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(https://www.psypost.org/open-access-scientific-publications-get-more-diverse-citations/) Open access scientific publications get more diverse citations
Aug 12th 2024, 10:00

A large-scale study of bibliographic data found that open-access research publications—those freely available on the internet—receive more diverse citations compared to paywalled publications, which require a subscription or payment to access. Authors who cite open-access publications (i.e., refer to them in their own scientific work) tend to come from a broader range of institutions, countries, geographic regions, and research fields. The paper was published in (https://doi.org/10.1007/s11192-023-04894-0) Scientometrics.
When a scientist completes a research study, they are generally expected to write a report describing the study and its findings and publish it in a scientific journal or another scientific outlet. However, not all scientific journals are the same. While some journals are highly prestigious, others are considered less valuable. The perceived value of a scientific paper is often influenced by the reputation of the journal in which it is published.
Many systems currently use the number of citations that articles in a scientific journal receive from other reputable journals to determine how influential and prestigious that journal is. A scientific work is cited when it is mentioned in another scientific work. The more citations a publication receives, the more influential it is considered to be.
National science funding bodies often use citation counts to assess the value of the scientific studies published in a particular journal. These evaluations play a crucial role in decisions about a researcher’s career. Whether a researcher keeps their job, secures funding, or advances in the academic hierarchy often depends on the reputation of the journals in which they publish.
This system allows publishers of highly cited journals to charge substantial fees for access, effectively paywalling their publications. Such practices make a significant portion of scientific research inaccessible to individuals and researchers from poorer countries or institutions that cannot afford these subscriptions or access fees.
To address this issue, some researchers and publishers have embraced open-access publishing. In this model, scientific publications are made freely available to everyone. Sometimes, authors pay a fee to the publisher to make their work open access. In other cases, nonprofit entities run their own scientific journals, which are freely accessible to the public.
Study author Chun-Kai Huang and his colleagues wanted to explore the relationship between open-access status and citation diversity. Specifically, they sought to determine whether making a scientific work freely available would lead to a broader range of researchers, geographically or across scientific disciplines, citing it.
To investigate this, the researchers analyzed all research outputs published between 2010 and 2019, resulting in a dataset of 19 million publications. They extracted data on the scientific publications that cited these works (420 million citation links), along with information about the citing publications and the affiliations of their authors. The data came from the Curtin Open Knowledge Initiative, a large-scale relational database tracking the open knowledge performance of research institutions worldwide.
The researchers used this data to calculate measures of citation diversity. Citation diversity was assessed based on the diversity of the institutions, countries, regions, subregions, and research fields of the citing authors. A paper’s citations were considered more diverse if the authors citing it came from a broader range of different institutions, countries, regions, subregions, or research fields.
The results showed that open-access publications had higher citation diversity across all the criteria considered. They were cited by authors from a greater number of different institutions, countries, subregions, regions, and research fields compared to paywalled publications. This difference was consistent across all the years studied and across almost all research fields.
“As the main result, we find that OA [open access] is associated with higher citation diversity, i.e., OPEN outputs receive more diverse citations as compared to CLOSED [paywalled] outputs. We refer to this phenomenon as OA citation diversity advantage. We find this advantage to be remarkably consistent across the many ways in which we have analyzed the data (bar the very few extreme cases), which addresses concerns of confounding factors mentioned above,” the study authors conclude.
The study highlights an important aspect of the dissemination of scientific results. However, the authors note that their study included only scientific publications assigned a Digital Object Identifier (DOI) by Crossref. They acknowledge that other agencies also assign DOIs, and these publications were not included in the analysis.
The paper, “(https://doi.org/10.1007/s11192-023-04894-0) Open access research outputs receive more diverse citations,” was authored by Chun‑Kai Huang, Cameron Neylon, Lucy Montgomery, Richard Hosking, James P. Diprose, Rebecca N. Handcock, and Katie Wilson.

(https://www.psypost.org/ai-is-surprisingly-good-at-predicting-narcissism-based-on-linkedin-profiles/) AI is surprisingly good at predicting narcissism based on LinkedIn profiles
Aug 12th 2024, 08:00

Our online profiles are more than just representations of our professional lives — they also appear to be windows into our personalities. A new study, published in the (https://doi.org/10.1111/joop.12531) Journal of Occupational and Organizational Psychology, reveals that LinkedIn profiles might offer significant insights into individual traits like narcissism and intelligence. This research suggests that machine learning algorithms can accurately infer these traits from the information available on LinkedIn, potentially offering a powerful tool for recruiters and organizations.
LinkedIn has grown into the most popular online professional network, with nearly a billion users globally. Recruiters and employers routinely use LinkedIn profiles to gauge potential hires, attempting to infer qualities such as professionalism, competence, and even personality traits. Despite its widespread use, there has been considerable debate over how accurately LinkedIn profiles can reflect a person’s traits. Previous research has produced mixed results, leaving it unclear whether LinkedIn provides reliable signals about individual traits.
