#neuroimaging — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #neuroimaging, aggregated by home.social.
-
DATE: September 11, 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 scans reveal how recurrent depression leaves a lasting mark on the amygdala
Brain scans reveal that elevated activity in the emotion-processing center is tied to a person’s history of recurrent depression, rather than their current mood. The large study, published in Psychological Medicine, suggests that each major depressive episode may leave a lasting biological mark that increases future vulnerability to the disease.
Major depressive disorder is a common psychiatric condition that affects millions of people worldwide. Currently, psychiatrists diagnose the condition based on clinical interviews and patient history. There are no biological markers, like a blood test or a brain scan, to guide treatment choices.
Functional magnetic resonance imaging, or fMRI, allows researchers to observe the brain in action by tracking blood-oxygen levels. When a brain region becomes active, it requires more oxygen, leading to localized changes in blood flow. The scanner detects these magnetic differences to map out neural activity.
Scientists often use this technology to study the amygdala, an almond-shaped structure deep inside the brain that processes fear and negative emotions. Early brain imaging research suggested that people with clinical depression have hyperactive amygdalae when looking at negative images. But a recent analysis of a massive dataset called the UK Biobank found no association between amygdala activity and current depression symptoms.
Jerke J. van den Berg, a biomedical researcher at the University of Amsterdam, and his colleagues designed a new study to better understand this discrepancy. They suspected that previous research might have missed the broader picture by only looking at a patient’s current mood. The researchers focused on a concept called the kindling theory.
The kindling theory proposes that an initial depressive episode makes the brain more sensitive to stress. After the brain has been sensitized by that first experience, it takes progressively less trauma to trigger a relapse. To test if the amygdala reflects this effect, the research team decided to look at a person’s lifetime history of depression, known as a trait, rather than their current symptoms, known as a state.
The researchers utilized data from the UK Biobank, a long-term population health study. They analyzed functional MRI scans from a subset of participants, totaling more than 11,000 individuals. While inside the scanner, participants completed a visual exercise called the Hariri task.
During the task, participants were shown a target image of an angry or fearful face at the top of a screen. They were then asked to select the matching face from two options at the bottom. This specific visual matching exercise is known to reliably stimulate the amygdala.
Brain activity can vary widely from person to person based on age, gender, and head movement during a scan. To account for this natural variation, the research team used a statistical technique called normative modeling. They analyzed scans from over 6,400 healthy participants to establish a baseline of expected amygdala activity. This works much like a pediatric growth chart, which maps out normal ranges for a child’s height and weight.
Next, the researchers evaluated how much the brain activity of nearly 5,000 other participants deviated from this baseline model. They categorized these individuals based on their self-reported mental health histories. The groups included healthy controls, people who had experienced a single depressive episode, those with moderate recurrence involving two to five episodes, and those with a high recurrence of six or more episodes.
For this initial cross-sectional analysis, the team focused strictly on participants who were currently in remission from their depression. The initial results were not statistically significant when the team analyzed the unaltered brain scans. However, once they applied the normative modeling technique to account for age and gender variations, a distinct pattern emerged.
The analysis revealed a measurable association between an individual’s history of depression and their amygdala response. Participants with a high recurrence of depressive episodes showed a heightened amygdala reaction to negative faces compared to healthy controls.
When the researchers looked at individuals actively experiencing a depressive episode, they found no distinct increase in brain activity compared to controls. This suggested that amygdala reactivity represents a long-term biological trait, rather than a temporary state reflecting current mood.
The researchers also wanted to know how medication might influence these brain signals. They noticed that a higher percentage of people in the severe recurrence group were taking antidepressants compared to those with a single past episode. They repeated their cross-sectional analysis, this time removing any participants who were actively taking antidepressant medications.
Excluding medicated individuals strengthened the observed differences between the healthy controls and the recurrent depression groups. The findings indicated that antidepressants might dampen the hyperactive amygdala signal associated with a history of recurrent depression. Because the medication reduced amygdala reactivity, including these participants in the initial data pool slightly masked the true extent of the brain changes.
To see how the brain changes over time, the team conducted a longitudinal analysis. They focused on a smaller group of participants who returned for a second brain scan roughly two and a half years after their initial visit. The researchers categorized these individuals based on whether they had suffered new depressive episodes between the two scans.
For this longitudinal evaluation, the team specifically analyzed people who were in remission during both of their imaging sessions. Participants who began the study with a history of just one depressive episode, but then experienced multiple new episodes before their second scan, exhibited an increase in amygdala reactivity over time.
This brain change supported the kindling theory. It suggests that new depressive episodes incrementally alter how the brain processes negative emotional information, leaving a biological mark even after symptoms fade.
