Introduction

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common developmental cognitive disorders of childhood. Prevalence rate of ADHD is 3–7% of children in the United States, and often continuing into adulthood (Barkley 1998). ADHD is usually characterized by a pattern of increased impulsivity, high levels of motor activity, and attentional problems that impair function in home, school, and social settings (American Psychiatric Association, APA 1994). Children with ADHD usually have functional impairment across multiple settings including home, school, and peer relationships. ADHD also has been shown to adverse effect on academic performance, vocational success and social-emotional development. The prevalence rate of ADHD has drastically increased. The increase is probably due to changes in diagnostic criteria. It is important to diagnose ADHD correctly otherwise they will not get proper treatment. Diagnosis of a disorder like ADHD is a complex issue for a clinician because of the high prevalence of co-morbid conditions. Misdiagnosis is likely to happen which may resulted in serious consequences, including school failure, depression, conduct disorder, failed relationships, and substance abuse (APA 1994). Therefore, proper diagnosis of ADHD children is very important. However, clinicians often cannot diagnose children with ADHD accurately consequently, these patients treated inappropriately in the primary care setting.

There is no laboratory test or set of physiological features that has been identified as an unequivocal marker for ADHD. That is, there is no “gold standard” for diagnosing ADHD. The disorder is behaviorally based; thus, behavioral observations are required to identify and correctly diagnose the disorder. It has been argued that ADHD is not a distinct diagnostic entity, but that it is a “symptom complex” characterized by multiple possible etiologies and a constellation of pathological behaviors (Weinberg and Brumback 1992; Weinberg and Harper 1993). The observed behaviors are interpreted subjectively by parents and teachers who describe these observations to clinicians. Clinicians often observe the child during clinical interviews and psychometric testing. Typically, parent, teacher and clinical observations are incorporated into a diagnostic decision. However, this subjective interpretation can lead to inter-observer differences, and can make ADHD diagnosis difficult. For example, the prevalence of behaviors related to hyperactivity as rated by a teacher can be higher than that rated by a clinician (Lambert et al. 1973). In contrast, the prevalence of behaviors related to hyperactivity can be lower if these behaviors must be judged to be present by more than one source (e.g., parent and teacher) (Sandberg et al. 1980).

Because there is no gold standard for diagnosing ADHD, it is important to make a distinction between how ADHD is defined and how it is diagnosed. The disorder is currently defined by criteria contained in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). The DSM-IV defines ADHD according to two behavioral domains: inattention and hyperactivity-impulsivity. Each domain contains nine possible symptoms; a child must have at least six of the nine symptoms to qualify for a diagnosis of ADHD. If the child has at least six symptoms on the inattention domain, s/he qualifies for the “ADHD-Predominantly Inattentive Type” diagnosis. If the child has at least six symptoms on the hyperactivity-impulsivity domain, s/he qualifies for the “ADHD-Predominantly Hyperactive-Impulsive Type” diagnosis. If the child has at least six symptoms on both inattention and hyperactivity-impulsivity domains, s/he qualifies for the “ADHD-Combined Type” diagnosis (DSM-IV).

Additional DSM-IV criteria specify that some inattentive or hyperactive-impulsive symptoms must have been present before the age of 7 years, although the diagnosis can be made at older ages. In addition, the symptoms must be present in at least two settings (e.g., home and school) and must cause impairment. That is, there must be evidence of interference with developmentally appropriate social, academic, or occupational functioning. The symptoms should not occur exclusively during the course of a pervasive developmental disorder (e.g., autism), schizophrenia, or any other psychotic disorder. Furthermore, the symptoms should not be better accounted for by another mental disorder (e.g., mood, anxiety, dissociative or personality disorders) (DSM-IV).

While ADHD is defined by the DSM-IV criteria, the symptom complex is diagnosed by a clinician. In the absence of a gold standard, the “reference standard” is the clinician’s judgment. Ideally, this decision would be based on information gathered from a number of sources (e.g., parent, teacher, observations of the child), and would be reached by consensus. That is, a number of qualified clinicians would confer in making the appropriate diagnosis. Nevertheless, the clinician’s decision is ultimately a subjective one, and this introduces a level of variability that is difficult to control particulary while evaluating a tool used for the diagnosis of ADHD. Moreover, the DSM has undergone several iterations over the past two decades, suggesting that ADHD is indeed a “symptom complex” characterized by behaviors that are difficult to agree upon. In addition to DSM IV criteria, there are some diagnostic tools available to diagnose ADHD.