This study sought to clarify LinkedIn’s predictive potential by using machine learning to analyze LinkedIn profiles. The researchers aimed to determine whether LinkedIn contains valid cues that could accurately reflect traits like narcissism and intelligence. They also wanted to see if machine learning algorithms, acting as “automated perceivers,” could outperform human recruiters in assessing these traits consistently and accurately.
“I’ve long been intrigued by how much our social media presence can reveal about us, especially when it comes to stable traits like personality,” said study author (https://www.linkedin.com/in/tobiashaertel/) Tobias M. Härtel, a tandem professor at the Osnabrück University of Applied Sciences and a people analytics specialist at BASF.
“It’s fascinating how platforms like Facebook can be used to predict things like personality traits from likes and status updates. But using that kind of personal data in recruitment processes raises a lot of ethical and legal questions. LinkedIn, on the other hand, is specifically designed for professional networking, so it seemed like a better fit for exploring these ideas. Plus, while recruiters often use LinkedIn to gauge a person’s traits, they don’t always get it right. So, I thought — why not see if machine learning can do better?”
To explore LinkedIn’s ability to signal traits, the researchers conducted a detailed analysis of LinkedIn profiles belonging to 406 German-speaking users. The participants, who were recruited online, provided their LinkedIn profiles and completed surveys measuring narcissism and intelligence. Narcissism was assessed using the Narcissistic Admiration and Rivalry Questionnaire, which captures both the self-promotional and defensive aspects of narcissism. Intelligence was measured through tests of fluid intelligence (reasoning and problem-solving) and crystallized intelligence (accumulated knowledge).
The researchers developed a set of 64 LinkedIn profile cues, based on theory and empirical research, that could potentially signal narcissism and intelligence. These cues included straightforward, objective information like the number of listed skills, as well as more subjective elements such as the presence of a smiling profile picture or the use of a background photo.
The study employed machine learning algorithms, specifically elastic nets, to analyze the relationship between these LinkedIn cues and the participants’ narcissism and intelligence scores. Elastic nets are a type of regression model that is particularly effective at handling large sets of interrelated data, making them ideal for this kind of analysis. The algorithms were trained to identify which LinkedIn cues were most predictive of narcissism and intelligence, and their performance was rigorously tested using a method called nested cross-validation to ensure the results were robust and reliable.
The machine learning algorithms demonstrated moderate to strong accuracy in predicting narcissism and intelligence based on LinkedIn profiles, with correlation coefficients of .35 and .41, respectively. These figures suggest that the algorithms were able to identify individuals with above-average levels of narcissism or intelligence about 67.5% to 70.5% of the time — a level of accuracy comparable to that achieved by previous studies using social media data from platforms like Facebook.
The analysis identified several LinkedIn cues that were particularly predictive of these traits. For narcissism, key indicators included having a background picture, listing public speaking skills, and showing fewer smiles in profile pictures. These findings align with the idea that narcissistic individuals often seek to project a grandiose image and are less inclined to display warmth or friendliness in professional settings.
For intelligence, important cues included listing schools with many followers, having a detailed description of educational and professional experiences, and posting accomplishments related to honors and awards. These cues reflect a focus on academic and professional achievements, which are strong indicators of intelligence.
“One big takeaway is that machine learning algorithms are surprisingly good at predicting traits like narcissism and intelligence just from LinkedIn profiles,” Härtel told PsyPost. “They can even be more accurate than coworkers reporting on personality or recruiters assessing applicants’ traits from resumés, LinkedIn profiles, or other social media information. This is because the algorithms consistently pick up on certain patterns, like how narcissists might be more likely to upload background pictures or list public speaking skills, and how intelligent individuals often showcase awards or follow prestigious schools. The study is a nice demonstration of how much information is actually packed into our LinkedIn profiles.”
While this study provides valuable insights, it is not without limitations. One limitation is the static nature of the data used in the study. LinkedIn profiles are dynamic, and users may update their profiles as they gain experience or as they become more familiar with how to present themselves on the platform. This means that the cues identified in this study might evolve over time, requiring continuous updates to the algorithms.
While the machine learning algorithms performed well in this study, the researchers caution against over-reliance on automated systems for making decisions about hiring or promotion. LinkedIn profiles offer only a snapshot of an individual’s professional identity, and while they can provide useful insights, they cannot fully capture the complexity of a person’s traits. For this reason, the researchers suggest that a hybrid approach, combining automated assessments with human judgment, might be the most effective strategy for organizations.
