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(https://www.psypost.org/dopamine-neurons-sensitivity-to-immune-system-gene-offers-clues-to-hyperactivity-and-behavioral-disorders/) Dopamine neurons’ sensitivity to immune system gene offers clues to hyperactivity and behavioral disorders
Dec 17th 2024, 08:00

Researchers at Duke Health have uncovered a connection between an immune system gene regulator, STAT1, and hyperactive behaviors in mice. Their study, published in the journal (https://www.sciencedirect.com/science/article/abs/pii/S0889159124007001) Brain, Behavior, and Immunity, demonstrates how prolonged activation of this gene regulator in dopamine neurons disrupts brain function, suggesting a potential link to neurodevelopmental disorders such as autism and ADHD. These findings highlight a possible therapeutic target for addressing these conditions.
The immune system and brain are closely interconnected, with many immune pathways also influencing brain development and behavior. STAT1 is a gene regulator activated during immune responses, particularly in fighting infections. However, researchers have observed that its prolonged activation can negatively affect brain function.
This raised questions about whether dysregulated STAT1 activity might contribute to neurodevelopmental disorders, which often involve behavioral and cognitive challenges. Given the prevalence of immune-related abnormalities in individuals with disorders like autism and ADHD, the research team aimed to explore STAT1’s role in brain function and behavior.
“As a neuroimmunologist, there are a few main reasons we were interested in this topic,” said senior author Anthony Filiano, an assistant professor in the departments of Neurosurgery and Pathology at Duke University School of Medicine and a faculty member in the Marcus Center for Cellular Cures.
“1. There have been large studies showing a connection with maternal infection and an increased risk of neurodevelopmental disorders. 2. The interferon pathway (a classical antiviral system) was enriched in the brains of individuals with neurodevelopmental conditions. 3. We and others have shown that interferons can regulate nerve cells, but the connection is unclear.”
“Last year, we found that neurons have a unique response to interferons,” Filiano explained. “That is unlike other cells that turn the system on and off quickly, to fight infection but not cause collateral damage; neurons had a prolonged response that revolved around the activation of the downstream factor STAT1. This was very surprising since neurons cannot be replaced.”
The researchers used genetically modified mice with a STAT1 mutation to simulate prolonged activation of the gene. These mice were bred at Duke Health in collaboration with Columbia University. The STAT1 mutation was introduced into specific brain cell types, including dopamine neurons, to investigate its effects. Dopamine neurons were chosen because of their critical role in regulating motivation, motor control, learning, and reward processing.
To assess the effects of this mutation, the team conducted a series of behavioral experiments. In the open field test, the mice were placed in an enclosed area where their movement patterns were monitored to evaluate activity levels and potential anxiety-related behaviors. The marble burying test was used to gauge repetitive and compulsive tendencies by counting how many marbles the mice buried within a set period. The tail suspension test involved suspending the mice by their tails to measure hyperactivity, while the pole descent test evaluated their motor skills as they climbed down a vertical pole.
Additional observations focused on grooming behaviors, where researchers recorded the time spent grooming to identify patterns of repetition. Finally, immunohistochemistry was employed to analyze neuronal activity. This method involved staining brain tissues to detect markers like c-Fos, which indicate active neurons. These combined approaches allowed the researchers to comprehensively examine how prolonged STAT1 activation impacts both behavior and underlying neural mechanisms.
The study found that prolonged activation of STAT1 in dopamine neurons significantly altered the behavior and brain function of mice. Mice with this genetic modification exhibited hyperactive tendencies, as evidenced by increased movement in the open field test and less immobility in the tail suspension test. These behaviors suggest a heightened level of activity compared to their unmodified counterparts.
In addition to hyperactivity, the mice demonstrated repetitive and compulsive actions. This was observed in the marble burying test, where the modified mice buried more marbles within the set timeframe. Such behavior points to increased compulsivity, often linked to neurodevelopmental abnormalities.
On a neurological level, the researchers noted changes in the caudate putamen—part of the brain’s basal ganglia and a critical region for learning, memory, motivation, and motor control. This brain region showed both a reduction in neuron count and lower neural activity in the affected mice, emphasizing the role of dopamine signaling in these behavioral patterns.
