#neuroimaging — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #neuroimaging, aggregated by home.social.
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DATE: August 19, 2026 at 02:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Childhood ADHD is linked to distinct developmental changes in brain white matter
An analysis of ABCD study data found that children with ADHD showed signs of reduced glial cellularity (measured as reduced restricted normalized isotropic diffusion) in 20 white matter tracts at 9 years of age. They also noticed signs of reduced axonal organization (measured as reduced restricted normalized directional diffusion) of nerve fibers in 16 white matter tracts between ages 9 and 14. The paper was published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.
Attention-deficit/hyperactivity disorder, or ADHD, is a neurodevelopmental condition that affects attention, impulse control, and activity levels. It usually begins in childhood, although many people continue to experience symptoms in adolescence and adulthood. Some people with ADHD mainly struggle with distractibility, forgetfulness, and organization, while others are more affected by restlessness and impulsive behavior. Many experience a combination of both patterns.
ADHD symptoms are persistent and interfere with everyday functioning. They can affect school or work performance, relationships, time management, and the ability to complete routine tasks. In fact, ADHD is frequently diagnosed when a child starts school because its symptoms often conflict with classroom expectations. It is estimated to affect approximately 3-10% of children.
Study author L. Nate Overholtzer and his colleagues note that white matter—the tissue in the brain made mainly of nerve fibers—facilitates efficient communication between brain regions, thereby supporting higher-order cognitive processes. These processes are commonly impaired in ADHD. With this in mind, they conducted a study aiming to characterize the relationship between ADHD and white matter microarchitecture across early adolescence. They also evaluated how these associations were affected by three classes of ADHD medications: amphetamine-based, methylphenidate-based, and nonstimulant medications.
They analyzed data from the Adolescent Brain and Cognitive Development Study (ABCD Study). The ABCD study is a large, long-term U.S. research project following thousands of children into adulthood to understand how brain development relates to health, behavior, and life experiences.
The analyses used data collected at enrollment (2016-2018), 2 years (2018-2020), and 4 years (2020-2022) after the start of the study. The total number of participants for whom data were available at the start of the study was 9,426. In year 2, it was 6,745, and it was down to 2,483 participants in year 4. At the start of the study, 1,150 participants (12.2%) had ADHD (658 of whom were taking ADHD medication). In year 2, there were 763 participants with ADHD (11.3%), and in year 4, there were 294 (11.8%).
The researchers used advanced diffusion magnetic resonance imaging (MRI) scans of the participants’ brains. Participants who were using non-ADHD psychiatric medications were excluded from the analysis to prevent pharmacological confounds. The study authors identified whether participants had ADHD based on caregiver responses to the computerized Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS) and the Medication Inventory Survey (MIS). The latter assessment also served to identify the type of ADHD medication participants were taking.
Results indicated that children with ADHD tended to show decreased restricted normalized isotropic diffusion in 20 white matter tracts at age 9, indicating reduced glial cellularity. The analyses also showed that ADHD was associated with enduring decreases in restricted normalized directional diffusion in 16 white matter tracts between the ages of 9 and 14, indicating reduced axonal organization. Additionally, an exploratory analysis indicated that the narrowing of isotropic diffusion differences between children with and without ADHD across early adolescence paralleled an overall age-related drop in ADHD symptoms across the participants.
Restricted normalized isotropic diffusion (RNI) and restricted normalized directional diffusion (RND) are advanced MRI measures that describe how water movement is restricted within brain tissue.
RNI reflects restriction that is similar in all directions and serves as an indicator of glial cellularity—the presence and morphology of support cells like astrocytes, microglia, and myelin-producing cells.
RND reflects restriction along a particular direction and, in brain white matter, provides information about the organization, packing, and alignment of nerve fibers (axons).
“Altogether, ADHD was robustly associated with reductions in isotropic diffusion in white matter tracts, suggestive of atypical glial cellularity during late childhood. Complementary reductions in directional diffusion of select tracts may suggest atypical axonal organization enduring across early adolescence,” the study authors concluded.
The study contributes to the scientific knowledge about the neural basis of ADHD. However, the study was limited by smaller sample sizes in later waves—partially because Year 4 data collection was still ongoing at the time of the data release, and because children with ADHD had higher exclusion rates due to factors like moving during MRI scans or taking other psychiatric medications. This could limit the generalizability of the results to the broader ADHD population.
The paper, “Developmental Differences in White Matter Microarchitecture in Youth with ADHD: Longitudinal Findings from the ABCD Study,” was authored by L. Nate Overholtzer, Katherine L. Bottenhorn, Hedyeh Ahmadi, Sarah L. Karalunas, Bradley S. Peterson, and Megan M. Herting.
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ADHD #WhiteMatter #BrainDevelopment #ABCDStudy #Neuroimaging #DiffusionMRI #GlialCells #AxonalOrganization #ChildhoodADHD #NeuroscienceResearch
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DATE: August 18, 2026 at 12:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: The human brain reorganizes itself at four distinct ages
URL: https://www.psypost.org/the-human-brain-reorganizes-itself-at-four-distinct-ages/
The structural organization of the human brain changes non-linearly over a person’s life, shifting at four distinct ages. A large study identified major transitions in brain network architecture around ages nine, 32, 66, and 83. The research was published in Nature Communications.
The brain is essentially a network of connected regions. The architecture of this network, known as its topology, dictates how well different areas communicate. Researchers measure this topology using mathematical concepts like integration, segregation, and centrality. Different topological structures have different strengths related to cognition and behavior.
Integration describes how easily information travels across the entire brain. A highly integrated network has many short paths connecting distant regions, optimizing it for rapid communication. Segregation refers to how the network divides into specialized local groups. A highly segregated network has dense local connections that support specialized processing tasks, like vision or motor control. Centrality identifies specific regions that act as highly active hubs for information transfer, making the network more resilient to damage.
Past research has linked brain topology to cognitive function and mental health during specific life stages. But the underlying principles of how this organization shifts across an entire human life have remained unmapped. Alexa Mousley, a researcher at the University of Cambridge, wanted to identify if there are specific turning points when the brain enters a new phase of developmental change.
To map these lifespan changes, the researchers gathered brain imaging data from nine different datasets. The combined data included 4,216 participants ranging in age from zero to 90 years old. Because the sample exceeded 2,000 individuals, this qualifies as a large study.
The team used a specific type of magnetic resonance imaging that tracks the movement of water molecules to map the physical wiring of the brain. They then harmonized the data from the different sources to account for variations in scanning equipment. From there, the scientists calculated 12 different metrics to describe the topology of each participant’s brain network. The network densities were strictly controlled to allow for fair comparisons across different ages.
To make sense of this highly detailed data, the team used a mathematical technique to project the network metrics into three-dimensional spaces. This machine learning approach filters out overlapping information to reveal the fundamental mathematical structure of complex data. By tracing the average trajectory of brain development through these spaces, the researchers could pinpoint where the trajectory abruptly changed direction. They defined these spots as turning points.
The analysis revealed four major turning points in the human lifespan. These occur around ages nine, 32, 66, and 83. These four points separate human life into five distinct epochs of brain development, with each epoch featuring its own unique pattern of structural change.
The first epoch spans from birth to age nine. During this childhood phase, the brain’s global integration decreases while local segregation increases. The extent to which neighboring regions connect to each other is the strongest predictor of a child’s age during this period. The end of this epoch coincides roughly with the onset of puberty and a known biological phase where the brain actively eliminates unused neural connections.
The second epoch lasts from age nine to 32. This phase encompasses adolescence and early adulthood. Over these years, the brain network becomes increasingly integrated and less segregated on a global scale. The balance between global efficiency and local specialization becomes the most defining feature of brain development during this time.
The turning point at age 32 represents the largest structural shift in the entire lifespan. It aligns with the known peak of white matter volume, which is the insulated wiring that connects brain regions. Following this peak, the third epoch stretches across three decades of adulthood, from age 32 to 66.
This middle adulthood epoch is a relatively stable period characterized by slower changes in network architecture. During these years, global integration begins to decline while local efficiency increases. Changes in network segregation drive the relationship between age and brain topology during this long phase.
The fourth turning point arrives at age 66, marking the transition into older age. From 66 to 83, the brain network shows a distinct shift toward increasing modularity. Modularity means the network separates into highly interconnected subgroups. The researchers note this pattern suggests a simplification of the brain’s structural network, which corresponds with expected age-related degradation in white matter.
The final epoch covers ages 83 to 90. In this late stage of life, the relationship between age and brain topology is quite weak. The only metric that tracks with age during this period is the centrality of individual nodes, meaning certain localized hubs become increasingly important for connectivity.
The study has some limitations that affect how the results should be interpreted. The data is cross-sectional, meaning it compares different people of different ages rather than following the same individuals over their entire lives. This design makes it impossible to establish causality or temporal dynamics within a single person. It prevents researchers from tracking how an individual’s specific brain topology changes over time.
