Nicole A. Tetreault, PhD and Matthew J. Zakreski, PsyD
Gifted individuals exhibit increased brain processing, receptivity, and behaviors for intellectual, emotional, sensory, creative, and motor functioning supported by many neuroscience studies. Several studies report increased brain volumes, efficiency of processing in brain networks, and utilization of different brain networks of high IQ (gifted) individuals as a core feature of their being. These increased brain volumes and efficiency networks play a critical role in the overexcitabilities of gifted individuals including the intensity of intellectual, emotional, motor, and sensory processing. Many molecular studies show associations of high IQ and giftedness with genetics, specifically genes involved in neurotransmission, neural plasticity, and development. Both genetics and brain size contribute to the constitution of gifted individuals, but even more important is investigating the intensity of processing in gifted individuals related to the brain and physiological response. Gifted individuals report experiencing the world differently, more intensely, which is a neurodiverse experience. Neurodiversity’s foundation centers on differential brain wiring and maturation, which are found in the gifted brain. We investigate, how does the perceived environment differ for gifted individuals in relation to their neuroanatomy? What are the environmental intensities and interactions perceived and experienced by gifted people? In this literature review, we provide a greater understanding of gifted individuals that engenders advocacy across all domains including intellectual, emotional, social, physiological and psychological processing, development, and the experience of gifted people. With science, insight, and compassion, we develop solutions to guide gifted individuals to thrive.
This paper represents an interdisciplinary effort for a greater understanding of the neuroanatomical, genetic, physiological, metabolic, hormonal, and sensory differences of gifted individuals. The integrative approach to neuroscience provides an understanding of the broad range of human physiology and the depths of the interactions between human body and mind (Ansari & Coch, 2006; Decety & Ickes, 2009). In such research, many questions emerge:
Are there biological correlates for intelligence? Are there physiological or biological differences of high-intelligence quotient (IQ) individuals compared to those with average levels of intelligence? Do metabolism, hormones, digestion, vision, and sensation differ in higher IQ individuals compared to general intelligence? Can overexcitabilities be measured in the body of higher IQ gifted individuals? Can the physiology of the overexcitabilities be measured in the body of high IQ gifted individuals? Can the physiology of the overexcitabilities be measured in the body and brain? Are biological correlates of intelligence valid? Can brain volume predict levels of intelligence and predict IQ?
The American Psychological Association (APA) defines intelligence as how “individuals differ from one another in their ability to understand complex ideas, to adapt efficiently to the environment, to learn from experience and to engage in forms of reasoning to overcome obstacles by taking thought (Nassar et al., 1996; Sternberg, 2013).” There is a long history of psychological research attempting to define and operationalize intelligence, from Spearman’s G, defined as higher cognitive functioning on all measures of performance (Jensen, 1998), to Cattell, Horn, and Carroll’s Three-Stratum Theory (McGrew, 2005). For the purposes of this paper, the authors will focus on the neuroscientific intelligence model of Parietal-Frontal Integration Theory (PFIT), which will be explored in more detail below (Jung & Haier, 2007).
Many intelligence measures that test the biological correlates of intelligence use neuroimaging, which is a process of acquiring images of the brain that form into a brain map (Filler, 2009; Haier et al., 1992). The current measures for determining intelligence and the relationship with the brain uses neuroimaging measures that include brain maps (Filler, 2009; Sandrone et. al, 2014; Sternberg, 2013). Brain maps are developed uses of technology also known as magnetic resonance imaging (MRI) to provide structural magnetic resonance imaging, diffuser tensor imaging, and functional magnetic resonance imaging (Filler, 2009). MRI studies that get further information on how these parts of the brain work and work together are known as functional MRI (fMRI) (Lachaux et al., 2007).
Functional magnetic resonance imaging (fMRI) is a technique that creates brain maps and shows activation of the brain during a task by measuring the blood oxygenation level dependent (BOLD) signal (Lachaux et al., 2007). The BOLD signal is relevant to fMRI studies because neurons do not have internal reserves of energy in the form of glucose and oxygen, so their firing causes a need for more energy to be brought in quickly (Raichle, 2010). Through a process called the hemodynamic response, blood releases oxygen to them at a greater rate than to inactive neurons, a process that can be detected by the different magnetic susceptibility of oxygenated versus deoxygenated blood (Lachaux et al., 2007). Essentially, the parts of the brain that are being used the most require the most replenishing, so their activity shows up more readily on scans (Filler, 2009; Sandrone et al., 2014; Haier, 2009).
Other measures that will be addressed in this paper include positron emission tomography (PET) and electroencephalogram (EEG) (Lachaux et al., 2007; Haier et al., 1992). Positron emission tomography (PET) measures glucose metabolism of neurons (Haier & Jung, 2008). The more glucose the brain uses indicates higher levels of cognitive load, as glucose is used as energy (Haier et al., 1992). The electroencephalogram (EEG) is a non-invasive technique used to measure electrical current within the brain, allowing researchers to determine the amount of effort the brain is using to complete tasks (Antonenko et al., 2010).