Interestingly, when the STAT1 mutation was restricted to other brain cell types, such as inhibitory neurons or microglia, these behavioral and neural alterations were absent. This finding underscores the unique sensitivity of dopamine neurons to prolonged STAT1 activation and highlights their pivotal role in regulating behavior.
“We created a transgenic mouse that had a clinical mutation in STAT1,” Filiano told PsyPost. “Using genetics, we were able to specifically insert the mutation in specific cell types. We found that driving a prolonged interferon/STAT1 response, particularly in the neurons of the basal ganglia, which has been implicated in ADHD, was sufficient to cause hyperactivity in mice.”
The study provides new insight into link between the immune system and brain function. But like all research, it has some limitations. The results were derived from mice, and their applicability to humans remains uncertain. Human brains are more complex, and additional studies are needed to confirm these findings in people.
Although STAT1 is a potential target for therapies, developing treatments that specifically modulate its activity in the brain without affecting other vital immune functions is challenging.
Future research could explore the mechanisms by which prolonged STAT1 activation disrupts dopamine signaling, examine its effects on other brain regions and cell types, and develop targeted therapies to modulate STAT1 activity specifically in brain cells without compromising essential immune responses.
“In our future work, we are dissecting how and why neurons have this unique response to interferons,” Filiano said. “There are many FDA-approved drugs targeting this pathway, but the challenge is to specifically target therapeutics to the right cells.”
The study, “(https://doi.org/10.1016/j.bbi.2024.11.018) Prolonged STAT1 signaling in neurons causes hyperactive behavior,” was authored by Danielle N. Clark, Shelby V. Brown, Li Xu, Rae-Ling Lee, Joey V. Ragusa, Zhenghao Xu, Joshua D. Milner, and Anthony J. Filiano.

(https://www.psypost.org/neuroscientists-uncover-how-the-brain-maps-behavioral-sequences/) Neuroscientists uncover how the brain maps behavioral sequences
Dec 17th 2024, 06:00

A new study published in (https://www.nature.com/articles/s41586-024-08145-x) Nature has identified brain cells that act like “map makers,” helping animals track their position within sequences of behaviors. These neurons, located in the medial frontal cortex, can encode abstract patterns of progress, enabling the brain to manage complex tasks such as planning, reasoning, and decision-making. The researchers found that these neurons function similarly to a music box, capable of flexibly organizing sequences of actions to adapt to changing goals.
Human behavior is highly structured, often involving elaborate sequences of actions to achieve specific goals. Whether it’s cooking a meal or solving a complex problem, these sequences require careful coordination. When tasks share common elements, the brain forms generalized frameworks called schemata, allowing it to adapt and learn new behaviors more efficiently.
While past research has implicated the medial frontal cortex in mapping task structures, forming schemata, and switching between tasks, the precise biological algorithms behind these functions have remained unclear. The researchers aimed to uncover how neurons encode abstract progress in complex, multi-goal tasks, helping to explain how the brain organizes and flexibly executes sequences of actions.
“Every day we solve new problems by generalising from our knowledge. Take cooking for example. When faced with a new recipe, you are able to use your background knowledge of similar recipes to infer what steps are needed, even if you have never made the meal before. We wanted to understand at a detailed cellular level how the brain achieves this and also to infer from this brain activity the algorithms being used to solve this problem,” explained Mohamady El Gaby, the first author on the study and postdoctoral neuroscientist in the Behrens lab at the Sainsbury Wellcome Centre at University College London and Nuffield Department of Clinical Neurosciences, University of Oxford.
The research team designed an experiment using mice and a structured maze task. The task required the mice to navigate a 3×3 grid maze to collect water rewards located at four goal positions (labeled A, B, C, and D) arranged in a repeating loop. Once the reward at location D was collected, the sequence reset, and the mouse needed to return to location A to continue the loop.
Although the spatial locations of the rewards changed between tasks, the overall sequence structure remained consistent. This setup allowed researchers to investigate whether the mice could learn an abstract framework (the sequence) independent of specific spatial layouts.
