Brain scans reveal two distinct physical subtypes of ADHD

A new study reveals that attention-deficit/hyperactivity disorder actually consists of at least two distinct structural brain subtypes, each with unique physical characteristics and behavioral symptoms. These structural differences suggest that patients may eventually benefit from highly personalized diagnostic and treatment strategies based on their specific biology. The research was published in the journal General Psychiatry.

Attention-deficit/hyperactivity disorder is a highly common neurodevelopmental condition affecting children and adolescents worldwide. Patients typically exhibit a variety of symptoms, which generally fall into categories like inattention, hyperactivity, and impulsivity. Because the condition looks different from one child to the next, medical professionals classify it into several clinical presentations based on these overlapping behaviors.

To understand the physical roots of the condition, researchers frequently use magnetic resonance imaging, commonly known as MRI, to look at brain structure. This non-invasive technology uses strong magnets and radio waves to create detailed, three-dimensional images of the central nervous system. Scientists specifically use these images to examine gray matter, which is the darker tissue in the brain that contains the main cell bodies of neurons.

Gray matter serves as the primary processing center of the human brain. It is responsible for routing sensory information, controlling voluntary movement, and managing complex functions like memories and emotions. Any structural changes in this vital tissue can profoundly alter a person’s cognitive abilities and daily behavior.

Scientists studying this disorder often look for structural alterations in specific brain regions, such as the prefrontal cortex, the amygdala, and the hippocampus. The prefrontal cortex handles executive functions like planning, decision making, and impulse control. The amygdala and hippocampus are deeply involved in emotional regulation, memory storage, and reward processing.

In the past, brain imaging studies of this disorder have produced highly inconsistent results. When researchers compared the brain scans of children with the condition to those of neurotypical children, the physical differences often appeared blurry or contradictory.

Tianzheng Zhong, a researcher at Shandong First Medical University in China, wanted to understand the reason behind these mixed results. Zhong and a team of colleagues suspected that the existing clinical categories did not fully capture the physical variation in the brains of patients. They hypothesized that the condition contains hidden physical subtypes, which correspond to different patterns of structural brain changes.

To test this idea, Zhong and the research team analyzed structural MRI data from a large public database. They rigorously filtered the information, excluding scans with poor image quality or incomplete clinical records. Their final sample included 135 children and adolescents diagnosed with the condition, alongside 182 neurotypical youths who served as a control group.

Initially, the researchers compared the overall gray matter of all the diagnosed patients directly to the neurotypical control group. In this broad comparison, the differences in gray matter were not statistically significant. The research team noted that the diverse physical manifestations of the condition likely caused the opposing structural changes to cancel each other out during the analysis.

To overcome this hurdle, the team used an advanced machine learning algorithm to group the patients based purely on their brain anatomy. This computational tool sorts through massive amounts of anatomical data to find hidden biological patterns that human observers might miss. The algorithm successfully identified two separate physical subtypes hidden within the patient group.

Once the patients were divided into these two categories, distinct physical and behavioral patterns emerged. The researchers noticed that the previously contradictory data began to make sense when viewed through the lens of these new groupings. Each subtype displayed its own unique signature of brain volume changes and behavioral tendencies.

The first subtype was characterized by an increase in gray matter across several areas of the brain. When the researchers looked closer, they found that these physical increases were heavily concentrated in the frontal regions and the cerebellum. The frontal regions handle higher-level cognitive functions like working memory, while the cerebellum manages attention and motor coordination.

Behaviorally, patients in this first subtype struggled the most with severe inattentiveness. The researchers noted that the structural changes in this group were strongly linked to an inability to maintain focus. The physical growth in these specific brain areas appeared to directly impact the patients’ attention spans.

The second subtype presented a nearly opposite physical reality. Patients in this group showed widespread reductions, or atrophy, in their gray matter compared to the neurotypical control group. This tissue loss was especially prominent in the bilateral cerebellum, the frontal regions, and the hippocampus.

The hippocampus is a specialized brain structure deeply involved in memory formation, spatial awareness, and internal motivation. In this second subtype, the structural decline in these regions was associated with higher overall disease severity. These patients exhibited both inattentive symptoms and highly hyperactive or impulsive behaviors.

After establishing these two subtypes, the researchers wanted to explore how these brain changes might progress as the condition worsens. Because they could not follow the same children over many years, they used a mathematical technique to simulate a timeline of the disease. They organized the brain scans in order of symptom severity, ranging from mild to severe, to create a pseudo-time series.

By arranging the data this way, the team could perform a causal network analysis. This statistical method helps identify which brain regions might be driving the physical changes associated with specific symptoms. They looked at how changes in one brain region might reliably predict changes in another region as the symptoms intensified.

For the first subtype, this progressive analysis showed strong links between specific brain nodes and the behavioral domain of inattention. The data indicated that attentional dysfunction is the primary biological impairment for these children. The frontal regions and the cerebellum acted as the primary hubs for these cascading structural changes.

For the second subtype, the causal network analysis revealed a much wider pattern of structural connections. The physical changes in these children were driven by multiple behavioral domains, including severe hyperactivity and impulsivity. This widespread pattern reflects the heightened overall severity of the condition in the second group, with the hippocampus acting as a major hub.

While the study provides a new way to categorize the condition, the researchers noted a few limitations to their approach. The data they used was cross-sectional, meaning it only provided a single snapshot of each child’s brain at one moment in time. The simulated timeline based on symptom severity may not perfectly capture how the disorder naturally develops as a child ages.

To confirm these progressive patterns, future studies will need to track the same groups of children over several years. Longitudinal research would allow scientists to watch these structural brain changes unfold in real time. Observing the brain as it grows and changes is essential for fully understanding neurodevelopmental conditions.

If validated by future research, these findings could eventually change how doctors diagnose and treat the condition. Currently, the disorder is often treated with a uniform approach, but these distinct physical subtypes suggest that personalized interventions might be highly effective. Understanding a child’s specific brain structure could help doctors choose the most appropriate therapy.

Patients falling into the first subtype might benefit the most from targeted cognitive training designed to strengthen their attention networks. Because their primary symptom is inattention, therapies that focus on building working memory and focus could be highly beneficial. This approach directly addresses the biological mechanisms at play in their specific brain subtype.

Alternatively, patients in the second subtype might require a completely different approach to disease management. Because they experience widespread gray matter loss and higher overall severity, they might require more intensive combinations of medication and behavioral therapy. Tailoring treatments to these distinct neuroanatomical profiles could pave the way for a new era of precision medicine in psychiatry.

The study, “Brain morphological changes across behaviour spectrums in attention-deficit/hyperactivity disorder,” was authored by Tianzheng Zhong, Feng Wang, Jianfeng Qiu, and Weizhao Lu. It offers a fresh perspective on the biological foundations of neurodevelopmental disorders.