“We do need to be careful about how we use this technology,” Härtel said. “There are definitely ethical implications to consider, especially when it comes to using these predictions in recruitment or other professional settings. Plus, LinkedIn profiles only show a part of who we are, so it’s important to remember that these algorithms aren’t capturing the full picture of someone’s personality. The algorithms are powerful, but they should be seen as a tool to complement human judgment, not replace it. And transparency is key — we need to make sure people understand how these tools are being used and how the decisions are being made.”
The study, “(https://bpspsychub.onlinelibrary.wiley.com/doi/full/10.1111/joop.12531) ‘LinkedIn, LinkedIn on the screen, who is the greatest and smartest ever seen?’: A machine learning approach using valid LinkedIn cues to predict narcissism and intelligence,” was authored by Tobias M. Härtel, Benedikt A. Schuler, and Mitja D. Back.

(https://www.psypost.org/sleeps-secret-power-timing-bedtime-closely-after-practice-sharpens-motor-skills/) Sleep’s secret power: Timing bedtime closely after practice sharpens motor skills
Aug 12th 2024, 06:00

When you learn a new skill, like playing the piano or mastering a golf swing, you might have heard that “practice makes perfect.” But according to a new study published in the (https://www.jneurosci.org/content/early/2024/07/22/JNEUROSCI.0325-24.2024) Journal of Neuroscience, the timing of your sleep could be just as important as the practice itself. Researchers found that motor memories—our brain’s way of holding on to skills and actions—are not just consolidated over time, but they can be significantly enhanced when sleep closely follows practice.
The research team set out to explore a longstanding debate in neuroscience: Does sleep play a role in consolidating motor memories, or is this process solely a function of time? While sleep has long been known to improve declarative memory—the type that helps us recall facts and events—its role in motor memory, such as learning new physical skills, has been less clear.
Previous studies suggested that motor memories, particularly those involved in adapting to new sensorimotor conditions, consolidate with time, independent of sleep. However, these studies did not consider the timing between training and sleep, which could be a critical factor.
In this study, the researchers proposed that motor memories might indeed benefit from sleep, but only when sleep occurs shortly after practice while the memory is still fresh and fragile. This hypothesis, if confirmed, would indicate that there are common mechanisms in how different types of memories are consolidated, whether they involve learning facts or mastering new skills.
“While it is well-established that sleep enhances conscious memories of facts and events, its role in consolidating memories of motor skills such as riding a bike remains a matter of debate,” said study author (https://orcid.org/0000-0001-9586-4255) Valeria Della-Maggiore, the director of the Physiology of Action Lab at the University of Buenos Aires, professor at the University of San Martin, and adjunct professor at McGill University.
“However, most studies challenging the role of sleep in motor learning have largely overlooked the significance of the time interval elapsed between training and sleep as a relevant factor. In our study, we aimed to investigate the proximity between training and bedtime as a main modulator given the potential impact of sleep in optimizing training and rehabilitation protocols.”
To test their hypothesis, the researchers conducted a series of experiments involving 290 participants, all right-handed, with no history of neurological or psychiatric disorders. The participants, ranging in age from about 20 to 28 years, were recruited from the School of Medicine at the University of Buenos Aires. Before and during the study, they maintained regular sleep schedules, which were monitored through self-reported logs.
The study used a visuomotor adaptation task, a well-established method for examining sensorimotor adaptation. Participants had to move a cursor on a computer screen to hit targets using a joystick. The trick was that sometimes the cursor’s movement was altered by an optical rotation, forcing participants to adapt their hand movements to hit the target accurately. This task allowed researchers to measure how well participants retained the ability to adapt to these changes—a measure of motor memory.
In the first experiment, 111 participants were split into five groups, each tested at different intervals after training, ranging from 15 minutes to nine hours. These intervals did not control when participants went to sleep, mimicking everyday situations where people train at different times of the day. Another group of participants trained and then slept before being tested 24 hours later.
In the second experiment, the researchers sought to find the most vulnerable period for memory consolidation by introducing an interference — another learning task — to see how quickly the motor memory from the first task would decay. A sample of 92 participants adapted to two opposite optical rotations separated by different intervals, from five minutes to 24 hours, with memory retention tested the next day.
In the final experiment, the researchers directly tested the hypothesis that sleep benefits sensorimotor adaptation memory only when it occurs soon after learning, within the identified critical window. A sample of 74 participants were divided into two main groups: one group trained on the task late at night and went to sleep shortly afterward, while the other group trained in the morning and did not sleep until much later. Both groups were tested 24 hours after their initial training.
To control for potential circadian effects, the researchers also included two additional control groups: one trained in the evening and tested in the morning after a full night’s sleep, and the other trained in the morning and tested in the evening without an intermediate sleep period.