The study involved 13 mice, which were trained across two phases. In the first phase, the mice were allowed to perform as many trials as needed to master each sequence. In the second phase, the mice were introduced to a “rapid-task regime” involving three new tasks daily, with only limited trials per task. This phase tested the mice’s ability to generalize the sequence structure and perform efficiently without extensive practice.
To examine the neuronal activity underpinning these behaviors, researchers used silicon probes to record activity from neurons in the medial frontal cortex. The probes allowed researchers to monitor how individual neurons fired in response to task progress, goal states, and other behavioral markers. By analyzing these patterns, the team could infer how the brain organizes information about tasks and sequences.
The researchers discovered that neurons in the medial frontal cortex encoded the mice’s progress toward specific goals in a sequence, a feature termed “goal-progress tuning.” These neurons fired in response to the animal’s position in the abstract task structure, rather than physical variables like time elapsed or distance traveled. This allowed the mice to maintain a flexible understanding of their progress, regardless of changes in the maze layout.
Additionally, a subset of neurons exhibited “state tuning,” meaning they were specifically active at certain points in the sequence (e.g., at goal A or B). These state-tuned neurons were organized into clusters or “modules,” with each module acting as a memory buffer for a specific part of the sequence. These modules allowed the brain to track and predict the sequence’s structure, enabling rapid adaptation to new tasks.
When the sequence structure was modified to include a fifth goal (ABCDE), the same neural systems adapted seamlessly, demonstrating the brain’s ability to generalize its task maps. This showed that the medial frontal cortex uses flexible, reusable “building blocks” to represent abstract task structures, rather than creating entirely new representations for each task.
“We found that the cells tracked the animal’s behavioural position relative to concrete actions. If we think of the cooking analogy, the cells cared about progress towards subgoals such as chopping the vegetables. A subset of the cells were also tuned to map the progress towards the overall goal, such as finishing preparing the meal. The ‘goal progress’ cells therefore effectively act as flexible building blocks that come together to build a behavioural coordinate system,” said El Gaby.
The researchers also identified a hierarchical organization in the neural activity, where simpler goal-progress signals were used to build representations of more complex task structures. These findings were modeled computationally using a framework called the structured memory buffer (SMB) model. According to this model, neurons are organized into modules that encode progress relative to specific behavioral steps. These modules form a dynamic network that can store and compute sequences of actions, allowing the brain to adapt quickly to new tasks.
While the study provides important insights, it is not without limitations. The findings are based on animal models, which, though highly informative, may not fully capture the complexity of human behavior. Further research is needed to confirm whether similar mechanisms operate in the human brain. Preliminary evidence suggests that equivalent circuits are active in healthy humans, but more studies are necessary to explore this connection.
Additionally, the study focused on relatively simple task structures. Future research could investigate how the brain handles more complex, multi-layered sequences or combines separate sequences into larger frameworks. Understanding these higher-order processes could bridge the gap between basic neural algorithms and the sophisticated behaviors seen in humans.
The researchers are also interested in how these patterns of brain activity emerge during development and learning. By examining how the brain builds and refines its task maps over time, scientists hope to uncover new strategies for enhancing learning and adaptability.
The study, “(https://doi.org/10.1038/s41586-024-08145-x) A cellular basis for mapping behavioural structure,” was authored by Mohamady El-Gaby, Adam Loyd Harris, James C. R. Whittington, William Dorrell, Arya Bhomick, Mark E. Walton, Thomas Akam, and Timothy E. J. Behrens.

(https://www.psypost.org/scientists-develop-ai-based-method-to-detect-adhd-by-analyzing-videos/) Scientists develop AI-based method to detect ADHD by analyzing videos
Dec 16th 2024, 12:00

A group of U.K. scientists has developed a machine-learning-based method to detect ADHD by analyzing the actions of individuals in video clips. These videos included recordings of study participants working on specific tasks, captured using multiple cameras from different angles. The authors report that this method outperformed alternative diagnostic systems in differentiating between individuals with and without ADHD. The research was published in (https://doi.org/10.1016/j.nsa.2024.104093) Neuroscience Applied.
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent patterns of inattention, hyperactivity, and impulsivity that interfere with functioning or development. Individuals with ADHD struggle to focus on tasks, follow instructions, or organize activities and are easily distracted by external stimuli. Hyperactivity symptoms can include excessive fidgeting, restlessness, or an inability to remain seated or quiet when appropriate.