Once the brain map is constructed, measurements of brain regions reveal whether there is a correlation with intelligence (Sandrone et al., 2014; Colom et al., 2010; Haier et al., 2004; Ritchie et al., 2015). To do so, intelligence must be measured in a manner that has high validity and reliability (Gignac & Bates, 2017). Several such measures exist and are available to researchers. In the brain map and genetic studies, intelligence measures include: Raven’s Progressive Matrices Test, Wechsler Intelligence Scale for Children (WISC, currently in its fifth edition), and Wechsler Adult Intelligence Scale (WAIS, currently in its fourth edition). These measures provide the researchers with both full-scale intelligence quotient (FSIQ) and scores for the individual subtests that make up the tests (Pietschnig et al., 2015). It is important to note, that IQ scores are designed to measure a specific domain of intelligence and only provide an approximation of intelligence, which is related to the particulars that are being tested, accessed, and evaluated.
The brain is the source of human cognition, the process of acquiring knowledge through hypothesis testing, experience, and sensory processing. Humans have approximately 100 billion neurons that construct complex networks for information processing and provide a broad range of individual variation for a human experience (Sandrone et al., 2014), much of which is predicated on individual neurocognitive differences in sensory perception (Luders et al., 2009). While all individuals demonstrate some idiosyncrasy in sensory perception networks, these differences are felt much more in neurodivergent populations such as gifted individuals (Liu et al., 2007; Luders et al., 2009; Thompson & Oehlert, 2010).
Sensory systems include; auditory (hearing), visual (sight), gustatory (taste), olfactory (smell), somatic sensation (touch) and vestibular (balance and movement) processing, which converge their sensory receptors and information on a single organ, the brain (Colom et al., 2010). The brain interprets sensory information in varying degrees and creates the scaffolding of perception (Liu et al., 2007; Gere et al., 2009). Gifted individuals experience and process sensory information at heightened levels compared to the general population (Nestor et al., 2015). Gifted individuals report smelling odors more distinctly and intensely than the general population, experience heightened tactile stimulation, increased visual perception, amplified levels of auditory stimulation and heightened gustatory sensation (Rinn & Majority, 2018; Vuyt et al., 2016; Gere et al., 2009).
The intensified gifted experience is best described by the concept of overexcitabilities (OEs): areas of the brain where gifted individuals have both an enhanced response to stimuli and the capacity to engage at a more intense level with said stimuli (Winkler & Voight, 2016; Piechowski, 1979). Dabrowski, the primary researcher who proposed this theory, recognized five areas of intensity: psychomotor, sensory, intellectual, imaginational, and emotional. (Piechowski, 1991). A gifted individual may have one, possess a few, or have all five overexcitabilities (Dabrowski & Piechowski, 1977). The authors will briefly describe these areas in the following paragraphs.
Psychomotor overexcitability is best described by an enhanced excitability of motor function, these are the children that are constantly active, and have high energy levels, speak rapidly, need vigorous physical activity and have the desire for constant action (Dabrowski & Piechowski, 1977; Piechowski, 1979, 1991). These children often speak, move and think rapidly and find flow in physical activities such as sports, building, and the performing arts. They are often misdiagnosed as having Attention-Deficit/Hyperactivity Disorder (ADHD) (Winkler & Voight, 2016; Webb et al., 2007).
Sensory overexcitability described as an elevated experience of sensory input originating from hearing, smell, touch, taste, and sight that can be enjoyable or painful (Dabrowski and Piechowski, 1977; Piechowski, 1979, 1991). Sensory OE has a more intense sensory awareness that can allow for a deep appreciation of the three-dimensional world, but the sensory processing can at times be overwhelming and can cause them to withdraw and “tune out (Gere et al., 2009).” Sensory OE can entice gifted individuals towards certain sensory experiences and make other sensations very aversive (Beduna et al., 2016).
Intellectual overexcitability is depicted by a prominent desire to gain knowledge, search for and understand the truth and to analyze and synthesize information (Dabrowski and Piechowski, 1977; Piechowski, 1979, 1991). Children that experience intellectual overexcitability have prodigious minds marked by an intrinsic drive to take in knowledge about the world. These children can be immersed in a book for hours, have a keen understanding of the world, and are determined problem-solvers (Kanazawa, 2010).
Imaginational overexcitability is described as enhanced imaginational play and creates worlds where fantasy and reality blend, have imaginary friends and are often described as “dreamers” by teachers and parents (Dabrowski & Piechowski, 1977; Piechowski, 1979, 1991). Children with great imaginational overexcitability are creative and have an intense desire to remain occupied with their fictional worlds and characters (Rinn et al., 2018). They can often appear disinterested in the world around them due to their all-encompassing engagement with their fantasy worlds (Bernhardt & Singer, 2012).
Emotional overexcitability is displayed by increased intense feelings, the ability for complex emotions, a strong ability for empathy and deep emotional expression (Piechowski, 1991). A highly emotional child has the capacity for deep emotional connection to humans, animals, and the planet (Zeidner & Matthews, 2017; Eklund et al., 2015). These children have an enhanced ability for compassion, empathy, and sensitivity in relationships, though their sensitivities can be disruptive to relationships if their peers do not share the depth and quality of their emotions (Bernhardt & Singer, 2012; Benbow, 1985).
The authors’ in-depth literature review focused on studies that elucidated the relationship between cognition and the role of the brain, the function of the brain and networks of the brain for high intelligence compared to average intelligence. These findings reflect the impact of an interdisciplinary approach to neuroscience and its practical applications to social behavior and education (Decety & Ickes, 2009). Thus, this in-depth literature review provides a framework to understand the complexities of the gifted experience and the spectrum of the human experience centered on cognition, behavior, and neurodiversity.