In the first experiment, where the timing between training and sleep was not controlled, there was no significant difference in memory retention between participants who slept and those who did not. This finding aligned with previous studies suggesting that motor memory consolidation does not rely on sleep when training is spread out over the day.
However, the second experiment revealed that motor memories were most fragile — hence, most in need of consolidation — within the first hour after training. During this time, introducing a second task significantly hindered the retention of the first task. This discovery highlighted a critical window during which the brain is most susceptible to interference.
The third experiment provided the most compelling evidence. When participants trained just before going to sleep, their memory retention was significantly better — by about 30% — than when they trained and stayed awake for several hours before sleeping. The improvement was tied to specific changes in brain activity during sleep, including increased density of sleep spindles (brief bursts of brain activity during non-rapid eye movement sleep) and their coupling with slow oscillations. These changes were particularly pronounced over the brain hemisphere opposite to the hand used in the task, suggesting that sleep actively consolidates motor memory by fine-tuning neural connections.
“We were surprised by the consistent memory enhancement of approximately 30% observed across independently trained groups,” Della-Maggiore told PsyPost. “We were pleased to replicate previous findings from our lab showing that sleep specifically modulates neural markers of memory consolidation over the brain hemisphere contralateral (opposite) to the trained hand.”
The implications of this study are far-reaching. If the timing of sleep can significantly enhance motor memory, this could change how we approach skill training and rehabilitation. Athletes might benefit from napping shortly after practice, and rehabilitation programs could be optimized by aligning therapy sessions with patients’ sleep schedules.
“Timing skill practice around your sleep schedule may significantly enhance your ability to retain and perform that skill,” Della-Maggiore said. “This simple adjustment could effectively boost motor learning and recovery in sports and rehabilitation settings.”
While these findings are promising, the study has limitations that future research should address. First, the motor learning tasks were highly controlled and may not fully represent more complex, real-life activities. Whether the same sleep-related benefits would apply to skills like playing a musical instrument or sports remains to be seen. Additionally, while the study controlled for sleep quality, it did not explore whether shorter naps could have a similar effect as a full night of sleep.
“We are now designing a study to determine whether our work applies to real-life activities, which will be crucial to assessing its translational impact,” Della-Maggiore said. “Our long-term goal is to determine whether the beneficial effects of sleep extend to real-life motor tasks, such as sports and the use of complex tools, and to explore its effectiveness in patients with motor injuries. We also aim to assess whether a short nap can be as beneficial as a full night of sleep. In parallel, we are developing sleep monitoring and wearable devices to enable personalized, data-driven neuro-interventions in home settings, beyond the clinical environment.”
The study, “(https://doi.org/10.1523/JNEUROSCI.0325-24.2024) Sleep consolidation potentiates sensorimotor adaptation,” was authored by Agustin Solano, Gonzalo Lerner, Guillermina Griffa, Alvaro Deleglise, Pedro Caffaro, Luis Riquelme, Daniel Perez-Chada, and Valeria Della-Maggiore

(https://www.psypost.org/surprising-findings-more-female-venture-capitalists-linked-to-less-funding-for-women-entrepreneurs/) Surprising findings: More female venture capitalists linked to less funding for women entrepreneurs
Aug 11th 2024, 18:00

Venture capital plays an important role in helping new businesses get off the ground. The field also has a stubborn gender gap.
More than 4 in 5 partners at U.S.-based venture capital firms are men, (https://www2.deloitte.com/content/dam/Deloitte/us/Documents/audit/us-audit-human-capital-survey-report-2023.pdf) surveys and (https://www.hks.harvard.edu/sites/default/files/2023-09/gender_and_culture_in_vc_literature_review_final.pdf) research show. Perhaps relatedly, VC firms overwhelmingly direct their funds to man-led businesses: In 2023, (https://news.crunchbase.com/diversity/us-vc-funding-female-founders-peaked-2023-ai-openai-anthropic/) only about 1 in 4 VC funds were allocated to woman-led companies, according to Crunchbase data.
(https://www.hks.harvard.edu/centers/wappp/research/past/venture-capital-entrepreneurship) Advocates for gender equity have long called for firms to have more female senior venture capitalists on their teams. The idea is that having more women making investment decisions will translate into more funding for woman-led businesses.
As a (https://www.umsl.edu/business/directory/xu-lei.html) professor of entrepreneurship, I wondered whether the facts supported this idea. So my co-authors and I analyzed funding decisions from more than 150 mid- and large-sized U.S.-based VC firms over eight years.
When women don’t support women
(https://doi.org/10.1016/j.jbusvent.2023.106368) What we found surprised us: Firms whose decision-making groups included more female senior venture capitalists offered less funding to woman-led businesses. Every additional senior female venture capitalist in a firm’s decision-making group was linked to a 0.46% decline in the proportion of newly funded woman-led businesses in its investment portfolio.