The disorder typically begins in childhood and can continue into adulthood. It adversely affects academic performance, work responsibilities, and social relationships. ADHD is most often diagnosed when a child starts school, as their behaviors are generally seen as disruptive and frequently result in poor academic performance. To mitigate these and other adverse consequences, timely diagnosis is of utmost importance.
Study author Yichun Li and his colleagues aimed to create an automated ADHD detection system. Their plan involved designing a trial to assess the actions and reactions of individuals with ADHD. Findings from this trial would then be used to develop a detection system based on recognizing human actions from video recordings. The system would classify individuals in the videos as either having ADHD or not.
The researchers first recorded videos of 10 adults diagnosed with ADHD and 12 without the disorder performing designated tasks. Among participants with ADHD, five were male and five were female. Of the participants without ADHD, eight were male and four were female. Participants’ ages ranged between 18 and 45 years. Individuals with ADHD were recruited by CNTW-NHS Foundation Trust, while healthy participants volunteered from Newcastle University in the U.K.
The videos were recorded from three fields of view—front, left, and right—using GoPro cameras. Additionally, the researchers recorded audio and used a keypad’s touch signal to capture tactile data. A screen displaying posters was placed within the participants’ line of sight, and various small objects, such as pens and spinners, were placed on the desk to serve as distractions, which individuals with ADHD are generally more susceptible to.
During the recordings, participants conducted a series of activities, including a 10-20 minute interview, the Cambridge Neurological Test Automated Battery, the beep reaction task (where participants respond to randomly generated beeps), and watching videos labeled as exciting. The entire process lasted about 1 to 1.5 hours.
The researchers created a machine-learning system that recognized elements and movements of the human body from the videos and identified the actions individuals were performing. The extracted information was used to generate various indexes indicating how much the behavior of the person in the video aligned with that expected of individuals with ADHD. Ultimately, the system classified individuals in the videos as having ADHD or not. The authors tested the system using different processing options and selected the best-performing one.
In the final tests, the system achieved a classification accuracy of 95.5%, outperforming similar classification systems based on magnetic resonance imaging (MRI), electroencephalography (EEG), or trajectory analysis. Additionally, the testing procedure was reported to be significantly less expensive.
“Experimental results demonstrate that our system outperforms state-of-the-art methods in terms of F1 score [a measure of prediction precision], accuracy, and AUC [area under the curve, another measure of how good a diagnostic system is]. Compared to conventional EEG [electroencephalography] and fMRI-based techniques [functional magnetic resonance imaging], our system is cost-effective, highlighting its potential for clinical practice. The collected data and results can be shared with doctors to support their diagnosis and follow-up procedures,” the study authors concluded.
The study presents a novel system for recognizing ADHD based on machine learning. However, the authors note that the system was less accurate in identifying females with ADHD. They attribute this to behavioral differences between males and females, with females exhibiting “prolonged small actions” that are more easily overlooked. Furthermore, the system’s performance on shorter video recordings was not as robust as on longer ones.
The paper, “(https://doi.org/10.1016/j.nsa.2024.104093) ADHD Detection Based on Human Action Recognition,” was authored by Yichun Li, Rajesh Nair, and Syed Mohsen Naqvi.

(https://www.psypost.org/people-overwhelmingly-choose-natural-products-from-chocolate-to-drugs/) People overwhelmingly choose “natural” products, from chocolate to drugs
Dec 16th 2024, 10:00

A new study sheds light on the deep-seated human preference for all things “natural.” Published in (https://journals.sagepub.com/doi/abs/10.1177/19485506241276027) Social Psychological and Personality Science, the research shows that this bias extends beyond words, influencing actual decisions—even when those decisions involve potential risks, like taking a drug or eating food that might cause discomfort.
The researchers behind the new study were intrigued by the well-documented naturalness bias—the widespread preference for items described as natural over synthetic. Prior studies have shown that this bias extends across various domains, from food and medicine to personal care products. However, much of the existing research relied on self-reported preferences in hypothetical scenarios, leaving a gap in understanding how the bias influences actual behavior.
