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Attention-deficit/hyperactivity disorder and autism spectrum disorder (ASD) often coexist in children and adults, but the range of cognitive abilities can vary widely in these patients. Researchers from around the world are leveraging symptom, cognitive assessment, and neurobiological measures to gain insights on how individuals with ADHD/ASD approach and solve problems.
Several experts discussed the progress of their research during the session, “Overlap and differences of ADHD and autism – new findings of functional imaging and cognition studies” at the World Congress on ADHD – Virtual Event.
“The overlap of these two disorders is a critical issue for our field,” said Sarah Karalunas, PhD, assistant professor of clinical psychology at Purdue University, West Lafayette, Ind., who moderated the session. Clinicians are often asked to make differential diagnoses between these two disorders. Only recently has the DSM-5 allowed their codiagnosis. “There’s increasing recognition that there may be shared cognitive and physiological features that reflect their shared risk and account for the high levels of symptom overlap,” said Dr. Karalunas.
Shared cognitive markers
Under the DSM’s change, “it’s now recognized that an estimated 20%-60% of children with ASD have comorbidities with ADHD, and around 20%-40% of children with ADHD have ASD symptoms,” said Beth Johnson, PhD, a research fellow with the Turner Institute for Brain and Mental Health at Monash University, Melbourne.
The shared overlap on genetic traits and comorbidities such as intellectual disability, anxiety, depression, and oppositional defiant disorder, make it difficult for clinicians to predict clinical outcomes, noted Dr. Johnson.
“We’re now understanding that they’re likely to be multiple autisms and ADHDs, that these symptoms exist on a spectrum of severity or ability,” she said. Dr. Johnson discussed a data-driven subtyping approach based on neurocognitive and symptom profiles in children with ADHD. The aim was to better understand how symptoms are managed across ADHD, ASD and comorbid ASD-ADHD.
As part of this research, her team recruited 295 controls and 117 children with ADHD who underwent clinical phenotyping and also completed working memory tasks, stop signal, and sustained attention tasks.
The researchers divided the children into four stable clusters based on the ADHD rating scale and autism questionnaire data: high ASD/ADHD traits, high ADHD/low ASD, low ADHD/moderate ASD, and low ADHD/ASD. Approximately half of the children with ADHD showed moderate to high ASD symptoms. Looking at neurocognition across the tasks, unsurprisingly, performance was lowest among the high-ASD/ADHD children, with performance on the stop signal being the most pronounced. “Notably, performance on the working memory task worsened with increasing ADHD symptoms,” she reported.
Drift model identifies information processing
Dr. Karalunas has also compared subgroups of ADHD and ASD children. “Our analysis examined whether cognitive impairments in ASD reflect a shared risk mechanism or co-occurring ADHD symptoms and why we see an overlap in these types of impairments,” she said.
Her study included 509 children with ADHD, 97 with ASD, and 301 controls (typical development). All three groups underwent a full cognitive assessment battery that measured attention arousal, basic processing speed, and working memory. Those tasks were collapsed into a series of variables as well as a set of tasks measuring response inhibition, switching, interference control, reward discounting, and measure of reaction time variability.
Four cognitive profiles emerged: a typically developing group, an ADHD group, an ASD group with low levels of ADHD symptoms and an ASD group with high levels of ADHD symptoms.
The ADHD group did worse on many of the tasks than the control group, and the ASD group with low ADHD levels also did poorly relative to the typically developing sample. This shows that autism – even in absence of co-occurring ADHD – demonstrates more cognitive impairment than typically developing kids. The ADHD group with high levels of autism did the most poorly across all of the tasks.
The findings also revealed a symptom severity pattern: the group with fewer symptoms did the best and the group with the most symptoms did the worst. “Overall, this reflects severity of impairment,” said Dr. Karalunas.
To identify measures more specific to either ADHD or autism, Dr. Karalunas and colleagues did a follow-up analysis to characterize cognitive performance. To accomplish this, they applied a drift-diffusion model to the same four cognitive profiles. The model assessed three parameters: drift rate, which relates to the speed or efficiency of information processing, boundary separation or speed accuracy trade-offs (impulsivity), and nondecision time such as motor preparation.
Using the same four cognitive profiles, they found that the ADHD group had slower drift rate relative to the control, although the two groups did not differ on boundary separation, which meant there were no differences on waiting to need to respond. The ADHD group had faster nondecision times. “This is a classic pattern, shown in the literature,” said Dr. Karalunas.
