Individual Alpha Peak Frequency, an Important Biomarker for Live Z-Score Training Neurofeedback in Adolescents with Learning Disabilities

Abstract

Learning disabilities (LDs) have an estimated prevalence between 5% and 9% in the pediatric population and are associated with difficulties in reading, arithmetic, and writing. Previous electroencephalography (EEG) research has reported a lag in alpha-band development in specific LD phenotypes, which seems to offer a possible explanation for differences in EEG maturation. In this study, 40 adolescents aged 10–15 years with LDs underwent 10 sessions of Live Z-Score Training Neurofeedback (LZT-NF) Training to improve their cognition and behavior. Based on the individual alpha peak frequency (i-APF) values from the spectrogram, a group with normal i-APF (ni-APF) and a group with low i-APF (li-APF) were compared in a pre-and-post-LZT-NF intervention. There were no statistical differences in age, gender, or the distribution of LDs between the groups. The li-APF group showed a higher theta absolute power in P4 (p = 0.016) at baseline and higher Hi-Beta absolute power in F3 (p = 0.007) post-treatment compared with the ni-APF group. In both groups, extreme waves (absolute Z-score of ≥1.5) were more likely to move toward the normative values, with better results in the ni-APF group. Conversely, the waves within the normal range at baseline were more likely to move out of the range after treatment in the li-APF group. Our results provide evidence of a viable biomarker for identifying optimal responders for the LZT-NF technique based on the i-APF metric reflecting the patient’s neurophysiological individuality.

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

“…Applying the well-known Altman Z-score model [43][44][45][46][47][48] by financial economist Altman [49] allows observing US bankrupt and non-bankrupt companies using 22 financial ratios and mathematical statistics method to establish the Z-score model with five financial variables. In particular, the approach of the Z-score model is well designed, and this model has been widely applied in various fields, such as for studying obesity risk [43], learning disabilities [44], BMI [45], insolvency risk [46], and coronary artery disease [47].…”

“…Applying the well-known Altman Z-score model [43][44][45][46][47][48] by financial economist Altman [49] allows observing US bankrupt and non-bankrupt companies using 22 financial ratios and mathematical statistics method to establish the Z-score model with five financial variables. In particular, the approach of the Z-score model is well designed, and this model has been widely applied in various fields, such as for studying obesity risk [43], learning disabilities [44], BMI [45], insolvency risk [46], and coronary artery disease [47]. Based on the five main variables required by the Z-score model [48,49], its definition mainly includes 10 financial ratios: working capital, total assets, working capital, statutory surplus reserve, undistributed earnings, earnings before interest and taxes (EBIT), common stock market value at the end of the quarter, total liabilities, and net operating income, and different calculations are given to construct similar regression equations to distinguish functions, so as to confirm whether the company is facing financial bankruptcy.…”

Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning

“…Scientific research uses a variety of performance matrices to assess prediction accuracy [ 60 , 61 , 62 ]. In binary classification problems, accuracy and F1-Score derived from Confusion matrics (CMs) have employed metrics for performance evaluation.…”

Exploring Inflammatory Status in Febrile Seizures Associated with Urinary Tract Infections: A Two-Step Cluster Approach

“…These patients represent around one-third of the epileptic children, and it seems that VNS holds promise in reducing more than half of the seizure events frequency in almost 50% of the studied patients after 2 years of follow-up [ 58 ]. The neurofeedback approach, however, is a non-invasive approach based on operant conditioning [ 60 ]. While the EEG signal is recorded, its particular metrics to be trained (power, amplitude, frequency) are analyzed by the computer providing feedback in real-time to the subject [ 60 , 61 ].…”

“…The neurofeedback approach, however, is a non-invasive approach based on operant conditioning [ 60 ]. While the EEG signal is recorded, its particular metrics to be trained (power, amplitude, frequency) are analyzed by the computer providing feedback in real-time to the subject [ 60 , 61 ]. As the signal is modulated toward the desired goal by comparison to the normative database embedded in the software, some “rewards” consisting of visual and/or auditory stimuli are offered to the trained subject.…”