“Most of us love nature. Waterfalls, mountains, forest trails, parks, and other green spaces provide endless enjoyment. Yet, can the love of nature and naturalness in general bias our beliefs in some situations?” said study author Brian Meier, a professor of psychology at Gettysburg College.
“My colleagues and I have been interested for some time in how terms related to ‘nature,’ ‘natural,’ or ‘naturalness’ might bias our perceptions of a host of items. In the present case, we focused on medicines. We have several papers showing that labeling a drug as ‘natural’ versus ‘synthetic’ affects attitudes, safety ratings, and choices in self-reports. Simply put, on average, people think natural drugs are better, safer, and a good choice compared to drugs described as synthetic or human-made.”
“Yet, much of this prior work has been carried out using hypothetical scenarios or situations where people tell researchers what they think they would feel or do,” Meier explained. “Therefore, it is unclear if people would actually use natural versus synthetic items when we measure their observable behavior.”
The researchers conducted four experiments, each designed to test participants’ choices and behaviors in different contexts.
The first experiment focused on a scenario involving a “performance-enhancing” drink. The participants, 174 college students, were informed that the study aimed to evaluate the effectiveness of a drink described as either natural or synthetic in improving strength. Participants completed an initial self-control task, where they measured their grip strength by holding a dynamometer at a set pressure. They were then offered the opportunity to try one of the drinks, described as natural or synthetic, before repeating the task.
In reality, both drinks were simply water, but the participants believed they were consuming a functional performance enhancer. The researchers recorded their choices and whether they followed through with drinking it. Remarkably, 84% of the participants opted for the drink labeled as natural, demonstrating a strong preference for naturalness even in this relatively low-risk scenario.
The second experiment introduced a more consequential context by simulating a medical procedure. Fifty-two students were told they could volunteer to test a blood-coagulating drug delivered via a finger prick. The drug was described as either natural or synthetic, but the finger stick itself was a sham; no actual lancet was used, and no drug was administered. To enhance the realism, the procedure was staged with typical medical equipment, such as gloves and alcohol wipes, and carried out by a research assistant wearing a lab coat.
Despite the potential discomfort or risk implied by the procedure, 73% of the participants who agreed to participate chose the natural version of the drug. This result indicated that even when a decision involved physical consequences, the naturalness bias remained a significant factor in participants’ choices.
The third experiment examined how the naturalness bias played out in the context of food, specifically chocolate consumption. Ninety-eight students were offered the chance to taste chocolate that was described as being made with either natural or synthetic cocoa. Participants were told that the cocoa might cause stomach discomfort, adding a layer of potential risk to their decision. After selecting and eating the chocolate, they rated its taste and any discomfort they experienced. Meier and his colleagues found that 84% of the participants who volunteered chose the natural cocoa chocolate.
The fourth and final experiment moved outside the laboratory and into a public setting. It included 200 participants, making it the most diverse of the studies. The researchers approached passersby on and around a university campus and asked them to evaluate and choose between stickers described as being made with either natural or synthetic ink. After rating the stickers for appeal and quality, participants were allowed to keep one as a token of appreciation.
Here, 66% of the participants chose a sticker labeled as natural. Although the stakes in this scenario were low, the preference for natural options was still evident. The participants’ ratings of the stickers also revealed that they perceived the natural ones to be of higher quality and more appealing.
“Most of the participants who completed the studies were biased by the term ‘natural’ in their behavior,” Meier told PsyPost. “That is, around 75-80% of participants in these studies chose a natural versus synthetic item and actually followed through with the behavior in question (e.g., chocolate consumption or drug ‘injection’). These results suggest to us that this naturalness bias can influence behavior that some people might consider risky.”
The researchers propose several future directions for this line of inquiry, including identifying individuals most and least susceptible to the bias, exploring ways to mitigate its influence, and examining how the bias impacts consequential medical behaviors, such as vaccine uptake.
The study, “(https://doi.org/10.1177/19485506241276027) Perceived Naturalness Biases Objective Behavior in Both Trivial and Meaningful Contexts,” was authored by Brian P. Meier, Eric E. Noreen, Li-Jun Ji, Michael B. Fellman, and Courtney M. Lappas.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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