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(https://www.psypost.org/reduced-pineal-gland-volume-observed-in-patients-with-obsessive-compulsive-disorder/) Reduced pineal gland volume observed in patients with obsessive-compulsive disorder
Oct 24th 2024, 10:00

A recent study published in (https://journals.sagepub.com/doi/abs/10.1177/00912174241287996) The International Journal of Psychiatry in Medicine has found that patients with obsessive-compulsive disorder (OCD) have significantly smaller pineal glands compared to healthy individuals. This finding suggests a possible link between the pineal gland and the underlying mechanisms of OCD.
OCD is a mental health condition characterized by persistent, unwanted thoughts or urges (obsessions) that lead to repetitive behaviors or mental acts (compulsions). People with OCD often engage in these behaviors in an attempt to reduce anxiety or prevent a feared event, but the relief is usually temporary, leading to a cycle of obsessive thoughts and compulsive actions. OCD affects about 1-2% of the population, and while its exact causes are not fully understood, research suggests a combination of genetic, environmental, and neurobiological factors contribute to its development.
“The study was motivated by the recognition that obsessive-compulsive disorder (OCD) is associated with several neurobiological factors, yet its underlying mechanisms remain largely elusive,” said study author Muhammed Fatih Tabara, an assistant professor of psychiatry at Firat University.
“Specifically, the pineal gland, which plays a role in regulating circadian rhythms and melatonin secretion, has not been thoroughly studied in relation to OCD. Prior research indicated hormonal alterations in conditions such as schizophrenia, bipolar disorder, and major depressive disorder, and suggested a potential link between melatonin-related disruptions and OCD. However, there had been no structural studies focusing on the pineal gland in patients with OCD.”
“Given the hyperactivity of the hypothalamic-pituitary-adrenal axis, elevated levels of cortisol, and altered melatonin secretion observed in OCD, it seemed plausible that changes in pineal gland volume might be involved in the pathophysiology of the disorder. This gap in the literature sparked an interest in examining whether pineal gland volumes in OCD patients differ from those in healthy controls, which might provide insight into the disorder’s biological underpinnings.”
To conduct the study, Tabara and his colleagues recruited 20 patients diagnosed with OCD from Firat University’s psychiatry department and 20 healthy control participants. All participants were aged between 18 and 65 and met specific inclusion criteria. The patients were diagnosed with OCD based on the criteria outlined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5).
The researchers ensured that none of the patients had other significant psychiatric conditions, a history of severe head trauma, or medical conditions that could affect the brain’s structure. Similarly, the control group consisted of individuals without psychiatric disorders or brain abnormalities. Both groups underwent high-resolution magnetic resonance imaging (MRI) scans using a 1.5-tesla system to capture detailed images of the brain, with a particular focus on the pineal gland.
To measure pineal gland volumes, the researchers used a semi-automated software tool that allowed them to define the boundaries of the gland with precision. A neuroradiologist who was unaware of the participants’ diagnoses conducted the measurements to minimize bias. After collecting the data, the researchers used statistical analyses to compare the pineal gland volumes between the OCD patients and the healthy controls.
The results showed a statistically significant difference in pineal gland volumes between the two groups. On average, the pineal glands of OCD patients were smaller (84.65 mm³) compared to those of the healthy control group (106.30 mm³). This finding was not influenced by demographic factors like age or gender, as these variables were evenly distributed across both groups.
Interestingly, the researchers found no significant differences in overall brain volumes or in gray and white matter between the two groups. This suggests that the reduction in pineal gland size is not part of a broader change in brain structure, but may instead be specific to the gland itself.
“The fact that the pineal gland volumes were notably smaller in the OCD group, despite no significant differences in overall brain, white matter, or gray matter volumes, was unexpected,” Tabara told PsyPost.
These findings point to a potential connection between OCD and the pineal gland, particularly given the gland’s role in regulating circadian rhythms and melatonin production. Previous research has shown that people with OCD often experience sleep disturbances and altered levels of hormones like cortisol and adrenocorticotropic hormone (ACTH), both of which are related to stress and the body’s circadian cycle.
Furthermore, studies have indicated that melatonin levels, which are typically higher at night to promote sleep, may be lower in people with OCD. The reduced size of the pineal gland observed in this study could be related to these hormonal changes, but the exact nature of this relationship remains unclear.
“The pineal gland is important because it regulates sleep-wake cycles through the production of melatonin, a hormone that influences circadian rhythms,” Tabara explained. “This finding suggests that biological factors, particularly those related to the pineal gland and melatonin production, might play a role in OCD. While this study is small and more research is needed, the results hint at a potential link between disrupted sleep patterns, hormonal imbalances, and the development of OCD.”
While the study provides evidence of a structural difference in the brains of people with OCD, it has some limitations. First, the sample size was relatively small, with only 20 patients and 20 healthy controls. “Future studies with larger and more diverse groups are needed to confirm the results,” Tabara said.
Another limitation is that the researchers did not measure hormonal levels in the participants. “While the study found a smaller pineal gland volume in OCD patients, it did not assess the hormonal changes, such as melatonin or cortisol levels, which are important in understanding the gland’s function,” Tabara noted. “Without these measurements, it’s difficult to directly link the structural changes to the biological processes related to OCD.”
