Researchers have identified specific neurological biomarkers for ADHD in the brain

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Researchers analyzing the data from MRI exams on nearly 8,000 children have identified biomarkers of attention-deficit/hyperactivity disorder (ADHD) and a possible role for neuroimaging machine learning to help with the diagnosis, treatment planning and surveillance of the disorder.

The results of the new study will be presented next week at the annual meeting of the Radiological Society of North America (RSNA).

According to the Centers for Disease Control and Prevention, ADHD is one of the most common neurodevelopmental disorders in childhood, affecting approximately 6 million American children between the ages of 3 and 17 years.

Children with the disorder may have trouble paying attention and controlling impulsive behaviors, or they may be overly active. Diagnosis relies on a checklist completed by the child’s caregiver to rate the presence of ADHD symptoms.

“There’s a need for a more objective methodology for a more efficient and reliable diagnosis,” said study co-author Huang Lin, a post-graduate researcher at the Yale School of Medicine in New Haven, Connecticut. “ADHD symptoms are often undiagnosed or misdiagnosed because the evaluation is subjective.”

The researchers used MRI data from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States. The ABCD study involves 11,878 children aged 9-10 years from 21 centers across the country to represent the sociodemographic diversity in the U.S.

“The demographics of our group mirror the U.S. population, making our results clinically applicable to the general population,” Lin said.

After exclusions, Lin’s study group included 7,805 patients, including 1,798 diagnosed with ADHD, all of whom underwent structural MRI scans, diffusion tensor imaging and resting-state functional MRI.

The researchers performed a statistical analysis of the imaging data to determine the association of ADHD with neuroimaging metrics including brain volume, surface area, white matter integrity and functional connectivity.

“We found changes in almost all the regions of the brain we investigated,” Lin said. “The pervasiveness throughout the whole brain was surprising since many prior studies have identified changes in selective regions of the brain.”

In the patients with ADHD, the researchers observed abnormal connectivity in the brain networks involved in memory processing and auditory processing, a thinning of the brain cortex, and significant white matter microstructural changes, especially in the frontal lobe of the brain.

“The frontal lobe is the area of the brain involved in governing impulsivity and attention or lack thereof—two of the leading symptoms of ADHD,” Lin said.

Volume changes in patients with ADHD. Children with ADHD tend to have lower cortical volume, especially in temporal and frontal lobes. Credit: RSNA and Huang Lin

Lin said MRI data was significant enough that it could be used as input for machine learning models to predict an ADHD diagnosis. Machine learning, a type of artificial intelligence, makes it possible to analyze large amounts of MRI data.

“Our study underscores that ADHD is a neurological disorder with neuro-structural and functional manifestations in the brain, not just a purely externalized behavior syndrome,” she said.

Lin said the population-level data from the study offers reassurance that the MRI biomarkers give a solid picture of the brain.

“At times when a clinical diagnosis is in doubt, objective brain MRI scans can help to clearly identify affected children,” Lin said. “Objective MRI biomarkers can be used for decision making in ADHD diagnosis, treatment planning and treatment monitoring.”

Senior author Sam Payabvash, M.D., a neuroradiologist and assistant professor of radiology at the Yale School of Medicine, noted that recent trials have reported microstructural changes in response to therapy among ADHD children.

“Our study provides novel and multimodal neuroimaging biomarkers as potential therapeutic targets in these children,” he said.

Co-authors are Stefan Haider, Clara Weber and Simone Kaltenhauser.


Attention deficit (hyperactivity) disorder (AD(H)D) is one of the most common neurodevelopmental disorders in children and adolescents, with a worldwide prevalence of about 5% (Polanczyk et al., 2007; American Psychiatric Association, 2013). AD(H)D is a chronic and debilitating disorder, affecting all aspects of life and is accompanied by permanent social and emotional overload and high psychological strain (Biederman, 1998; Birnbaum et al., 2005; Biederman and Faraone, 2006; Loe and Feldman, 2007; Wilens et al., 2011). A substantial percentage of up to 60% of children remain affected into adulthood (Weiss and Hechtman, 1993). While hyperactivity may decrease over time, inattention and impulsivity often persist (American Psychiatric Association, 2013).

A wide range of timing deficits have been linked to AD(H)D (Barkley et al., 2001; Smith et al., 2002; McInerney and Kerns, 2003; Falter and Noreika, 2011; Noreika et al., 2013; Lesiuk, 2015), including several timeframes ranging from milliseconds up to years and including auditory, visual, and motor timing as well as temporal foresight problems (Falter and Noreika, 2011; Noreika et al., 2013).