Additionally, the researchers used fixed network density thresholds for their main analysis to allow for fair comparisons between different ages. While they conducted secondary tests to verify their choices, this thresholding process might obscure some smaller individual differences in total brain connectivity. The analysis also did not separate the data by biological sex, leaving it unknown whether these major turning points happen at different ages for men and women.
Finally, the oldest age group contained just 93 participants, which lowered the statistical power of the analysis for that specific epoch. The associations in this late-aging epoch were mostly not statistically significant. It is also highly possible that the people in their late 80s who participated in these imaging studies are exceptionally healthy compared to their peers. This selection bias could skew the results for the oldest epoch, making their brains look more resilient than average.
The study, “Topological turning points across the human lifespan,” was authored by Alexa Mousley, Richard A. I. Bethlehem, Fang-Cheng Yeh, and Duncan E. Astle.
URL: https://www.psypost.org/the-human-brain-reorganizes-itself-at-four-distinct-ages/
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #BrainTopology #LifespanTurningPoints #Neuroscience #BrainDevelopment #AdultBrain #AgeAndBrain #Neuroimaging #WhiteMatter #BrainNetwork #NatureCommunications
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DATE: August 17, 2026 at 06:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Brain scans reveal two distinct biological profiles of migraine
URL: https://www.psypost.org/brain-scans-reveal-two-distinct-biological-profiles-of-migraine/
A recent analysis of brain scans has revealed that people who experience migraines can be grouped into two distinct biological categories. These categories are based on how the brain is wired and physically structured, offering a new way to understand the disorder beyond traditional symptom checklists. The findings were published in the journal Cephalalgia.
Migraine is a neurological condition that causes severe head pain, sensitivity to light, and other debilitating symptoms. Doctors currently classify the disorder based on how often attacks occur and whether a patient experiences an aura, which refers to visual or sensory disturbances preceding the headache. This symptom-based approach, outlined in the International Classification of Headache Disorders, often fails to predict which treatments will work best for individual patients.
The biological differences between people with migraines remain largely unmapped. Researchers suspect that categorizing patients based on brain biology, rather than just symptom frequency, might eventually improve treatment strategies. Stanford University researchers Jaiashre Sridhar and Danielle D. DeSouza led a team to investigate whether patterns in brain imaging could identify hidden biological subgroups.
To do this, the research team used two types of magnetic resonance imaging, or MRI. Structural MRI measures the physical dimensions of the brain, such as the volume and thickness of the outer layer known as the cerebral cortex, as well as deeper subcortical structures. Functional MRI tracks blood flow to observe how different brain regions communicate when a person is at rest, a metric called functional connectivity.
The researchers first analyzed combined structural and functional brain scan data from 111 individuals with migraines and 51 healthy controls. They used a mathematical algorithm to simplify the massive amount of data and group the patients based on shared biological patterns. This exploratory approach was designed to let the data dictate the groups rather than relying on prior clinical labels.
This combined analysis identified two biological subgroups with distinct brain profiles and clinical experiences. One group tended to be older, had lived with migraines longer, and reported higher levels of daily disability. This higher-burden group also experienced longer individual headache durations and lower confidence in their ability to manage pain.
In this higher-burden group, functional MRI scans showed elevated connectivity between deeper brain structures and cortical networks responsible for attention, movement, and visual processing. Structurally, these individuals also exhibited reduced brain volume across several cortical regions, including the frontal, parietal, and temporal lobes, compared with the other subgroup. Many of these heightened functional connections were also elevated relative to the healthy control group.
The second subgroup presented a milder biological profile. Their brain structure was largely preserved in comparison to the first group. Their functional connectivity patterns and brain volumes were not statistically significant when compared to the healthy control group.
After identifying the combined groups, the researchers conducted a secondary analysis using only the functional connectivity data. They applied the same mathematical grouping process to see how the patients would cluster based solely on how different brain regions communicate.
This functional-only model produced two subgroups that closely matched the groups found in the initial combined analysis. Patients with higher clinical burden again clustered together, exhibiting similar patterns of elevated brain connectivity. When grouped this way, the resulting clusters did not display any differences in brain structure, indicating that functional connectivity drove most of the initial subgroupings.
Next, the team ran a third clustering model using exclusively structural MRI data. They grouped the same patients based entirely on the thickness and volume of their brain tissue.
This structural-only analysis generated two entirely different patient clusters that had almost no overlap with the groups formed by the combined or functional data. While these two new groups showed widespread differences in brain volume, they exhibited no differences in functional connectivity. This divergence indicates that structural variations represent a completely separate dimension of migraine biology than functional variations.
To verify the stability of their findings, the researchers performed a final sensitivity analysis. Instead of looking at broad functional networks, they repeated the combined analysis using a much more detailed map that divided the brain into over a hundred smaller, specific regions.
The results of this fine-grained analysis strongly mirrored the original combined model. Between 90 and 95 percent of the participants were assigned to the exact same subgroups as before. This consistency suggests that the biological groups are robust, regardless of the scale used to map the brain.
While these biological groupings provide a new perspective on migraines, the research relies on data collected at a single point in time. It is not possible to know whether prolonged migraines alter the brain over the years, or if these brain differences exist first and influence how the condition develops. The clinical differences between the two subgroups were also relatively subtle, and the groups did not align with traditional categories like chronic or episodic migraine.
The researchers noted that this was a modestly sized study, meaning the results will need to be verified in larger populations. The study also did not track the exact phase of the patients’ migraine cycle during the brain scans, such as whether they were actively having a migraine or in a resting phase. Additionally, the researchers did not account for all preventive medications the participants might have been taking at the time.
Future research will need to track larger groups of patients over extended periods to see how these biological profiles evolve and whether they can eventually guide medical care.
The study, “Neuroimaging-based subtyping of migraine identifies clinically distinct phenotypes,” was authored by Jaiashre Sridhar, Mahsa Babaei, Bharati M. Sanjanwala, Robert P. Cowan, and Danielle D. DeSouza.
URL: https://www.psypost.org/brain-scans-reveal-two-distinct-biological-profiles-of-migraine/
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #MigraineBiology #Neuroimaging #BrainConnectivity #StructuralFunctionalMRI #MigraineSubtypes #CephalalgiaStudy #BrainNetworks #PersonalizedMedicine #NeurologyResearch #MigrainePhenotypes
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DATE: August 12, 2026 at 08:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Evolving political views linked to changes in brain activity
URL: https://www.psypost.org/how-shifting-political-loyalties-alter-brain-activity-over-time/
When people alter their political loyalties, the way their brains respond to political messages changes alongside those shifting allegiances. A small study tracked individuals over two and a half years and found that evolving feelings about political groups altered brain activity in regions related to emotion and memory. These results, published in Communications Psychology, detail how social identity deeply shapes the way the human brain processes political information.
Political identity has traditionally been measured by a person’s abstract beliefs or policy preferences, such as identifying as liberal or conservative. In recent years, political polarization has become more personal, with group loyalty often overriding specific policy positions. Many voters now view politics through a lens of strict group membership, prioritizing whether a politician belongs to their specific faction over the details of proposed legislation. This shift has led researchers to ask how changes in worldview manifest physically within the brain over time.
Most neuroimaging studies compare different groups of people at a single moment in time. Very few track how an individual’s neural responses to the exact same information might shift as their own perspectives evolve. Finding an environment to test this is difficult, because adult political opinions tend to remain stable under normal conditions.
The research was conducted by neuroscientists Gal Boiman, Tal Ohad, and Yaara Yeshurun at Tel Aviv University, along with their colleagues Yohay Zvi and Noa Katabi. The team took advantage of a prolonged period of political instability in Israel starting in 2019. During this time, repeated elections and unexpected alliances disrupted traditional party lines, creating a rare opportunity to observe people as their political loyalties shifted in real time.
To study this phenomenon, the researchers recruited a group of participants for an initial brain scanning session in April 2019. More than two years later, in August 2021, 21 of those individuals returned for a second scan. Because the sample included fewer than 50 participants, it is considered a small study. During both sessions, participants lay inside a functional magnetic resonance imaging scanner, a machine that measures brain activity by tracking blood flow.
While inside the scanner, the participants watched identical sets of videos. The video clips included campaign advertisements from left-wing, right-wing, and centrist parties, as well as speeches by political figures. A documentary clip about a man who converted an old bus into a house served as a neutral baseline.
After each scanning session, the participants answered extensive questionnaires in a separate room. These surveys measured their agreement with the videos, their emotional engagement, and their specific sentiments toward the political figures shown. The questions asked participants to rate feelings like trust, pride, anger, and disgust. During the 2021 session, participants also answered questions about how their opinions had changed since the first viewing.
The research team used the survey responses to calculate a score representing how much each person’s interpretation of the videos had altered. They divided these changes into two main categories. One category measured shifts in abstract ideology, such as opinions on specific government policies. The second category measured changes in group identity, capturing how participants felt about specific politicians and political factions.
Next, the researchers compared the brain scans from 2019 with the scans from 2021. They looked for differences in brain activity across thousands of tiny, three-dimensional blocks of brain tissue, known as voxels. The researchers mapped these differences to see which parts of the brain changed the most and which remained stable. To ensure accuracy, they filtered out baseline changes that occurred when participants watched the neutral documentary video.
The analysis revealed a hierarchy of brain adaptation. Brain regions responsible for basic sensory processing, such as the visual and auditory cortices, showed the least amount of change between the two sessions. When participants watched the same videos two years later, their visual and auditory centers reacted in almost the exact same way.
In contrast, the largest differences in brain activity appeared in areas deep within the brain that manage memory, emotion, and reward. These areas included the amygdala, the hippocampus, and the striatum. The amygdala helps process emotional reactions, the hippocampus is heavily involved in forming and retrieving memories, and the striatum plays a role in recognizing rewards.
The researchers then looked for links between the altered brain activity and the survey scores measuring shifts in interpretation. They found that changes in brain activity directly correlated with how much a participant’s interpretation of a video had changed. The brain activity shifted the most when participants watched videos of politicians who had made unexpected alliances or changed their political positioning during the two-year gap.
When the researchers separated the survey scores into ideology and group identity, a distinct pattern emerged. The alterations in brain activity were strongly associated with changing feelings about political groups and specific figures. In contrast, the relationship between brain activity and changes in abstract ideological beliefs was not statistically significant. This suggests that shifting loyalties to a political team are more closely tied to how the brain processes information than changing opinions on policy.
The study design relies on observing natural changes over time, meaning it can only identify associations rather than establish direct causes. It remains unknown whether changing political attitudes cause the brain’s processing pathways to alter, or if underlying neural shifts lead to new political attitudes. The authors note that the small sample size prevented them from comparing different demographic groups, such as strictly conservative versus liberal voters. This limitation makes it impossible to say if one end of the political spectrum experiences these brain changes differently than the other.
The findings also depend on real-world political developments, which introduces some contextual variables. A participant might interpret a video differently in 2021 not just because their internal worldview changed, but because the politician in the video had taken new actions in the real world. Future research could use controlled laboratory settings to isolate personal shifts in opinion from external political events.
The study, “Changes in political attitudes are associated with changes in neural responses to political content,” was authored by Gal Boiman, Tal Ohad, Yohay Zvi, Noa Katabi, and Yaara Yeshurun.
URL: https://www.psypost.org/how-shifting-political-loyalties-alter-brain-activity-over-time/
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #PoliticalIdentity #BrainActivity #Neuroscience #PoliticalPersuasion #EmotionMemory #AmygdalaHippocampus #VoterLoyalty #Neuroimaging #PoliticalContent #BlockbusterResearch
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DATE: August 11, 2026 at 06:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Cognitive abilities help explain regional brain aging patterns in anxiety and depression
A new study published in the Journal of Affective Disorders suggests that the advanced brain aging often seen in people with anxiety and depression is partly associated with variations in their cognitive performance. By accounting for cognitive skills like memory and processing speed, scientists observed that the apparent effect of these psychiatric conditions on brain aging decreased by roughly twenty to twenty-five percent. This indicates that cognitive differences play an important role in understanding brain health in individuals with neuropsychiatric conditions.
Biological aging of the brain can sometimes diverge from chronological aging. Using structural magnetic resonance imaging, machine learning algorithms can predict a person’s brain age by comparing their brain structure to a large dataset of healthy individuals. The difference between this predicted age and the person’s actual age is called the brain age gap. A positive gap indicates an older-appearing brain, which is linked to cognitive decline, health risks, and various neurological conditions.
Neuropsychiatric disorders like depression and anxiety are associated with increased brain age gaps. People with these conditions also frequently experience cognitive difficulties that affect their attention, executive function, and processing speed. Executive function refers to a set of mental skills that include working memory, flexible thinking, and self-control. Because cognitive decline is a common feature of mood disorders, it is often difficult to tell if brain aging differences reflect the psychiatric diagnosis itself or the accompanying cognitive variation.
Previous neuroimaging research typically looked at global brain age, which assumes aging happens uniformly across the entire brain. This approach can obscure specific regional effects.
“Our main motivation was that most previous brain-age studies summarize the entire brain using a single number,” said Owen M. Vega, a doctoral candidate in the neuroscience graduate program at the University of Southern California and a researcher at the Ethel Percy Andrus Gerontology Center in the Leonard Davis School of Gerontology.
“While useful, that approach assumes the brain ages uniformly and provides little insight into why certain disorders are associated with advanced brain aging. We wanted to move beyond prediction toward biological understanding,” Vega told PsyPost.
By mapping brain age at a regional level and accounting for cognitive performance, the researchers aimed to identify the specific neural systems affected and link them to underlying cellular processes.
“Ultimately, this brings us closer to understanding the biological mechanisms that contribute to psychiatric brain aging rather than simply measuring that it exists,” Vega explained.
The authors analyzed data from 21,424 older adult participants in the UK Biobank. Participants were classified into four mutually exclusive groups based on their diagnostic status. The sample included 12,285 individuals with no psychiatric diagnosis, 1,746 with anxiety only, 4,267 with depression only, and 1,563 with comorbid anxiety and depression. Comorbidity means the individual met the criteria for both conditions.
To estimate regional brain ages, the scientists processed structural brain scans through a deep neural network, breaking the brain down into 187 distinct cortical and subcortical regions. Participants also completed several cognitive assessments measuring fluid intelligence, reaction time, and symbol substitution. These test results were statistically combined into a single principal component score representing general cognitive performance. The models also controlled for participant sex, years of education, and socioeconomic deprivation to isolate the variables of interest.
The researchers first ran a statistical model that did not account for cognitive performance. They found widespread regional brain age gap elevations across the psychiatric groups compared to the diagnosis-free participants. On average, brains in the anxiety group appeared about 1.01 years older than chronological age. Brains in the depression group appeared 1.05 years older, and brains in the comorbid group appeared 1.14 years older.
These elevated brain ages were widely distributed but particularly pronounced in specific areas. The largest gaps were observed in the anterior frontal and orbitofrontal regions, as well as the temporal pole. These areas are heavily involved in emotion regulation and reward processing.
Vega noted that while the overall increases are relatively small, they provide a starting point for exploring the biology of mental health.
“The effects are statistically robust but modest in size, with average differences of about one year. However, they should not be interpreted as the whole story,” Vega told PsyPost. “Averaging across the entire brain masks much larger regional differences. The real significance of this work lies in identifying where these changes occur.”
By pinpointing these spatial patterns, scientists can relate them to specific genes and molecular pathways, moving the field past simple summary measures.
“This moves brain-age research beyond a single summary measure toward understanding the mechanisms that may contribute to psychiatric illness and cognitive vulnerability,” Vega added.
Next, the authors ran a second model that included the participants’ general cognitive performance scores. Factoring in cognition reduced the magnitude of the brain age gaps by approximately twenty to twenty-five percent. The mean gap dropped to 0.80 years for the anxiety group, 0.84 years for the depression group, and 0.78 years for the comorbid group. Despite this reduction, the effects remained present, indicating that diagnostic status contributes to brain aging independent of cognitive ability.
“The main takeaway is that anxiety and depression are associated with subtle but measurable differences in how the brain ages, and those differences are not spread evenly across the brain,” Vega said.
By showing how these estimates change when mental skills are factored into the equations, the study refines how scientists understand brain health in clinical populations.
“We also found that part of the observed brain-age signal is associated with cognitive performance, showing that cognition is an important piece of the picture,” Vega explained. “More broadly, our work suggests that brain aging in psychiatric disorders reflects specific biological patterns rather than a single, uniform process, which may ultimately help researchers develop more biologically meaningful biomarkers.”
Higher cognitive performance was associated with a younger-looking brain, suggesting a protective effect. This association was noticeably stronger in all three psychiatric groups compared to the diagnosis-free participants. Interestingly, the brain regions most strongly associated with cognitive performance differed from the regions most affected by the psychiatric diagnoses.
Cognitive associations were strongest in subcortical and ventral regions of the brain. These included the thalamus, pallidum, and hippocampus, which are structures located deep beneath the cerebral cortex that are essential for memory formation and information integration. This dissociation suggests that psychiatric status and cognition exert distinct but overlapping influences on different neural systems.
The researchers also looked beyond the magnetic resonance imaging scans to see if their regional brain age maps aligned with other biological data, such as transcriptomics. Transcriptomics is the study of RNA molecules in cells, which reveals how specific genes are turned on or off to drive cellular activity.