Exploring how pineal gland volume relates to sleep patterns, melatonin levels, and circadian disruptions in OCD patients could provide a more comprehensive understanding of the biological mechanisms behind the disorder. Ultimately, this line of research could lead to new therapeutic approaches that target the circadian system or melatonin regulation, offering potential new treatment options for people with OCD.
“The long-term goals for this line of research are to deepen the understanding of the biological mechanisms underlying OCD and to explore the role of the pineal gland and its associated hormones, particularly melatonin, in the disorder’s pathophysiology,” Tabara said. “By understanding the role of the pineal gland and circadian regulation in OCD, the research could contribute to the development of novel therapeutic strategies. This might include treatments that address melatonin imbalances or target the circadian system, potentially improving sleep and reducing OCD symptoms.”
“One additional point to emphasize is the novelty of this study. As the first investigation to examine the pineal gland volume in patients with OCD, it opens a new avenue for exploring the biological factors contributing to the disorder. Although the sample size is small and further studies are needed, this research adds an important piece to the puzzle of OCD’s complex pathophysiology.”
“Moreover, while the study focused on structural changes, future work should aim to integrate functional studies—like measuring melatonin levels, sleep patterns, and circadian rhythm disturbances—to provide a more comprehensive understanding of how pineal gland abnormalities might contribute to the behavioral and cognitive symptoms seen in OCD,” Tabara continued.
“Finally, we hope that this study encourages others to investigate underexplored neurobiological components of psychiatric disorders, which could potentially lead to better diagnostic markers and innovative treatments in the future.”
The study, “(https://doi.org/10.1177/00912174241287996) Reduced pineal gland volume in patients with obsessive-compulsive disorder,” was authored by Murad Atmaca, Sevler Yildiz, Muhammed Fatih Tabara, Mehmet Gurkan Gurok, Mustafa Yildirim, and Hanefi Yildirim.

(https://www.psypost.org/4-week-exposure-to-bright-light-increases-the-volume-of-the-brains-left-hippocampal-dentate-gyrus/) 4-week exposure to bright light increases the volume of the brain’s left hippocampal dentate gyrus
Oct 24th 2024, 08:00

A recent study conducted in Japan on individuals with mood disorders found that a 4-week exposure to bright light resulted in an increased volume of the left hippocampal dentate gyrus, a region of the brain associated with memory and mood regulation. Participants in the experimental group were exposed to bright light (10,000 lux) for 30 minutes each morning, while the control group was exposed to dim light (50 lux). The findings were published in (https://www.nature.com/articles/s41398-023-02688-9) Translational Psychiatry.
Bright light therapy involves exposure to intense artificial light to help regulate circadian rhythms and improve mood. It is commonly used to treat seasonal affective disorder, a type of depression that occurs during winter months due to reduced sunlight. The therapy works by mimicking natural sunlight and helping reset the body’s internal clock, which is often disrupted in mood disorders.
Typically, bright light therapy requires sitting near a specialized lightbox that emits 10,000 lux of cool-white fluorescent light for 20 to 30 minutes each morning. This exposure helps promote the release of serotonin, a neurotransmitter associated with mood stabilization. In addition to seasonal affective disorder, bright light therapy has been shown to benefit individuals with non-seasonal depression, insomnia, and jet lag.
The treatment is most effective when done consistently, usually within the first hour after waking. Side effects are generally minimal but may include eyestrain, headaches, or nausea, which can often be reduced by adjusting the duration or timing of sessions.
Study author Hirofumi Hirakawa and his colleagues wanted to explore the effects of bright light therapy on the volume of the hippocampal dentate gyrus region of the brain. They also wanted to know whether an increase in the volume of this brain region will be accompanied by the alleviation of depressive symptoms.
The hippocampal dentate gyrus is a part of the brain involved in neurogenesis, the creation of new neurons, and plays a key role in memory formation. Previous research has suggested that reduced neurogenesis and lower activity in this area are linked to mood disorders. The researchers hypothesized that bright light therapy might stimulate neurogenesis in this region, leading to an increase in volume and a reduction in depression symptoms.
The study included 24 adults diagnosed with either major depressive disorder or bipolar disorder. Participants had an average age of 38 years, and 12 of them were men. The participants were randomly assigned to one of two groups: one group received bright light therapy every morning for 30 minutes over four weeks, while the control group was exposed to dim light for the same duration and at the same time.
Participants completed assessments of depression symptoms using the Hamilton Depression Rating Scale and the Beck Depression Inventory, as well as manic symptoms using the Young Mania Rating Scale. They also rated their mood on a visual analog scale each morning. Magnetic resonance imaging (MRI) was used to measure changes in brain volume before and after the treatment.
The results showed a significant increase in the volume of the left hippocampal dentate gyrus in the group that received bright light therapy, while no such change was observed in the dim light group. Both groups experienced a reduction in depressive symptoms, but the improvement was more pronounced in the bright light group.
A decrease in manic symptoms was observed only in the bright light group. However, this group had slightly higher manic symptoms at the start of the study, so their scores became comparable to the control group by the end. Additionally, the researchers found a strong association between the increase in left hippocampal dentate gyrus volume and improvements in mood and depressive symptoms.