Deficits were found in sensorimotor synchronization, duration discrimination, duration reproduction, delay discounting tasks (Noreika et al., 2013), melodic and rhythm processing, and musical performance (Groß et al., 2022). Research has suggested an association between timing deficits and behavioral measures of impulsiveness and inattention indicating that timing deficits may play a key role in AD(H)D (Noreika et al., 2013).

Further AD(H)D-specific features, such as genetic risk variants (Riglin et al., 2016; Demontis et al., 2019), biochemical variations (Elia et al., 2011), neuromorphological (Castellanos et al., 2002; Mostofsky et al., 2002; Nakao et al., 2011; Seither-Preisler et al., 2014; Serrallach et al., 2016; Hoogman et al., 2017, 2019; Firouzabadi et al., 2021; Pereira-Sanchez and Castellanos, 2021) or neurofunctional differences (Kuperman et al., 1996; Seither-Preisler et al., 2014; Serrallach et al., 2016; McVoy et al., 2019; Müller et al., 2019) have been described. This evidence adds to the validity of AD(H)D, which is characterized by the key symptoms of hyperactivity, impulsivity, and/or inattention (American Psychiatric Association, 2013; World Health Organization, 2019), as a neurodevelopmental disorder.

The auditory cortex (AC) is broadly connected and provides detailed information to and receives precise feedback from multiple different brain structures, including attentional networks, demonstrating the interdependence between auditory and attentional functions (Scheich et al., 2011; Seither-Preisler et al., 2014). There is evidence that AD(H)D frequently overlaps with (Central) Auditory Processing Disorder [(C)APD] (Riccio et al., 2005). (C)APD is characterized by difficulties in identifying and discriminating among sounds despite having normal peripheral hearing (Dawes and Bishop, 2009). In addition, patients with (C)APD may show behavioral problems, encompassing inattention and distractibility, features also known from AD(H)D (American Speech-Language-Hearing Association, 2005). Dawes and Bishop (2009) already questioned the reason for this overlap and asked whether auditory processing problems lead to inattention or whether attentional deficits affect auditory perception.

The AC processes auditory information and can be divided into three functional areas, being the core, belt and parabelt regions. The AC follows a hierarchical processing order with the primary information streaming out of the core area to reach the belt region, which in turn connects broadly to the parabelt and auditory-related cortex (Hackett and Kaas, 2004).

The Heschl’s gyrus (HG) includes both primary core and secondary belt areas (Seither-Preisler et al., 2014). It is known, that there are large inter-hemispheric and inter-individual morphological variants of HG, including single HG, common stem duplication, complete posterior duplication or multiple duplications (Schneider et al., 2005; Seither-Preisler et al., 2014; Marie et al., 2015; Benner et al., 2017; Turker et al., 2017; Dalboni da Rocha et al., 2020). While the left-hemispheric AC is linked to rapid temporal processing, which is optimal for speech discrimination, the right AC is responsible for spectral processing, which is important for frequency discrimination (Zatorre et al., 2002; Seither-Preisler et al., 2014).

The planum temporale (PT) can be found posteriorly to the HG and is part of the auditory association cortex. A decisive role in subserving auditory functions that underlie music and speech processing is attributed to the PT (Meyer et al., 2012). The left PT is seen to be primarily associated with decoding sub-segmental, rapidly changing acoustic cues (about 40 Hz) important for phonemic perception, while the right PT is preferentially responsible for processing supra-segmental, slowly changing cues (about 4 Hz) substantial for prosodic and rhythmic information (Meyer et al., 2012).

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-5 (American Psychiatric Association, 2013), mainly applied in the United States, distinguishes between ADHD combined presentation, ADHD predominantly inattentive presentation and ADHD predominantly hyperactive-impulsive presentation.

In contrast, the International Statistical Classification of Diseases, ICD-10 (World Health Organization, 2019), mainly used in Europe, emphasizes primarily one (sub-)type of this disorder, which is defined by the existence of symptoms of all three behavioral categories (hyperactivity, impulsivity, and attention deficit, F 90.0).

However, “attention deficit disorder without hyperactivity” can be found in the ICD-10 under the heading “other specified behavioral and emotional disorders with onset in childhood and adolescence” (F 98.80). This fact reflects the different cultural perception in dealing with the heterogeneity of the disorder (Luo et al., 2019). So far, all AD(H)D presentations/subtypes receive the same therapies mainly consisting of stimulant medication, such as methylphenidate and/or behavior modification therapy (MTA Cooperative Group, 2004).