Since the average funding round in our sample was $5.4 million, that suggests adding one extra female senior venture capitalist into a VC decision-making group translates into woman-led businesses receiving about $25,000 less funding.
To be clear, my team isn’t saying that individual female venture capitalists are to blame for this state of affairs. Our work was not aimed at assigning personal responsibility. We simply found that having more women in VC decision-making circles was associated with less funding of woman-led businesses.
On its face, this may seem like a paradox. But it’s consistent with (https://www.hks.harvard.edu/sites/default/files/2023-09/gender_and_culture_in_vc_literature_review_final.pdf) previous research that shows male dominance is entrenched in the U.S. entrepreneurial finance market. According to our interviews with female entrepreneurs and senior venture capitalists, this fosters a culture where women (https://doi.org/10.5465/amj.2013.0718) tend to defer to their male counterparts.
Research also suggests that women in male-dominated spaces have incentives to (https://doi.org/10.1016/j.leaqua.2015.12.007) distance themselves from less-powerful women to improve their status. That might help explain why female senior venture capitalists would hesitate to fund woman-led startups.
The value of trust and neutrality
My team also found, however, that (https://doi.org/10.1016/j.jbusvent.2023.106368) two key factors can mitigate this effect.
First, when senior venture capitalists in a decision-making group had worked together previously, we didn’t see the same negative impact. That suggests trust matters.
And when a group includes politically neutral senior venture capitalists, which we judged by looking at public political donation records, it reduces the negative effects on funding for woman-led businesses. This is because politically impartial decision-makers improve and facilitate (https://www.jstor.org/stable/25610729) group communication and consensus building.
Our findings suggest that VC firms might want to explore innovative approaches to fighting gender bias. For example, they could invite outside female investment professionals who have connections with many incumbent senior venture capitalists to work as consultants. These professionals could then independently assess investment proposals and offer advice to VC firms’ decision-making groups.
In some cases, efforts to elevate women in the workplace may pay off. For example, an analysis of all companies listed on the (https://www.spglobal.com/spdji/en/indices/equity/sp-composite-1500/#overview) S&P Composite 1500 index from 2004 to 2015 found that (https://doi.org/10.1177/0001839214530951) calls for greater gender diversity in the boardroom were linked to the inclusion of more female directors.
But as our research suggests, efforts to promote diversity aren’t always so successful, especially in those male-dominated contexts such as the U.S. entrepreneurial finance market. Indeed, they can backfire if they fail to address underlying cultural biases and power dynamics.
To be clear, our study isn’t a call to abandon the pursuit of diversity among venture capitalists. Instead, it underscores the importance of persisting until women achieve equal status in business and society at large.
 
This article is republished from (https://theconversation.com) The Conversation under a Creative Commons license. Read the (https://theconversation.com/more-women-in-venture-capital-doesnt-mean-more-funding-for-female-led-businesses-new-research-suggests-heres-why-232654) original article.

(https://www.psypost.org/ddl-920-scientists-discover-compound-that-restores-lost-memories-in-alzheimers-model/) DDL-920: Scientists discover compound that restores lost memories in Alzheimer’s model
Aug 11th 2024, 16:00

A potential game-changer in Alzheimer’s treatment has emerged from UCLA Health, where scientists have identified a compound that can restore memory function in mice with symptoms of the disease. This groundbreaking discovery, centered around a molecule called DDL-920, could pave the way for a novel approach to treating Alzheimer’s that goes beyond merely slowing the disease’s progression. The study, published in (https://doi.org/10.1073/pnas.2400420121) The Proceedings of the National Academy of Sciences, demonstrated that DDL-920 effectively “jumpstarted” the brain’s memory circuitry.
Alzheimer’s disease is a progressive neurological disorder that primarily affects older adults, leading to the deterioration of memory, cognitive abilities, and, eventually, the ability to carry out simple daily tasks. The disease is characterized by the accumulation of amyloid plaques and tau tangles in the brain, which disrupt the communication between neurons and ultimately lead to cell death.
Over time, this neuronal loss results in significant shrinkage of the brain and a decline in cognitive functions, including memory, reasoning, and the ability to perform familiar tasks. Alzheimer’s disease is the most common cause of dementia and has no cure, making it one of the most pressing public health challenges as the global population ages.
Existing therapies, including some recently approved drugs, focus on reducing the amyloid plaques in the brain, which are a hallmark of the disease. While these treatments can slow the progression of cognitive decline, they do not reverse the damage that has already occurred in the brain, particularly the loss of memory and cognitive functions.
The UCLA team recognized the limitations of existing treatments and set out to explore a new strategy. Instead of focusing on removing amyloid plaques, they aimed to find a way to restore the brain’s memory circuits.