“One of the most striking findings was that several independent biological analyses converged on the same underlying systems,” Vega said. “Regional brain-aging patterns identified from MRI aligned with transcriptomic enrichment and biological pathways in a remarkably consistent way.”
This overlap suggests that the structural differences visible on brain scans are directly tied to cellular and genetic changes.
“That convergence gives us greater confidence that these patterns reflect meaningful biology rather than isolated statistical findings, and suggests that regional brain age can serve as a bridge between neuroimaging and molecular neuroscience,” Vega added.
The cross-sectional design of the study relies on data collected at a single point in time. This prevents researchers from establishing the sequence of events.
“A key caveat is that these results are not causal. Our findings do not demonstrate that anxiety or depression directly accelerate brain aging,” Vega said. “Instead, they identify patterns of brain-aging vulnerability associated with psychiatric illness and cognitive performance.”
Tracking individuals over multiple years is necessary to determine if cognitive differences precede advanced brain aging or reflect the downstream consequences of an aging brain. Bidirectional influences are highly likely in these conditions.
“Longitudinal studies will be needed to determine how these relationships evolve over time and whether they predict future cognitive decline,” Vega explained. “The goal was to refine the interpretation of previous brain-age findings and pave the way to clinical research, not to claim a direct mechanism.”
The diagnostic classifications were derived from a combination of self-reported surveys and clinician-confirmed records. The available data lacked details regarding symptom severity, illness duration, and the age of onset. The researchers were unable to determine if the older brain ages were linked to more severe, chronic, or recurrent forms of mental illness. Residual misclassification or reporting bias might also introduce variability into the data.
The UK Biobank predominantly consists of White European participants who are often healthier than the general population. This demographic makeup limits how well these findings apply to more diverse groups worldwide. Environmental factors, cultural differences, and early-life stressors that influence brain aging were not fully captured in the dataset. Future research should prioritize replicating these findings in more ethnically diverse cohorts.
Future research will continue to explore the genetic and molecular factors that drive these localized brain changes.
“Our next step is to relate regional brain-age maps to other spatially organized biological data,” Vega said. “We are now integrating regional brain-age maps with transcriptomic, genetic, and cellular datasets to identify the biological pathways associated with vulnerability to psychiatric brain aging.”
By building a more comprehensive biological profile, the team aims to improve risk assessments for aging adults.
“Ultimately, we hope this work will improve biologically informed risk stratification, help identify individuals at greatest risk for later cognitive decline, and reveal biological systems that may become targets for future therapeutic interventions,” Vega concluded.
The study, “Cognitive performance modulates regional brain age differences in clinical anxiety and depression,” was authored by Owen M. Vega, Phoebe Imms, Nikhil N. Chaudhari, Wendy J. Mack, Nahian F. Chowdhury, and Andrei Irimia.
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DATE: August 8, 2026 at 09:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Brain scans reveal widespread structural and functional changes in patients following COVID-19 infection
A recent review of 49 brain imaging studies reveals that COVID-19 is associated with widespread structural and functional changes in the human brain. The findings indicate that the virus affects regions responsible for memory, emotion, and executive function, which may help explain the neurological symptoms many patients experience. The research was published in the journal Cerebral Cortex.
Following the initial outbreak of the coronavirus, many patients began reporting enduring neurological issues, such as brain fog, fatigue, and memory loss. A team of scientists led by researchers Li Chen, Huan Lan, and Wenxiong Liu synthesized data from existing medical studies to build a comprehensive picture of how the virus impacts the central nervous system. They focused on research utilizing magnetic resonance imaging, a technology that uses strong magnetic fields to generate detailed maps of the brain’s internal anatomy and activity.
To evaluate the extent of the damage, the researchers looked at different types of brain tissue. Gray matter consists of the brain’s neuron cell bodies, which process information, while white matter contains the nerve fibers that connect these processing centers. Functional imaging techniques measure blood flow or oxygen levels to track how different brain areas communicate in real time.
The research team conducted a systematic review, gathering 49 previously published studies that compared the brain scans of people who had contracted COVID-19 with those of healthy individuals. These studies included patients in various stages of the disease, ranging from acute infection to long-term recovery. Some of the individual studies were small, involving as few as 10 participants, while others evaluated more than 200 patients.
By pooling the results, the researchers identified consistent patterns of abnormalities across several key brain regions. The frontal lobe, which handles decision-making and cognitive control, frequently showed structural changes. The temporal and parietal lobes, areas involved in sensory processing and attention, also exhibited noticeable differences in patients who had recovered from the virus.
Structural changes in the frontal lobe might explain the loss of top-down cognitive control observed in some patients. The researchers note that the prefrontal cortex is highly sensitive to psychosocial stress. This means the stress of the pandemic itself, alongside the biological infection, might contribute to these structural shifts.
The temporal lobe includes areas like Heschl’s gyrus, a region that processes auditory information. Patients recovering from COVID-19 often experience difficulties processing sound, which can contribute to chronic fatigue as the brain works harder during daily listening activities. The parietal lobe, a region involved in visual and spatial attention, also showed structural changes linked to impaired attention allocation.
In many of these regions, COVID-19 patients displayed a reduction in overall gray matter volume and a thinning of the outer cortical layer. This tissue loss could result from reduced oxygen supply or severe immune system inflammation during the infection. Conversely, a few studies reported localized increases in gray matter volume. The researchers suggest this could represent temporary swelling caused by vascular injury, or it could be the brain’s attempt to compensate for damaged tissue by growing new neural connections.
The review highlighted extensive structural issues in the brain’s white matter. To measure this, scientists track the microscopic diffusion of water molecules along nerve fibers. In COVID-19 patients, this water movement was often abnormal, indicating that the protective coating around the nerve fibers had degraded. These microscopic changes were present even in patients who experienced only mild to moderate respiratory symptoms and had otherwise normal-looking gray matter on standard medical scans.
Functional imaging scans revealed altered patterns of spontaneous brain activity. When patients were simply resting in the scanner, their brains showed abnormal synchronization between different regions. The limbic system, a network of deep brain structures including the insula, hippocampus, and amygdala, frequently exhibited connectivity issues. Because these areas regulate emotion and memory, functional disruptions here often correlated with clinical symptoms like anxiety, depression, and post-traumatic stress.
When patients were asked to perform working memory tasks during their scans, their brains displayed altered activation patterns. This suggests that the nervous system had to reorganize its resources to maintain normal cognitive performance. Other functional scans focused on the olfactory network, the brain regions responsible for processing smell. Patients suffering from a persistent loss of smell showed disrupted connectivity in this specific network, which also correlated with lower scores on short-term verbal memory tests.
Tests measuring cerebral blood flow found lower than normal circulation in several brain areas, including the frontal and temporal lobes. This restricted blood supply limits the delivery of oxygen and nutrients, which might contribute to difficulties with attention, language processing, and executive function. The review found that deep subcortical nuclei, such as the thalamus, were particularly vulnerable to this reduced blood flow. These deep relay centers have high metabolic demands and rely on adjacent blood vessels that lack secondary backup circulation.
The reviewed studies rely heavily on cross-sectional data, meaning they capture a single snapshot of the brain after infection rather than tracking changes over an extended period. Because the vast majority of these studies lack pre-infection baseline brain scans, it is difficult to prove definitively that COVID-19 directly caused all the observed changes. Individual biological differences present before the pandemic might account for some of the variations in brain structure and function.
The researchers point out that the clinical status of the patients varied widely across the studies. Grouping together individuals with acute infections and those experiencing long-term recovery makes it challenging to isolate how the brain heals over time. Some studies also focused only on specific, predefined brain regions rather than scanning the entire brain. This targeted approach can artificially inflate the statistical differences between patients and healthy controls.
Future research utilizing unbiased, whole-brain analyses and long-term tracking could map the specific trajectory of these neurological changes. Additional follow-up investigations are required to clarify whether COVID-19-related brain alterations are permanent, or if they are reversible with therapeutic interventions and time.
The study, “Widespread structural and functional brain alterations in COVID-19: a systematic review of MRI studies,” was authored by Li Chen, Huan Lan, Wenxiong Liu, Chao Zuo, Graham J. Kemp, Song Wang, Qiyong Gong, and Xueling Suo.
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DATE: August 7, 2026 at 12:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Brain wiring patterns linked to harm avoidance in obsessive-compulsive disorder
Researchers have identified a specific pattern of brain wiring associated with the tendency to excessively avoid potential harm, a common trait in obsessive-compulsive disorder and other psychiatric conditions. The study, published in Neuropsychopharmacology, shows that denser microscopic connections between two distinct brain regions relate to higher levels of this avoidance behavior across different diagnostic groups. These structural brain differences offer a biological target for researchers developing future psychiatric treatments.