“Our study results suggest that BLT [bright light therapy] exhibits a dose-response effect, wherein light exposure of a higher intensity is more efficacious than that of a lower intensity in terms of increasing the volume of the left DG [dentate gyrus region of the brain] and alleviating depression symptoms. Thus, BLT may trigger neurogenesis [the creation of new neurons] in the left DG of patients with mood disorders, indicating a novel mechanism of action of BLT,” the study authors concluded.
The study sheds light on a mechanism through which bright light therapy might help with depression symptoms. However, it should be noted that the study was conducted on a small group of participants. Future research with larger sample sizes is needed to confirm these findings and further explore the potential of bright light therapy as a treatment for mood disorders.
The paper, “(https://doi.org/10.1038/s41398-023-02688-9) Increased volume of the left hippocampal dentate gyrus after 4 weeks of bright light exposure in patients with mood disorders: a randomized controlled study,” was authored by Hirofumi Hirakawa, Takeshi Terao, Koji Hatano, Masanao Shirahama, Tsuyoshi Kugimiya, Kentaro Kohno, Hiroyuki Matsuta, Tsuyoshi Shimomura, and Minoru Fujiki.

(https://www.psypost.org/concept-cells-and-pronouns-neuroscientists-shed-light-on-key-aspect-of-language-comprehension/) Concept cells and pronouns: Neuroscientists shed light on key aspect of language comprehension
Oct 24th 2024, 06:00

A recent study from the Netherlands Institute for Neuroscience published in (https://www.science.org/doi/10.1126/science.adr2813) Science provides new insights into how individual brain cells in the hippocampus react to pronouns during reading. The researchers discovered that certain neurons in this part of the brain, which initially responded to specific nouns, were later reactivated when participants read pronouns referring to those nouns. The new findings offer a glimpse into how the brain connects concepts while processing sentences.
The study focused on one of the more intricate aspects of language comprehension: pronoun resolution, or how the brain identifies the correct noun a pronoun refers to. For example, when reading, “Alice and Bob went hiking. She carried the backpack,” we immediately recognize that “she” refers to Alice, even though her name isn’t repeated. This ability to seamlessly connect pronouns to their corresponding nouns is critical for following a narrative and understanding context.
The researchers sought to understand how individual neurons that have a preference for a specific concept, known as “concept cells,” contribute to this process of linking words with their meanings. Previous research had shown that these cells respond selectively to specific concepts—such as a person’s name or image—but it was unclear whether they also played a role in tracking pronouns and their antecedents (the nouns they refer to).
“In the end, I’m interested in the bigger picture: How does such a small thing (a single cell that only fires or not) contribute to something so complex as our memory?” said study author (https://scholar.google.com/citations?user=BBUjmJcAAAAJ&hl=nl&oi=sra) Doris Dijksterhuis, who now works as a postdoctoral researcher at University Hospital Bonn. “How is information represented on a single-cell level? With this study, we can look at how a single concept, represented by a single neuron (a concept cell), is represented during reading and what this can tell us about how we build up a story (memory) in our head.”
The researchers worked with 22 patients who were undergoing treatment for epilepsy. As part of their treatment, these patients had electrodes implanted in their hippocampus to monitor seizure activity. This setup allowed the researchers to record the electrical activity of individual neurons while the patients performed a reading task. The patients, who were being treated in hospitals in the Netherlands and the UK, consented to participate in this research alongside their medical treatment.
The experiment had two main parts: a screening session and a reading task. In the screening session, participants were shown images of familiar people, including celebrities, friends, and family members. The researchers monitored the patients’ brain activity to identify concept cells—neurons that responded specifically to certain people. For example, if a cell consistently fired when the participant saw a picture of the character “Shrek” but not for other images, that neuron was identified as a “Shrek concept cell.”
In the second part, the reading task, participants were shown sentences on a computer screen. The first sentence introduced two individuals (for instance, “Shrek and Fiona went to a restaurant”). The second sentence contained a pronoun referring back to one of the characters (e.g., “He ordered a drink”). After reading both sentences, the participants answered a question to ensure they understood who the pronoun referred to. During this task, the researchers recorded the activity of neurons in the hippocampus, focusing on whether the concept cells responded not just to the proper nouns but also to the pronouns that referred to those nouns.
Dijksterhuis and her colleagues found that concept cells in the hippocampus responded not only when a participant read their preferred noun (such as “Shrek”) but also when the corresponding pronoun appeared later in the sentence. For example, when the participants read “Shrek went to a restaurant,” the Shrek cell became active. Later, when they read the sentence “He ordered a drink,” the same neuron fired again in response to the pronoun “he,” provided that it referred back to Shrek.
This finding shows that the brain can dynamically link pronouns to the correct individuals, even when the proper noun isn’t explicitly repeated. The researchers also found that the activity of these neurons could predict whether the participant would correctly answer a question about who the pronoun referred to. If the concept cell was strongly active when the pronoun appeared, the participant was more likely to correctly identify the antecedent of the pronoun. On trials where the concept cell activity was weaker, participants were more likely to make errors.