The researchers focused on a specific type of brain cell called parvalbumin interneurons. These cells are known to generate gamma oscillations, which are high-frequency brain rhythms essential for memory and cognition. In individuals with Alzheimer’s disease, these oscillations are significantly reduced, leading to impaired cognitive function.
The research team identified a molecule, DDL-920, that could potentially target and enhance the activity of these parvalbumin interneurons. DDL-920 was designed to block certain receptors in these neurons that typically act as brakes, slowing down the gamma oscillations. By inhibiting these receptors, the researchers hoped to boost the neurons’ activity and restore normal oscillation patterns, thereby revitalizing memory circuits.
To test the effectiveness of DDL-920, the researchers conducted experiments on mice genetically modified to display symptoms of Alzheimer’s disease. Both these mice and healthy mice were subjected to a cognitive task known as the Barnes maze. This task involves a circular platform with one escape hole, and it is used to measure spatial learning and memory in rodents.
After assessing the baseline cognitive abilities of the mice, the researchers administered DDL-920 to the Alzheimer’s model mice twice daily for two weeks. They then retested the mice to see if their ability to remember and locate the escape hole had improved.
After the two-week treatment period, the Alzheimer’s model mice that received DDL-920 performed almost as well as the healthy mice in the Barnes maze, indicating a significant improvement in memory. These treated mice were able to recall the location of the escape hole nearly as effectively as their healthy counterparts, a promising sign that the compound had successfully restored some level of cognitive function.
The researchers observed no abnormal behavior or side effects in the treated mice, such as hyperactivity or other motor dysfunctions, which often complicate the development of new neurological drugs. This lack of visible side effects was particularly encouraging, suggesting that DDL-920 could be a safe candidate for further testing in humans.
While these findings are promising, the researchers caution that much more work is needed before DDL-920 can be considered a viable treatment for humans. The next steps involve rigorous testing to ensure that the compound is safe and effective in humans. This will include exploring the appropriate dosage, understanding how the compound is metabolized in the human body, and determining any potential long-term side effects.
The study opens up new avenues for treating other neurological conditions characterized by reduced gamma oscillations, such as depression, schizophrenia, and autism spectrum disorder. The ability of DDL-920 to enhance these oscillations suggests it could have broader applications beyond Alzheimer’s disease, potentially benefiting individuals with a range of cognitive impairments.
The study, “(https://www.pnas.org/doi/10.1073/pnas.2400420121) A therapeutic small molecule enhances γ-oscillations and improves cognition/memory in Alzheimer’s disease model mice,” was authored by Xiaofei Wei, Jesus J. Campagna, Barbara Jagodzinska, Dongwook Wi, Whitaker Cohn, Jessica T. Lee, Chunni Zhu, Christine S. Huang, László Molnár, Carolyn R. Houser, Varghese John, and Istvan Mody.

(https://www.psypost.org/neuroscientists-identify-brain-network-critical-for-creative-idea-generation/) Neuroscientists identify brain network critical for creative idea generation
Aug 11th 2024, 14:00

Creativity is one of the most enigmatic and celebrated human capacities, driving everything from artistic expression to scientific innovation. But what actually happens in the brain when we think creatively? A recent study led by researchers from the University of Utah Health and Baylor College of Medicine sheds new light on this question, revealing that the brain’s Default Mode Network (DMN) plays a critical and causal role in generating creative ideas.
The study, published in the journal (https://doi.org/10.1093/brain/awae199) BRAIN, found that when participants engaged in a task requiring creative thinking—specifically, coming up with novel uses for everyday objects—the DMN became highly active. Furthermore, when researchers temporarily dampened activity in specific regions of the DMN, the participants’ creative abilities noticeably diminished, even though other cognitive functions, such as mind wandering, remained unaffected. This discovery not only strengthens the association between the DMN and creativity but also demonstrates that the network is essential for the generation of original ideas.
The DMN is a complex network of brain regions that is most active when we are at rest, lost in thought, or daydreaming. The DMN has been associated with introspective processes like recalling memories, envisioning the future, and considering the thoughts of others. It has also been implicated in a range of mental health conditions, such as depression, where the network appears to be overactive.
While numerous studies using brain imaging techniques like functional MRI have linked the DMN to various cognitive functions, including creativity, these studies have largely been correlational. They showed that DMN activity often accompanies creative thought, but they could not prove that the DMN is necessary for creativity to occur. This study aimed to move beyond correlation to establish a direct causal relationship between DMN activity and creative thinking.
To explore the role of the DMN in creativity, the researchers recruited 13 participants who were undergoing treatment for epilepsy at Baylor St. Luke’s Medical Center. These participants had intracranial electrodes implanted in their brains for clinical monitoring. This unique setup allowed the researchers to record electrical activity from specific brain regions with high temporal and spatial precision while the participants performed various cognitive tasks.