Obsessive-compulsive disorder involves intrusive thoughts and repetitive behaviors. Two primary dimensions often drive these symptoms. One is harm avoidance, an intense sensitivity to potential threats and an urge to prevent them. The other is incompleteness, a persistent feeling that things are imperfect or not quite right.
These traits are not exclusive to obsessive-compulsive disorder. Harm avoidance frequently occurs in anxiety disorders and post-traumatic stress disorder. Incompleteness is a hallmark symptom of obsessive-compulsive personality disorder, a distinct condition characterized by rigid perfectionism and a need for control.
Because these behavioral traits appear across various mental health conditions, researchers suspect they might share common biological roots in the brain. Previous brain imaging studies hinted at a relationship between the prefrontal cortex and the severity of these symptoms. The prefrontal cortex is the area of the brain that handles high-level cognitive functions, including risk assessment and emotional regulation.
University of Pittsburgh psychiatry researcher João Paulo Lima Santos led a team to investigate the brain architecture underlying these specific behavioral traits. Along with senior researchers Steven A. Rasmussen and Mary L. Phillips, Lima Santos aimed to replicate earlier findings in a new group of participants. The researchers also wanted to see if these brain patterns appear in people with other psychiatric diagnoses, pointing to a universal biological mechanism.
The study utilized diffusion magnetic resonance imaging, a specialized scanning technique. This technology allows researchers to map white matter in the brain. White matter consists of the insulated nerve fibers that act as communication cables, transmitting signals between different brain regions.
Through a computational process called whole-brain tractography, algorithms trace the path of water molecules as they diffuse along these nerve fibers. Because water moves more easily along the length of a fiber rather than across its boundaries, mapping this diffusion reveals the brain’s internal wiring diagram.
The research team focused on tracts connecting the prefrontal cortex to subcortical regions deep within the brain. Specifically, they looked at the thalamus, which acts as the brain’s central relay station for sensory and motor signals. They also examined connections to the striatum, a cluster of neurons involved in reward processing and habit formation.
To quantify the microscopic structure of these connections, the scientists measured fractional anisotropy. This metric indicates the density and directional alignment of white matter fibers. Higher fractional anisotropy suggests a denser, more structurally organized bundle of nerve connections.
The researchers recruited a diverse set of participants for their primary analysis. This included a small study group of 38 healthy controls and another group of 47 individuals diagnosed with obsessive-compulsive disorder. Participants completed specialized clinical questionnaires to measure their baseline levels of harm avoidance and incompleteness.
In their first statistical model, the researchers analyzed the healthy controls and the participants with obsessive-compulsive disorder. They found that higher fractional anisotropy in the connections between the dorsomedial prefrontal cortex and the thalamus related to higher levels of harm avoidance. This pattern appeared in both the left and right hemispheres of the brain.
The dorsomedial prefrontal cortex is heavily involved in evaluating situational demands and preparing responses to potential threats. A denser connection between this area and the thalamus might reflect an overactive system for perceiving danger. This heightened sensitivity could biologically drive the behavioral patterns of harm avoidance.
In the same group, the researchers also looked at the feeling of incompleteness. They observed that higher fractional anisotropy in the left dorsomedial prefrontal-thalamic connection was associated with higher incompleteness scores. The team found no statistical relationship between prefrontal-striatum connections and either symptom dimension.
The researchers looked at other secondary metrics of water diffusion in the brain, including radial diffusivity and axial diffusivity. These additional metrics yielded no statistical associations with the behavioral traits.
Next, the researchers expanded their analysis to test whether these structural associations exist outside of typical obsessive-compulsive disorder. They added a small group of 21 participants diagnosed with obsessive-compulsive personality disorder. In this expanded pool, the structural links to both harm avoidance and incompleteness remained the same.
The team then added another 20 participants who had non-obsessive-compulsive psychiatric conditions, such as panic disorder, social anxiety, and post-traumatic stress disorder. In this broader group, the association between both the left and right dorsomedial prefrontal-thalamic connections and harm avoidance persisted. The link to incompleteness was not statistically significant in this specific model.
To test the robustness of their findings, the scientists created a final, combined dataset. They merged their current participants with data from an older, original study pool containing 42 healthy controls and 44 people with obsessive-compulsive disorder. This created a much larger and more clinically diverse sample.
In this combined analysis, higher fractional anisotropy in the left dorsomedial prefrontal-thalamic connection once again tracked with higher levels of both harm avoidance and incompleteness. The connection in the right hemisphere lost its statistical association with harm avoidance in this expanded group.
Across all the different models, the connection in the left hemisphere consistently predicted the severity of harm avoidance. This persistence suggests that the left prefrontal-thalamic pathway might serve as a universal biological mechanism for threat sensitivity across different psychiatric populations. The right hemisphere connection appears more sensitive to the specific makeup or severity of the patient group.
The study design is observational and prevents researchers from determining cause and effect. It is unclear if denser white matter tracts cause heightened threat sensitivity or if a lifetime of hypervigilant behavior alters the brain’s physical structure.
The participant groups for the individual psychiatric conditions were relatively small. These small sample sizes limit the statistical power of the specific within-group analyses. Larger studies with greater variability in symptom severity are necessary to confirm these structural brain patterns.
The researchers used automated software to label the different regions of the brain. While standard in the neuroimaging field, this automated method might miss subtle anatomical differences between individual people. Future research could combine automated tools with individualized brain mapping for greater precision.
The neuroimaging protocol relied on single-shell diffusion magnetic resonance imaging. This older scanning method captures less microscopic detail than newer multi-shell techniques. More advanced imaging could provide a more nuanced picture of the specific white matter fiber segments involved in these psychiatric conditions.
While the researchers found that current psychiatric medications did not alter the results, the study did not track long-term medication use. Future longitudinal studies will need to monitor how pharmaceutical treatments might physically change these white matter connections over time.
The study, “Medial prefrontal-thalamic white matter microstructure is associated with harm avoidance in OCD: a discovery and transdiagnostic replication study,” was authored by João Paulo Lima Santos, Amelia Versace, Manan Arora, Michele A. Bertocci, Henry W. Chase, Simona Graur, Lisa Bonar, Chiara Maffei, Anastasia Yendiki, Christina L. Boisseau, Suzanne N. Haber, Steven A. Rasmussen, and Mary L. Phillips.
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DATE: August 6, 2026 at 12:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: ADHD medication helps children focus by stabilizing brain networks, new study suggests
URL: https://www.psypost.org/adhd-medication-helps-children-focus-by-stabilizing-brain-networks/
A small study shows that a common medication for attention deficit hyperactivity disorder helps children focus by stabilizing how different brain regions communicate with one another. The research, published in Translational Psychiatry, suggests that this brain stabilization is tied directly to better attention and task performance.
The brain constantly balances two opposing states, known as flexibility and stability. Flexibility allows a person to easily switch between different tasks or thoughts. Stability allows a person to ignore distractions and maintain focus on a single goal.
Children with attention deficit hyperactivity disorder, or ADHD, often struggle to maintain this balance. Their brain networks tend to change connections more rapidly. This frequent shifting can manifest as fluctuating attention, impulsive actions, and heightened sensitivity to rewards.
Foundational models of the disorder suggest that these symptoms arise from disruptions in specific neurological circuits. One circuit orients attention through executive control, while another tunes sensitivity to rewards through motivational control. Because these disruptions are widespread across multiple brain systems, looking at how the entire brain communicates is necessary to understand the condition.
Methylphenidate is a common first-line treatment for the disorder. The drug works by blocking the reuptake of dopamine and norepinephrine, which increases the levels of these chemical messengers in the brain. Dopamine and norepinephrine help regulate attention, executive control, and motivation.
The medication effectively reduces symptoms for many children, but up to 30 percent of patients do not experience improvements. A better understanding of how the drug alters brain function on a mechanical level is necessary to explain this variation in effectiveness.
Tehila Nugiel, a psychology researcher at Florida State University, led a team to investigate how methylphenidate influences the balance of brain flexibility and stability. The researchers suspected that the medication might reduce the rapid shifting of brain network connections, driving the brain into a more stable state.
Historically, researchers looked at brain connectivity by averaging activity over several minutes. Newer mathematical methods allow scientists to model how these networks reconfigure on a second-by-second basis. This high-resolution timeline is better suited for capturing the fleeting shifts in focus that characterize the disorder.
To test their hypothesis, the researchers designed a small study involving 31 children between the ages of 8 and 12 who had been diagnosed with ADHD. None of the participants had ever taken stimulant medication before.
Each child visited a laboratory for two separate brain scanning sessions, spaced about a week apart. One hour before entering the magnetic resonance imaging, or MRI, scanner, the children received either a single dose of methylphenidate or a placebo pill. Neither the researchers nor the children knew which pill was given on which day.
Inside the scanner, the children completed a standard test of sustained attention and impulse control. They viewed a series of sports balls on a screen and were instructed to press a button for certain balls and withhold their press for others. This tests a person’s ability to maintain focus without any external incentives.
After the standard version, the children completed a rewarded version of the same task. In this round, they saw feedback after each image, earning pennies for fast, correct responses and for correctly withholding a button press. The rewarded task tests how the brain adapts when performance is tied to an immediate, tangible benefit.
While the children completed these tasks, the researchers recorded their brain activity. Functional MRI tracks blood oxygen changes in the brain, allowing scientists to see which areas are communicating at any given moment. The researchers calculated whole brain flexibility, which measures how frequently different regions of the brain change their functional connections over short timescales.
The researchers also tracked behavioral performance during the scanning sessions. They measured response time variability, which indicates fluctuations in attention, and overall task accuracy.
When the children took methylphenidate, their whole brain flexibility decreased during both tasks. The connection patterns between different brain regions became more stable and persisted for longer periods of time.
This stabilization in the brain matched improvements in behavior. On the medication, the children displayed steadier attention, meaning their response times were less erratic on both tasks. They also achieved higher overall accuracy during the rewarded task.
To understand how the drug affected each child personally, the researchers compared the change in brain activity to the change in test scores. They found a direct relationship between the neural changes and the behavioral improvements. The individuals who experienced the largest decreases in brain flexibility on the medication also showed the greatest improvements in steady attention and accuracy.
These findings provide a biological explanation for how the medication aids cognition. By stabilizing whole brain network dynamics, the drug appears to reduce the neurological noise that often disrupts focus.
There are a few caveats to consider regarding the study design. The experiment involved a single dose of medication given to children who had never taken stimulants. Chronic use of the drug over months or years might alter brain network dynamics differently than an acute dose.
Additionally, the tests performed inside an MRI scanner isolate very specific cognitive processes. These controlled tasks do not perfectly mimic the complicated, distracting environments that children navigate in their daily lives.
The results also highlight notable individual differences among the participants. While the medication stabilized the brain and improved performance for most of the children, a small subset experienced the opposite effect. For these children, the drug increased brain flexibility and led to poorer task performance.
This variation offers a potential clue as to why stimulants fail to reduce symptoms in some individuals. Future research involving larger groups of participants could help scientists predict which patients will benefit from the medication and which might respond better to alternative treatments.
The study, “Methylphenidate stabilizes dynamic brain network organization during tasks probing attention and reward processing in stimulant-naïve children with ADHD,” was authored by Tehila Nugiel, Nicholas D. Fogleman, Monica G. Lyons, Margaret A. Sheridan, and Jessica R. Cohen.
URL: https://www.psypost.org/adhd-medication-helps-children-focus-by-stabilizing-brain-networks/
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DATE: August 4, 2026 at 09:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Inflammation corresponds to altered brain wiring in borderline personality disorder
People with borderline personality disorder may experience higher levels of immune system inflammation that relate to structural differences in the brain’s wiring. A recent small study found that individuals with the condition showed reduced integrity in specific brain networks alongside elevated inflammatory markers. Understanding this biological relationship could offer new ways to view the physical mechanisms behind emotional dysregulation. The research was published in the journal Psychoneuroendocrinology.
Borderline personality disorder is a psychiatric condition characterized by intense emotional instability, impulsivity, and difficulties in interpersonal relationships. Historically, psychological trauma was viewed as the primary origin of the condition. Researchers are increasingly investigating the underlying biological and neurological factors that might accompany these symptoms. The latest research looks beyond outward behavior to map the physical architecture of the central nervous system.
White matter is the brain’s internal communication network. It consists of long nerve fibers that connect different regions of the brain, allowing them to share information. Myelin, the protective coating around these fibers, acts like insulation on an electrical cable to keep neural signals moving efficiently. When the microscopic structure of white matter is altered, it can disrupt how different areas of the brain regulate emotions and process incoming information.
Simultaneously, researchers have observed that people with various psychiatric conditions often exhibit low-grade systemic inflammation. The immune system releases proteins called cytokines to signal inflammation throughout the body. There is growing interest in how these circulating inflammatory proteins might interact with the physical structure of the brain. Chronic immune activation is thought to influence how the brain develops and maintains its cellular architecture over time.
Piotr Podwalski, a researcher at Pomeranian Medical University in Poland, and his colleagues designed a study to explore these overlapping systems. They wanted to investigate whether people with borderline personality disorder showed measurable differences in white matter and immune markers compared to healthy individuals. They also sought to determine if higher levels of inflammation correspond to reduced white matter integrity in the patient group. Understanding these overlapping systems could eventually lead to new medical interventions that target the immune system to help manage psychological symptoms.
To conduct the small study, the research team recruited 40 women diagnosed with borderline personality disorder and 37 healthy women of similar ages. The researchers restricted the participant pool to females to reduce biological and clinical variations, as men and women often express symptoms of the disorder differently. The participants underwent clinical assessments and provided blood samples in the morning after fasting.
The researchers analyzed the blood samples for specific inflammatory biomarkers, including interleukin-6 and C-reactive protein. When the body encounters stress or infection, immune cells release interleukin-6, which then prompts the liver to produce C-reactive protein. Chronic elevation of these proteins indicates a persistent state of low-grade inflammation. This ongoing immune response can negatively impact healthy tissues, including the delicate architecture of the nervous system.
The researchers then used a specialized type of magnetic resonance imaging to scan the participants’ brains. This imaging technique tracks how water molecules diffuse through brain tissue. In an unrestricted environment, water molecules move randomly in all directions. Inside the brain’s white matter, water diffuses primarily along the length of the nerve fibers.
By tracking this directional movement, scientists can calculate a metric known as fractional anisotropy. Lower scores on this metric suggest that the microscopic organization of the nerve fibers has been disrupted or damaged. The researchers used this calculation to map out the integrity of major fiber bundles throughout the brain.
When comparing the two groups, the researchers initially found elevated levels of interleukin-6 and C-reactive protein in the participants with borderline personality disorder. The initial results indicated a heightened immune response in this clinical group. However, when the researchers adjusted their statistical models to account for body mass index and smoking habits, the differences in inflammation between the two groups were not statistically significant.
The brain imaging analysis revealed distinct structural differences regardless of lifestyle factors. The participants with borderline personality disorder displayed reduced white matter integrity in two specific pathways in the left hemisphere of the brain. These pathways are known as the superior longitudinal fasciculus and the superior thalamic radiation. Both of these neural pathways are highly active during complex cognitive tasks.
The superior longitudinal fasciculus is a long bundle of nerve fibers that connects the front of the brain to regions in the back. This specific pathway is heavily involved in language processing, memory, and the regulation of emotions. The superior thalamic radiation is another fiber bundle that links a deep brain relay center to the outer cortex. Disruptions in these pathways can impair the brain’s ability to filter sensory information and exert control over emotional responses.
The research team then combined the blood test data with the brain imaging results to look for specific relationships. They discovered an inverse correlation between the inflammatory markers and the structural integrity of the superior longitudinal fasciculus. Participants who had higher levels of circulating inflammation generally exhibited lower structural integrity in this specific brain network.
The research design relied on a single snapshot in time, which limits how the results can be interpreted. It is not possible to determine if elevated inflammation directly causes the observed alterations in brain structure. An alternative explanation is that structural brain differences and psychological distress trigger an inflammatory response in the body.
The study sample consisted entirely of women, meaning the results may not apply to men with borderline personality disorder. The two groups of participants also differed in their average body mass index, smoking habits, and years of education. While the researchers used statistical techniques to adjust for these variables, lifestyle factors are known to heavily influence both immune function and brain health over time.
Future investigations will need to track participants over several years to observe how inflammatory markers and brain structures change together. Tracking these biological measures across different developmental stages could map the sequence of events in the brain. Researchers may also incorporate more diverse groups of participants to see if these patterns hold true across the broader population.
The study, “Inflammatory biomarkers and white matter microstructure in borderline personality disorder: A cross-sectional study,” was authored by Piotr Podwalski, Bartosz Dawidowski, Kamil Lipiński, Łukasz Franczak, Patryk Wysocki, Marcin Jabłoński, Krzysztof Wietrzyński, Piotr Plichta, Ernest Tyburski, Łukasz Zwarzany, Andrea Amerio, Błażej Misiak, Wojciech Poncyljusz, and Jerzy Samochowiec.
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DATE: July 27, 2026 at 12:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Brain structure differences in autism map onto serotonin receptor locations
URL: https://www.psypost.org/brain-structure-differences-in-autism-map-onto-serotonin-receptor-locations/
A neuroimaging study found that autistic individuals whose cortical thickness deviated most from that of neurotypical peers tended to experience greater social and communication difficulties. In autistic individuals, the cortical regions showing the greatest differences in thickness relative to neurotypical individuals also tended to have a higher density of serotonin receptors. The research provides evidence linking brain structure, neurochemistry, and behavioral traits in autism. The paper was published in Autism Research.
Autism is a neurodevelopmental condition that affects how a person communicates, interacts with others, processes sensory information, and experiences the world. It is called a spectrum because its characteristics and level of support needs vary widely between individuals.
Some autistic people have difficulty interpreting social cues, maintaining conversations, or understanding unwritten social rules. Others are able to communicate fluently but still find social interaction tiring, confusing, or overwhelming. Repetitive movements, strong preferences for routines, intense interests, and unusual responses to sounds, lights, textures, or smells are also common.
Autism begins early in development, although it may not be recognized until later in childhood or adulthood. It is a lifelong form of neurological difference that may bring both difficulties and strengths. Many autistic people show beneficial qualities such as strong attention to detail, deep knowledge in areas of interest, logical thinking, creativity, or exceptional memory.
Study author Livio Tarchi and his colleagues note that previous research indicates consistent structural differences between the brains of individuals with autism and their neurotypical peers. The authors suggest these differences might be connected to the brain’s neurotransmitter systems. Neurotransmitters are chemical messengers that carry signals between brain cells. The researchers specifically focused on systems using serotonin, dopamine, and glutamate.
The scientists investigated how structural differences in the brain might map onto the spatial distribution of these chemical messengers. They analyzed data from the Autism Brain Imaging Data Exchange. This public dataset contains physical measurements, behavioral assessments, and brain scans collected across twenty different sites.
The data used in this analysis came from 1,035 participants. The sample included 505 autistic individuals and 530 neurotypical individuals. The average age of participants was about 17 years old. Both groups were predominantly male, reflecting historical diagnosis patterns.
The study authors used structural magnetic resonance imaging (MRI) data to calculate deviations from expected cortical thickness. Cortical thickness refers to the depth of the brain’s outer layer of gray matter, which is responsible for complex thought and sensory processing. They measured this thickness across thousands of individual points, called vertices, on the surface of the brain. These measurements were adjusted for both the sex and age of each participant.
Next, the researchers compared these structural measurements against reference maps of neurotransmitter receptor density. Receptors are protein structures on cells that receive chemical messages. These reference maps were derived from separate, previously published imaging studies. This allowed the authors to see if areas with unusual thickness in autistic individuals aligned with regions known to have high concentrations of specific neurotransmitters.
The results showed widespread deviations in cortical thickness in the brains of autistic participants compared to neurotypical participants. The structural differences tended to be larger in areas of the brain with a higher density of serotonin receptors. The researchers did not find a similar spatial link for dopamine or glutamate receptors. At the individual level, greater deviations in cortical thickness were associated with greater difficulties in social and communication domains.
These findings provide evidence for a neurobiological link between autism, brain structure, and serotonin. However, the participants in this study were mostly male. Because brain development and cortical thickness can vary by sex, studies involving more female participants might yield different results. Future research could help clarify these connections and guide tailored support strategies for autistic individuals.
The paper, “Autism and Cortical Thickness Deviation From Neurotypical Controls: Evidence for a Spatial Association With Serotonin Receptors,” was authored by Livio Tarchi, Arne Doose, Julius Hennig, Fabio Bernardoni, Joseph A. King, Tiziana Pisano, Giovanni Castellini, Valdo Ricca, Inge Kamp-Becker, and Stefan Ehrlich.
URL: https://www.psypost.org/brain-structure-differences-in-autism-map-onto-serotonin-receptor-locations/
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DATE: July 24, 2026 at 08:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Learning a new skill triggers both temporary cell swelling and lasting structural growth in the human brain
A new study published in PLoS Biology has found that learning a new motor skill sets off two different types of cellular changes in the human brain. The findings suggest that the brain experiences a temporary swelling of cell bodies followed by a long-lasting growth of cellular extensions in specific regions. This dual response offers a deeper understanding of how the human brain physically adapts when we learn something new.
Neuroplasticity refers to the brain’s ability to remodel its physical structure in response to new experiences. This biological process supports learning and memory, and it also influences a person’s vulnerability to neurological conditions.
Valeria Della-Maggiore, an associate professor at the National University of San Martin and the University of Buenos Aires, led the research. She also serves as an adjunct professor at McGill University and directs the Physiology of Action Lab.
“Structural plasticity, the brain’s ability to remodel its connections in response to experience, is fundamental to learning and memory and shapes development and degenerative disorders,” she told PsyPost. She explained that most human studies over the past two decades have used standard MRI protocols to detect changes in brain microstructure, assuming these changes were always plastic in nature.
“Yet animal studies show that cells may undergo structural changes that do not always reflect synapse remodeling,” Della-Maggiore said. “To disambiguate plastic from non-plastic processes, we combined ultra-high-gradient diffusion MRI with SANDI, a biophysical model that allows making inferences at the level of cellular compartments, that is, cell bodies and cell processes.”
To measure structural changes in humans, scientists have typically relied on a brain scanning technique called diffusion tensor imaging, or DTI. This method measures how water molecules move and diffuse through brain tissue. By tracking this water movement, scientists can infer changes in the brain’s microscopic structure.
DTI blends the signals from various parts of the brain tissue together. “DTI captures a single, global signal: it can tell you that a change in one region lasts longer than in another, but not what underlies it,” Della-Maggiore said. Because of this blending, the technique cannot easily distinguish between a permanent structural change and a temporary biological reaction.
To address this limitation, the authors utilized highly sensitive magnetic resonance imaging paired with the specialized mathematical model called Soma and Neurite Density Imaging, or SANDI. Rather than grouping all tissue signals together, SANDI separates the scanning signals into three distinct categories. These categories include the cell bodies, the cellular extensions called neurites, and the extracellular fluid surrounding the cells.
“This study was only possible through a genuinely multidisciplinary effort, in which neuroscientists, experts in diffusion MRI, mathematicians and modeling specialists, and engineers worked together around a single scientific question,” Della-Maggiore said.
The collaboration included her lab along with the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital, and the Cardiff University Brain Research Imaging Centre. “Bringing these different forms of expertise into alignment is what made it possible to extract biological insight from a non-invasive measurement, something no single discipline could have achieved on its own,” she added.
The study included 29 healthy adults between the ages of 18 and 36, consisting of 16 females and 13 males. All participants were right-handed and reported no history of neurological or psychiatric conditions. They completed a motor sequence learning task involving typing a specific five-number sequence on a keyboard using the four fingers of their left, non-dominant hand. The exact sequence was 4-1-3-2-4, with the number 4 representing the index finger and the number 1 representing the pinky finger.
Participants were instructed to type the sequence as quickly and accurately as possible. They completed 15 practice blocks of this finger-tapping sequence. Each block consisted of 12 sequences and was separated by 25 seconds of rest. The entire training session took about 15 to 20 minutes.
To assess how well the participants retained the skill overnight, they were asked to complete eight additional practice blocks 24 hours later. To track brain activity and physical changes, the scientists used an ultra-high-gradient MRI scanner, which offers exceptional sensitivity for capturing microscopic tissue details. They collected functional MRI scans to measure active brain regions during the task. They also collected advanced diffusion MRI scans at three specific points: before the practice session began, 30 minutes after the practice ended, and 24 hours later.
The behavioral data showed that participants improved their typing speed and accuracy primarily during the short rest periods between practice blocks. The functional brain scans aligned with this observation, revealing increased activity in the brain’s memory and motor regions during these brief breaks. However, the most specific discoveries emerged from the SANDI model used to track cellular changes.
“When you learn a new skill, two processes of different spatial and temporal dynamics take place in your brain at the cellular level,” Della-Maggiore said. “One is transient and occurs at the level of cell bodies, which increase in size across all brain regions engaged by the task. The other is persistent, confined to the regions specifically involved in learning, and occurs at the level of cell processes, compatible with structural plasticity.”
The researchers found that DTI scans alone missed a layer of detail. “Our approach revealed something DTI cannot see, that the regions showing lasting changes also carry a transient response,” Della-Maggiore explained. “In other words, beneath what DTI reads as a single persistent effect, there are in fact two distinct processes unfolding on different timescales.”
Specifically, the researchers found a temporary increase in the apparent density of cell bodies across all the brain areas engaged by the task. These areas included the hippocampus, the primary motor cortex, the posterior parietal cortex, and the precuneus. This physical change was observed 30 minutes after the practice session. By the 24-hour mark, the cell bodies in these regions had returned to their normal baseline size.
“The second [surprise] was the spatial pattern: a transient change at the level of the cell body appeared uniformly across all regions engaged by learning, whereas the sustained change in cellular processes was confined to those regions specific to the learned skill,” Della-Maggiore said. “It was this dissociation, in both space and time, that let us infer different biological processes underlying these responses: a homeostatic process such as swelling of cell bodies induced by increased neuronal activity, and cell-process remodeling mediating genuine structural plasticity.”
The authors propose that this short-lived cell expansion is a temporary biological reaction to balance out intense cellular activity. When brain cells are highly active, they experience an imbalance of ions. To correct this imbalance, water flows into the cells, causing them to temporarily swell.
In addition to the temporary swelling, the SANDI model revealed a second, longer-lasting change in specific areas of the brain. The researchers observed a sustained increase in the density of cellular extensions in the precuneus and the posterior parietal cortex. These cellular extensions include structures like dendrites and axons, which connect different brain cells to one another.
This increase in cellular extensions persisted a full day after the learning task. The researchers noticed a direct link to task performance. “Notably, the more a person improved, the stronger this second change was,” Della-Maggiore said.
Interestingly, this long-lasting structural remodeling did not occur in the hippocampus. The hippocampus is a brain region known for helping encode new memories early in the learning process. The findings suggest that while the hippocampus is engaged initially, the long-term structural changes required to retain a motor skill happen in the outer layers of the brain, known as the cortex.
“The broader message is that a change in brain structure is not, in itself, evidence of learning-related plasticity,” Della-Maggiore said. “Being able to separate these processes in a living brain, non-invasively, provides something that did not exist before in human neuroscience: a mechanistic window onto brain plasticity, allowing us to begin inferring biological mechanisms directly in humans rather than relying on animal models.”
Interpreting these findings requires acknowledging a few limitations related to the scanning technology. The SANDI model estimates relative signal fractions of cell components rather than providing a direct physical measurement of cellular volume. The technique relies on specific mathematical assumptions about how water moves in the brain.
“Our approach does not quantify cells or cell processes directly,” Della-Maggiore explained. “It infers how much different cellular components contribute to the MRI signal, based on a biophysical model whose interpretation is grounded in animal and histological evidence.”
She added that referring to changes in cell bodies or cell processes involves principled inferences, not microscopic observations. “The strength of the method lies in tracking how these signals evolve over time, compared against the person’s own baseline,” she said.
The study focused on a specific finger-tapping task in a small group of healthy young adults. Different types of learning, such as studying a new language or solving complex math problems, might engage different cellular mechanisms. “Our broader aim is to keep refining this approach to probe the biological mechanisms of plasticity in ever greater detail, directly in humans,” Della-Maggiore said.
The researchers hope to apply this multi-compartment imaging approach to other areas of neuroscience. “Beyond learning, distinguishing genuine, adaptive remodeling from other processes could prove valuable in contexts such as development, aging, and disease, including conditions like neurodegeneration or neuroinflammation, where telling apart healthy from harmful structural change is both difficult and clinically important,” she said.
“The results move the field beyond descriptive diffusion changes toward mechanistic inference, which is particularly valuable for studies of learning, development, and disease,” Della-Maggiore concluded.
The study, “Learning engages transient and sustained cellular mechanisms in the human brain,” was authored by Guillermina Griffa, Marco Palombo, Abraham Yeffal, Hong-Hsi Lee, Agustin Solano, Susie Y. Huang, and Valeria Della-Maggiore.
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #BrainPlasticity #Neurobiology #LearningAndMemory #Neuroimaging #SANDI #DTI #MotorSkillLearning #DiffusionMRI #Hippocampus #Cortex
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On the Emergence of Neuroforecasting
Knowledge in the neurosciences, theory and methodology, is increasingly applied to improve and deepen our understanding of consumer decision processes, affect and cognition, and behaviours, in a young field known as consumer neuroscience; practical implementation of this knowledge on consumers in marketing management is known as neuromarketing. Relevant specialisitations in the neurosciences include neuropsychology, neuroeconomics, and neurobiology. The research has largely focused on […]https://consumergateway.org/2026/07/13/on-the-emergence-of-neuroforecasting/
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NeuroVFM trains a medical AI on 5 million uncurated brain scans without human labels
https://1ban.news/neurovfm-neuroimaging-foundation-model-vol-jepa/
#1ban #neurovfm #neuroimaging #foundation #model #tech -
A neuroimaging study revealed that a small subpopulation of individuals with schizophrenia who have a history of severe physical violence display heightened brain activity when anticipating punishment, rather than when receiving a reward or an actual punishment.
#Neuroscience #Psychiatry #Neuroimaging #Schizophrenia #sflorg
https://www.sflorg.com/2026/07/ns07062601.html -
Neuroimaging-derived biotypes of self-reported insomnia symptoms are differentially associated with affective symptoms
https://1ban.news/neuroimaging-biotypes-insomnia-affective-risk/
#1ban #neuroimaging #biotypes #insomnia #affective #sleep -
ggseg now draws brains without sf.
It installs without needing compiled GDAL, GEOS, or PROJ — so it runs on locked-down laptops, HPC clusters, and in the browser (webR/shinylive). Same geom_brain(), identical figures.
How & why 👉
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At booth #39-40 today, Artinis & @NIRx Medical Technologies are demoing #fNIRS + #TMS, a powerful combination for brain stimulation research that gives you both the trigger and the response. Come, join us at 1 PM!
Visit booth #44 for:
🔹 APEX EEG + Brite fNIRS
🔹 SAGA EEG + NIRSport2 demos!https://www.artinis-nirx.com/ohbm-2026-bordeaux #🧠 #OHBM2026 #BrainStimulation #Neuroscience #Neuroimaging
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🧠 Could the brain reveal vision loss more accurately than traditional eye tests?
🔗 Using Steady-State Visual Evoked Potentials to Characterize Wide-Ranging Retinopathy Linked to CRB1: Implications for Clinical Trials. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0042
📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj
#Neuroscience #Ophthalmology #RetinalDiseases #GeneTherapy #EEG #BrainResearch #DigitalHealth #PrecisionMedicine #Neuroimaging
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Cerebellar flatmaps now in #ggsegverse!
We have now shipped across the ggsegverse the possibility to visualise the cerebellum parcellations also, based on the SUIT flatmap from the Diedriksen lab.
Read more about it https://ggsegverse.github.io/news/cerebellar-support/
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Call for Papers: Human Neuroimaging Education Special Issue
Share training programs, open resources, AI in education and more.
Deadline: 31 Aug 2026
Guidelines: https://apertureneuro.org/for-authors
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#fMRT, macht die #Hirnaktivität sichtbar, doch die Interpretation wird häufig hinterfragt. Auch Forschende der @FAU ermittelten in einer #Studie eine Diskrepanz. Die Autoren sprechen jedoch nicht von Kritik an der Methode, sondern von #Erkenntnisgewinn für die fMRT-Bildgebung, wobei ein zweiter Blick neues offenbart...
Interessiert an mehr? Den #HintergrundArtikel von Larissa Tetsch findet ihr hier: https://www.laborjournal.de/editorials/3465.php
#Laborjournal #LifeSci #Neuroimaging #Neuroscience #Hirnforschung
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Thank you for the incredible response to our recent #fNIRS Introduction Courses! Our next stop is #Sydney 🇦🇺
📅 Friday, April 24
⏰ 10 AM – 5 PM
📍 University of Sydney, Camperdown/Darlington CampusJoin us for a full day of hands-on learning and expert insights. Spots are limited 👉 https://zurl.co/HYf2l
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Mind-bending visualization of brain spirals sweeping across the cortex — a stunning re-creation from Gong et al. Watch neural waves come alive and rethink how activity travels through the brain. Perfect for neuroscience lovers and visual explorers! #neuroscience #brain #neuroimaging #visualization #science #research #neuro #English
https://video.davidsterry.com/videos/watch/f4613fb1-0c22-4fb2-b95d-2505e5125204 -
New ggsegverse update!
I finally got around to pre-release new ggseg.extra (notice name change) package, for creating new atlases.
https://ggsegverse.github.io/news/ggseg.extra/
It's full of new features, and likely lots of new bugs.
I'd love folks to test how it works, I've really tried making things more robust and I hope its payed off! -
neuromapr 0.2.1 has been accepted and published on CRAN!
Very excited to get this out to users in the simplest way possible, and hope the #rstats #neuroimaging community finds it useful!
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🧠 From setup to real-time decoding: how fNIRS-BCIs actually work. In Part 1 of our #fNIRS #BCI: Methodology and (clinical) application possibilities" webinar series, Dr. Bettina Sorger from Maastricht University & Dr. Franziska Klein from OFFIS guide you through system setup, experimental design, and the fundamentals of online analysis.
They also discuss the strengths & limitations of fNIRS compared to other BCI modalities.
▶️ https://zurl.co/pemtc
#Neuroscience #Neuroimaging