Existing Tools for a Diagnosis of ADHD

According to DuPaul et al. (1991) the approach to diagnosis of ADHD may incorporates: (1) parent and teacher interviews; (2) parent and teacher rating scales; (3) direct observations of behavior; and (4) academic record. There are certain objective tools such as continuous performance tests (CPTs) that are available to diagnose ADHD.

Rating Scales

Rating scales which are most commonly used to diagnose ADHD are the parent-completed Child Behavior Checklist, the Teacher Report Form (TRF) of the Child Behavior Checklist, the Conners Parent and Teacher Rating Scales, the ADD-H: Comprehensive Teacher Rating Scale (ACTeRS), and the Barkley Home Situations Questionnaire and School Situations Questionnaire. These rating scales yield valuable information about overt symptoms efficiently. However, there are certain limitations in the use of rating scales in getting valid ratings. For example, factors like raters’ identification with the ratees is one of the factor which may introduce bias and limit the effectiveness of rating scale. Often the rater is known/related to persons being rated. He may identify with some persons positively and with some persons negatively. Persons identified positively are likely to be rated high despite some unfavorable characteristics whereas persons identified negatively are likely to be rated low despite some favorable and sound characteristics. In both the situations the rating would be a misleading index and would reduce the effectiveness of the rating. Sometime vagueness in the meaning of the trait being rated may affect the rating negatively. For example, some traits or dimensions to be rated are vague and abstract ones. As a consequence, their meaning varies from rater to rater and thus naturally affects the consistency in ratings.

Most of the rating scales require the raters to rate the ratees in any one of different categories like superior, excellent, very good, or best, good, average, below average. When a rater rates a ratee as superior, the question arises as to what he standard is against which a ratee is being classified as superior. Is he being compared with the top 5% or top 10% or, is he being compared with he middle cases? The raters have no uniform standard before them so that the interpretation of category “superior” may not have an identical meaning to all. In the absence of a uniform standard, the interpretation of a category varies from rater to rater. One rater’s “superior” may be another rater’s simply “very good”. This naturally lowers the consistency of ratings and thereby, their reliability. Raters’ personal characteristics also tend to influence the ratings. Some raters are conservative and, therefore, they tend to rate persons almost in the middle. They rarely rate a person very high or very low. Some raters are tough and, therefore, rarely rate anybody high. Thus personal experiences of raters are likely to color their judgment and also their ratings. In such situations the rating done by one rater cannot be compared with the rating done by another rater. In addition, these subjective and behaviorally biased ratings do not objectively measure attentional variables, which are impaired in ADHD.

Continuous Performance Tests

Continuous Performance Tests (CPTs) are computer-based tests designed to measure inattention and impulsivity. It provides a quick assessment of current abilities for sustained attention. A number of CPTs are commercial available to private clinician for the assessment of children with ADHD: Gordon Diagnostic System (GDS) (Gordon 1983), Test of Variables of Attention (TOVA) (Greenberg and Waldman 1993), Conners’ Continuous Performance Task (CCPT) (Conners 1995), and the Intermediate Visual and Auditory Continuous Performance Test (IVA) (Sandford 1995). CPTs involve the presentation of stimuli that include a set number of predetermined targets to which subjects respond. CPTs vary in duration (a few minutes to half an hour), sensory modality (visual or auditory), stimuli (numbers, letters, drawings, and colors), and nature of the task (single target, double target, or sequential recognition). The differences among CPTs are factors to consider when comparing CPT performances. Most common measures assess omission errors, or misses, or commission errors, or false alarm and reaction time.

Two of the primary criticisms of the use of CPTs are discussed here. First is related to its reliability and validity; second, CPTs involve high rates of false negatives, and its limited ability to discriminate between ADHD and other clinical disorders.

The commercially available CPTs fail to report acceptable levels of test–retest reliability. The TOVA, for example, does not even report test–retest reliability, but rather, split-half reliability coefficients (Greenberg 1996). Split-half reliability may be appropriate for a test that is given to a strong practice effect, but that is hardly true of the TOVA. Test–retest reliability is essential to consider in a test that is likely to be used serially (e.g., to evaluate an ADD patient’s response to stimulant medications). The Conners CPT-II reports test–retest reliability, but only on 23 participants, and not on raw scores, but on derived scores (Conners 2000). The GDS lacks strong validity. It may be useful while conducting a comprehensive evaluation of children suspected of attentional problems. There are a number of important confounds that could affect the score obtained on GDS and other computerized measures. Wherry et al. (1993) investigated the validity of the GDS and the results were fairly poor. These authors stated that “The results failed to demonstrate the discriminant validity of any GDS score regardless of the behavior rating used.” As Barkley and others (Barkley and Grodzinsky 1994) have noted, in order for a test to be diagnostically useful, it must be able to not only identify the children with ADHD (sensitivity), but it must also accurately identify children without ADHD (specificity).

