#neuroimaging — Public Fediverse posts
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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 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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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #harmavoidance #OCD #prefrontalthalamicconnection #white matter #diffusionMRI #neuroimaging #mentalhealthresearch #transdiagnostic #dorsomedialPFC #brainwiring
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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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Probably one of the works I am mostly proud from my team against #brain #tumor! Out now on Nature Communication bio:
https://www.nature.com/articles/s42003-024-06119-3
Practically we predict via simple #machinelearning brain rewiring after #surgery and recovery, hopefully allowing brainsurgeons better planning to reduce aphasia and motor deficits. Moreover we investigated whether the functional signal inside oedema and other cancer tissues has a meaning.
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"Get a new perspective on EEG: Convolutional neural network encoders for parametric t-SNE."
https://www.biorxiv.org/content/10.1101/2022.12.08.519691v1#Neuroscience #Neuro #Brain #Neuroimaging #EEG #DimensionalityReduction #tSNE #DeepLearning #NeuralNetworks