In other results, an interesting pattern began to evolve
Both ASD groups, for example, had much wider boundary separations, which meant they were waiting to be sure before they responded than the ADHD or typically developing groups. In contrast, the two ADHD groups had much faster non-decision times, whereas the two non-ADHD groups had similar nondecisions times.
Unlike the previous analysis, which saw a symptom severity pattern develop, “we’re getting two parameters that seem to track much more specifically to specific symptom domains,” observed Dr. Karalunas.
The results suggest there’s a substantial overlap in cognitive impairments in ADHD/ASD. “But we have pretty strong evidence at this point that these similarities are not accounted for by symptom overlap, especially for things like response and inhibition, working memory and processing speed. These seem to be independently related to ADHD and autism, regardless of the level of comorbid ADHD symptoms in the autism group,” said Dr. Karalunas.
The hope is to expand on these types of analyses to address the interaction of cognition-emotion and social cognition, and empirically define groups based on cognitive performance, she said.
Neurocognitive studies
Researchers have also been studying neural networks to assess ASD and ADHD. Roselyne Chauvin, PhD, a postdoctoral associate at Washington University, St. Louis, discussed the concept of “a task generic connectome,” in which researchers look for a common network between targeted task paradigms to get closer to a common alteration across impairments.
In her research, Dr. Chauvin and colleagues looked at connectivity modulations across three tasks: working memory, reward processing tasks, and stop signal tasks, comparing ADHD patients to siblings and controls. The ADHD group showed reduced sensitivity or a smaller number of connections modulated in the tasks compared with the other groups. Researchers wondered where those missed connections were located.
Dividing the cohorts into task generic and task specific groups, Dr. Chauvin and colleagues found that the ADHD group lacked common processing skills. They were also able to identify reproducible missing circuits in the ADHD participants. Among the cohorts, there was a higher modulation of task-specific edges in the ADHD group.
The ADHD patients seemed to be using more task-tailored alternative strategies that were more challenging and suboptimal.
She also previewed her ongoing work with the EU-AIMS Longitudinal European Autism Project (LEAP) database to study ASD-ADHD comorbidity. In this project, she and her colleagues looked at several tasks: probing emotion processing, inhibitory control, theory of mind, and reward anticipation. Comparing ASD groups with or without ADHD comorbidity or a shared connection, she and her team were able to devise a functional profile predictive of ADHD severity. As an example, “for the connection only used by the ASD with ADHD comorbidity, the more they were using those connections of higher amplitude in the modulation, inside this subset of connection, the higher they would have ADHD severity,” said Dr. Chauvin.
Neural correlates of different behavioral and cognitive profiles haven’t been widely studied, according to Charlotte Tye, PhD, who’s based at the Institute of Psychiatry, Psychology & Neuroscience, King’s College, London. Electroencephalography is a useful technique for understanding the neural correlates of cognitive impairments and teasing apart different models of co-occurrence in ASD and ADHD.
Dr. Tye and colleagues tested this approach in a cohort of boys aged 8-13 years diagnosed with ASD and/or ADHD, measuring EEG while the children did various continuous performance tasks to assess changes in brain activity. Examining P3 amplitude (event-related potential components) they found that children with ADHD or ADHD+ASD showed an attenuated amplitude of the P3, compared with typically developing children and those with ASD.
“This suggests children with an ADHD diagnosis exhibited reduced inhibitory control,” said Dr. Tye. In contrast, children with ASD showed reduced conflict monitoring as indexed by altered N2 amplitude across task conditions.
These, and other studies conducted by Dr. Tye and colleagues indicate that children with ADHD show reduced neural responses during attentional processing, whereas autistic children show typical neural responses, supporting specific profiles.
“Autistic children with a diagnosis of ADHD appear to show the unique patterns of neural responses of autism and ADHD, supporting an additive co-occurrence rather than a distinct condition. This contributes to identification of transdiagnostic subgroups within neurodevelopmental conditions for targeting of personalized intervention, and suggests that children with co-occurring autism and ADHD require support for both conditions,” said Dr. Tye.
An important takeaway from all of these findings is “we can’t look just at how someone does overall on a single test,” said Dr. Karalunas in an interview. “There is a tremendous amount of variability between people who have the same diagnosis, and our research really needs to account for this.”