It is also necessary to investigate the sensitivity and specificity of the diagnostic measures. In the opinion of one expert panel (Dulcan and Popper 1991) CPTs (Greenberg and Waldman 1993; Barkley 1990; Conners 1985; Wigal et al. 1998) are not generally useful in diagnosis because they suffer from low specificity and sensitivity (Lovejoy and Rasmussen 1990; Trommer et al. 1988). They are useful, however, as research tools. It is possible that CPTs may be useful in measuring some isolated symptoms of ADHD. Various CPTs have yielded false negative rates from 20 to 37% or higher (Greenberg and Waldman 1993; Barkley 1991), leading researchers to caution that “normal’ CPT performance should not be used as evidence to rule out a diagnosis of ADHD. Rielly et al. (1999) used the GDS CPT to evaluate and classify children with a preschool history of language disorders for the presence of ADHD. They used the 5th and 25th percentiles as thresholds for 11 possible outcomes on three GDS tasks. For total commission errors from the vigilance portion of the CPT, sensitivity was 60% and specificity was 46% at the 5th percentile; at the 25th percentile these values were 88 and 23%, respectively (Table 1). The authots concluded that the GDS may have some clinical utility in ruling out a diagnosis of ADHD in children with a history of language disorders, but may not be sufficient to confirm an ADHD diagnosis in the population (Rielly et al. 1999). Description of the other CPTs and its sensitivity and specificity is presented in Table 1.

Table 1 Description of various CPTs and its sensitivity and specificity
Full size table

In order to evaluate the efficacy of the CPT in the diagnosis of ADHD, it is important to consider if CPTs can help in discriminating ADHD from other co-morbid conditions. Children with ADHD are likely to have conduct disorder (CD), Oppositional Defiant Disorder (ODD) or Learning Disability (LD) (APA 1994; Loney and Millich 1982; Epstein et al. 1991). A diagnostic tool not only helps to differentiate ADHD from normal children (non-specific diagnosis), but also among disorders like CD, ODD and LD that tend to co-occur with ADHD (differential diagnosis). A number of studies have been found that CPTs have been inconsistent in differentiating ADHD from other clinical groups (Halperin et al. 1992).

One of the main reason that why CPTs are less able to discriminate ADHD from normal children and children with ODD/CD or LD is that CPT assess on aspect of attention such as sustained attention, which is impaired in ADHD. Indeed impairments, in sustained attention are common to a certain extent to all children with psychiatric disorders (Swaab-Barneveld et al. 2000). Therefore, it is important to asses those cognitive functions which are specifically impaired in ADHD and inculcate this information in the diagnostic system. This will provide a more sensitive as well as specific tool to tap the cognitive impairments in ADHD. Recent experimental literatures on ADHD have identified unique underlying cognitive dysfunction, specific to ADHD, are briefly discussed in the next section. In addition, few recent studies also demonstrate the differences in cognitive profile in ADHD subtypes (ADHD-combined type (ADHD/CT) and ADHD-inattentive type (ADHD/IT) in terms of disinhibition (Nigg et al. 2002), and alerting and executive attentional network (Booth et al. 2001; Oberlin et al. 2005).

Specific Cognitive Deficits in ADHD

Recent experimental literature reveals that children with ADHD are characterized by specific deficits in the monitoring of attentional resources. These deficits in turn negatively affect the cognitive processes, such as response inhibition, error monitoring, attentional disengagement, (Logan 1985; Rabbit 1968; Schachar et al. 2004), decision making processes (Garon 2006), and emotion regulation (Maedgen 2000; Walcott 2004).

