord, a popular platform among students. The human peer group met with their human conversation partners in person at the beginning of the study. The chatbot and peer group participants were asked to send one meaningful message each day. Each participant was asked to perform their assigned task and take a short survey for 14 consecutive days. The participants were also assessed via a survey pre and post-study.

What did they find?

The authors found that only the human peer group showed a decrease in loneliness post-study. A similar pattern was found for positive mood and perceived isolation. Interestingly, both the human peer group and the chatbot group displayed decreased negative mood post-study compared to the control group. Participants were allowed to use their Discord room for one week following the study. The percentage of the human peer group that continued to engage with their human conversation partner in the Discord room was more than double the percentage in the group that continued communication with their chatbot during this one-week time period. These findings overall suggest that interactions with a human peer may be more beneficial than interactions with an AI chatbot to reduce loneliness.

What’s the impact?

This study’s results suggest a “middle ground” role of AI chatbots on emotional outcomes in individuals vulnerable to loneliness, demonstrating that a chatbot can reduce negative affect, while a human’s support may have a more positive impact in the longer term. This study sheds light on when to use AI or human-based support in vulnerable populations to reduce loneliness. The authors suggest that it is better to view AI as a tool to ameliorate rather than mimic human connection.

Access the original scientific publication here.

This Week's BrainPost:

• Hearing Loss: Can Cell Therapy Restore Hearing Loss? Neuroscience. 3 min read. 

Read this on our website. 

Can Cell Therapy Restore Hearing Loss?

Post by Anastasia Sares

The takeaway

In an experimental cell therapy treatment, researchers transplanted young, immature auditory cells from the ears of newborn mice into older mice whose hearing had been damaged. These cells, surrounded by a new environment, matured and repaired the hearing of the older mice. 

What's the science?

Deep in the inner ear are cells called hair cells that are responsible for taking sound from the environment and turning it into electrical signals to be sent to the brain. These cells are attached to a membrane in the ear that vibrates with incoming sounds, and the movement of this membrane causes the cells to fire. However, as we age, hair cells experience significant wear and tear, and they are sensitive to insults like loud noise exposure, infections, and head trauma. There are even certain classes of drugs that can cause hair cells to die off as a side effect. Hair cells generally do not regenerate in adults, so when they die, it causes an irreversible loss in hearing. While there are some solutions for hair cell loss, such as cochlear implants, these all come with their own drawbacks.

Cell therapy is a kind of medical treatment that involves taking cells from one organism and transplanting them into another (or from one organ/tissue to another within the same organism). It includes treatments like bone marrow transplants and stem cell therapy. When trying to apply this kind of therapy to hearing loss, there are several challenges: will the transplanted cells develop into functioning hair cells? Will the procedure be too invasive and cause additional damage to the inner ear? Will the procedure result in significantly better hearing in the end?

This week in Neuroscience, Liu and colleagues showed that cells can be successfully transplanted into the inner ear and restore hearing function in mice that have drug-induced hearing loss.

How did they do it?

The authors first induced hearing loss in a group of adult mice by administering an antibiotic known to cause hair cell death (kanamycin). Then, relying on previous research, they extracted a very specific type of cell from the inner ears of newborn mice and grew these cells in a gel matrix, where the cells clumped together to form small “organoids” that were ready to become hair cells. They then injected these cells into the left inner ear of each adult mouse (with the right ear serving as a control).

To measure the mice’s hearing, the authors used the auditory brainstem response, which records electrical activity from the brainstem to measure how well the early auditory system responds to sound. They made these measurements before hearing loss, after hearing loss, and after cell therapy.

What did they find?

The authors confirmed that hair cells had died and that the adult mice’s hearing was severely impaired after being exposed to kanamycin. After cell therapy, hair cells survived the transplant and did not cause any additional hearing loss in the mice. The new cells were able to repopulate the inner ear, and hearing thresholds recovered substantially (though not completely) following the cell therapy.

What's the impact?

This study shows that it is possible to restore the function of hair cells and significantly improve hearing using cell therapy. Cell therapy may be a viable way to address the previously irreversible problem of hearing loss that affects millions of people worldwide.

Access the original scientific publication here.

This Week's BrainPost:

• Aging: Brain Stages of Aging Across the Lifespan. Nature Communications. 3 min read. 

Read this on our website. 

