Real-life data analysis conducted with a large multi-specialty health and psychiatry clinic in the US.

Data was collected from 2,253 patients who visited a psychiatric clinic between January 2015 and December 2020 to explore the potential benefits of using BNA™ as a disease management program in psychiatric practice. These patients were grouped based on their primary diagnosis: 1155 had MDD, 468 had GAD, and 709 had ADHD. In total 539 patients regularly underwent BNA recordings with the Auditory Oddball (AOB) Task. See Table 1 for the demographical information of all patients.

Key findings from using BNA™:

• Depressed patients showed a 15% increase in compliance to MDD therapy (both antidepressants and TMS)
• The need for switching antidepressant medication reduced by over 50%.
• MDD, anxiety, and ADHD, experienced more than double the improvements in general functioning.
• Antidepressant response rates increased by 10%.
• The rate of treatment non-responders in MDD decreased by 17%.

Psychiatric Care and EEG

Psychiatric disorders have a significant impact on individuals and families, but many do not receive adequate care. Routine electroencephalography (EEG) could improve disease management in the psychiatry by providing valuable information about brain function that can assist in diagnosis, treatment planning, and monitoring. EEG is a non-invasive and relatively inexpensive tool that measures the brain’s electrical activity and can detect changes in brain wave patterns that may be associated with certain psychiatric conditions¹. For example, abnormal EEG findings have been reported in individuals with general anxiety disorder ², depression ^3–5, and attention deficit disorder (ADHD) ^6,7 and may be used to inform treatment decisions and personalize care. Additionally, EEG has the potential to be used as a biomarker for predicting treatment response^8,9 and monitoring the effects of interventions and diseases on brain function^10,11. However, extensive preprocessing, training, and expertise required to interpret EEG data can be a barrier to its widespread use in clinical settings. In addition to the challenge of expert interpretation, the variability in brain wave patterns across individuals can make it difficult to discern abnormal findings, in the absence of normative data¹².

BNA™: Towards a Change in Psychiatric Care

Brain Network Analysis (BNA™) technology offers a promising solution to the challenge of widespread use of EEG in psychiatric practice. BNA™ employs sophisticated algorithms to automatically analyze EEG recordings taken during rest and cognitive tasks, producing easy-to-interpret results for quantitative EEG (QEEG) and event-related potentials (ERP) analysis. By automatically providing insights into electrophysiology and the associated cognitive functioning, BNA™ eliminates the need for preprocessing, manual analysis, or advanced interpretation skills. Additionally, BNA™ offers a significant advantage by comparing a patient’s EEG to a comprehensive normative dataset of healthy individuals of the same age, allowing clinicians to draw meaningful conclusions quickly.

Methods

This analysis explores the potential benefits of using BNA™ as a disease management program in psychiatric practice. Data was collected from patients who visited a psychiatric clinic between January 2015 and December 2020. The study included 2,253 patients. These patients were grouped based on their primary diagnosis: 1155 had MDD, 468 had GAD, and 709 had ADHD. In total 539 patients regularly underwent BNA™ recordings with the Auditory Oddball (AOB) Task. Seventy-eight of these patients changed primary diagnosis once and one patient changed twice during the analysis period. They were consequently assigned to more than one diagnostic group. See Table 1 for the demographical information of all patients.

We compared the clinical outcomes of patients who received BNA™ assessments to those who received standard care without BNA™ recordings. We used the Clinical Global Impression Scale (CGI-S) to measure changes in symptom severity at baseline and endpoint, as well as the Global Assessment of Function scale (GAF) to assess changes in overall functionality. Additionally, the study evaluated the impact of BNA™ on response and remission rates in depression using the Patient Health Questionnaire (PHQ-9) specifically for the MDD group. 

In order to address potential variations in treatment duration across datasets, we conducted additional analyses on subsets of the data where the maximum treatment length was capped at 12 months. This approach ensured that any observed effects were not solely due to differences in treatment duration between datasets.

Table 1. Demographical Information on the patients involved in the analysis.

The BNA™ recordings included the AOB task only. On average, patients in the BNA™ group underwent 3 to 4 such recordings throughout their treatment, with the corresponding reports being made available to clinicians via the BNA™ platform within 48 hours of each recording.

Key Findings

1. BNA™ Associated with Higher Symptom Reduction

A significantly larger CGI-S improvement was detected in the BNA™ group relative to the No-BNA™ group in MDD patients (Figure 1 left; F(1,431)=5.45, p=0.020, η_p²=0.01), GAD patients (Figure 1 middle; F(1,201)=5.23, p=0.023, η_p²=0.023) and ADHD patients (Figure 1 right; F(1,331)=5.27, p=0.022, η_p²=0.012). All effects remained statistically significant in the 12 months subset analysis (p=0.027 in MDD, p=0.021 in GAD, p=0.02 in ADHD). 

All effects remained statistically when limiting the analysis to datasets with up to 12 months of treatment duration (p=0.027 in MDD, p=0.021 in GAD, p=0.02 in ADHD).

Figure 1 The average endpoint change in CGI-S in each patient group (MDD, GAD, ADHD). Error bars represent the standard error of the mean.