The participants were asked to perform three different tasks while their brain activity was recorded. The first was a creative thinking task known as the Alternate Uses Task, where participants had to list as many novel uses as possible for everyday objects like a chair or a paperclip. The second task was a mind-wandering task, where participants simply let their thoughts roam freely while focusing on a neutral visual stimulus. The third task was a sustained attention task, which served as a control to compare against the creative and mind-wandering tasks.
The researchers focused on two specific types of brain waves: theta waves, which are associated with long-range communication between brain regions, and gamma waves, which are linked to local neural activity. They also used electrical stimulation to temporarily disrupt the activity in specific regions of the DMN to see how this would affect the participants’ performance on the creative thinking task.
Participants were asked to complete three distinct tasks while their brain activity was recorded: a creative thinking task known as the Alternate Uses Task, a mind-wandering task, and a sustained attention task. The Alternate Uses Task required participants to generate as many novel uses as possible for common objects, such as a chair or a paperclip. The mind-wandering task involved participants fixating on a neutral visual stimulus while allowing their thoughts to wander freely. The sustained attention task served as a control, requiring participants to focus on a simple visual cue and respond accordingly.
The researchers focused on analyzing two specific types of brain waves: theta waves, which are associated with long-range communication between brain regions, and gamma waves, which are linked to local neural activity. By examining the patterns of these brain waves, the researchers could infer how different regions of the DMN and other brain networks interacted during the tasks. Additionally, in a subset of participants, the researchers used electrical stimulation to temporarily disrupt activity in specific regions of the DMN. This allowed them to observe how dampening DMN activity affected participants’ performance on the creative thinking task.
The findings revealed that the DMN plays a critical role in creative thinking. During the Alternate Uses Task, the DMN exhibited a distinctive pattern of activity: it became highly active when participants were presented with an object and asked to think of novel uses for it. As participants generated and evaluated their ideas, the DMN synchronized with other brain networks involved in executive functions, such as decision-making and problem-solving. This synchronization suggests that the DMN helps generate creative ideas, which are then evaluated and refined by other cognitive processes.
Importantly, when the researchers disrupted DMN activity using electrical stimulation, participants’ ability to come up with creative ideas was significantly impaired. Their responses became less original and more predictable, indicating that the DMN is not just associated with creativity but is necessary for generating novel ideas. Interestingly, the disruption did not affect participants’ performance on the mind-wandering task, suggesting that the DMN’s role in creativity is distinct from its involvement in other forms of introspective thought.
These findings provide strong evidence that the DMN is crucial for creative thinking, particularly in the generation of original ideas. The study also highlights the distinct roles that different brain networks play in creativity, with the DMN working in concert with other regions to support the complex cognitive processes involved in creative thought. By demonstrating a causal link between DMN activity and creativity, the researchers have opened new avenues for understanding the neural basis of creativity and its potential clinical implications.
While the findings of this study are compelling, there are some limitations that should be acknowledged. First, the study was conducted with a small sample size of 13 participants, all of whom were undergoing treatment for epilepsy. Additionally, the study focused on a specific subset of brain regions within the DMN, and it is likely that other regions and networks also play important roles in creativity.
The study, “(https://academic.oup.com/brain/advance-article/doi/10.1093/brain/awae199/7695856) Default mode network electrophysiological dynamics and causal role in creative thinking,” was authored by Eleonora Bartoli, Ethan Devara, Huy Q Dang, Rikki Rabinovich, Raissa K Mathura, Adrish Anand, Bailey R Pascuzzi, Joshua Adkinson, Yoed N Kenett, Kelly R Bijanki, Sameer A Sheth, and Ben Shofty.

(https://www.psypost.org/breakthrough-ai-tech-enables-at-home-tracking-of-parkinsons-disease-progression/) Breakthrough AI tech enables at-home tracking of Parkinson’s disease progression
Aug 11th 2024, 12:00

For the millions of people living with Parkinson’s disease, tracking the progression of symptoms can be challenging and time-consuming. But now, researchers at the University of Florida have developed a new video-processing system that could revolutionize this process, making it easier to monitor the disease from home with unprecedented precision.
The research, published in the journal (http://dx.doi.org/10.1109/TNSRE.2024.3416446) IEEE Transactions on Neural Networks, describes a video-processing system developed by Diego Guarin, an assistant professor at the University of Florida’s College of Health and Human Performance. This system uses machine learning to analyze videos of patients performing a simple hand movement test, revealing tiny, often imperceptible changes in motor function that could signal the progression of Parkinson’s disease.
“The beauty of this technology,” said Guarin, “is that a patient can record themselves performing the test, and the software analyzes it and informs the clinician how the patient is moving so the clinician can make decisions.”