Brain Stages of Aging Across the Lifespan

Post by Lila Metko 

The takeaway

Topology in neuroscience is the study of the motifs that arise when observing how neural connections are interrelated and arranged. This research revealed that there are five major phases of topological development, with a significant topological turning point occurring between each phase at approximately the ages of 9, 32, 66, and 83 years old. 

What's the science?

Many scientists who study the development of the human brain look for linear trends in the developmental trajectory, or differences between age groups. To date, there are few papers that look at development outside of the confines of linear relationships. Recently, in Nature Communications, Mousley and colleagues analyzed brain imaging data from a large sample of individuals aged zero to 90 and found that rather than smooth trends in topographical organization, the human brain undergoes major age-specific turning points in topography. 

How did they do it?

The study included nine separate data sets, so the first step was to harmonize the data to ensure the differences between the data sets, such as the brain scanners and data acquisition protocols used, did not create nuisance variation. A double harmonization method was used to harmonize the data across atlas and study. Next, the authors used UMAP (Uniform Manifold Approximation and Projection), a manifold learning method, to project high-dimensional data onto a lower-dimensional space. Doing this makes highly complex data easier to understand while still maintaining its intrinsic structure. They mathematically analyzed the lower-dimensional manifold to find topological turning points within the data set. Finally, to understand the topological characteristics of these turning points, they did a principal components analysis of the data with eleven different topological metrics. These topological metrics measured how efficient communication was throughout a network, how many non-overlapping, communicating nodes can be found within a network, and to what extent different nodes are particularly important for network function. 

What did they find?

The major topological turning points were characterized by differences in three properties: segregation, integration, and centrality. Segregation is defined as the partitioning of the network into subgroups, integration measures the ease of communication across the network, and centrality is network communication’s dependency on a few nodes that are particularly important for network function. Segregation measures loaded most heavily onto principal component 1 (PC1), and integration measures onto PC2. Both segregation and centrality load most heavily onto PC3. Significant shifts in PC 1 and 2 (network subdivision and network communication ease) are found at roughly the ages of 9 and 32. At roughly the age of 66, there are significant shifts in segregation, integration, and centrality (network subdivision, network communication ease, and dependence on central nodes). At around age 83, there is a significant shift in integration, or network communication. The two epochs with the biggest differences in trajectories were epochs three and four, suggesting that topological change is particularly dissimilar in the years before around age 66 and after around age 66. 

What's the impact?

This study is the first to examine a large dataset with the goal of finding true qualitative transitions in brain topology, not just age group comparisons or linear trends. This paper allows us to see aging as unique stages rather than just progression and decline. Moreover, understanding the impact of therapeutics on different brain aging stages can allow for a more targeted approach.

Access the original scientific publication here.

This Week's BrainPost:

• Prosocial Behavior: Brain Rhythms Translate Empathy Into Prosocial Behavior. Science. 3 min read. 

Read this on our website. 

Brain Rhythms Translate Empathy Into Prosocial Behavior

Post by Amanda Engstrom 

The takeaway

Orexin neurons in the anterior cingulate cortex generate theta oscillations that transform empathic perception into prosocial behavior, revealing a precise circuit linking emotional understanding to helpful behavior.

What's the science?

Empathy allows animals to perceive and share others’ emotional states and drives prosocial behaviors, which are essential for societal cohesion and well-being. In mice, empathy has been associated with changes in theta oscillations (slow, repeating electrical patterns that coordinate communication between brain regions) in the anterior cingulate cortex (ACC). The ACC has been suggested to be a “central hub” for empathy and prosocial behaviors, projecting into multiple brain regions involved in these complex behaviors. 

The neuropeptide orexin regulates arousal, stress, and emotional processing and promotes theta oscillations. However, it remains unclear what upstream circuits modulate these oscillations and how they influence empathy and prosocial behaviors. This week in Science, Kim and colleagues examine how orexin modulates ACC theta oscillations and their relationship to prosocial behaviors. 

How did they do it?

The authors evaluated empathy’s effects on prosocial behavior using an observational fear-conditioning paradigm (one mouse watches another receive a foot shock) combined with a consolation assay measuring allogrooming (the observer mouse grooming the foot-shocked mouse). They tested two paradigms: an experience-dependent observer (EXP), which had previously received a foot shock, and a naïve observer with no prior fear experience. They measured vicarious freezing (the observer mouse freezes at the tone when the experimental mouse receives the foot shock) and allogrooming in both groups and evaluated the impact of consolation on the shocked mouse using an open-field test. To dissect the neuronal mechanisms involved in these behaviors, the authors recorded ACC theta oscillations (5–7 Hz) during behavior. They conducted fiber photometry recordings using a genetically encoded orexin sensor (OxLight1), and using optogenetics, they inhibited ACC-projecting orexigenic circuits in observing mice only during the observation period when the experimental mouse received a foot shock. 

