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.

In this study, we measured the neuro-auditory profile of 82 adults (27 ADHD, 30 ADD, 25 controls) via MRI and MEG. All three groups (ADHD, ADD, and controls) revealed distinct neuro-auditory profiles (Figure 1). In the left hemisphere, both ADHD and ADD showed reduced gray matter volumes of the left HG, resulting in diminished left HG/PT ratios. In the right hemisphere, subjects with ADHD were characterized by lower right HG/PT ratios and ADD by a similar right HG/PT ratios compared to controls. Controls and ADD had a well-balanced hemispheric response pattern, ADHD a left-right asynchrony.

Discriminant analysis revealed that mainly the left HG/PT, the left HG and the absolute P1 latency asynchrony separated the ADD and ADHD groups from the control group, while the right P1 latency, the right HG, the right HG/PT and the right PT separated the control and ADD groups best from the ADHD group (Figure 2). Thus, the structural and functional asymmetry and the lateralization between the right and left hemisphere seem to play an important role in subtype/presentation differentiation. In this study, patients were diagnosed according to the ICD-10-GM. The ICD-10-GM mainly differentiates between two subtypes of AD(H)D (attention deficit hyperactivity disorder and attention deficit disorder without hyperactivity), while the DSM-5 distinguishes between ADHD combined presentation, ADHD predominantly inattentive presentation and ADHD predominantly hyperactive-impulsive presentation. This reflects the still existing uncertainty regarding the exact number of AD(H)D subtypes/presentations. Further, to date, there is no consensus on how to deal with the heterogeneity of AD(H)D. There are studies distinguishing AD(H)D subtypes/presentations (Lee et al., 2008; Seither-Preisler et al., 2014; Serrallach et al., 2016; Groß et al., 2022) and studies considering AD(H)D as a single group (Mostofsky et al., 2002; Nakao et al., 2011; Hoogman et al., 2017, 2019). Differentiating subtypes/presentations in future studies, could help to shed more light on subtype/presentation specific characteristics.

In our preceding study, using the same methodology and similar group sizes, we focused exclusively on children and adolescents, under the age of 17 years (Serrallach et al., 2016). In that study, significant group differences in AC morphology and function were found between the ADHD, the ADD and the control groups, anticipating that auditory functions are indicative for ADHD and ADD (Serrallach et al., 2016). The distinct susceptibility of the AC for ADHD/ADD specific patterns could be explained through the fact that various attentional processes are an integral part within the auditory system. In our current study with adults, all groups (ADHD, ADD, and controls) exhibited a distinct reduction in P1-asynchrony compared to children with ADHD and ADD and unaffected children (Serrallach et al., 2016). It is noticeable, that the reduction is more pronounced in the ADD than the ADHD group.

Combining the results of our preceding study on only children and adolescents (Serrallach et al., 2016) with the current study with only adults, we conclude that characteristic neuro-auditory profiles for ADHD, ADD, and controls can be found in both, the children/adolescent and adult population. We have previously shown that individual differences in the gross morphology of the AC are extremely stable over time and are likely to be mediated by genetic dispositions and/or by prenatal and early environmental influences (Seither-Preisler et al., 2014; Serrallach et al., 2016). The enlarged PTs seen in the patient groups (ADHD and ADD) may originate from diminished or delayed pruning (Iglesias et al., 2005), which potentially leads to oversized anatomical structures and functionally inefficient neural networks (Seither-Preisler et al., 2014; Serrallach et al., 2016; Groß et al., 2022). These morphological anomalies could hinder the build-up of reliable interconnections between bilaterally homotopic regions via the corpus callosum (Westerhausen et al., 2009). The development of compensatory, alternative neural connections could result in the observed atypical bilateral asynchronous P1 latencies in ADHD and ADD in children/adolescents (Serrallach et al., 2016), and ADHD in adults.