Human Intelligence and the Brain
Spearman’s G is the measurement of general cognitive function and basic processing ability, which is considered intelligence (Kanazawa, 2010; Luders et al., 2009; Sternberg, 2013). In order to measure intelligence, professionals assess a person’s cognitive ability using measures and psychometrically-sound test instruments (Keith & Reynolds, 2010). These measures indicate that intelligence is not a single construct but multivariate (Levitt et al., 2015; McGraw, 2005). For the purposes of this paper, the researchers will focus on two such aspects: crystallized and fluid intelligence (Catell, 1971).
Crystallized intelligence is defined as the amount of knowledge that individuals attain over their lifespan that they can express (Catell, 1971). Crystallized intelligence often increases with age, education, and experience (Barbey, 2018), though enough individual differences exist to suggest that certain aspects of it may be biologically encoded (Brown, 2018). Regardless, a person’s life experiences, including access to and quality of education, are highly impactful to measures of crystallized intelligence (Hülür et al., 2018; Sousa, 2016). It should be noted that levels of crystallized intelligence are both a predictor and a measure of giftedness (Thompson & Oehlert, 2010).
Fluid intelligence is defined as the ability for analytical reasoning, which is often dependent on the processing speed of information and memory (Toga & Thompson, 2005). It is highly impacted by the amount of white matter in a person’s brain, as the highly myelinated neurons facilitate rapid and efficient transfer of information (Kievit et al., 2016). The amount of one’s fluid intelligence is often the necessary factor in unlocking higher-order cognitive functioning including controlled attention and logical reasoning (Au et al., 2015). As such, research has linked fluid intelligence to many key outcomes across the lifespan, including life satisfaction and goodness of fit with career (Au et al., 2015; Kievet et al., 2016).
As these theories of intelligence propagated, researchers pondered where intelligence would be “found” in the brain (Sternberg, 2013; Ritchie et al., 2015). One of the earliest studies revealed that, upon measuring, a person’s whole brain size was positively associated with superior intelligence (Jensen, 1998; McDaniel, 2005). Since then, the brain has been the focus of many studies of higher intelligence and to date; many studies validate the hypothesis that the brain is the center for human cognition and intelligence (Kanazawa, 2010; Luders et al., 2009; Sternberg, 2013). Various technological methods create human brain maps that measure brain volume, surface area, network connectivity, and processing speed (Filler, 2009) and, thus, quantify those aspects’ relationships to intelligence as a means to measure behavior (Colom et al., 2010; Koziol et al., 2010).
Cortical Brain Maps Expanded in Intelligence
Through the modern uses of MRI technology, constructing brain maps of individuals and groups provides an in vivo analysis of the brain the regions, structures and functions (Basten et al., 2015; McDaniel, 2005; Lachaux et al,, 2007). Brain maps allow for visualizing the brain while measuring the relationship of the brain to intelligence and performance (Filler, 2009; Wang, 2012). The structural images of the brain allow for regional representations of grey matter and white matter (Ashburner & Friston, 2000).
Grey matter in the brain is involved in information processing and also captures the number of neurons and dendritic density in a region of interest (Colom et al., 2009). Grey matter is comprised of neurons, which are the basic computational components of information processing (Haier et al., 2010), though these cells are unmylienated (Kievet et al., 2016). Grey matter comprises the surface of the cerebral cortex and cerebellar cortex, as well as aspects of the brain stem (Colom et al., 2009). The grey matter neurons thus provide a network for information convergence, manipulation, processing, and storage (Haier et al., 2010; Colom et al., 2010; Sousa, 2009).
The white matter measurement in the brain reflects axonal projections and thickness, as well as the amount of myelin in a trap and is involved in information flow and efficiency (Colom et al., 2009; Schmithorst et al., 2005). It is subcortical in nature (Colom et al., 2010; Koziol et al., 2010). White matter tracts in the brain provide for rapid processing of information and the white matter networks between two regions allows for the relay of information (Kievet et al., 2016; Sousa 2016; Miller, 1994). The speed of the information varies on the white matter tracts themselves, such as the abundance, the distance and the strength of the connections across brain regions (Barbey, 2018), which are measured via multiple sound imaging techniques (Filler, 2009).
The first brain imaging study to measure IQ and brain volume tested subjects on a subset of four WISC test measures and reported a 0.51 correlation between brain size and IQ scores (Willerman et al., 1991). The brain size measures were corrected for the subjects’ body sizes. The researchers drew the conclusion that differences in human brain size are relevant, if not all-encompassing, to explaining differences in intelligence test performance (Willerman et al., 1991). It is important to note the researchers did not account for extreme IQ groups, which may have increased the level of the correlation (Jung & Haier, 2007).
The next study to determine intelligence and brain association had the subjects complete a WAIS-R (the first revision of the adult intelligence scale) after performing several MRI scans to determine brain structure, size, and segmentation (Andreasen et al., 1993). The results showed positive associations between the volume of brain regions including the cerebellum, hippocampus, temporal lobes and found that total grey volume matter was associated with IQ (Andreasen et al., 1993). The cerebellum is essential for coordination, smooth movements, and timing of actions and is composed of the largest output layer of neurons in the brain (Sardone et al., 2014; Lee et al., 2006). The hippocampus is involved in memory formation and consolidation (Colom et al., 2009; Haier et al., 2010; Sousa, 2016). The temporal lobe is essential for language processing and auditory processing (Colom et al., 2013; Sousa, 2016).