“Concept cells hold a super abstract representation of their preferred concept,” Dijksterhuis told PsyPost. “Even a word that on its own is ambiguous, but gets meaning in a specific sentence, can reactivate a concept cell (when it refers to its preferred concept). This also means that cells in the hippocampus contribute to our understanding of pronouns. Plus, now we know that we can study the memory processes that are involved in reading on a single cell level.”
Interestingly, the researchers also explored ambiguous sentences in which two people of the same gender were introduced. In these cases, participants had to decide for themselves which person the pronoun referred to.
“Something interesting happened when we showed ‘ambiguous sentences,'” Dijksterhuis explained. “These were, for example, as follows: ‘Donald Trump and Shrek walked into a bar. He sat at the table.’ In that sentence, ‘he’ could refer to either one, so we ask the participant to choose who ‘he’ referred to by picking the person that they saw—in their head—sit at the table.
“When we looked at the activity of, for example, the Shrek cell, we saw that the neural response to the noun ‘Shrek’ was higher on trials where the patient afterward chose Shrek as the person that ‘he’ referred to, compared to trials where the patient chose the other person. This means that when the presentation of Shrek was stronger, the patient was more likely to choose Shrek.”
However, as with all research, there are some limitations. The study focused on fairly simple sentences where the pronoun referred to a person based on gender alone. Real-world language, however, often involves more complex sentence structures and contextual clues that go beyond gender.
“You’re always limited with what you can do with the patient: the task can’t be too difficult or too long,” Dijksterhuis said. “But I think we did a great job with such a short and simple experiment.”
Future research could build on this study by examining how the brain handles more complex forms of pronoun resolution, such as in sentences where the pronoun’s referent isn’t obvious based solely on gender. For instance, in the sentence, “The teacher told the student that he would need to study harder,” the pronoun “he” could refer to either the teacher or the student, depending on context. Investigating how the brain resolves these kinds of ambiguities could deepen our understanding of how language comprehension operates at the neuronal level.
Another area for future research is to explore how different elements of a story, such as characters, settings, and actions, are represented and integrated in the hippocampus.
“At the moment, I am continuing with similar experiments at my current research position at the University Hospital in Bonn with Professor Florian Mormann,” Dijksterhuis said. “I am hoping to find out more about how we bind meaning to words and how we create pictures/stories in our head and how the individual parts are bound together (Shrek walks into a bar and sits down –> I see Shrek at a table in a bar –> How do these three elements (Shrek, table, bar) come together to form this complex picture?). This, in turn, will hopefully tell us something about the underlying memory processes and the role of single neurons in this.”
“I would like to stress how amazing it is that we can work with these patients,” Dijksterhuis added. “They are often happy to participate and they provide us with a very unique opportunity to record from neurons while they perform a task. I am very thankful for all the patients that we have worked with.”
The study, “(https://doi.org/10.1126/science.adr2813) Pronouns Reactivate Conceptual Representations in Human Hippocampal Neurons,” was authored by D. E. Dijksterhuis, M. W. Self, J. K. Possel, J. C. Peters, E. C. W. van Straaten, S. Idema, J. C. Baaijen, S. M. A. van der Salm, E. J. Aarnoutse, N. C. E. van Klink, P. van Eijsden, S. Hanslmayr, R. Chelvarajah, F. Roux, L. D. Kolibius, V. Sawlani, D. T. Rollings, S. Dehaene, and P. R. Roelfsema.

(https://www.psypost.org/new-study-rural-americans-trust-government-less-no-matter-whos-president/) New study: Rural Americans trust government less, no matter who’s president
Oct 23rd 2024, 18:00

A recent study published in (https://journals.sagepub.com/doi/abs/10.1177/1532673X241273220) American Politics Research sheds light on the connection between geography, identity, and political trust in the United States. The findings reveal that both living in rural areas and identifying with rural regions are linked to lower levels of trust in the federal government. This trend persists regardless of whether a Republican or Democratic president is in office, offering new insights into the political divide between rural and urban America.
James R. G. Kirk of the University of Notre Dame conducted the study to explore whether political trust varies across the urban-rural spectrum. While there have been many observations about political behaviors and attitudes differing between urban and rural populations, less is known about how place and place-based identity affect trust in the federal government. The authors were motivated by the fact that political distrust seems to be more prominent in rural areas.
For example, during the COVID-19 pandemic, vaccination rates were significantly lower in rural counties compared to urban ones, pointing to a possible distrust of government-led health initiatives. Additionally, rural areas have been strongholds for anti-establishment politics, particularly under Donald Trump’s leadership, which often centered on skepticism of government and elites. These observations raised a crucial question: are Americans in rural areas inherently less trusting of the government?
To explore this, Kirk used data from two large national surveys conducted during the 2016 and 2020 election cycles—the American National Election Studies (ANES). These surveys, which include a wide range of political and demographic questions, allowed him to examine trust in government across different geographic contexts and time periods.
He specifically wanted to understand how rurality, both in terms of where people live and how they identify, influenced trust in the federal government. In addition to measuring geographic location, the 2020 survey asked respondents how they perceived their own identity in terms of whether they considered themselves a “rural person” or more aligned with urban or suburban life.
