Open access peer-reviewed chapter
Abstract
Previous studies have shown that quantitative electroencephalography (qEEG) provides measures of brain wave voltage and symmetry within each of the standard bandwidths. These qEEG measures are neurophysiological correlates of brain wave signatures for various aspects of cognition and behavior and are susceptible to neurofeedback training for improving human performance. Using exam scores and an individualized self-inventory (ISI) of psychosocial interactions, we provide unique data for probing behavioral and cognitive performance of medical students. Increments in voltage within the standard theta (4–7 Hz) and beta (15–20 Hz) frequencies and decrements in the theta–beta ratio (TBR) suggest improvements in attentional control. Associations between right-sided frontal alpha asymmetry (fAA) and ISI scores for negative self-perceptions suggest a novel qEEG signature for emotional balance. These findings suggest that changes in qEEG voltages and asymmetries may be predictive of improvements in attentional control, cognitive performance, and psychosocial skills, as well as serving as surrogate markers for neurofeedback training-related changes in neuroplasticity.
Keywords
- electroencephalography
- academic performance
- psychosocial interactions
- theta–beta ratio
- frontal alpha asymmetry
1. Introduction
Academic learning requires a person to interact with other individuals [1]. Depression and anxiety may be good predictors of learning disabilities and academic underachievement [2] probably because the emotional state is a marker for an individual’s ability to manage their psychosocial interactions [3]. There are numerous examples of brilliant persons with high intelligence quotients who do not do anything meaningful with their intelligence because of difficulties with psychosocial interactions. Such persons may be deficient in emotional intelligence or balance [4]. For medical students, overall health and wellness appear to be correlated with their academic performance [5]. Academic achievement in medical school is a good example of a situation wherein stress can unmask latent mental or emotional imbalances which, in turn, lead to academic underachievement or failure [6]. Previous studies from our laboratory suggest that quantitative electroencephalographic (qEEG) measures of theta–beta ratio (TBR) and frontal alpha asymmetry (fAA) may be useful neurophysiological correlates of academic achievement and negative perceptions of psychosocial interactions in medical students [7, 8, 9, 10]. TBR is reported to be a qEEG marker for a person’s capacity to focus their attention on salient information [11]. Between a control human structure–function course introductory exam and a comparison structure–function exam on different topics, we have reported significant increments in voltage within the standard theta and beta frequencies combined with a significant decrement in TBR [7]. These findings were associated with no significant changes in the magnitude of voltages in the standard delta and alpha bandwidths and, therefore, suggest an overall increase in attention control for our pilot study cohort. Frontal (F8–F7) alpha asymmetry (fAA) is reported to be a qEEG marker for negative emotions. In our pilot study cohort, we previously reported a significant negative correlation between the magnitude of F8–F7 frontal alpha asymmetry and “depressed” score on an individualized self-inventory (ISI) of the cohort’s perceptions of their psychosocial interactions, which purported to be indicative of negative perceptions of themselves [10].
This chapter is intended as a primer for neurophysiological evaluation of qEEG brain maps and demonstrates how qEEG technology is becoming a useful tool for probing the human brain. The relatively inexpensive qEEG brain maps showing electrical activity are analogous to the much more expensive functional magnetic resonance imaging (fMRI) maps; therefore, validating qEEG as the “poor man’s” fMRI. We will demonstrate the utility of qEEG by interpreting the qEEG maps of individual medical students exhibiting the highest and lowest TBRs and fAAs.
2. Methodology
The methods and protocols described herein were approved by the American University of Antigua College of Medicine Research Council which served as the University’s Institutional Review Board (IRB). The study subjects provided their informed consent. A total of 10 male subjects were recruited; 1 subject discontinued the study due to his ill health. Females were excluded because of the potential confounding influence of hormonal changes associated with their menstrual cycles. Nine individuals underwent 5–10 minutes of eyes-closed (EC) qEEG measurements at 19 standard electrode sites [12, 13, 14, 15, 16, 17, 18, 19, 20]. The Brain Master Discovery System (Brain Master Technologies, Inc., Bedford, OH, USA) was used to take the qEEG readings 3 days before each of the first two summative block examinations covering standard first-semester integrated basic science courses. For the purposes of this chapter, the qEEG voltage brain maps were selected for the subjects with the highest and lowest exam scores to demonstrate the spectrum of changes found to be associated with academic achievement.