There is evidence that the core deficit linked with ADHD involves sustained and selective attention that is necessary to perform a given task (Greenham 1998). These individuals exhibit several deviations from their developmental level, sufficient to create impairments in major life activities. Most of these impairments have been studied using objective measures to demonstrate distinct attentional problems. The ADHD individuals show deficits in subsystems of attention such as alerting, orienting, and executive network. Some of the sustained attention problems among ADHD may also be linked with deficits in alerting mechanisms (Posner and Raichle 1994), which are critical for normal cognitive functioning. Earlier work using spatial orienting task suggested that ADHD children have difficulty in maintaining an alert state (sustained attention) in the absence of warning signal (Swanson et al. 1991). More recent studies using the Attentional Network Task (ANT) have replicated problems with alerting in ADHD mainly due to the inability to maintain the alert state when no warning signal was used (Blane and Marrocco 2004; Booth et al. 2001). Other studies using task similar to ANT have also shown some evidence of abnormalities in alerting and/or executive control in ADHD in terms of slowed response times to abrupt visual cues especially when faced with conflicting spatial cues (Oberlin et al. 2005). It should be noted that impairment in attention processes also results in abnormal functioning in many higher order cognitive operations that involve inhibition of a pre-potent response, interference control and emotion regulation.

A number of studies provide consistent empirical support for the assumption that individuals with ADHD have a deficit in executive control processes, one of which is response inhibition (Barkley 1997; Sergeant et al. 1999). Earlier studies reported that response inhibition deficits have also been associated with CD (Hurt and Naglieri 1992; Oosterlaan et al. 1998). However, most previous studies have failed to control for comorbid diagnosis and it has been suggested that the association between disinhibition and CD might be accounted for by the large overlap with ADHD (Pennington and Ozonoff 1996). On the other hand, it is also possible that the association between disinhibition and ADHD is caused by the overlap with CD. Finally, Lynam (1998) has argued that it is the combination of high levels of ADHD symptoms and conduct problems that is associated with poor inhibitory control, and this comorbid group has also been shown to have the most serious negative outcomes (Moffitt 1990). Therefore, Nigg et al. (1998) suggested that it is very important to take into consideration that, even though children diagnosed with CD, may not meet the criteria for comorbid ADHD, they may still have considerably higher levels of ADHD symptoms compared to normal controls. As a consequence, it is important to treat data dimensionally, not categorically, and control for comorbid symptoms at a subclinical level (Nigg et al. 1998). Studies which have controlled for subclinical symptoms, reported that disinhibition is specific to ADHD and not primarily related to CD (Nigg et al. 1998; Berlin and Bohlin 2002). Berlin and Bohlin (2002) have reported that the correlation between response inhibition and conduct problems was no longer significant when controlling for hyperactivity, whereas the correlation between disinhibition and hyperactivity did remain significant when controlling for conduct problems. These findings indicate that the association between inhibition and conduct problems was caused by the large overlap between conduct problems and hyperactivity. It also provides further support for the role of response inhibition in understanding the mechanisms behind hyperactivity. A meta-analytic study also provides clear evidence for an inhibitory dysfunction in ADHD, whereas evidence for such a deficit for ODD/CD was less robust (Oosterlaan et al. 1998). ADHD is normally associated with slower responses during a stop signal task (Schachar and Logan 1990) which requires responses to “go” trials and inhibition of responses during “no go” trials. Earlier, it was suggested that the ADHD individuals are deficient at monitoring responses during “no go” trials. However, meta-analytic results have shown potential milder problems during “go” responses as well, indicating that problems in response organization and arousal may also play a role in the disorder (Oosterlaan et al. 1998; Sergeant et al. 1999). More specifically, the impairments in executive control processes manifest as poor post error slowing and decrement in accuracy when faced with response conflict during a flanker task. Such deficits in monitoring ongoing behavior during the task were confirmed by the reduced event-related potential amplitude difference between correct and incorrect trials compared to normal controls (van Meel et al. 2007; Wang et al. 2004). Recent fMRI study indicates a functional anatomical asymmetry of response inhibition between ADHD and normal children.

Cognitive performance of individuals with ADHD is also characterized by large moment-to-moment fluctuations in cognitive control reflected by a highly inconsistent and inaccurate response style. It has been suggested that abnormal error processing underlies this failure to implement adequate control that provides top–down adjustment of elementary mental operations (Logan 1985). ADHD individuals get slowed after fewer inhibition failures than normal controls and when they slow, they slowed to a lesser extent. This pattern of performance suggests that ADHD individuals differ from normally developing individuals in behavioral adjustment to errors. Another study examined error monitoring as one of the executive control measures being impaired in ADHD using the event related potential (ERP) methodology. The error-related negativity (ERN) was the same for ADHD and the control group, the former showed diminished error positivity (Pe). Based on these findings, the authors concluded that individuals with ADHD are normal in early error monitoring processes related to error detection as indexed by ERN. However, they show abnormal response strategy adjustments and are deviant in later error monitoring processes related to Pe which may also be associated with the subjective/emotional, conscious evaluation of the error (Leube et al. 2003). This is also an indication of the abnormal emotional regulation in these individuals. Yet in another study brain electrical activity was recorded during the stop signal task. Individuals with ADHD showed abnormal scalp distribution in P3a and reduce ERN in dorsal anterior cingulate cortex suggesting a global deficit in cognitive control operations in these individuals (Wiersema et al. 2005). ERP amplitude differences between correct and incorrect responses were also found to be diminished in ADHD. ADHD individuals also show larger deficit in control processes necessary for disengagement from one task and preparation for a subsequent task. It has been suggested that behavioral deficits observed in ADHD are a result of deficient resource allocation policies. No association has not been reported between error monitoring and reading disability, ODD/CD, or anxious behavior or generalized impairment (Schachar et al. 2004).

