Brain Mapping Performance as an Occupational Therapy Assessment Aid in Attention Deficit Hyperactivity Disorder

Method

This study used a cross-sectional design and focused on a defined number of outputs that were calculated with power spectral analysis (Thatcher et al., 2005). Excessive θ activity is commonly known as the primary neurological landmark of ADHD. Several previous studies have revealed that approximately 90% of children diagnosed with ADHD have increased θ relative power (Markovska-Simoska & Pop-Jordanova, 2017; Monastra, 2008); therefore, θ relative power could be useful in differentiating ADHD from other psychiatric disorders. The θ relative power was selected for further differentiation of BMP in school-age children with ADHD (n = 153) and without ADHD (n = 152).

The participants were ages 7–12 yr and were informed of the objectives and procedures of the testing, and their legal guardians signed a statement of informed consent before their participation in the study. Ethical approval was obtained (MU-CIRB 2017/106.0606). We identified participants’ inclusion and exclusion criteria by briefly reviewing their medical history forms. The inclusion criteria for the group without ADHD included no neurological symptoms, no previous head injury, no history of substance abuse, and a normal IQ as determined by three registered clinical psychologists. The exclusion criteria included poor performance at home and school during the past 2-yr period, which was determined by an occupational therapist asking participants’ parents and assessing their daily prescribed psychiatric medications at least 90 days before the screening interview.

For the group with ADHD, the inclusion criteria were a pediatrician’s diagnosis of ADHD and a normal IQ confirmed by the registered clinical psychologists. This group was recruited from three outpatient services in both public and private metropolitan hospitals. The participants in this group were receiving individual or group psychosocial therapy. They had also been taking stable doses of medication (Ritalin^® and Concerta^®) for more than 4 mo and participated in psychosocial therapy with three occupational therapists 2 times per month (3-hr sessions). The participants had Clinical Global Impression Improvement Scale (Busner & Targum, 2007) scores of 3 (minimally improved; n = 118) and 4 (no change; n = 35). The exclusion criteria included any of the following medical problems: premature birth, poor consciousness, head injury, cerebral palsy, convulsions, paroxysmal headaches, tics, pervasive developmental disorder, substance disorders, mental retardation, epileptic seizures, severe hearing and vision loss, and comorbidity with oppositional defiant disorder disorder and conduct disorder.

Participants’ QEEGs were recorded in a 45-min assessment period of brain mapping (via Brain Master Discovery 24E^TM; Brain Master Technologies, Bedford, OH) in a comfortable sitting position with no distracting light or sound. Electrodes were placed over the 19 relative θ band regions (Fp1, Fp2, F3, F4, F7, F8, Fz, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1, and O2) following the protocol of the international 10–20 system with only 12 channels used (Arns et al., 2013; Markovska-Simoska & Pop-Jordanova, 2017; Monastra et al., 2001; Snyder & Hall, 2006). Data (via NeuroGuide^TM software; Applied Neuroscience, Largo, FL) were captured for 10 min, with eyes continuously open during a resting state, and then translated to QEEG or z scores of a normative database. All QEEGs were analyzed using stepwise multiple regressions with IBM SPSS Statistics (Version 16; IBM Corp., Armonk, NY). This method yielded good-fit models (adjusted R ² at p < .05) for interpretation of each brain location in a percentage of θ relative power comparison between the two groups of participants, which was termed brain mapping performance.

Results

Of the 305 total participants, 78 boys and 75 girls were included in the ADHD group and 79 boys and 73 girls, matched for age and IQ, were included in the non-ADHD group (Table 1). When the signals of the 12 channels were analyzed, the developed BMP model output values of 49.64% for participants in the ADHD group and 64.70% for participants in the non-ADHD group, respectively (Table 2). The comparisons indicated that the ADHD group showed 15.10% weaker primary brain function than the non-ADHD group. For electrodes with a cutoff of 60%, the ADHD group corresponded to an average value of 69.03% (F4, Fz, T5, and T6), and the non-ADHD group corresponded to an average value of 78.97% (Fp2, F4, Fz, Cz, T4, T5, and T6). The comparison indicated that the ADHD group had 9.67% weaker primary brain function than the non-ADHD group.

Discussion

This study reports a novel model based on collaboration between researchers in the fields of biomedical engineering and occupational therapy. We identified three outcomes that were associated with BMP, which could allow an integrative assessment of QEEG and its strong functional predictors before setting occupational goals for clients with ADHD (Chu & Reynolds, 2007a, 2007b). This model could serve as an additional assessment tool for occupational therapists that could be used in parallel with SMART (specific, measurable, attainable, relevant, time-oriented) goals.

First, poor BMP, that is, decreased percentages (overloaded θ), was demonstrated to be associated with a low frequency of brain localization (Markovska-Simoska & Pop-Jordanova, 2017; Monastra, 2008; Snyder & Hall, 2006). These data were clearly differentiated between the two groups, which were matched for age and IQ. The highest differentiated percentages in both groups were localized at Cz, indicating a role of primary sensorimotor functions (Brodmann Areas 6, 4, 3, 12, and 24; Hart et al., 2012; Rubia et al., 2014; Snyder et al., 2015; Sonuga-Barke et al., 2016; Wolraich et al., 2011). This finding could indicate that participants in the ADHD group had 3 times lower sensorimotor function than those in the non-ADHD group, which is consistent with the results of previous studies that reported deficits in the primary motor cortex, the prefrontal and frontal areas, and the central sensorimotor strip in children with ADHD (Barry et al., 2003; Lazzaro et al., 1998; Monastra et al., 1999). However, the participants in the ADHD group demonstrated increased cognitive–emotional valence or emotional awareness (Fp1) and language comprehension (T3) compared with those in the non-ADHD group. Because the Cz position covered Brodmann Area 24, the anterior cingulate gyrus indicated no feedback mechanism to emotion-related brain regions (limbic system), leading to a deficiency in emotional management, low motivation, and lack of a sense of body movement and motor imagery (Hart et al., 2012; Rubia et al., 2014; Snyder et al., 2015; Sonuga-Barke et al., 2016; Wolraich et al., 2011).