Attention-deficit/hyperactivity disorder and autism spectrum disorder (ASD) often coexist in children and adults, but the range of cognitive abilities can vary widely in these patients. Researchers from around the world are leveraging symptom, cognitive assessment, and neurobiological measures to gain insights on how individuals with ADHD/ASD approach and solve problems.
Several experts discussed the progress of their research during the session, “Overlap and differences of ADHD and autism – new findings of functional imaging and cognition studies” at the World Congress on ADHD – Virtual Event.
“The overlap of these two disorders is a critical issue for our field,” said Sarah Karalunas, PhD, assistant professor of clinical psychology at Purdue University, West Lafayette, Ind., who moderated the session. Clinicians are often asked to make differential diagnoses between these two disorders. Only recently has the DSM-5 allowed their codiagnosis. “There’s increasing recognition that there may be shared cognitive and physiological features that reflect their shared risk and account for the high levels of symptom overlap,” said Dr. Karalunas.
Shared cognitive markers
Under the DSM’s change, “it’s now recognized that an estimated 20%-60% of children with ASD have comorbidities with ADHD, and around 20%-40% of children with ADHD have ASD symptoms,” said Beth Johnson, PhD, a research fellow with the Turner Institute for Brain and Mental Health at Monash University, Melbourne.
The shared overlap on genetic traits and comorbidities such as intellectual disability, anxiety, depression, and oppositional defiant disorder, make it difficult for clinicians to predict clinical outcomes, noted Dr. Johnson.
“We’re now understanding that they’re likely to be multiple autisms and ADHDs, that these symptoms exist on a spectrum of severity or ability,” she said. Dr. Johnson discussed a data-driven subtyping approach based on neurocognitive and symptom profiles in children with ADHD. The aim was to better understand how symptoms are managed across ADHD, ASD and comorbid ASD-ADHD.
As part of this research, her team recruited 295 controls and 117 children with ADHD who underwent clinical phenotyping and also completed working memory tasks, stop signal, and sustained attention tasks.
The researchers divided the children into four stable clusters based on the ADHD rating scale and autism questionnaire data: high ASD/ADHD traits, high ADHD/low ASD, low ADHD/moderate ASD, and low ADHD/ASD. Approximately half of the children with ADHD showed moderate to high ASD symptoms. Looking at neurocognition across the tasks, unsurprisingly, performance was lowest among the high-ASD/ADHD children, with performance on the stop signal being the most pronounced. “Notably, performance on the working memory task worsened with increasing ADHD symptoms,” she reported.
Drift model identifies information processing
Dr. Karalunas has also compared subgroups of ADHD and ASD children. “Our analysis examined whether cognitive impairments in ASD reflect a shared risk mechanism or co-occurring ADHD symptoms and why we see an overlap in these types of impairments,” she said.
Her study included 509 children with ADHD, 97 with ASD, and 301 controls (typical development). All three groups underwent a full cognitive assessment battery that measured attention arousal, basic processing speed, and working memory. Those tasks were collapsed into a series of variables as well as a set of tasks measuring response inhibition, switching, interference control, reward discounting, and measure of reaction time variability.
Four cognitive profiles emerged: a typically developing group, an ADHD group, an ASD group with low levels of ADHD symptoms and an ASD group with high levels of ADHD symptoms.
The ADHD group did worse on many of the tasks than the control group, and the ASD group with low ADHD levels also did poorly relative to the typically developing sample. This shows that autism – even in absence of co-occurring ADHD – demonstrates more cognitive impairment than typically developing kids. The ADHD group with high levels of autism did the most poorly across all of the tasks.
The findings also revealed a symptom severity pattern: the group with fewer symptoms did the best and the group with the most symptoms did the worst. “Overall, this reflects severity of impairment,” said Dr. Karalunas.
To identify measures more specific to either ADHD or autism, Dr. Karalunas and colleagues did a follow-up analysis to characterize cognitive performance. To accomplish this, they applied a drift-diffusion model to the same four cognitive profiles. The model assessed three parameters: drift rate, which relates to the speed or efficiency of information processing, boundary separation or speed accuracy trade-offs (impulsivity), and nondecision time such as motor preparation.
Using the same four cognitive profiles, they found that the ADHD group had slower drift rate relative to the control, although the two groups did not differ on boundary separation, which meant there were no differences on waiting to need to respond. The ADHD group had faster nondecision times. “This is a classic pattern, shown in the literature,” said Dr. Karalunas.