Attention Deficit Hyperactivity Disorder (ADHD) individuals are also characterized by the abnormality in reward responsivity that interferes with decision making. ADHD individuals are characterized by specific motivational style called “delay aversion” (Ernst et al. 2003). Delay aversion is the tendency to escape or avoid delay, results in preference for small immediate over large delayed rewards. This is again an indcation of abnormal emotional/motivational mechanism underlying ADHD. Recent positron emission tomography study suggested that neural circuits engaged during decision making differ in subjects with ADHD and healthy comparison subjects. Ventral and dorsolateral prefrontal cortex and the insula were activated during performance of the decision-making task in both the ADHD and healthy groups; however, activation in the ADHD group was less extended and did not involve other regions, such as anterior cingulate and hippocampus that sub serve emotion/memory processes. This difference may explain observed deficits in motivated behaviors in ADHD (Wu et al. 2006).

Computerized Neurocognitive Diagnostic System Based on Specific Cognitive Deficits: A Proposal

Diagnosis of ADHD has been dependent on clinician judgment. The existing tools available for the diagnosis of ADHD include rating scales (parents/teachers), clinical observations or CPTs. These tools are not susceptible enough to discriminate ADHD from other developmental disorder (differential diagnosis) as well as from normals. Experimental evidence informs about the cognitive mechanisms specific to ADHD. An objective assessment of cognitive impairments can explain the behavioral symptoms as well as could help in differential diagnosis. This is particularly important in the context of ADHD, as comorbidity is highly prevalent in ADHD. Therefore, we propose to develop a diagnostic system based on a combination of tests which tap specific cognitive deficits in ADHD in addition to the information about behavioral symptoms collected with other tools like rating scales and diagnostic criteria (DSM-IV). Such a diagnostic system will provide the clinician with objective data based upon the child’s actual performance and will allow for observation in a paradigm likely to elicit inattention and impulsiveness. The clinical relevance of such a system will not only be limited to diagnosis but will also help to plan the treatment strategies including cognitive rehabilitation.

The rationale for using a more comprehensive neurocognitive screening battery is that ADHD is not simply a disorder of sustained attention. ADHD is marked by impairment in complex functional systems such as response inhibition, error monitoring, attentional disengagement, and decision making. Neuropsychological literature also indicates that although many tests show cognitive impairment in ADHD, no one test is sufficient to make the diagnosis of its own. ADHD patients are a diverse group; individuals may demonstrate deficits in one test, or in one cognitive domain, but not in another. Combinations of tests are more likely to yield useful information than any one single test (Konver et al. 1998; Doyle et al. 2000; Nigg et al. 2002; Schmitz et al. 2002; Gupta et al. 2006). CPTs like GDS, TOVA, CCPT, and IVA restrict the scope of the assessment of other cognitive impairments, such as response inhibition, error monitoring, and delay aversion that are specific to ADHD.

In addition, standardized, computer based laboratory measures of attention can provide objective data less influenced by factors such as rater bias. Computer based laboratory measures do offer both the researcher and clinician an opportunity to incorporate data derived from a child’s actual behavior. It would also be helpful in order to detect attention problems in children who are not overtly hyperactive or impulsive (ADHD predominantly inattentive type) and might fall within the normal range on parent or teacher ratings of behavior.