The BMP component at the Cz position strongly indicates a need for occupational goals related to sensorimotor intervention (Chu & Reynolds, 2007a). Therefore, occupational therapy services could implement programs to improve motor planning (C3), upper limb hand function coordination (C4), and attentional shifting (Pz).

Second, a recent report (Yang et al., 2018) that analyzed the Cz position (via the TBR) showed benefits of neurofeedback training (20 sessions over 8 wk), which reduced core symptoms of ADHD (i.e., inattention, impulsivity, and oppositional defiance). We did not conduct neurofeedback training, although BMP showed different average percentages at the Cz position (23.20% for the ADHD group vs. 84.40% for the non-ADHD group). BMP also showed differences in executive function (EF) in five brain locations (Fp1, Fp2, FZ, F7, and F4) in both groups; namely, a 7.28% difference (49.42% for the ADHD group vs. 56.70% for the non-ADHD group) was observed.

This outcome was in accordance with the effectiveness of the Cognitive–Functional (Cog–Fun) intervention conducted by medical doctors and occupational therapists according to a recent randomized controlled trial (Hahn-Markowitz et al., 2016, 2017). The authors showed weak improvement in EF (via the Behavior Rating Inventory of Executive Function; Gioia et al., 2000) in a group of participants with ADHD, which was maintained for 3 mo. Surprisingly, no significant processing planning memory (F3) or sustained attention emotion (F8) functions were found in our group of participants without ADHD, whereas those with ADHD exhibited significant F3 and F8 functions (52.60% for F3 and 23.60% for F8). In this study, we found this positive functioning might be predominantly associated with the ADHD group.

We noted that the non-ADHD group exhibited compensatory mechanisms with higher selective and sustained attention memory function (F4; the highest percentages at 86.90%) than either processing planning memory (F3) or sustained attention (F8) function in a resting state. Moreover, the ADHD group exhibited a higher percentage of language comprehension (T3) than the non-ADHD group, indicating occupational deprivation of EF (Hwang et al., 2015), writing (Bruce et al., 2006), academic presentation (Wassenberg et al., 2010), and everyday communication (Hayden et al., 2018). When subcomponents of the T3 region were detailed, three regions (T4, T5, and T6) were better predictors of the primary functional outcome of the ADHD group, whereas two regions (T4 and F7) were associated with the non-ADHD group. Thus, occupational therapy services could focus on social–emotional learning programs (Chu & Reynolds, 2007a, 2007b), including personality emotional tonality (T4), meaning construction (T5), and facial recognition (T6), for clients with ADHD, which could help them lead a balanced life of pleasure and mindfulness.

Finally, the lowest percentages of θ relative power were considered in both groups. Participants in the ADHD group were found to have weaker working memory (F7) than participants in the non-ADHD group. Evidence has indicated that a portion of EF is characteristic of poor learning performance in people with ADHD (Mawjee et al., 2017; Yang et al., 2017); learning performance can be improved by the Cog–Fun intervention, however (Hahn-Markowitz et al., 2017). Interestingly, significant predictors of ADHD included F7 for intentional motivation and Fz for personality change, a finding supported by previous studies (Gomez & Corr, 2014; Sobanski et al., 2010). These studies reported personality changes in relation to reduced intentional motivation in the ADHD group from childhood to adulthood.

The current findings agree with those of previous studies showing that people without ADHD have the lowest percentages of θ theta relative power at the working memory (F7) and cognitive emotion valence (Fp1) positions, which may indicate high connectivity and brain plasticity. However, a previous study (Liu et al., 2017) that used 5 wk of computerized working memory training showed that patients with ADHD can be trained in working memory by occupational therapists. QEEG can provide a strong predictor of primary functional outcomes in children and, thus, could be a low-cost and safe (Kaiser, 2007) technique for identifying occupational goals in clients with ADHD.

Study Limitations and Recommendations for Future Studies

A limitation of this study was the inclusion of patients with the hyperactive and impulsive type of ADHD, which might have affected the data analysis. The TBR should be further studied to use QEEG in neurofeedback training in addition to occupational therapy for ADHD. Further interventions should be assessed with longitudinal designs. Other beneficial outcomes could also be examined, such as whether self-efficacy among parents and teachers of children with ADHD improve while engaging in social and emotional learning activities and whether the psychosocial context of the parents, teachers, or children with ADHD can be modified.

Implications for Occupational Therapy Practice

The outcomes of this study have the following implications for occupational therapy practice:

Conclusion

This pilot study shows that BMP could be used as an additional assessment tool for setting occupational goals for neurological, psychosocial, and behavioral performance interventions. The findings of this study indicate that the children with ADHD had greater emotional awareness and language comprehension than those without ADHD. To enhance the effective delivery of occupational therapy services, QEEG training will allow clinicians to interpret the functional connectivity of the human brain, manage artifacts, and better understand reporting techniques. These features will allow individually tailored programming with disease progression and allow clients to be compared at pre- and postassessment intervals.