The currently used behavior-based methodology carries the risk of misdiagnosis with possible negative side effects for individuals, the society, the economy and the health care system. It may have shortcomings in reproducibility and subtype differentiation (Willcutt et al., 2012). As a result of this current diagnostic pathway, AD(H)D tends to be overdiagnosed in boys and underdiagnosed in girls (Bruchmüller et al., 2012; Mowlem et al., 2019). This mentioned overdiagnosis in boys/men and underdiagnosis in girls/women may lead to unnecessary (medication-) treatment in males and lower than necessary access to (medication-) treatment in females (Mowlem et al., 2019). As females often present less externalizing and consequently more internalizing symptoms, they are often misdiagnosed with depression or anxiety (Quinn and Madhoo, 2014). A lack of a clear or a delayed AD(H)D diagnosis and accompanied denied or late treatment such as medication, psychotherapy and psychoeducation may lead to lower educational achievement, long term social problems, higher substance abuse, financial and employment difficulties, higher rates of criminality, imprisonment and motor vehicle accidents (Hamed et al., 2015). This underlines the need for an observer-independent and reliable biomarker (Thome et al., 2012) in the diagnostic work-up of patients with AD(H)D. As revealed by the discriminant analysis, mainly the absolute P1 latency, the left HG/PT and the left HG separated the disorder groups (ADD and ADHD groups) from the control group, while primarily the right side with the right P1 latency, the right HG, the right HG/PT and the right PT separated the control and ADD groups best from the ADHD group (Figure 2). In addition, this combined brain-based morphological and functional approach may lead to an advanced differentiation of AD(H)D subtypes. It has been stated that the development of AD(H)D markers is challenged by etiological and phenotypic complexity and heterogeneity, AD(H)D subtypes, maturation across the age spectrum and comorbidities (Thome et al., 2012; Firouzabadi et al., 2021). Not only do patients with ADHD and ADD differ on a behavioral level (Lahey and Carlson, 1991), but also in auditory processing and music performance (Serrallach et al., 2016; Groß et al., 2022). Compared to controls, children/adolescents with ADHD scored lower in complex rhythmic and melodic perception tasks while children/adolescents with ADD showed no auditory impairments (Serrallach et al., 2016). In rhythmic reproduction, adolescents with ADD performed worse compared to controls, while adolescents with ADHD scored lower in pitch improvisation than controls or adolescents with ADD (Groß et al., 2022). Against this background, and as so far, all AD(H)D presentations/subtypes receive the same therapies, the question arises if more focus should be given in the diagnostic work-up to identify subtypes/presentations in order to develop more presentation/subtype tailored therapies.

We are aware of several limitations in our study. First, our study had a cross-sectional design. Second, the number of subjects in our cohort is rather small. Third, psychiatric diagnoses, even though made by specialized psychiatrists, were performed by different psychiatrists in Germany and Switzerland. Subsequent, longitudinal studies with larger series and homogenized psychiatric diagnoses are needed to confirm these neuromorphological and neurofunctional correlates in the AC. In addition to an independent replication/external validation of the results, future studies should also focus on the question whether the magnitude of the P1 asynchrony is also correlated with the severity of the disorder in adults. Further, as it is known that the morphology of the HG is very variable (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) and include single HG, common stem duplication, complete posterior duplication or multiple duplications, future studies should include an assessment of morphotypes and their distribution in specific groups.

In conclusion, until today, no observer-independent diagnostic approach is available for AD(H)D and the official diagnostic classification systems, being mainly the ICD-10 in Europe and the DSM-5 in the United States, are not entirely consistent. We measured the neuro-auditory profile of 82 adults (27 ADHD, 30 ADD, 25 controls) via MRI and MEG. We found distinct neuro-auditory profiles for all groups (ADHD, ADD, and controls). In the left hemisphere, both ADHD and ADD showed reduced gray matter volumes of the left HG, resulting in diminished left HG/PT ratios. In the right hemisphere, subjects with ADHD were characterized by lower right HG/PT ratios and ADD by a similar right HG/PT ratio compared to controls. Controls and ADD had well-balanced hemispheric response patterns, ADHD a left-right asynchrony. In the future, observer-independent neuromorphological and/or neurofunctional biomarkers could support the diagnostic work-up of patients with AD(H)D in order to become more definite in diagnosing AD(H)D and its subtypes/presentations.

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.

The studies involving human participants were reviewed and approved by the Ethikkommission Ostschweiz (EKOS). The patients/participants provided their written informed consent to participate in this study.

BS, SW, and PS substantially contributed to the conception and design of the work. BS and PS were involved in the acquisition of the data and performed the neurological analyses. BS, CG, and MC were involved in the interpretation of the findings. MC performed the statistical analysis. BS drafted the work. PS contributed to the figures. MC, CG, PS, and SW performed a critical revision of the manuscript. SW obtained funding. All authors gave their approval to the final version for submission.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.