As the imaging capabilities of neuroscience continued to improve, researchers began using the innovative technique of Voxel Based Morphometry (VMB), where MRI images are divided in an automated manner into separate regions to increase readability and to better trace functionality throughout the brain (Ashburner & Friston, 2000). The pioneering study to measure intelligence and brain regions using VMB found significant correlations with the relative sizes of the cingulate gyrus and anterior cingulate cortex with increased FISQ scores (Haier et al., 2004). The anterior cingulate cortex is involved in executive functioning and decision-making (Lee et al., 2006; Sousa, 2016).
In this same study, Haier and colleagues also found significant correlations in the gray matter that include the frontal lobes, temporal lobes, parietal, and occipital lobes and found a significant white matter correlation near BA39 (Haier et al., 2004). The frontal lobes are critical for complex decision-making and hypothesis testing (Nestor et al., 2015; Colom et al., 2013; Sousa, 2009). The temporal and parietal lobes are critical for sensory processing, including sight, sound, and smell, as well as language interpretation and formation (Liu et al., 2007). The occipital lobe is the brain region dedicated to visual processing (Sousa, 2016; O’Boyle et al., 2005).
Given the granularity of data achieved by the VMB method of assessing brain regions, a logical extension of that data is to test whether there is a correlation between the ranks of G loadings (Sternberg, 2013; Colom et al., 2010; Gignac & Bates, 2017). G explained several of the FISQ correlations with grey matter volumes in anterior cingulate cortex (ACC) and frontal cortices (FC), parietal, and temporal lobes (Colom et al., 2006; Nestor et al., 2015). The ACC and FC are crucial for hypothesis testing, determining the valence of information, interpretation of information, and decision-making (Colom et al., 2006; Ohtani, Nestor, Bouix, Saito, Hosokawa, & Kubicki, 2014; Vandervert, 2009; Sousa, 2009).
Further Literature Review of Brain Imaging and Intelligence
Increased imaging technology allowed researchers to study the relationships between brain regions and intelligence at a deeper level. A study of the relationship between intelligence and brain regions of 30 adults tested for IQ performance and brain volume found correlations with the posterior lobe of the right cerebellum (Lee et al., 2006). Gong and colleagues reported in their IQ study of adults that that those who are above average intelligence showed an increase in grey matter volume (Gong et al., 2005). These increases in both the anterior cingulate cortex and frontal cortices correlated positively with a person’s IQ (Gong et al., 2005).
Using a systematic protocol for measuring cortical thickness, Karama and colleagues analyzed a sizable group of children and adolescents (Karama et al., 2009). They found increased cortical thickness in lateral prefrontal cortex and the occipital cortex and hippocampus areas were correlated with g scores (Karama et al., 2009). Thus, IQ scores are positively correlated with brain size when adjusting for age and gender and altering the effects of G removed the correlations of test scores and cortical thickness (Karama et al., 2009; Colom et al., 2013; Ritchie et al., 2015).
A study of 65 adults measuring intelligence and cortical thickness showed positive correlations between intelligence and cortical thickness bilaterally in the prefrontal cortex and temporal cortex (Luders et al., 2009). Furthermore, several neuroimaging studies report neural correlates of executive functioning with the bilateral activation of the prefrontal cortex (Colom et al., 2007, Colom et al., 2009, Haier et al., 2004). The prefrontal cortex is the seat of executive functioning skills in the human brain, which includes self-regulation, time management, task initiation, and behavioral inhibition (Sousa, 2016; Nestor et al., 2015). Thus, greater executive functioning allows for increased cognitive control, which is a key feature of gifted individuals (Thompson & Oehlert, 2010; Thompson & Toga, 2005). Also, it has been reported that gifted individuals have increased cerebellar functioning, which is related to rapid information processing and information consolidation (Vandervert, 2009).
A study by Geake and Hansen (2005) that measured fluid intelligence with a letter sequence problem solving, high IQ individuals compared to general intelligence using fMRI found activations in the bilateral frontal, parietal and the occipital regions as well as bilateral anterior cingulate cortex. These findings are consistent with other studies that found a positive relationships between the ACC activation and intelligence (Lachaux, et al,, 2007; O’Boyle et al., 2005). In addition, the researchers reported a linear relationship between verbal IQ and the BOLD signal in the frontal lobes of gifted individuals, where higher verbal IQ correlates with increased BOLD signals in the frontal lobes (Geake & Hansen, 2005; Raichle, 2010).
Lee and colleagues report increased BOLD signals when gifted individuals compared to general intelligence were tested in an fMRI set up (in vivo brain imaging when undergoing a task) for various g loading measures and the gifted children exhibited increased activity bilateral prefrontal cortices with increased g loaded tests (Lee et al., 2006). Lee (2006) reported the gifted group had bilateral activation of the prefrontal cortex with greater signal than average IQ (Lee et al., 2006). Using event-related potential (ERP) measuring the brain response to a specific visual search task that compared gifted to average age school children in China, gifted children better spatial and temporal coordination neural networks (Zhang et al., 2014). These studies provide evidence that the prefrontal cortex plays a crucial role in cognitive tasks with increased difficulty and may be enhanced in higher IQ individuals (Colom et al., 2013; McDaniel, 2005).
What is the conclusion from all these studies? Clearly, there are significant relationships between locations of the brain and intelligence, the size of those locations and intelligence, and the way that those locations connect and intelligence (McDaniel, 2005; Pietschnig et al., 2015). A meta-analysis, or analysis of the findings of several analyses, of 37 neuroimaging findings shows that there is a connection between entire brain volume and intelligence (McDaniel, 2005).
Brain Maps, Development, Growth, and the Connections with IQ
Shaw and colleagues designed a study for intellectual ability and cortical development in 300 children and adolescents by creating brain maps using magnetic resonance imaging to determine the gray matter and white matter of the brain tissue to develop brain maps (Shaw et al., 2006). For the study design, they obtained IQ scores using a WISC test and found that high IQ gifted individuals had a rapid increase in brain growth in adolescents and the gifted adolescents’ cerebral cortices were significantly larger than average (Shaw et al., 2006). In particular, the frontal cortex was expanded in high IQ gifted adolescents (Shaw et al., 2006). Given the important role the frontal cortex plays in intellectual functioning, this finding seems to solidify the relationship between brain size, brain function, and intelligence (Haier, 2009; Pietschnig et al., 2015).
Brain Maps and the Parieto-Frontal Integration Theory (P-FIT) Model in High Intelligence
Jung and Haier (2007) proposed a model of brain-based intelligence that they call the parieto-frontal integration theory (P-FIT). This model developed by the analysis of 37 neuroimaging studies that measure intelligence that include crystallized intelligence, fluid intelligence, and game reasoning (Jung & Haier, 2007). The P-FIT model was proposed as a method to determine the commonalities across many neuroimaging studies for intelligence measures (Colom et al., 2010; Colom et al., 2009).
The analysis of 37 neuroimaging studies found 28 brain regions associated with superior intelligence (Jung & Haier, 2007). This model represents the crucial incorporation/convergence between the parietal sensory association cortices and the frontal lobes of the brain (Jung & Haier, 2007; Jung et al., 2013). These regions connected by white matter tracks that transform information from one region of the brain to another. In particular, the white matter structures that connect the parietal and frontal brain regions are the arcuate fasciculus and superior longitudinal fasciculus (Jung & Haier, 2007; Jung et al., 2013). It is possible that these white matter tracks play a crucial role in the individual differences in intelligence. Potentially these white matter tracts are essential for rapid information transfer from one region to the other (Jung & Haier, 2007).
Jung and Haier’s (2007) P-FIT model uses the basic assumption that information is commonly processed by sensory measures such as auditory and visual manner which implies that the temporal and the occipital lobes are the first regions for information processing (Desco et al., 2011). The next assumption is the information from auditory and visual cortices travel to the parietal cortex where elaboration of information is integrated (Colom et al., 2010; Colom et al., 2009). The information then travels to the frontal cortex to analyze and hypothesize an outcome and once information is analyzed (Jung et al., 2013). The anterior cingulate cortex is involved in decision-making and responds for error detection and recognition, and finally, their proposal is white matter is critical for integrating and transferring information across all regions of the brain (Jung & Haier, 2007; Lee et al., 2006; Haier, 2009).
Is important to note that the P-FIT model defines a network of brain regions associated with higher performance on tests of intelligence and comprehensive measures (Desco et al., 2011). Sensory processing of the major five senses includes sight, taste, smell, hearing, and touch (Liu et al., 2007). Our sensory level and experience of the five senses that include all involve brain regions (Rinn & Majority, 2018; Vuyt et al., 2016; Gere et al., 2009). Sensory information is processed in the brain and involves the first integration of sensory information; second value measures placed on the sensory information based on past experience for next the step, which is decision-making and outcomes in response to the sensory information (Liu et al., 2007; Gere et al., 2009). The entire process involves many brain regions and white matter tracks that connect the different brain regions for information and intelligent response (Kievet et al., 2016; Sousa, 2016). According to the P-FIT model, the entire brain is involved in processing information (Jung & Haier, 2007). Therefore, individuals of superior intelligence exhibit greater connectivity across regions (Colom et al., 2010; Sousa, 2009), which may be a key feature of increased gray matter volumes and white matter tracts that allow for rapid information processing (Haier et al., 2010; Kievet et al., 2016).
White Matter Brain Maps, Elevated Intellect, and Processing
Miller (1994) stated many lines of evidence suggest that the intellectual capacity is reliant on white matter tracts and myelin integrity. Greater nerve conduction and speed are related to a larger axon diameter (Aboitiz et al., 1992) and the size of an axon-associated with the thickness of myelin (Dicke & Roth, 2016). Greater myelin bundle and increased axon diameter are thought to play a crucial role in cognitive development and information processing speed (Jung & Haier 2007; Ohtani et al., 2014; Venables & Raine, 2016).
It has also been reported that there is a link between the reduction of myelination around the fourth decade and a slowing in cognitive functioning (Hale et al., 1997; Dicke & Roth, 2016). Thus, it is hypothesized that the decline in cognitive function is associated with the reduction in myelination throughout the brain (Schubert et al., 2017).
Schmithorst (2005) and colleagues explored and studied the relationship of white matter tracts and subjects that were tested with the WISC-III and found positive correlations within the frontal and occipito-parietal white matter bilaterally and proposed that the region is the arcuate fasciculus (Schmithorst et al., 2005). Also, they found a greater correlation of white matter measures and verbal intellectual ability compared to nonverbal ability (Schimithorst et al., 2005). They concluded that the projections of white matter connecting Broca’s area to Wernike’s area are sensitive variation among individuals for intellectual achievement (Schimithorst et al., 2005; Catani & Mesulam, 2008). The arcuate fasciculus is a white matter tract that connects to crucial language areas, which include Broca’s area (inferior frontal gyrus) and Wernicke’s area (superior temporal gyrus), and the myelinated axons are bidirectional between the two regions (Catani & Mesulam, 2008). The white matter tracts relay information across the brain, connecting different brain regions (Schubert et al., 2017). Hence, more intact white matter tracts in the brain are directly related to an increase in information processing (Vandervert, 2009; Barbey, 2018; Pietschnig et al., 2015).
Glucose Metabolism in the Bright Brain and Neuronal Efficiency
Positron emission tomography measures the amount of labeled glucose usage or blood flow to a particular region of interest (Lachaux, et al., 2007; Jung & Haier, 2008). Haier and colleges (1992) tested subjects using the Raven’s Advanced Progressive Matrices and measured the glucose metabolic rate in the brains of the subjects while they performed the test at their own pace. An interesting finding from this study was the inverse correlation of the glucose metabolic rate and performance on the test. Importantly high scores on the test related to a lower glucose metabolic rate, which could be greater neuronal efficiency in individuals with better performance (Haier et al., 1992).
Neuroimaging Studies of Mathematically Gifted Children while in Flow
An fMRI study of mental rotation comparing six mathematically gifted males to six male’s average intelligence found that both groups activated the frontal-parietal network during the task of mental rotation (O’Boyle et al., 2005). Moreover, activations were significantly larger for gifted individuals in the right anterior cingulate cortex left inferior parietal lobe and left premotor area (O’Boyle et al., 2005). In particular, this study highlighted that gifted individuals have greater bilateral activation of the parietal lobes, anterior cingulate and frontal cortex (O’Boyle et al., 2005). Additionally, it was noted that the network in gifted individuals was qualitative and quantitatively different than an average IQ individuals and therefore hypothesize that cingulate, parietal and frontal cortices may be critical for information processing network (O’Boyle et al., 2005). Subsequent studies have drawn similar conclusions, deepening the understanding about the morphological differences of the gifted brain (Koziol et al., 2010; Thompson & Oehlert, 2010; Vuyt et al., 2014; Pietschnig et al., 2015).
Lee (2006) measured the BOLD signal in a fMRI study that compared gifted and age-matched adolescents testing high and low G loading tasks and found that the gifted group had increased bilateral activation in the lateral prefrontal cortex, anterior cingulate cortex and the parietal cortex. Importantly, high intelligence is associated with greater frontal lobe activation compared to average intelligence (Lee et al., 2006; Colom et al., 2013). A gifted individual’s ability to harness this activation seems highly linked to that individual’s ability to be successful in academic, social, and occupational environments (Nestor et al., 2015).
In a study of mathematically gifted precocious youth, 13 mathematically gifted and 14 age-matched controls were examined using fMRI while performing Ravens Advanced Progressive Matrices (Desco et al., 2011). Throughout the task both the gifted and average mathematical ability subjects showed significant activations of the parietal-frontal network (PFIT) (Colom et al., 2009). Importantly, the math-gifted group showed activation clusters that were always bilateral and greater number of regions were recruited, in particular the right hemisphere (Dresco et al., 2011). The authors hypothesize that the increase and bilateral pattern of activation in the parietal and frontal regions of mathematically gifted individuals are related to the elevated skills in visual spatial processing and logic reasoning.
In a study to determine the role of white matter microstructure in mathematically gifted individuals, Navas-Sanchez and colleagues (2014) performed diffusion tensor imaging (DTI) analysis, which measures the amount of white matter in mathematically gifted and adolescents and control brains (Navas‐Sánchez et al., 2014). They found that the IQ had a significant positive correlation with greater volume of the corpus callosum providing evidence that efficiently transferring information between hemispheres is critical for greater intellectual capabilities (Navas-Sanchez et al., 2014). Additionally, mathematically gifted adolescents showed increase in white matter tracts that connects frontal regions with the basal ganglia and parietal regions, which can account for increased fluid reasoning working memory and creativity of these gifted children (Desco et al., 2011; O’Boyle et al., 2005; Sousa, 2009).
In an attempt to better understand the role of the fronto-parietal cortices in math gifted adolescents, and electroencephalogram (EEG) sourcing element analysis was used to investigate the synchronization between frontal and parietal cortices in adolescents with math precociousness (Antonenko et al., 2010). The EEG demonstrated greater levels of activation and synchronization, demonstrating that the precocious brain generates more energy in its processing (Antoneko, et al, 2010). Additionally, Zhang and colleagues report that mathematically gifted individuals have a greater fronto-parietal connection, which can allow for greater flexibility for rapid network reconfiguration (Zhang et al., 2014). More connection leads to more integration, which allows the brain to work both more effectively and efficiently in areas of interest (Nestor et al., 2015; Schubert et al., 2017)
Evidence of Increased Emotional Intelligence in High IQ Individuals
Emotional intelligence is described as having increased empathy-increased theory of mind the ability to understand and anticipate what another individual is experiencing and or feeling (Zeidner & Matthews, 2017; Beduna & Perrone-McGovern, 2016). Gifted individuals are often considered to have increased emotional capacity and emotional intelligence (Fonseca, 2015; Rinn et al., 2018; Zeidner & Matthews, 2017). It is common for these individuals to describe feelings things more intensely more deeply their emotions are heightened compared to average intelligence (Gere et al., 2009; Eklund et al., 2015). There is evidence to support that gifted individuals have structural, morphological, and performance differences within their brains that contribute to these enhanced emotional experiences (Decety & Ickes, 2009; Thompson & Oehlert, 2010; Vuyk et al., 2016).
Yu (2008) and colleagues performed a study using diffusion tensor imaging (DIT) to determine the white matter structure and tracts of the corpus callosum, cingulum, uncinate fasciculus, optic radiation and corticospinal tract in individuals and determined the correlations with a Full-Scale IQ (FISC) in high, average and low IQ individuals (Yu et al., 2008). They found that high IQ individuals showed an increase in the volume of the right uncinate fasciculus and are important for the neural basis of high intelligence (Yu et al., 2008). The right uncinate fasciculus is essential for emotional intelligence and its role in empathy (Decety & Ickes, 2009; Olson et al., 2015).
A study of lesion patients of the right and uncinate fasciculus exhibited impairment in emotional empathy task (Oishi et al., 2015). The right uncinate fasciculus is an essential white matter tract that connects orbitofrontal cortex, temporal pole, insula, and the amygdala and is critical for relaying emotional information across these brain structures (Olson et al., 2015; Beduna et al., 2016). Hence, the right uncinate fasciculus is an essential white matter tract for emotional empathy (Oishi et al., 2015). More importantly, the right uncinate fasciculus is expanded gifted individuals compared to average intelligence, which could be one element for the increased abilities of emotional intelligence and emotional empathy (Vuyk et al., 2016; Rinn, et. al, 2018; Urben et al., 2018).
In a recent study of white matter and grey matter contributions to intelligence investigators used DTI diffusion tensor imaging method to determine the relationship between IQ measured with a WISC and tractography was used to determine the white matter connections and integrity between medial orbital frontal cortex and the right anterior cingulate cortex pathways and functional networks (Ohtani et al., 2014). The study reported increased gray matter volumes for medial orbital frontal cortex (OFC) and right anterior cingulate cortex (rACC) and increased white matter connectivity for the left posterior medial orbital frontal cortex and anterior cingulate cortex connectivity in higher IQ individuals (Ohtani et al., 2014). This is an important finding for understanding the emotionally gifted brain.
OFC is multimodal where this brain region receives multiple sensory inputs including taste, smell, and auditory, visual, somatosensory and visceral information (Liu et al., 2007; Colom et al., 2006). OFC has several projections of the amygdala, anterior cingulate cortex, insula, hypothalamus, hippocampus, striatum, and frontal cortex and in particular, this brain region is specifically connected to the limbic system (Ohtani et al, 2014). Rostral ACC inputs originate from the amygdala and rACC codes the emotional salience of these inputs (Urben et al., 2018). The salience of emotional and motivational information is specific to rACC and rACC regulates emotional responses (Nestor et al., 2013; Kanazawa, 2010). Both OFC and rACC are essential for various forms of emotional processing (Urben et al., 2018; Vuyk et al., 2016).
Penke and colleagues (2012) studied 402 subjects using DTI and found that brain-wide white matter tracts integrity is associated with increased processing speed in general intelligence (Penke et al., 2012). Hence the more intact the white matter tract the greater the increase in information processing (Penke et al., 2012; Sousa, 2009). The white matter tracts relay information across the brain to different brain regions (Nestor et al., 2015; Schubert et al., 2017; Barbey, 2018). It is important to note that gifted individual’s exhibit increased white matter tracts that include arcuate fasciculus, uncinate fasciculus, and the connectivity between OFC cortex and rACC (Schimthorst et al., 2005, Yu et al., 2008, Ohiani et al., 2014) which are all crucial for emotional processing and empathy (Barbey, 2018; Vuyk et al., 2016).
The Gifted Brain– A Neurodiverse Brain, and Experience
The brain is a complex organ that interprets sensory information, relays information across different brain regions for hypothesis testing, the salience of information, memory consolidation and storage of past experiences and carries out executive functioning for decision making (Thompson & Oehlert, 2010; Thompson & Toga, 2005). High IQ individuals have increased grey matter volume in 28 brain regions (Jung & Haier, 2007) and also have expanded white matter tracts across the brain which include; the corpus callosum, actuate fasciculus, uncinate fasciculus, and the connectivity between OFC cortex and rACC (Navas-Sanchez et al., 2014; Schmithorst et al., 2005; Yu et al., 2008; Ohiani et al., 2014).
Importantly, these differences in volume of these brain regions and white matter tracts are related to the neurodiversity across the intelligence spectrum (Colom et al., 2010; Desco et al., 2011; Koziol et al., 2010). The volume of the brain region is a representation of the number and size of neurons (Ohiani et al., 2014; Dicke & Rothe, 2016). These increased brain volumes thus indicate an increased potential of those relevant brain structures to perform more effectively and efficiently (Colom et al., 2009; Keith & Reynolds, 2010; Haier et al., 2010).
Gifted individuals have increased brain volume in 28 regions compared to individuals of general intelligence and gifted individuals thus have bigger brains (Pietschnig et al., 2015; Matthews, 2005). The regions involved in sensory processing are larger in gifted individuals and could account for the psychomotor, sensory and imaginational OEs (Winkler & Voight, 2016). Gifted individuals have greater bilateral activation in frontal cortex when solving complex math equations compared to average intelligence, and the increased activation could be related to the intensity of the intellectual OE (O’Boyle et al., 2005; Sousa, 2009). Also, greater activated brain in ACC, OFC; frontal, temporal, parietal cortices could have increased intensities and sensory processing and related to the OEs (Liu et al., 2007; Eklund et al., 2015).
Gifted individuals have greater white matter connectivity compared to general intelligence that can lead to differences in processing speed (Luders et al., 2009; Toga & Thompson, 2005). Some gifted individuals experience rapid processing speed, whereas others have a reduction in processing speed associated with a greater volume of information processing and in-depth thinking and problem solving, associated with all five OEs (Dabrowksi & Piechowski, 1977; Vuyk et al., 2016; Gere et al., 2009). Furthermore, gifted individuals have increased brain regions related to emotional intelligence and have larger connectivity across emotional brain regions, which can provide for greater awareness of emotional intelligence and empathy (Bernhardt & Singer, 2012; Decety & Ickes, 2009).
Given psychology’s long history with the study of intelligence, it is not surprising that gifted individuals have received formal research attention (McGrew, 2005). However, it was not until the development of advanced imaging techniques such as fMRI, PET, and EEG, that neuroscience was able to fully grasp the complexities of this population (Filler, 2009; Nestor et al., 2015; Gignac & Bates, 2017). The gifted brain is significantly different in terms of morphology, functionality, structure, internal networks, and processing rate (Basten et al., 2015; Hülür et al., 2015; Kanazawa, 2010; Thompson & Oehlert, 2010).
Gifted individuals often report feeling significantly different to their neurotypical peers (Rinn & Majority, 2018; Fonseca, 2015; Bernhardt & Singer, 2012). These differences occur in sensory processing, social functioning, emotional regulation, processing speed, intellectual engagement, and memory function (Sandrone et al., 2014; Sousa, 2009; Rinn et al., 2018). Many gifted children, as well as their parents, friends, and teachers, report confusion and even dismay about these differences, as they can be extreme and debilitating (Fonseca, 2011). Given the scarcity of information available in the public sphere about giftedness, more knowledge would be very beneficial to helping these systems meet the needs of these individuals.
Neuroscience holds the key to helping the gifted population receive more informed and nuanced care in medicine, psychotherapy, education, and community support. The data presented in the studies referenced in this paper demonstrate that many of the differences in the gifted population, both external and internal, are best explained by differences in the gifted brain. Informing the population with this knowledge can help create an environment better suited to meet their needs and, hopefully, help them to better unlock their vast potential.
Aboitiz, F., Scheibel, A., Fisher, R., & Zaidel, E. (1992). Fiber composition of the human corpus collosum. Brain Research. 598(1-2). 143-53.
Antonenko, P., Paas, F., Grabner, R., & Van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review. 22(4), 425-438.
Andreasen, N. C., Flaum, M., Swayze, V., O’Leary, D. S., Alliger, R., Cohen, G., … & Yuh, W. T. (1993). Intelligence and brain structure in normal individuals. American Journal of Psychiatry. 150, 130-130.
Ansari, D., and Coch, D. (2006). Bridges over troubled waters: Education and cognitive neuroscience. Trends in Cognitive Sciences. 10(4), 146-151.
Ashburner, J., and Friston, K. (2000). Voxel-based morphometry: The methods. Neuroimage. 11(6), 805-21.
Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: a meta-analysis. Psychonomic Bulletin & Review. 22(2), 366-377.
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences. 22(1), 8-20.
Beduna, K., and Perrone-McGovern, K. M. (2016). Relationships among emotional and intellectual overexcitability, emotional IQ, and subjective well-being. Roeper Review, 38(1), 24-31.
Benbow, C. P. (1985). Intellectually gifted students also suffer from immune disorders. Behavioral and Brain Sciences, 8(3), 42-42. doi: 10.1017/S0140525X00001059
Benbow, C. P. (1986). Physiological correlates of extreme intellectual precocity. Neuropsychologia, 24, 719-725.
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence. 51, 10-27.
Bernhardt, B. and Singer, T. (2012) The neural basis of empathy. Annual Review of Neuroscience. 35. doi: 10.1146/annurev-neuro-062111-150536. Review. PubMed PMID: 22715878.
Brown, R. E. (2016). Hebb and Cattell: the genesis of the theory of fluid and crystallized intelligence. Frontiers in Human Neuroscience. 10, 606.
Catani, M., & Mesulam, M. (2008). The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex. 44(8), 953-961.
Colom, R., Burgaleta, M., Román, F., Karama, S., Alvarez-Linera, J., Abad, F., Martínez, K., Quiroga, M.Á., and Haier, R. (2013). Neuroanatomic overlap between intelligence and cognitive factors: Morphometry methods provide support for the key role of the frontal lobes. Neuroimage. doi: 10.1016/j.neuroimage.2013.01.032. PubMed PMID: 23357078.
Colom, R., Haier, R.J., Head, K., Álvarez-Linera, J., Quiroga, M.A., Shih, P.C., and Jung, R.E., (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37(1). 124–135.
Colom, R., Jung, R.E., and Haier, R.J. (2006) Distributed brain sites for the g-factor of intelligence. Neuroimage. 31(3): 1359-65. PubMed PMID: 16513370.
Colom, R., Karama, S., Jung, R.E., and Haier, R.J. (2010). Human intelligence and brain networks. