#neuroimaging — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #neuroimaging, aggregated by home.social.
-
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.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#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
-
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/
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#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
-
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/
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#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
-
🧠 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
-
🟡 Special Launch Event at the Artinis #Hyperscanning Summer School!
Monday, 23rd of June
3:30–5:00 PM CEST
Demonstration by David Zijderveld (@artinis Medical Systems )
🧠 Using Brite Ultra to Study Large Group Interactions 🧠Be part of the exclusive live demo of the brand-new Brite Ultra —see it in action for the first time!
Don’t miss this one! 👉 https://events.teams.microsoft.com/event/cae257a8-5e2f-4143-8ec7-0a7003ea78fc@59671240-e400-45e0-802e-35aa686f7c52 -
📣 Unique #job #opportunity: The #MPIEA invites applications for a #W2 (#tenure track) #Faculty Member (m/f/d) in the field of Imaging #Neuroscience, with a specialization in high-field (7T) #neuroimaging. Find out more at ae.mpg.de/jobs.