What did they find?

Both naïve and EXP observers exhibited vicarious freezing during observation and increased allogrooming after foot shock reunion. However, EXP mice displayed stronger vicarious freezing and more allogrooming compared to naïve mice. Self-grooming increased after observing the foot shock, but did not differ between groups. Notably, emphatic-like behaviors required visual attention - an observer mouse looking away during the foot shock showed no effects. Additionally, the authors found that the increased allogrooming by EXP observers resulted in less anxiety-like behavior in the mice that received the foot shock. Together, these data suggest that shared experiences enhance prosocial behaviors but don’t alter self-directed care.

To dissect the mechanism behind these behaviors, the authors show that both naïve and EXP observers have increased 5- to 7- Hz theta oscillations in the ACC while mice are observing the foot shock and during allogrooming (i.e. empathy and prosocial behavior), but not self-grooming. Concordantly, there was a selective increase in orexin activity at the same time, but only in EXP mice, suggesting that orexin-dependent increases require shared experience rather than observation alone. Inhibiting orexin input to the ACC specifically during the observation period reduced theta power and allogrooming in EXP mice and had no effect in naïve mice. These findings suggest that orexinergic inputs drive ACC theta oscillations to modulate affective empathy and prosocial action when there is shared experience.

What's the impact?

This study found that orexin-dependent increases in ACC theta oscillations link affective empathy to prosocial comforting behaviors. It demonstrates that social behavior relies on specific neuromodulatory rhythms to translate emotional experience into action. Elucidation of this upstream mechanism can provide potential targets for treating disorders that lead to empathy deficits. 
 

Access the original scientific publication here.

This Week's BrainPost:

• Learning: Practice Does Not Always Make Perfect: How Reward Timing Impacts Learning. Nature Neuroscience. 3 min read. 

Read this on our website. 

Practice Does Not Always Make Perfect: How Reward Timing Impacts Learning

Post by Annika Matthiesen 

The takeaway

This study challenges the idea that we learn faster just by repeating something more often. Instead, it shows that learning depends on timing, and even when rewarded events are spaced far apart, overall learning progresses at the same pace with fewer experiences.

What's the science?

Most theories of reward learning in neuroscience argue that the learning rate is a flexible parameter with no fixed rule, and that dopamine signals whether something is better or worse than expected. However, psychology has shown that learning can improve when experiences are spread out over time, though no clear biological rule has been established.

This week in Nature Neuroscience, Burke and colleagues sought to understand how learning rates vary under different conditions.

How did they do it?

The authors trained several groups of mice to associate a sound cue with a drop of sugar water, varying the waiting period in between trials by tenfold. They measured learning by tracking anticipatory licking and monitored dopamine release in the nucleus accumbens, a key reward-related brain region, using a dopamine-sensitive virus and an implanted lens. Comparing across groups, they identified a relationship between reward spacing and learning and used mathematical models to predict both behavior and dopamine response. To test their mathematical model, they also tracked learning using partial reinforcement, giving rewards on only some trials to increase the time between rewards, keeping cue frequency the same. In addition, they examined reward omission trials, where an expected reward was withheld, to test whether dopamine signals changed when rewards were unexpectedly absent.

What did they find?

The researchers found that learning rate depends on the time between rewards, not just how often a cue and reward are paired. When rewards were spaced farther apart, animals learned more from each reward and needed fewer trials to form the association. However, the proposed mathematical learning rate prediction model did not fully hold at the most extreme condition, where rewards were separated by a very long interval. Even so, the researchers showed that the timing rule held under partial reinforcement, where rewards were delivered only some of the time, further confirming that learning scales with the interval between rewards. Additionally, dopamine signals followed the same pattern as behavior, and the emergence of dopamine responses to reward omission occurred much later than cue responses, challenging traditional reinforcement learning models. Overall, the findings reveal a fundamental timing-based rule that governs how the brain updates associations during reward learning.

What's the impact?

This study is the first to show that learning rate follows rules based on the time between cue–reward experiences, rather than simply increasing with repetition. In simpler terms, learning depends not just on practice, but on how that practice is timed. Practice alone does not make perfect, and carefully spaced timing may be key to more effective learning, reshaping how we think about education and habit formation.
 

Access the original scientific publication here.