The results suggest that patients receiving BNA™ experienced more significant symptom relief and functional improvement compared to those who did not receive BNA™ management. A noteworthy aspect of the study was that this outcome was consistent across patients diagnosed with MDD, GAD, or ADHD, which suggests that BNA™ has the potential to enhance psychiatric care for a diverse range of patients.

2. BNA™ Linked To Overall Better Functioning

A significantly larger GAF improvement was detected in the BNA™ group in comparison to the No-BNA™ group in MDD (Figure 2 left; F(1,913)=13.30, p=0.0002, η_p²=0.012), GAD (Figure 2 middle; F(1,342)=34.16, p<0.0001, η_p²=0.084) and ADHD (Figure 2 right; F(1,604)=4.80, p=0.029, η_p²=0.006). All effects remained statistically when limiting the analysis to datasets with up to 12 months of treatment duration (p=0.004 in MDD, p=0.001 in GAD, p=0.045 in ADHD).

Figure 2 The average endpoint change in GAF scores in each patient group (MDD, GAD, ADHD). Error bars represent the standard error of the mean.

The results indicate that individuals in the BNA™ group showed an overall increased level of functioning over time. Clinically, this improvement can indicate that the patients experiencing a reduction in symptoms, are better able to cope with daily stressors, and are able to engage in activities that were previously difficult or impossible to undertake. Like the results for the CGI-S improvements, this outcome was consistent across patients diagnosed with MDD, GAD, or ADHD, which suggests that BNA™ has the potential to enhance psychiatric care for a diverse range of patients.

3. BNA™ Associated to Improved MDD Treatment Outcome

The analysis of MDD patients indicated significantly greater treatment-induced improvements in the BNA™ group compared to the No-BNA™ group (Figure 2; χ²=9.83, p=0.007). This finding was further validated using logistic regression while controlling for variables such as age, gender, baseline PHQ-9, insurance plan, and the number of clinic visits (z=3.08, p=0.002, odds ratio=2.27, CI=[1.34-3.83]). Moreover, patients in the BNA™ group required significantly fewer antidepressant changes, including switching and dose adjustments, compared to those in the No-BNA™ group (Figure 4; F(1,394)=5.50, p=0.019, ηp2=0.015), indicating that BNA™ use was associated with a reduction in the trial-and-error approach to treatment selection.

Figure 3 Response and remission rates in BNA™ and No-BNA™ groups of MDD patients.

Figure 4 Changes in antidepressant medication in BNA™ and No-BNA™ groups of MDD patients. Error bars represent the standard error of the mean.

4. Enhanced MDD Treatment Compliance In The BNA™ Group

MDD patients in the BNA™ group demonstrated significantly higher treatment compliance rates, defined as adhering to the prescribed treatment plan for longer than 45 days, compared to the No-BNA™ group (Figure 5; 81.4% and 66.9%, respectively; χ²=19.77, p=0.001). This finding was further confirmed through logistic regression analysis, controlling for age, gender, and baseline PHQ-9 scores (z=3.325, p=0.008, odds ratio=1.93, CI=[1.32-2.96]). Patients were treated with antidepressants, transcranial magnetic stimulation (TMS) or both.

Figure 5 Comparison of Treatment Compliance Rates between BNA™ and No-BNA™ Groups in MDD Patients.

Optimal therapeutic outcomes and the effectiveness of medical interventions heavily rely on treatment compliance. Empowering patients to actively participate in their healthcare and monitor their progress is crucial in this regard. By providing patients with insights into their brain health and facilitating communication between physicians and patients through easily understandable reports, BNA™ technology could play a significant role in fostering patient engagement and promoting treatment compliance.

Conclusion

The findings presented in this white paper strongly support the potential of the BNA™ technology to transform psychiatric care by automating the analysis of electroencephalography (EEG) data. The study demonstrated that BNA™ significantly improved disease management in psychiatric patients suffering from MDD, GAD, ADHD. Patients who underwent BNA™ assessments experienced greater symptom reduction, improved overall functioning. Additionally, MDD patients, who received BNA™ not only required fewer treatment adjustments but also demonstrated a higher level of adherence to their treatment plans compared to their counterparts without BNA™. The positive outcomes observed across diverse patient groups furthermore highlight the versatility and potential of BNA™ in personalized psychiatric care, enabling clinicians to tailor treatment plans and improve therapeutic outcomes.

The automated analysis provided by BNA™ eliminates the need for preprocessing, manual analysis, or advanced interpretation skills, making EEG utilization more accessible in clinical settings. By comparing a patient’s EEG to a comprehensive normative dataset of healthy individuals within the same age range, BNA™ enables clinicians to quickly and meaningfully interpret the results. This approach can enhance diagnostic accuracy, treatment planning, and monitoring of psychiatric and cognitive conditions.

In conclusion, BNA™ represents a significant advancement in psychiatric care, addressing the challenges associated with EEG utilization. By automating EEG analysis and providing comprehensive insights into brain function, BNA™ has the potential to revolutionize disease management, enhance treatment outcomes, and improve the overall well-being of individuals with psychiatric disorders. Future research and implementation efforts should continue to explore the broader application of BNA™ in clinical practice, further validating its effectiveness and expanding its potential benefits to a larger patient population.