The qEEG measurements were performed with subjects seated comfortably with their eyes-closed, in a dimly lit room. After manual editing with the New Mind Maps online editing tool (New Mind Technologies, Roswell, GA, USA), the mean ± SD of the voltage amplitudes, stated in μV, and the mode frequency in each bandwidth, expressed in Hz, were measured for the following standard qEEG frequencies: delta, theta, alpha, and beta. The average theta-beta ratio of the voltages recorded at each of the 19 standard electrode sites was computed. All qEEG recordings were performed at the same time in the morning, after overnight fasting to avoid confounding factors related to food ingestion and metabolism. The subjects refrained from recreational drugs such as alcohol, marijuana, nicotine, and caffeine for the 24 hours before the qEEG recording session. None of the patients reported using prescription or recreational drugs within the previous month, according to self-report. Preliminary results suggest that eye opening, which is known to inhibit alpha brainwave voltage amplitude, resulted in changes in alpha voltage that were not uniformly reproducible. The qEEG measurements were therefore only interpreted for the closed-eye condition. Frontal alpha asymmetry (fAA) was computed from the mean alpha voltages at the frontal recording sites F7 (left) and F8 (right) as follows: ([F8 − F7]/F8 × 100). The inferior frontal gyrus is an area where “mirror neurons,” hypothesized to process information about psychosocial interactions have been identified [21]. Negative asymmetry values are indicators of right-sided, nondominant hemispheric alpha asymmetry resulting from greater activation of the right frontal cortex. Negative fAA was defined as a qEEG recording where the average alpha voltage at the left frontal F7 scalp electrode was greater than the voltage at the symmetrical right frontal F8 scalp electrode. Positive asymmetry values are indicators of left-dominant hemisphere alpha asymmetry. Positive fAA was defined as qEEG where the mean alpha voltage at the right frontal F8 scalp electrode was greater than the voltage at the symmetrical left frontal F7 scalp electrode.
Within 8 hours of qEEG measurement, each study subject completed an Interactive Self-Report Inventory (ISI, New Mind Technologies, Roswell GA, USA) online at the New Mind Maps website (https://www.newmindmaps.com). For the purposes of this chapter, the ISI scoring of “depressed” psychosocial interactions was used to demonstrate the correlation and potential utility of the fAA measurement. Depressed individuals are expected to have had negative thoughts about themselves based on a correlation coefficient > 0.8 between relevant inventory items and the Beck’s Depression Inventory (New Mind Technologies, Roswell GA, USA). For the purposes of this chapter, the qEEG asymmetry brain maps were selected for the subjects with the highest and lowest ISI “depressed” scores to demonstrate the spectrum of changes found to be associated with a subject’s negative perceptions of their psychosocial interactions.
3. Results
3.1 Theta–beta ratio
Theta–beta ratio (TBR) is reported to be a qEEG marker for a person’s capacity to focus their attention on salient information. For this cohort, between the control block 1 and the comparison block 2 human structure–function course examinations, we have reported significant increments in voltage within the standard theta and beta EEG frequencies combined with a significant decrement in the theta–beta ratio. (TBR) These findings were associated with no significant changes in the magnitude of voltages in the standard delta and alpha bandwidths and, therefore, suggest an overall increase in attention control for the cohort.
Figure 1 presents qEEG maps comparing the control exam (upper panel) and comparison exam (lower panel) measures of the voltages from the subject earning the highest block 2 exam score of 90%.
Figure 1.
qEEG voltage maps for the subject with the highest exam score. Green circles indicate the scalp recording sites where the voltages are in the normal reference range (OK = normal) for each of the standard delta (1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), and beta (15–20 Hz) EEG bandwidths. Light blue (LO = low) and dark blue (VLO = very low) circles indicate voltages 1 and 2 standard deviations below the normal reference range; red (HI = high) and yellow (VHI = very high) circles are voltages 1 and 2 standard deviations above the normal reference range, respectively. Upper panel: eyes-closed control map recorded 3 days before the first control exam covering introductory material taught in the subject’s first structure–function course exam. Lower panel: eyes-closed comparison map recorded 3 days before the comparison exam covering material taught in the subject’s second human structure–function course exam, 5 weeks after the control recording. Theta and beta voltages are used to compute theta–beta ratio, a qEEG marker for attentional control.