Emerging literature points out the fact that AD(H)D has far-fetching, long term implications and impacts on various areas of individual life, the society, the economy and the health care system. Comorbid disorders, such as oppositional defiant disorder, conduct disorder, learning disabilities, anxiety disorder, and depression are frequent in individuals with AD(H)D (Biederman et al., 1991). AD(H)D is seen as a significant risk factor for developing cigarette-, alcohol- or drug-use disorders (Wilens et al., 2011).

Links between AD(H)D and poor academic and educational outcomes as well as lower socioeconomic status (Biederman and Faraone, 2006; Loe and Feldman, 2007), substantial declines in full-time employment and household income (Biederman and Faraone, 2006) have been reported.

A remarkable body of literature has demonstrated that several aspects of AD(H)D such as treatment, increased rates of comorbid psychiatric disorders, high accident rates, work loss, and criminality lead to significant higher direct and indirect medical costs (Birnbaum et al., 2005). Particularly, overdiagnosis systematically leads to inflated healthcare costs due to unnecessary labeling, unneeded tests and inappropriate therapies (Moynihan, 2012).

At present, AD(H)D is primarily diagnosed on the basis of patterns of observable behavior, clinical symptoms and diagnostic schemes according to established diagnostic systems (ICD-10 and DSM-5) that not necessarily reflect the underlying neurobiological systems and pathomechanisms (Thome et al., 2012). Hence, it can be expected that a group of disorders with similar symptomatology as AD(H)D but differing pathogenesis are subsumed under the term AD(H)D (Thome et al., 2012). Moreover, with the current diagnostic approach the inter-rater agreement of AD(H)D is low to moderate, a finding that is found across a lot of procedures of psychopathology (Willcutt et al., 2012).

In the light of the above, in 2012, the World Federation of Societies of Biological Psychiatry (WFSBP) task force on biological markers and the World Federation of ADHD called for validated biomarkers of AD(H)D (Thome et al., 2012). The Biomarker Definitions Working Group defines a biological marker (biomarker) as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (Biomarkers Definitions Working, 2001). As of yet, however, no commonly accepted brain-based method exists that could contribute to a more objective diagnostic procedure for AD(H)D.

In last decades, there have been growing attempts in searching for brain-based correlates in neurological and psychiatric diseases in general (Bremner et al., 1995; Müller et al., 2019) and in AD(H)D in particular (Castellanos et al., 2002; Thome et al., 2012; Firouzabadi et al., 2021).

There is a large body of literature on differences in neurophysiology including attention, memory, executive functions, language skills, spatial abilities and olfactory functions, risk genes identification, biochemical alterations, proteomic variations and neuroimaging, including structural (conventional, volumetric, and diffusion tensor imaging) and functional (task-based and resting state) magnetic resonance imaging (MRI) (Thome et al., 2012; Firouzabadi et al., 2021; Pereira-Sanchez and Castellanos, 2021).

A recent large-scale study using the ENIGMA- (Enhanced Neuroimaging Genetics Through Meta-Analysis) ADHD sample compared the cortical thickness and surface area between 2,246 subjects with ADHD and 1,932 control subjects and found subtle lower surface area in frontal, temporal, and cingulate regions and thinner cortical thickness in the temporal pole and fusiform gyrus in children.

These differences in surface area and cortical thickness were not evident in the adolescent or adult group (Hoogman et al., 2019). Lately, artificial intelligence modeling has been increasingly applied to structural and functional imaging with promising results helping to identify imaging features relevant to the diagnosis of AD(H)D (Sun et al., 2018; Firouzabadi et al., 2021).

In previous investigations, we could show that children with AD(H)D, compared with non-affected subjects, show differing morphology of the HG and PT, with decreased gray matter volume of HG and enlarged gray matter volume of the PT resulting in a considerably lower HG/PT ratio. In addition, the primary auditory-evoked responses in the magnetoencephalography demonstrated a characteristic pattern for children with AD(H)D. Compared with non-disorderd peers, children with AD(H)D showed bilateral asynchrony of the P1 evoked response (Seither-Preisler et al., 2014; Serrallach et al., 2016).

Hence, the aim of the present study was to find further evidence and evaluate if the neural correlates of ADHD and ADD in the AC of children can also be found in the AC of adults.

reference link :https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9121124/


Original Research: The findings will be presented at the 108th Scientific Assembly and Annual Meeting of the Radiological Society of North America

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