#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 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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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #BrainAging #AnxietyDepression #CognitivePerformance #RegionalBrainAge #Neuroimaging #MentalHealthBiology #BrainAgeGap #CognitionAndBrain #Transcriptomics #BiomarkersInMentalHealth
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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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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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Neurophotonics gets a cool new cover! Inside, depth-resolved fiber photometry @ferruccio-phc.bsky.social and much more!
#Neurophotonics #Neuroscience #Neuroimaging #BrainImaging #OpticalImaging #Microscopy
#studyBRAIN -
Chris Xu talks to Neurophotonics about the 'toy land of optics' and how to channel positive vibes! Thank you Tianyu Wang for conducting this fantastic interview!
https://www.spiedigitallibrary.org/journals/neurophotonics/volume-12/issue-03/030402/Exploring-the-depths-of-light--a-conversation-with-Professor/10.1117/1.NPh.12.3.030402.full
#Neurophotonics #Neuroscience #Neuroimaging #BrainImaging #OpticalImaging #Microscopy -
Neurophotonics' Special Section on Voltage Imaging is open for submissions! Edited by Amanda Foust, Kai Wang, François St-Pierre and Ahmed Abdelfattah
https://spiedigitallibrary.org/journals/neurophotonics/call-for-papers#voltageimaging
#Neurophotonics #Neuroscience #OpticalImaging #Microscopy #Neuroimaging #BrainImaging -
Unraveling the Secrets of Brain Chemistry: The Dance of Serotonin and Beyond!
#BrainChemistry #Serotonin #MentalHealth #Neuroimaging #GABA #BioactivePeptides #Genetics #WellBeing #Neuroscience #MoodBoost #ScienceExplained #SerotoninTransporter #Mindfulness #AnxietyRelief #HealthAndWellness
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Hello world ! I am a Developmental Cognitive Neuroscientist #biology #psychology #neuroscience & Associate Professor at the #university of #caen #france
I Research #neonate and #infant #brain #development and #neurodevelopmentaldisorders using #neuroimaging #eeg #fnirs #mri with an emphasis on #somatosensation and #sensoryprocessing
I work with my awesome #decoderesearch team at the #comete1075 lab, with the help of strong coffee, loud music, and cats.
My conversational abilities include #science #babies horse riding and vintage sci-fi
#introduction #introductions #neurodevelopment #cognitivedevelopment