3.2 Theta bandwidth changes for the subject with the highest exam score
Five recording sites exhibit no changes from normal theta frequencies coded green, four sites show theta increases from 1 standard deviation below normal (light blue) to normal (green), seven sites show theta increases from 2 standard deviations below normal (dark blue) to 1 standard deviation below normal (light blue), and three sites show theta increases from 2 standard deviations below normal (dark blue) to normal (green). These findings are indicative of an overall increase in theta voltage for the subject with the highest exam score. The predominant change in theta is observed at 14 of 19 sites wherein increases in theta voltage are found.
3.3 Beta bandwidth changes for the subject with the highest exam score
Two recording sites exhibit no changes from normal beta frequencies coded green, five sites show beta increases from normal (green) to 1 standard deviation above normal (red), nine sites show beta increases from 1 standard deviation below normal (light blue) to 1 standard deviation above normal (red), one site shows a beta increase from 2 standard deviations below normal (dark blue) to 1 standard deviation above normal (red), and two sites shows beta increases from normal (green) to 2 standard deviations above normal (yellow). These findings are indicative of an overall increase in beta voltage for the subject with the highest exam score. The predominant change in beta is observed at 12 of 19 sites wherein increments in beta voltage are found.
3.4 Theta-beta ratio for the subject with the highest exam score
The theta and beta voltage changes resulted in larger increases in beta than in theta voltage, thereby decreasing the theta-beta ratio (TBR) by 11.7% from 0.93 to 0.84. This range of TBR indicates that the subject with the highest exam score is functioning with 7%–16% more beta than theta bandwidth voltage.
Figure 2 shows representative qEEG maps comparing the control block 1 (upper panel) and comparison block 2 (lower panel) voltage measurements from the subject earning the lowest block 2 exam score of 48%.
Figure 2.
qEEG brain maps for the subject with the lowest exam score. Green circles indicate the scalp recording sites where the voltages are in the normal reference range (OK = normal) for each of the standard delta (1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), and beta (15–20 Hz) EEG bandwidths. Light blue (LO = low) and dark blue (VLO = very low) circles indicate voltages 1 and 2 standard deviations below the normal reference range; red (HI = high) and yellow (VHI = very high) circles are voltages 1 and 2 standard deviations above the normal reference range, respectively. Upper panel: eyes-closed control map recorded 3 days before the first control exam covering introductory material taught in the subject’s first structure–function course exam. Lower panel: eyes-closed comparison map recorded 3 days before the comparison exam covering material taught in the subject’s second human structure–function course exam, 5 weeks after the control recording.
3.5 Theta bandwidth changes for the subject with the lowest exam score
One recording site shows no change in theta voltage from normal (green), five sites show no changes in theta voltage from 1 standard deviation below normal (light blue), nine sites show no changes in theta voltage from 2 standard deviations below normal (dark blue), and four sites show decreases in theta voltage from normal (green) to 1 standard deviation below normal (light blue). These findings indicate minimal changes in theta voltage for the subject with the lowest exam score. The predominant change in theta is observed at 4 of 19 sites wherein decrements from normal to 1 standard deviation below normal are found.
3.6 Beta bandwidth changes for the subject with the lowest exam score
A total of 16 recording sites show no change in beta voltage from 1 standard deviation below normal (light blue), one site shows no change in beta voltage from 2 standard deviations below normal (dark blue), one site shows a decrease in beta voltage from normal (green) to 1 standard deviation below normal (light blue), and one site shows an increase in beta voltage from 2 standard deviations below normal to 1 standard deviation below normal. These findings also indicate minimal changes in beta voltage for the subject with the lowest exam score. The predominant change in beta is observed at 16 of 19 sites wherein no changes from 1 standard deviation below normal are found.
3.7 Theta-beta ratio for the subject with the lowest exam score
As for the subject with the highest exam score, the theta and beta voltages in this subject with the lowest exam score also resulted in larger increases in beta than in theta voltage, thereby decreasing the theta-beta ratio (TBR) by 16.1%, from 1.55 to 1.30. This range of TBR indicate that the subject with the lowest exam score is functioning with 30%–55% more theta than beta bandwidth voltage.
3.8 Frontal alpha asymmetry
Frontal (F8–F7) alpha asymmetry (fAA) is reported to be a qEEG marker for negative emotions. In the same cohort, we previously reported significant negative correlation between the magnitude of F8–F7 frontal alpha asymmetry and “depressed” score on an individualized self-inventory (ISI), purported to be indicative of negative perceptions of a person’s psychosocial interactions. No significant correlations were found between the ISI and the exam scores.