The sample used in this study was broad and representative, including respondents from across the United States, both rural and urban. Kirk employed different measures of rurality to capture both physical location (whether someone lives in a rural, suburban, or urban area) and place-based identity (whether someone identifies as being from a rural area, regardless of where they currently live). This distinction was important because rural identity, as proposed in earlier research, might go beyond geographic boundaries and reflect a deeper, cultural or psychological connection to rural life, possibly influencing attitudes toward the government in unique ways.
Kirk used statistical methods to analyze the data, including regression models to test whether rurality—both in residence and in identity—was associated with lower levels of trust in the federal government. He controlled for a variety of factors, such as political party identification, income, education, age, and gender, to isolate the impact of rurality on political trust.
The findings confirmed Kirk’s hypothesis: people who live in rural areas or who identify as being from a rural area tend to trust the federal government less. This held true across both the 2016 and 2020 surveys, even though they covered very different political environments—one under the presidency of Barack Obama, a Democrat, and the other under Donald Trump, a Republican. Kirk noted that this consistency suggests that the urban-rural divide in political trust is not merely a function of party politics. In other words, rural distrust of the government persists regardless of which party is in power.
One of the key insights from the study is that rurality, both as a place and an identity, has a substantive effect on political trust. The impact of rurality on trust in government was found to be greater than half of the effect size of political party affiliation or ideological conservatism. This suggests that where someone lives, or how they identify with rural life, can shape their attitudes toward government in a way that is comparable to, if not stronger than, the influence of partisanship.
Interestingly, Kirk found that rural distrust of the federal government was evident under both Democratic and Republican administrations. Typically, trust in government is influenced by partisan alignment—Republicans tend to trust the government more when a Republican is president, and Democrats trust it more under a Democratic president. However, the rural-urban gap in trust persisted across both partisan contexts, indicating that rural Americans may feel alienated from the federal government regardless of which party holds power.
The study has several important implications. First, it reinforces the idea that America’s urban-rural divide extends beyond voting patterns and ideological preferences to include fundamental attitudes about government institutions. The persistence of rural distrust, even under a Republican president, suggests that there is a deeper, more ingrained skepticism in rural communities that transcends party politics. This distrust may be linked to broader feelings of disenfranchisement and the belief that the government is out of touch with rural concerns, a theme that has been highlighted in previous research on rural identity.
Kirk also pointed out several limitations to his study. One limitation is that while he was able to measure both rural residence and rural identity, the data from the surveys was self-reported. This means that respondents’ perceptions of their own identity and geographic location may not perfectly align with objective measures of rurality. For instance, someone living in a small town might identify more with rural life, or vice versa, which could introduce some variability in the data. Additionally, the study focused specifically on trust in the federal government, but it is possible that rural Americans have different levels of trust in state or local governments, or in specific government programs, that were not captured in this research.
Looking forward, Kirk suggests that future studies should explore the underlying causes of rural distrust in greater detail. He recommends examining how different aspects of rural life, such as economic conditions, access to services, and cultural factors, might contribute to political attitudes. Furthermore, there is a need to investigate how rural political distrust intersects with other identities, such as race and ethnicity, to provide a more nuanced understanding of the urban-rural divide in political trust.
The study, “(https://doi.org/10.1177/1532673X241273220) Landscape of Distrust: Political Trust Across America’s Urban-Rural Divide,” was published online on August 13, 2024.

(https://www.psypost.org/new-high-efficiency-neural-chip-could-revolutionize-treatment-of-brain-disorders/) New high-efficiency neural chip could revolutionize treatment of brain disorders
Oct 23rd 2024, 16:00

Researchers from several prominent universities in China have developed a new neural stimulation chip designed to make neural modulation safer and more effective for treating neurological conditions. The team, led by Professor Biao Sun and Associate Professor Xu Liu, created a state-of-the-art, 8-channel device capable of delivering high-voltage stimulation with remarkable efficiency. The chip, which achieves 98% power efficiency and includes sophisticated charge-balancing mechanisms, could significantly improve treatments for conditions like Parkinson’s disease, epilepsy, and spinal injuries. It also has potential applications in brain-machine interfaces and advanced prosthetics.
Neural modulation, the use of electrical stimuli to alter nervous system activity, is an exciting area of research that holds promise for treating a variety of neurological disorders. By directly stimulating the brain or other nerve systems, these techniques can help alleviate symptoms of diseases like Parkinson’s or control epileptic seizures. However, one of the main challenges in this field is delivering the electrical stimulus to neurons in a way that is both effective and safe. Stimulating neurons requires precise control of the electrical charge to avoid damaging surrounding tissue, while also ensuring that the power consumption remains efficient.
To address this issue, Professor Sun and his collaborators designed a novel neural stimulation chip that delivers exponentially decaying currents. This type of current is more power-efficient than the traditional constant-current methods often used in neural modulation. By making the process safer and more efficient, the team’s innovation could significantly enhance current treatment options for patients and open up new possibilities for future brain-machine interface technologies.
The study was conducted through a collaborative effort between researchers from four institutions: Tianjin University, Beijing University of Technology, Tianjin University of Traditional Chinese Medicine, and the Southern University of Science and Technology. The core of their research was the development of an 8-channel neural stimulation chip. This chip, fabricated using 180-nanometer BCD CMOS technology, has a compact core area of just 13.25 square millimeters. It was designed to handle high-voltage outputs, up to 30 volts, which allows it to work with high-impedance electrodes often used in neural stimulation.
To test the chip’s performance, the researchers carried out a range of laboratory and animal experiments. First, they confirmed that the device could effectively trigger action potentials—electrical signals produced by neurons—and induce muscle contractions in test settings. Importantly, the team paid special attention to ensuring that the electrical stimulation did not leave any harmful residual charges, which could lead to ion imbalances and tissue damage over time.
To achieve this, each channel of the chip includes an active charge-balancing circuit and a dual-slope control system, which significantly reduces residual charges to less than 3 nanocoulombs per cycle. This level of precision ensures that the device can operate safely over many cycles without risking tissue damage, even when delivering high-voltage stimulation.
In animal experiments, the chip was tested on anesthetized rats, where it successfully stimulated both the vagus nerve and the sciatic nerve. These tests confirmed that the chip could induce motor responses in the rats without causing any observable tissue damage, validating its potential for use in biological settings.
The study’s key finding is the development of a neural stimulation chip that combines safety and high power efficiency, a combination that has been difficult to achieve in previous designs. The chip’s exponential waveform output was found to be particularly beneficial, as it allows for greater efficiency in charge transfer, which is a major challenge when working with high-impedance electrodes. These electrodes are commonly used in neural stimulation, but they create resistance that makes it harder to deliver enough charge without increasing the risk of tissue damage.
The chip’s ability to deliver stimulation efficiently at up to 30 volts while maintaining charge imbalance at less than 3 nanocoulombs is a major improvement over existing technologies. In laboratory tests, the researchers found that the chip operates at an efficiency rate of 98.1% at a 20-volt output, which is significantly higher than most existing devices.
(Credit: Biao Sun/Tianjin University, Hao Yu/ Southern University of Science and Technology, Xu Liu/Beijing University of Technology.)
In terms of real-world application, the researchers are optimistic that this chip could be used in a range of medical devices designed to treat neurological conditions. By improving the safety and efficiency of neural stimulation, the chip could lead to better treatment outcomes for patients with conditions like Parkinson’s disease and epilepsy, and may also enable more advanced brain-machine interface systems, which could allow paralyzed individuals to control prosthetic limbs or other devices with their thoughts.
While the study represents a significant advancement in neural modulation technology, the researchers acknowledged a few limitations in their work. First, although the chip performed well in the controlled laboratory and animal experiments, more extensive testing is needed to confirm its effectiveness and safety in humans. Neural tissue in humans is more complex than that of rats, and the electrode-tissue interface may behave differently in clinical applications.
Additionally, while the chip achieved impressive charge balance and power efficiency, further refinements could be made to better capture the complexity of neural interactions in more diverse biological settings. Future research could focus on improving the device’s ability to handle a wider range of stimulation protocols, especially in more intricate neural networks.
The study, “(https://elspub.com/papers/j/1812845598994948096.html) An 8-channel high-voltage neural stimulation IC design with exponential waveform output,” was authored by Xu Liu, Zeyu Lu, Juzhe Li, Xue Zhao, Lin Zheng, Weijian Chen, Gengchen Sun, Jiaqi Sun, Liuyang Zhang, Shenjun Wang, Biao Sun, and Hao Yu.

(https://www.psypost.org/new-machine-learning-model-finds-hate-tweeting-mainly-originates-from-right-leaning-figures/) New machine learning model finds hate tweeting mainly originates from right-leaning figures
Oct 23rd 2024, 14:00

Social media platforms have struggled to accurately detect hate speech, especially given the different definitions and contexts of harmful content. A new study in (https://www.sciencedirect.com/science/article/pii/S0885230824000731) Computer Speech & Language introduces a machine learning model that improves detection by training on multiple datasets. The researchers found that right-leaning figures generated significantly more hate speech and abusive posts than left-leaning figures. This innovative model shows promise in better identifying and moderating hate speech across platforms like Twitter and Reddit.
The rise of social media has created new challenges in managing harmful content, with hate speech being a major issue. Platforms like Twitter, Facebook, and Reddit have struggled to efficiently and accurately detect and remove such content. Automated detection methods, primarily based on machine learning, have been employed to identify hate speech. However, existing methods often fail when applied to new datasets, partly due to the inconsistent definitions of hate speech across different contexts and platforms.
For example, a model trained to detect racist language may perform poorly when tasked with identifying misogynistic or xenophobic comments. The absence of a universal definition of hate speech further complicates the issue. Given this limitation, the research team aimed to create a more robust model that could recognize hate speech across a variety of domains and datasets, improving the accuracy of detection across platforms.
“Our group’s long-term research goals include understanding the creation and spread of online harmful content,” said study author Marian-Andrei Rizoiu, an associate professor leading the (https://www.behavioral-ds.science/) Behavioral Data Science lab at the University of Technology Sydney.