The study relied on a large dataset, but the researchers noted that the effect sizes were relatively small. These findings do not mean that a functional MRI scan can be used to diagnose depression in a clinical setting right now. The results are not robust enough to predict an individual’s exact risk of a relapse based on a single brain scan.
The data collection methods also presented certain limitations. The study depended on participants accurately recalling their own mental health histories, which can introduce memory biases. People might misremember exactly how many distinct depressive episodes they experienced over the course of their lives.
The mental health questionnaires also combined treatments for nerves, anxiety, and depression into a single metric. Because of this, the researchers could not strictly isolate the effects of anxiety disorders from the effects of clinical depression. Future studies will need to track larger groups of symptomatic individuals over extended periods of time to untangle these variables.
Scientists hope that advancing neuroimaging techniques will eventually reduce the normal fluctuations seen in brain scans. Over time, mapping the biology of recurrent depression could help psychiatrists tailor treatments to a patient’s individual history, moving away from the current trial-and-error approach to prescribing medication.
The study, “Normative amygdala fMRI response during emotional processing as a trait of depressive symptoms in the UK Biobank,” was authored by Jerke J. van den Berg, Henricus G. Ruhé, Henk A. Marquering, Liesbeth Reneman, and Matthan W. A. Caan.
-------------------------------------------------
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 #DepressionResearch #Amygdala #fMRI #Neuroimaging #KindlingTheory #UKBiobank #MentalHealthAwareness #BiomarkersInDepression #LongitudinalStudy #NeuroscienceAdvances
-
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 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 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 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 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
-
Multilayer Network Neuroscience: Decoding the Brain’s Complex Systems
#NetworkNeuroscience #BrainConnectivity #Connectome #Neuroscience #BrainMapping #GraphTheory #MultilayerNetworks #BrainResearch #Neurotechnology #PersonalizedMedicine #BrainHealth #CognitiveScience #Neuroimaging #BrainScience
-
Multilayer Network Neuroscience: Decoding the Brain’s Complex Systems
#NetworkNeuroscience #BrainConnectivity #Connectome #Neuroscience #BrainMapping #GraphTheory #MultilayerNetworks #BrainResearch #Neurotechnology #PersonalizedMedicine #BrainHealth #CognitiveScience #Neuroimaging #BrainScience
-
Multilayer Network Neuroscience: Decoding the Brain’s Complex Systems
#NetworkNeuroscience #BrainConnectivity #Connectome #Neuroscience #BrainMapping #GraphTheory #MultilayerNetworks #BrainResearch #Neurotechnology #PersonalizedMedicine #BrainHealth #CognitiveScience #Neuroimaging #BrainScience
-
Multilayer Network Neuroscience: Decoding the Brain’s Complex Systems
#NetworkNeuroscience #BrainConnectivity #Connectome #Neuroscience #BrainMapping #GraphTheory #MultilayerNetworks #BrainResearch #Neurotechnology #PersonalizedMedicine #BrainHealth #CognitiveScience #Neuroimaging #BrainScience
-
The Neuroscience Behind Empathy, Mood Disorders, and Social Interaction
#Neuroscience #HumanMind #SocialSkills #BrainScience #MentalHealth #Neuropsychology #MindReading #BrainRegions #EmotionRegulation #MentalDisorders #Autism #BipolarDisorder #LieDetection #Neuroimaging #EvolutionOfTheBrain
-
- Open position -
Postdoc in Advanced MRI Acquisition Methods at Dept Neurophysics, MPI-CBS, Leipzig
Interested in pushing the limits of MRI and in-vivo histology using latest 7T and Connectom (300mT/m) scanners?
Closing date: 9th March 2023
More info on our research: https://www.cbs.mpg.de/departments/neurophysics
Apply on: https://www.cbs.mpg.de/vacancies/open-positions using reference PD 1/23
Please boost!
-
Are you interested in how #quantitative #MRI (#qMRI) parameters relate to #cell types estimated by #gene #expression in the #human #brain? 🤔
What impact have different #magnetic #field strengths (#3T, #7T)? 🧲
What do the different #relaxation and #MT parameters reflect? 📏
@LukeJoelEdwards and our team of collaborators wanted to know. You can now find some interesting answers published in https://doi.org/10.1093/cercor/bhac453 -
FYI scientists esp #radiologists and those involved in #neurosciences
Clinica: An Open-Source Software Platform for Reproducible Clinical #Neuroscience Studies
https://www.frontiersin.org/articles/10.3389/fninf.2021.689675/full
Clinica is a set of automatic pipelines for processing and analysis of multimodal #neuroimaging data (T1-weighted #MRI, #DiffusionMRI, and #PET data) & tools for statistics, #MachineLearning, and #DeepLearining
It relies on the #BrainImaging data structure (BIDS)