Parkinson’s disease is a complex neurological disorder that gradually impairs a person’s ability to control their movements. Currently, there is no cure, and treatments focus on managing symptoms rather than stopping the disease’s progression. One of the biggest challenges in treating Parkinson’s disease is accurately monitoring how it progresses over time, particularly in its early stages when changes can be subtle and easily missed during standard clinical assessments.
Traditional methods for assessing Parkinson’s rely heavily on the Movement Disorder Society – Unified Parkinson’s Disease Rating Scale, a system that, while widely used, has its limitations. This scale is based on a clinician’s observation of a patient’s movements, scored on a 5-point scale that can be subjective and lacks the fine granularity needed to detect small, yet significant, changes. Additionally, this method requires patients to visit a clinic, which may not always be feasible, especially for those with limited mobility.
Recognizing these limitations, Guarin and his team sought to develop a more objective and sensitive method for tracking motor symptoms in Parkinson’s patients. Their goal was to create a tool that could be used easily at home, providing continuous monitoring and more precise information about the disease’s progression.
To test their new system, the researchers analyzed video data from 66 people with Parkinson’s disease and 24 healthy individuals. All participants performed a standardized finger-tapping test, which involves quickly tapping the thumb and index finger together 10 times. This test is commonly used to assess bradykinesia, a slowing of movement that is a hallmark of Parkinson’s disease.
The videos were recorded under controlled conditions at the University of Florida’s Health facility using a standard camera setup. Each participant sat in front of a camera while a trained clinician guided them through the finger-tapping test.
The videos were then processed using a custom machine learning pipeline developed by Guarin’s team. This pipeline uses Google’s MediaPipe, a software that can track hand movements by identifying 21 key points on each hand. From these points, the system calculates various movement metrics, such as the speed and amplitude of finger taps, as well as more complex measures like movement variability and the time taken to complete each tap cycle.
The study focused on comparing three different machine learning approaches to predict the severity of Parkinson’s disease based on these video-derived movement features. These approaches included a traditional multiclass classification model, an ordinal binary classification model, and a novel tiered binary classification model. The latter was specifically designed by the researchers to account for the fact that different movement features may be more or less important at different stages of the disease.
The results of the study were promising. The new tiered binary classification model outperformed the other methods, achieving an accuracy of 85% when distinguishing between healthy individuals and those with Parkinson’s disease, and an 86% accuracy overall when classifying the severity of the disease. This is a significant improvement over traditional methods and highlights the potential of this approach for clinical use.
“We found that we can observe the same features that the clinicians are trying to see by using a camera and a computer,” Guarin said. “With help from AI, the same examination is made easier and less time-consuming for everyone involved.”
One of the key findings of the study was that certain movement features were more predictive of disease severity at different stages. For example, early in the disease, features like the speed and amplitude of finger taps were most indicative of severity. However, as the disease progressed, measures of movement variability became more important. This insight suggests that a one-size-fits-all approach to assessing Parkinson’s may not be effective and that different aspects of motor function should be emphasized depending on the stage of the disease.
“We’ve seen that, with Parkinson’s disease, the opening movement is delayed, compared to the same movement in individuals that are healthy,” Guarin said. “This is new information that is almost impossible to measure without the video and computer, telling us the technology can help to better characterize how Parkinson’s disease affects movement and provide new markers to help evaluate the effectiveness of therapies.”
The study also found that the new system could detect very subtle changes in movement that might go unnoticed by clinicians using the traditional 5-point rating scale. This could be particularly valuable for detecting the early signs of Parkinson’s, potentially allowing for earlier intervention and better management of the disease.
While the findings are encouraging, the study has several limitations that need to be addressed in future research. One major limitation is that the video recordings were all made under controlled conditions with a clinician present to guide the participants. This may not reflect real-world conditions, where patients would be recording themselves at home without professional guidance. The researchers plan to address this by testing their system in more natural settings, where variations in camera angle, lighting, and patient positioning could affect the accuracy of the movement analysis.
Michael S. Okun, the director of the Norman Fixel Institute and medical advisor for the Parkinson’s Foundation, described the automated video-based assessments as a potential “game changer” for clinical trials and care.
“The finger-tapping test is one of the most critical elements used for diagnosis and for measuring disease progression in Parkinson’s disease,” Okun said. “Today, it takes an expert to interpret the results, but what is transformative is how Diego and three Parkinson’s neurologists at the Fixel Institute were able to use AI to objectify disease progression.”
The study, “(https://ieeexplore.ieee.org/document/10568188) Characterizing Disease Progression in Parkinson’s Disease from Videos of the Finger Tapping Test,” was authored by Diego L. Guarín, Joshua K. Wong, Nikolaus R. McFarland, and Adolfo Ramirez-Zamora.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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