Empirical Support for the Above Proposal

We carried out a study (Gupta et al. 2010) in which children with ADHD, ODD, and normal controls were examined on four tasks: Stop-signal task (SST), attentional disengagement task (ADT), attentional network task (ANT) (child version), and delay aversion task (DAT). In the SST required inhibition of a motor response. Stop-signal reaction time (SSRT), mean delay, and post-error slowing (PES) were the measure of this task. ADT required switching the attention from one task to another task (task-switching). All participants performed a task in which they had to respond to two different task rules, requiring to discriminating the value of a number presented on a computer screen and deciding how many numbers were present on the screen. Switch costs (SC; difference of RT between the switch trials and nonswitch trials) were the measure of this task. The child ANT is a complex task that provides a measure of efficiency of the attentional function of alerting, orienting, and executive control (Rueda et al. 2004). The ANT uses differences in reaction time (RT) between different conditions to measure the efficiency of each function. In the target display arrow of five fish presented either below or above fixation. Each target was preceded by one of four warning cue conditions: a center cue, a double cue, a spatial cue, or no cue. In the center cue condition, an asterisk is presented at the location of the fixation cross. In the double cue condition, an asterisk appears at the locations of the target above and below the fixation cross. Spatial cues involve a single asterisk presented in the position of the upcoming target. Children are instructed to pay attention to the middle fish, and to respond based on whether it is pointing to left or right by pressing the corresponding key. In the executive attention children are presented with fish surrounded by congruent and incongruent flankers. Differences in RT between incongruent trials and congruent trials give the score of executive attention. Differences in RT between nocue condition and double cue give the score of alerting function. Differences in RT between center cue and spatial cue give the score of orienting function. Alerting, orienting, and conflict score were the measure of this task. In the delay aversion task participants were required to choose between a small rewards (1 point) delivered after a short delay (1 s) or a large reward (2 points) delivered after a long delay (20 s). Thus, the child could choose between a small, immediate reward (SDR) and a large, delayed reward (LDR). %LDR and %SDR were the measure of this task.

We obtained the Receiver Operating Characteristic (ROC) for each parameter of each task to calculate the sensitivity and specificity to see which parameters individually classify the participants with their respective group correctly. Effect size of the parameters such as %LDR (or %SDR), SC, SSRT, PES, mean delay, and grand mean was more with the range of (0.49–0.66) as compared to other parameters such as alerting, orienting, and conflict score with the range of 0.033–0.004. Therefore, in the further analysis only these six parameters were combined through multinomial logistic regression to determine which parameters together can classify the children in their respective group correctly. Overall percentage of the correct classification of the individual seven parameters was 64–72%. These parameters were then combined with multinomial logistic regression. We have tried all the possible combinations of the parameters. By combining any of the two parameters overall correct classification was 72–87%. By combining three it was 82–92%, by four it was 89–95%, by five it was 96–98%, by six it was 97–98%, and by seven the overall percentage of the correct classification was increased to 97%. Together all the six parameters correctly classified 97.8% of the participants, with ADHD children classified correctly 100%, ODD children 100%, and control with 93.3%. This study indicates that cognitive mechanisms underlying ADHD such as response inhibition, error monitoring, attentional disengagement, delay aversion were together not only helpful in diagnosis of children with ADHD rather it also helpful in differential diagnosis. Thus, these cognitive markers should be incorporate in the diagnostic system, which will provide sensitive and specific tool to tap the cognitive impairments, which may help in differential diagnosis.

Conclusion

Diagnosis of a disorder like ADHD is a complex issue for a clinician because of the high prevalence of co-morbid conditions. Misdiagnoses is likely to happen which may in turn affect the treatment. Reasons for misdiagnosis are due to the dependence on subjective reports based on rating scales, interviews from parents while arriving at a diagnostic decision. The subjective reports yield valuable information efficiently and systematically. However, there are some factors, which limit or affect valid ratings such as “rater bias”. Children with ADHD often show strong associations with disorders such as anxiety, depression, conduct disorder (CD), Tourette’s syndrome (TS) and oppositional defiant disorder (ODD) (Angold et al. 1999). Furthermore, ADHD, particularly the inattentive symptoms, are also associated with reading disability (Maedgen 2000). Therefore, differential diagnosis is especially important in ADHD due to both the frequent co-occurrence of ADHD with other disorders and to the potential for other symptoms to produce symptoms that overlap with the behavioral criteria for ADHD. Recent experimental literature has identified cognitive mechanisms underlying ADHD. Thus, there is need to incorporate information on cognitive mechanisms underlying ADHD and inculcate such information in the diagnostic system, which will provide a more sensitive as well as specific tool to tap the cognitive impairments, which may help in differential diagnosis. Such a diagnostic system will provide the clinician with objective data based upon the child’s actual performance and will allow for observation in a paradigm likely to elicit inattention and impulsiveness. Moreover, multiple sources of information, both subjective and objective need to be incorporated in the diagnostic procedure to ensure accurate diagnosis of ADHD.