In other results, an interesting pattern began to evolve
Both ASD groups, for example, had much wider boundary separations, which meant they were waiting to be sure before they responded than the ADHD or typically developing groups. In contrast, the two ADHD groups had much faster non-decision times, whereas the two non-ADHD groups had similar nondecisions times.
Unlike the previous analysis, which saw a symptom severity pattern develop, “we’re getting two parameters that seem to track much more specifically to specific symptom domains,” observed Dr. Karalunas.
The results suggest there’s a substantial overlap in cognitive impairments in ADHD/ASD. “But we have pretty strong evidence at this point that these similarities are not accounted for by symptom overlap, especially for things like response and inhibition, working memory and processing speed. These seem to be independently related to ADHD and autism, regardless of the level of comorbid ADHD symptoms in the autism group,” said Dr. Karalunas.
The hope is to expand on these types of analyses to address the interaction of cognition-emotion and social cognition, and empirically define groups based on cognitive performance, she said.
Neurocognitive studies
Researchers have also been studying neural networks to assess ASD and ADHD. Roselyne Chauvin, PhD, a postdoctoral associate at Washington University, St. Louis, discussed the concept of “a task generic connectome,” in which researchers look for a common network between targeted task paradigms to get closer to a common alteration across impairments.
In her research, Dr. Chauvin and colleagues looked at connectivity modulations across three tasks: working memory, reward processing tasks, and stop signal tasks, comparing ADHD patients to siblings and controls. The ADHD group showed reduced sensitivity or a smaller number of connections modulated in the tasks compared with the other groups. Researchers wondered where those missed connections were located.
Dividing the cohorts into task generic and task specific groups, Dr. Chauvin and colleagues found that the ADHD group lacked common processing skills. They were also able to identify reproducible missing circuits in the ADHD participants. Among the cohorts, there was a higher modulation of task-specific edges in the ADHD group.
The ADHD patients seemed to be using more task-tailored alternative strategies that were more challenging and suboptimal.
She also previewed her ongoing work with the EU-AIMS Longitudinal European Autism Project (LEAP) database to study ASD-ADHD comorbidity. In this project, she and her colleagues looked at several tasks: probing emotion processing, inhibitory control, theory of mind, and reward anticipation. Comparing ASD groups with or without ADHD comorbidity or a shared connection, she and her team were able to devise a functional profile predictive of ADHD severity. As an example, “for the connection only used by the ASD with ADHD comorbidity, the more they were using those connections of higher amplitude in the modulation, inside this subset of connection, the higher they would have ADHD severity,” said Dr. Chauvin.
Neural correlates of different behavioral and cognitive profiles haven’t been widely studied, according to Charlotte Tye, PhD, who’s based at the Institute of Psychiatry, Psychology & Neuroscience, King’s College, London. Electroencephalography is a useful technique for understanding the neural correlates of cognitive impairments and teasing apart different models of co-occurrence in ASD and ADHD.
Dr. Tye and colleagues tested this approach in a cohort of boys aged 8-13 years diagnosed with ASD and/or ADHD, measuring EEG while the children did various continuous performance tasks to assess changes in brain activity. Examining P3 amplitude (event-related potential components) they found that children with ADHD or ADHD+ASD showed an attenuated amplitude of the P3, compared with typically developing children and those with ASD.
“This suggests children with an ADHD diagnosis exhibited reduced inhibitory control,” said Dr. Tye. In contrast, children with ASD showed reduced conflict monitoring as indexed by altered N2 amplitude across task conditions.
These, and other studies conducted by Dr. Tye and colleagues indicate that children with ADHD show reduced neural responses during attentional processing, whereas autistic children show typical neural responses, supporting specific profiles.
“Autistic children with a diagnosis of ADHD appear to show the unique patterns of neural responses of autism and ADHD, supporting an additive co-occurrence rather than a distinct condition. This contributes to identification of transdiagnostic subgroups within neurodevelopmental conditions for targeting of personalized intervention, and suggests that children with co-occurring autism and ADHD require support for both conditions,” said Dr. Tye.
An important takeaway from all of these findings is “we can’t look just at how someone does overall on a single test,” said Dr. Karalunas in an interview. “There is a tremendous amount of variability between people who have the same diagnosis, and our research really needs to account for this.”