Figure 3 shows representative qEEG maps from the subject with the highest “depressed” ISI score (upper panel) and from the subject with the lowest “depressed” ISI score (lower panel).
Figure 3.
qEEG symmetry maps for the subjects with the highest and lowest “depressed” ISI scores. Green circles joined by horizontal lines indicate the symmetrical homologous right and left scalp recording sites where the voltages are ≤0.5 μV (EVN = even) for each of the standard delta (1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), and beta (15–20 Hz) EEG bandwidths. Blue (LO = low) and red (HI = high) circles joined by horizontal red lines (UVN = uneven) indicate the recording sites where the voltages are >0.5 μV lower and higher than the symmetrical homologous site, respectively. Upper panel: eyes-closed map recorded on the same day as completion of the Individualized Self Inventory (ISI) for the subject with the highest “depressed” ISI score. Lower panel: eyes-closed map recorded on the same day as completion of the ISI for the subject with the lowest “depressed” score. The highest and lowest “depressed” ISI scores are indicative of increased and decreased negative perceptions of a person’s psychosocial interactions, respectively.
3.9 Frontal alpha asymmetry in the subject with the highest “depressed” score
F8–F7 fAA in the subject with the highest “depressed” score shows F8 voltage 1 standard deviation above normal (coded red) combined with F7 voltage 1 standard deviation below normal (coded blue). There is no normal asymmetry in this map for any of the qEEG bandwidths because none of the symmetrical right–left recording sites are less than 1 standard deviation from each other, considered equal, even or no asymmetry.
3.10 Frontal alpha asymmetry in the subject with the lowest “depressed” score
F8–F7 fAA in the subject with the lowest “depressed” score shows F8 voltage 1 standard deviation below normal (coded blue) combined with F7 voltage 1 standard deviation below normal (coded red). Normal in this map is shown for the theta voltage at recording sites T4–T3 wherein both voltages are less than 1 standard deviation from each other, considered equal or even asymmetry. It is noteworthy that the F8–F7 voltages for this subject with the lowest “depressed” ISI score are reverse of those in the subject with the highest “depressed” score. This pattern is indicative of right-sided fAA in the subject with the highest “depressed” score and left-sided fAA in the subject with the lowest “depressed” score.
4. Discussion
4.1 Neurophysiological markers for academic underachievement
Most people believe that academic underachievement is caused by low intelligence, motivation, or social background [22, 23, 24]. This view fails to explain the substantial proportion of extremely bright, driven, and affluent individuals who display academic underachievement. Academic underachievement has long been hypothesized to be caused by inter-individual variations in preferred learning styles (such as auditory vs. visual learning). For instance, it has been demonstrated that in a first-year chemistry course at an Australian institution, introverted Myers-Briggs Personality Type students outperform extroverted students [25]. Students with reflective personalities and visual learning style are reported to have the best academic performance in an ophthalmology course for fifth-year students in a Chilean medical school [26]. Although learning style preferences are hard to measure, most curricula demand that students successfully use a variety of learning methods [25].
To date, research on optimizing academic performance has encountered an inability to translate what is known about learning style preferences to how effectively students use the critical nodes and hubs in their cerebral cortex for learning. This barrier to learning outcomes research has been effectively overcome by the advent of computer-based technologies for measuring electrical and metabolic functions of the cerebral cortex such as qEEG, functional MRI (fMRI), and psychometric testing. While fMRI provides information about metabolic activity in the brain, quantitative qEEG is primarily a measure of electrical activity. Due to the tight coupling between metabolic and electrical signals in the brain [27], each can be thought of as a surrogate for the other. In that context, an inexpensive qEEG record can be useful as a surrogate for a very expensive fMRI recording [28]. qEEG has been validated by the US Food & Drug Administration as a medical device and diagnostic method for identifying children diagnosed with attention deficit disorder (ADD) and attention deficit hyperactivity disorder (ADHD) syndromes (FDA News Release July 15, 2013). qEEG has also been shown to be useful in selecting children and adults who are likely to respond to psychostimulant treatment [11, 12, 13]. qEEG is also increasingly used for neurofeedback training in sports and academia. qEEG profiles indicative of improved athletic performance in major league baseball players [29], and Olympic athletes with performance decline following injury have been reported [14]. qEEG-based neurofeedback training has also been shown to be effective in improving the neurosurgery skills of ophthalmic microsurgeons [15]. A relatively specific qEEG signature indicates working memory deficits in low-achieving high school students, compared with high-achieving students [16]. Specific changes in qEEG, indicating increased brain performance, also as a result of certain yoga practices [17, 18] suggest the ability of yoga training to produce improvements in academic performance.
4.2 The problem of academic underachievement in medical school
Concerns about academic under-preparedness associated with the transition from the preprofessional phase to the professional phase of undergraduate medical education are likely to be the same as those associated with transition from the preclinical to the clinical phase [30]. A key feature of low academic achievement in the first two preclinical years of medical school appears to be an underappreciation of the volume and complexity of the information needed to be learned. Students are often hampered by not having developed a systematic method for handling complex information during their preprofessional undergraduate training. A “tried-and-true,” albeit highly individualized, method of mastering complex information is usually needed to provide the impetus for developing confidence in test taking skills and for meeting the challenges of translating complex basic science knowledge into evaluation, problem solving and differential diagnosis. The outcome of this difficult transition is twofold: (i) a large number of students exhibit low academic achievement on formative and summative examinations designed to test medical information processing skills and substandard performance on standardized (NBME, USMLE Step 1) examinations designed to evaluate readiness for entry into the clinical phase of undergraduate medical training; and (ii) many students exhibit high academic performance on their formative and summative course examinations but low performance on the standardized NBME and USMLE. We have previously reported that TBR, a measure of attentional control, is negatively correlated with academic achievement in medical students [7, 8, 9]. In this chapter, we depict and interpret the qEEG brain maps demonstrating the spectrum of deviations from the normative reference values for qEEG theta and beta voltages and higher versus lower TBR seemed to identify students exhibiting neurophysiological and cognitive deviations from the norms, which could explain their underachievement. To our knowledge, this is the first report depicting qEEG brain maps of individuals at two ends of a spectrum of academic performance on a medical school exam. The significance of the qEEG maps is quite obvious from the global changes in color-coded range of voltages shown in Figure 1 and 2. The map of the subject with the highest exam score shows colors indicative of substantial increases in both their average theta frequency and their average beta frequency voltages Figure 1. The map of the subject with the lowest exam score shows colors indicative of very little change in their theta and beta voltages (Figure 2). We speculate that these color-coded voltage changes suggest that the subject with the highest exam score might have improved neural plasticity, compared to the subject with the lowest exam score. We envisage such usage of qEEG as a putative marker for identifying individuals who might benefit from neurofeedback or other types of brain training and educational counseling. Such targeted interventions might be expected to change the qEEG brain maps and neural plasticity associated with studying for a high-stakes medical school examination, as described in this chapter, or preparing for other rigorous academic challenges.
4.3 Evaluating the brain networks involved in facilitating complex learning
The advent of qEEG and fMRI technologies have led to the discovery of important correlations in the electrical and metabolic profiles between areas of the cerebral cortex considered to be the main nodes and hubs for learning and memory. The key networks are the cingulate, arcuate and uncinate. In this study, the qEEG data were interpreted by measuring, within each of the EEG brain wave bandwidths (beta, alpha, theta, delta), magnitude (electrical power), dominant frequency and coherence between recording sites. We have successfully used alpha and beta coherences to demonstrate correlations between the amount of communication between the cortical tissue in the vicinity of each interhemispheric recording site, a measure of network development, integrity, and function in this same cohort of medical students with apparent attentional dysregulation and academic underachievement [9]. Our study is corroborated by research demonstrating correlations between the brain’s executive functions and the frontal theta, alpha and beta interhemispheric coherences of 168 Iranian university students in their twenties [31], as well as by a metanalysis suggesting that the brain’s executive function is a positive predictor of academic performance in primary school children [32].
4.4 The cingulate network in complex learning
The cingulate network is also known as the dorsal pathway for cognition, and activity in this network represents the cerebral cortex’s neutral or idling gear. The cingulate network may be the equivalent of a computer’s default mode network, involving areas that are operating when a person’s eyes are closed and in a state of “day-dreaming” or not involved in deep thought and problem-solving [33]. Linkage of electrical magnitude (voltage), dominant frequency, and coherence in the prefrontal (Fp1, Fp2), frontal (F3, F4, F7, F8), central (C3, Cz, C4), parietal (P3, Pz, P4) and occipital (O1, O2) electrodes is thought to reflect activity of the cingulate default mode network. The cingulate network is named for the anterior and posterior cingulate cortical areas, the anterior involved in high-level information processing of socialization, empathy, outcome/error monitoring and action planning. Error-related negative emotional responses are found to be diminished in individuals with anterior cingulate lesions [34]. The posterior cingulate is linked to interpreting emotional salience and both the anterior and posterior cingulate have strong connectivity to the insular cortex, the main area for integrating and interpreting interoceptive responses [35]. The cingulate network appears to contain critical nodes and hubs for motivating emotional aspects of learning and memory [36]. Mastery of a lengthy, rigorous medical curriculum may require normal range of electrical activity in the cingulate network to support empathy-motivated and self-corrective learning paradigms. qEEG deviation from the norm in the cingulate network could, therefore, provide a key signature for academic underachievement in a medical curriculum. We have previously reported that right-sided fAA, a measurement of negatively valenced emotions, is positively correlated with negative self-perceptions of the psychosocial interactions among medical students [10]. In this chapter, we show and interpret the qEEG brain maps demonstrating a spectrum of fAA correlating with negative self-perceptions. We also provide the first depiction in the scientific literature of qEEG brain maps from subjects at both ends of an emotional scale from high to low “depressed” ISI score and from high to low nondominant hemispheric, right-sided fAA. These findings should be interpreted cautiously because of a report that there was no correlation between these variables in a robust multiverse analysis of five “clinically depressed” populations [37].
4.5 The hippocampus in complex learning
The hippocampus is a gray structure in the center of the brain and is necessary for normal learning, memory, mood, and emotion [38]. Learning and memory are also highly dependent on neurogenesis. The adult brain generates new brain cells at the rate of 700 neurons per day in the hippocampus and by the age of 50, humans are thought to exchange the entire population of neurons with which they were born [39]. In adult laboratory animals, blocking brain neurogenesis limits the animal’s ability to navigate the environment, a function highly dependent on working memory [40] and blocking neurogenesis also results in depression and the inability of antidepressant medications to work [41]. Brain-derived neurotrophic factor (BDNF) is thought to be the main stimulator of neurogenesis in the human brain [42]. Aerobic exercise, learning and sexual activities, 20%–30% calorie restriction, diets high in flavonoids (e.g., curcumin), resveratrol (e.g., blueberries, grapes, dark chocolate, and red wine), omega-3 fatty acids (e.g., walnuts, fatty fish such as salmon), folic acid and zinc are known to increase BDNF and neurogenesis, whereas diets high in saturated fats, sugars and ethanol, vitamins A, B and E deficiencies, sleep deprivation, stress, aging, inflammation, as well as exposure to high plasma levels of cortisol decrease BDNF and neurogenesis [43]. We have previously shown robust correlations between hippocampal neurogenesis and physical activity, maze learning and environmental enrichment in normal healthy rats, as well as in rats recovering from kainite-induced epileptic seizures [44, 45, 46]. The results of these studies support the notion that interventions which increase hippocampal neurogenesis are also likely to increase the cognitive learning and memory functions of the human brain [47]. Studying the effects of hippocampal neurogenesis was beyond the scope of the qEEG studies reviewed herein. However, modafinil, a drug known to stimulate neurogenesis, is reported to decrease qEEG voltage, across all the standard EEG frequencies [48]. This finding of a limitation on qEEG voltage suggests that in the presence of active neurogenesis, we may not be able to observe the large global changes in theta and beta power indicative of the putative improvement in neural plasticity depicted in the (Figure 1) brain map from the subject with the highest exam score.
5. Conclusion
This chapter introduces qEEG brain map interpretation in newly matriculated medical students transitioning from undergraduate science to the preclinical basic medical science phase of their medical training exhibiting a spectrum from high to low theta-beta ratio (TBR), a putative measure of attentional control, and in students showing a spectrum of right-sided and left-sided frontal alpha asymmetry (fAA), a putative measure of negative emotions. The qEEG changes in TBR are highly correlated with academic performance on a first-semester human structure–function (anatomy-physiology) exam and the qEEG changes in fAA are highly correlated with “depressed” scores on an individualized self-inventory of their psychosocial interactions. These brain maps suggest that changes in qEEG voltages and asymmetries may be predictive of changes in attentional control, cognitive performance, and psychosocial skills and may serve as surrogate markers for neurofeedback training related changes in neuroplasticity and in cognitive learning and memory functions of the human brain.
Acknowledgments
This research is supported by a grant from the American University of Antigua College of Medicine. The authors are grateful for the expert technical assistance of Dr. Vasavi R. Gorantla for performing the qEEG recordings.
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