“We, therefore, needed a detector for hate speech to be able to track such content online. The issue with existing classifiers is that they capture very narrow definitions of hate speech; our classifier works better because we account for multiple definitions of hate across different platforms. Historically, literature has trained hate speech classifiers on data manually labeled by human experts. This process is expensive (human expertise is slow and costly) and usually leads to biased definitions of hate speech that account for the labeller’s points of view.”
To tackle the issue of generalization, the researchers developed a new machine learning model using Multi-task Learning. Multi-task Learning allows a model to learn from multiple datasets simultaneously, which helps the model capture broader patterns and definitions of hate speech. The idea is that learning from multiple sources at once can reduce biases and improve the model’s ability to detect hate speech in new or unseen contexts.
The researchers trained their model using eight publicly available hate speech datasets, gathered from platforms such as Twitter, Reddit, Gab, and others. These datasets varied in their definitions and classifications of hate speech, with some focusing on racism, others on sexism, and still others on abusive language more generally. This broad approach helped the model learn from diverse sources, making it less likely to overfit to a specific type of hate speech.
In addition to using existing datasets, the researchers also created a new dataset called “PubFigs,” which contains over 300,000 tweets from 15 American public figures. The figures selected for this dataset included both right-wing and left-wing political figures. By including this new dataset, the researchers tested how well their model could detect hate speech from high-profile individuals and in political contexts.
The model they developed was based on a pre-trained language model known as BERT (Bidirectional Encoder Representations from Transformers). This model is widely used in natural language processing tasks due to its ability to understand and generate human-like text. The researchers modified BERT by attaching separate classification layers for each dataset, allowing the model to handle different types of hate speech. During training, these classification layers worked together to optimize the model’s ability to detect a general definition of hate speech across all datasets.
The Multi-task Learning model outperformed existing state-of-the-art models in detecting hate speech across different datasets. It showed improved accuracy in identifying hate speech, especially when applied to datasets it had not seen during training. This was a key improvement over previous models, which tended to perform well only on the specific datasets they were trained on but struggled when exposed to new data.
For example, in one of the experiments, the researchers used a “leave-one-out” approach, where the model was trained on all but one dataset and then tested on the remaining dataset. In most cases, the new model outperformed other hate speech detection models, particularly when tested on datasets that involved different definitions or types of hate speech. This demonstrates the model’s ability to generalize and adapt to new kinds of harmful content.
“There is typically no single definition of hate speech; hate speech is a continuum, as hate can be expressed overtly using slurs and direct references or covertly using sarcasm and even humor,” Rizoiu told PsyPost. “Our study develops tools to account for these nuances by leveraging multiple training datasets and a novel machine learning technique called transfer learning.”
Another interesting finding from the study came from applying the model to the PubFigs dataset. Of the 1,133 tweets classified as hate speech, 1,094 were posted by right-leaning figures, while only 39 came from left-leaning figures. In terms of abusive content, right-leaning figures contributed 5,029 out of the total 5,299 abusive tweets, with only 270 coming from the left-leaning group. This means that left-leaning figures accounted for just 3.38% of the hate speech and 5.14% of the abusive content in the dataset.
Among the right-leaning figures, certain individuals stood out for their high levels of problematic content. Ann Coulter, a conservative media pundit known for her provocative views, was responsible for nearly half of the hate speech in the dataset, contributing 464 out of the 1,133 hate-labeled tweets. Former President Donald Trump also posted a significant number of problematic tweets, with 85 classified as hate speech and 197 as abusive content. Other prominent right-wing figures, such as Alex Jones and Candace Owens, also had high levels of flagged content.
On the other hand, left-leaning figures posted far fewer problematic tweets. For example, Senator Bernie Sanders, former President Barack Obama, and former First Lady Michelle Obama had no tweets labeled as abusive. Alexandria Ocasio-Cortez had only 4 tweets classified as hate speech and 4 tweets classified as abusive, while Ilhan Omar had 23 tweets classified as hate speech and 46 tweets classified as abusive.
“What surprised us was the fact that abusive speech appears not to be solely the traits of right-leaning figures,” Rizoiu said. “Left-leaning figures also spread abusive content in their postings. While this content would not necessarily be considered hate speech in most definitions, they were abusive.”
The content of the hate speech and abusive posts also differed between right-leaning and left-leaning figures. For right-leaning figures, the hateful content often targeted specific groups, including Muslims, women, immigrants, and people of color.
“We find that most hate-filled tweets target topics such as religion (particularly Islam), politics, race and ethnicity, women and refugees and immigrants,” Rizoiu said. “It is interesting how most hate is directed towards the most vulnerable cohorts.”
In comparison, the left-leaning figures’ tweets were less focused on inflammatory rhetoric. The few instances of problematic content from this group were often related to discussions of social justice or political topics.
While the study showed significant improvements in hate speech detection, there were still some limitations. One issue was the challenge of handling subtle or covert forms of hate speech. The researchers noted that their model might miss more nuanced expressions of hate that don’t use overtly harmful language but still contribute to a hostile environment. Future research could explore how to enhance the model’s ability to detect these more subtle forms of hate.
Additionally, the study’s reliance on labeled datasets presents a potential limitation. While Multi-task Learning helps reduce the biases inherent in individual datasets, these biases are not completely eliminated. The datasets used in the study, like many others, are subject to human labeling, which can introduce inconsistencies or inaccuracies.
“While our model builds more encompassing definitions and detections of hate speech, they still depend on the original datasets’ labelling,” Rizoiu explained. “That is, we average over human expert viewpoints, but if they are all biased similarly (say, they are all academics who share a similar bias), then even our encompassing model will have these general biases.”
“Our group’s research is modelling the spread of online content via the digital work-of-mouth process. We concentrate particularly on harmful content (misinformation, disinformation, hate speech) and its effects on the offline world. For example, we want to understand why people engage with harmful content, what makes it attractive, and why it spreads widely.”
“Detection is only the first phase in addressing an online issue,” Rizoiu added. “The question is how we develop and deploy effective methods in the real online world that can protect against harmful content without impeding rights such as free speech. Works like our study provide effective detection approaches that online platforms could incorporate to protect their users, particularly the most vulnerable, such as children and teens, from hate speech.”
The study, “(https://doi.org/10.1016/j.csl.2024.101690) Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures,” was authored by Lanqin Yuan and Marian-Andrei Rizoiu.

(https://www.psypost.org/children-often-visiting-coasts-rivers-and-lakes-are-more-likely-to-practice-pro-environmental-behaviors-when-they-grow-up/) Children often visiting coasts, rivers, and lakes are more likely to practice pro-environmental behaviors when they grow up
Oct 23rd 2024, 12:00

A study conducted in Austria found that individuals who were exposed to blue spaces (such as coasts, rivers, and lakes) during childhood tend to feel more connected to nature as adults. This stronger connection to nature, in turn, is linked to more frequent visits to both blue and green spaces (such as parks, forests, and meadows) in adulthood, and a greater likelihood of engaging in pro-environmental behaviors. The research was published in the (https://doi.org/10.1016/j.jenvp.2023.102225) Journal of Environmental Psychology.
As human settlements expand and industrial development increases, efforts to preserve natural environments become increasingly important. On an individual level, pro-environmental behaviors can significantly mitigate the negative impacts of human activity on nature. These behaviors include actions such as recycling, avoiding single-use plastics, properly disposing of waste in natural environments, and reducing energy consumption. All of these actions aim to protect ecosystems, reduce pollution, and conserve natural landscapes.
The study, led by Patricia Stehl and her colleagues, sought to investigate what factors influence whether people engage in these pro-environmental behaviors. The researchers proposed that childhood exposure to blue spaces might foster a deeper emotional connection to nature, which could then encourage environmentally responsible behaviors in adulthood.
To explore this, the researchers gathered data from 2,370 Austrian adults, representing a cross-section of the population in terms of age, gender, and region. Participants ranged in age from 18 to 89, and half of them were women. The study was conducted using an online survey provided by the polling company YouGov.
Participants were asked to report on various aspects of their environmental behavior. This included self-reported pro-environmental actions (based on 12 yes-or-no items), memories of childhood blue space exposure, the frequency of recent visits to blue and green spaces, and their feelings of connectedness with nature, which were measured using the Inclusion of Nature in Self Scale.
The results showed that participants who recalled frequent visits to blue spaces during childhood also reported stronger feelings of connectedness with nature and more frequent visits to natural environments in adulthood. These feelings of nature connectedness, in turn, were associated with a greater likelihood of engaging in pro-environmental behaviors.
The researchers tested a statistical model that proposed a causal relationship: childhood exposure to blue spaces strengthens nature connectedness in adulthood, which then leads to more visits to natural landscapes, ultimately resulting in more pro-environmental behaviors. The results supported this model, indicating that the pathway from childhood exposure to blue spaces to adult environmentalism is possible.
However, the researchers noted that the direct relationship between childhood blue space exposure and adult pro-environmental behavior was weak. This suggests that while childhood experiences with nature can influence environmental behaviors later in life, the effect is relatively small.
The study also uncovered some demographic differences. Women reported more pro-environmental behaviors than men, although men reported more frequent visits to green spaces. Unemployed individuals exhibited stronger pro-environmental behaviors than employed participants, though employed and retired participants reported stronger feelings of nature connectedness. Participants from higher-income households spent more time in nature compared to those from lower-income households.
“Growing detachment from the natural world may hinder the development of nature connectedness and PEBs [pro-environmental behavior]. We provide evidence for a positive relationship between (recalled) childhood blue space exposure and adult PEBs, which may be partly explained by adult nature connectedness and recent nature visits. These relationships were found among a large heterogenous sample in landlocked Austria, highlighting the importance of inland blue spaces, compared to the more frequently studied coastal ones,” study authors concluded.
The study sheds light on the links between recalled childhood visits to blue spaces and pro-environmental behaviors in adulthood. However, the associations reported were very weak and only detectable due to the large sample size.
The paper, “(https://doi.org/10.1016/j.jenvp.2023.102225) From childhood blue space exposure to adult environmentalism: The role of nature connectedness and nature contact,” was authored by Patricia Stehl, Mathew P. White, Valeria Vitale, Sabine Pahl, Lewis R. Elliott, Leonie Fian, and Matilda van den Bosch.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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