Attention-deficit/hyperactivity disorder and autism spectrum disorder (ASD) often coexist in children and adults, but the range of cognitive abilities can vary widely in these patients. Researchers from around the world are leveraging symptom, cognitive assessment, and neurobiological measures to gain insights on how individuals with ADHD/ASD approach and solve problems.
Several experts discussed the progress of their research during the session, “Overlap and differences of ADHD and autism – new findings of functional imaging and cognition studies” at the World Congress on ADHD – Virtual Event.
“The overlap of these two disorders is a critical issue for our field,” said Sarah Karalunas, PhD, assistant professor of clinical psychology at Purdue University, West Lafayette, Ind., who moderated the session. Clinicians are often asked to make differential diagnoses between these two disorders. Only recently has the DSM-5 allowed their codiagnosis. “There’s increasing recognition that there may be shared cognitive and physiological features that reflect their shared risk and account for the high levels of symptom overlap,” said Dr. Karalunas.
Shared cognitive markers
Under the DSM’s change, “it’s now recognized that an estimated 20%-60% of children with ASD have comorbidities with ADHD, and around 20%-40% of children with ADHD have ASD symptoms,” said Beth Johnson, PhD, a research fellow with the Turner Institute for Brain and Mental Health at Monash University, Melbourne.
The shared overlap on genetic traits and comorbidities such as intellectual disability, anxiety, depression, and oppositional defiant disorder, make it difficult for clinicians to predict clinical outcomes, noted Dr. Johnson.
“We’re now understanding that they’re likely to be multiple autisms and ADHDs, that these symptoms exist on a spectrum of severity or ability,” she said. Dr. Johnson discussed a data-driven subtyping approach based on neurocognitive and symptom profiles in children with ADHD. The aim was to better understand how symptoms are managed across ADHD, ASD and comorbid ASD-ADHD.
As part of this research, her team recruited 295 controls and 117 children with ADHD who underwent clinical phenotyping and also completed working memory tasks, stop signal, and sustained attention tasks.
The researchers divided the children into four stable clusters based on the ADHD rating scale and autism questionnaire data: high ASD/ADHD traits, high ADHD/low ASD, low ADHD/moderate ASD, and low ADHD/ASD. Approximately half of the children with ADHD showed moderate to high ASD symptoms. Looking at neurocognition across the tasks, unsurprisingly, performance was lowest among the high-ASD/ADHD children, with performance on the stop signal being the most pronounced. “Notably, performance on the working memory task worsened with increasing ADHD symptoms,” she reported.
Drift model identifies information processing
Dr. Karalunas has also compared subgroups of ADHD and ASD children. “Our analysis examined whether cognitive impairments in ASD reflect a shared risk mechanism or co-occurring ADHD symptoms and why we see an overlap in these types of impairments,” she said.
Her study included 509 children with ADHD, 97 with ASD, and 301 controls (typical development). All three groups underwent a full cognitive assessment battery that measured attention arousal, basic processing speed, and working memory. Those tasks were collapsed into a series of variables as well as a set of tasks measuring response inhibition, switching, interference control, reward discounting, and measure of reaction time variability.
Four cognitive profiles emerged: a typically developing group, an ADHD group, an ASD group with low levels of ADHD symptoms and an ASD group with high levels of ADHD symptoms.
The ADHD group did worse on many of the tasks than the control group, and the ASD group with low ADHD levels also did poorly relative to the typically developing sample. This shows that autism – even in absence of co-occurring ADHD – demonstrates more cognitive impairment than typically developing kids. The ADHD group with high levels of autism did the most poorly across all of the tasks.
The findings also revealed a symptom severity pattern: the group with fewer symptoms did the best and the group with the most symptoms did the worst. “Overall, this reflects severity of impairment,” said Dr. Karalunas.
To identify measures more specific to either ADHD or autism, Dr. Karalunas and colleagues did a follow-up analysis to characterize cognitive performance. To accomplish this, they applied a drift-diffusion model to the same four cognitive profiles. The model assessed three parameters: drift rate, which relates to the speed or efficiency of information processing, boundary separation or speed accuracy trade-offs (impulsivity), and nondecision time such as motor preparation.
Using the same four cognitive profiles, they found that the ADHD group had slower drift rate relative to the control, although the two groups did not differ on boundary separation, which meant there were no differences on waiting to need to respond. The ADHD group had faster nondecision times. “This is a classic pattern, shown in the literature,” said Dr. Karalunas.
In other results, an interesting pattern began to evolve
Both ASD groups, for example, had much wider boundary separations, which meant they were waiting to be sure before they responded than the ADHD or typically developing groups. In contrast, the two ADHD groups had much faster non-decision times, whereas the two non-ADHD groups had similar nondecisions times.
Unlike the previous analysis, which saw a symptom severity pattern develop, “we’re getting two parameters that seem to track much more specifically to specific symptom domains,” observed Dr. Karalunas.
The results suggest there’s a substantial overlap in cognitive impairments in ADHD/ASD. “But we have pretty strong evidence at this point that these similarities are not accounted for by symptom overlap, especially for things like response and inhibition, working memory and processing speed. These seem to be independently related to ADHD and autism, regardless of the level of comorbid ADHD symptoms in the autism group,” said Dr. Karalunas.
The hope is to expand on these types of analyses to address the interaction of cognition-emotion and social cognition, and empirically define groups based on cognitive performance, she said.
Neurocognitive studies
Researchers have also been studying neural networks to assess ASD and ADHD. Roselyne Chauvin, PhD, a postdoctoral associate at Washington University, St. Louis, discussed the concept of “a task generic connectome,” in which researchers look for a common network between targeted task paradigms to get closer to a common alteration across impairments.
In her research, Dr. Chauvin and colleagues looked at connectivity modulations across three tasks: working memory, reward processing tasks, and stop signal tasks, comparing ADHD patients to siblings and controls. The ADHD group showed reduced sensitivity or a smaller number of connections modulated in the tasks compared with the other groups. Researchers wondered where those missed connections were located.
Dividing the cohorts into task generic and task specific groups, Dr. Chauvin and colleagues found that the ADHD group lacked common processing skills. They were also able to identify reproducible missing circuits in the ADHD participants. Among the cohorts, there was a higher modulation of task-specific edges in the ADHD group.
The ADHD patients seemed to be using more task-tailored alternative strategies that were more challenging and suboptimal.
She also previewed her ongoing work with the EU-AIMS Longitudinal European Autism Project (LEAP) database to study ASD-ADHD comorbidity. In this project, she and her colleagues looked at several tasks: probing emotion processing, inhibitory control, theory of mind, and reward anticipation. Comparing ASD groups with or without ADHD comorbidity or a shared connection, she and her team were able to devise a functional profile predictive of ADHD severity. As an example, “for the connection only used by the ASD with ADHD comorbidity, the more they were using those connections of higher amplitude in the modulation, inside this subset of connection, the higher they would have ADHD severity,” said Dr. Chauvin.
Neural correlates of different behavioral and cognitive profiles haven’t been widely studied, according to Charlotte Tye, PhD, who’s based at the Institute of Psychiatry, Psychology & Neuroscience, King’s College, London. Electroencephalography is a useful technique for understanding the neural correlates of cognitive impairments and teasing apart different models of co-occurrence in ASD and ADHD.
Dr. Tye and colleagues tested this approach in a cohort of boys aged 8-13 years diagnosed with ASD and/or ADHD, measuring EEG while the children did various continuous performance tasks to assess changes in brain activity. Examining P3 amplitude (event-related potential components) they found that children with ADHD or ADHD+ASD showed an attenuated amplitude of the P3, compared with typically developing children and those with ASD.
“This suggests children with an ADHD diagnosis exhibited reduced inhibitory control,” said Dr. Tye. In contrast, children with ASD showed reduced conflict monitoring as indexed by altered N2 amplitude across task conditions.
These, and other studies conducted by Dr. Tye and colleagues indicate that children with ADHD show reduced neural responses during attentional processing, whereas autistic children show typical neural responses, supporting specific profiles.
“Autistic children with a diagnosis of ADHD appear to show the unique patterns of neural responses of autism and ADHD, supporting an additive co-occurrence rather than a distinct condition. This contributes to identification of transdiagnostic subgroups within neurodevelopmental conditions for targeting of personalized intervention, and suggests that children with co-occurring autism and ADHD require support for both conditions,” said Dr. Tye.
An important takeaway from all of these findings is “we can’t look just at how someone does overall on a single test,” said Dr. Karalunas in an interview. “There is a tremendous amount of variability between people who have the same diagnosis, and our research really needs to account for this.”
FROM ADHD 2021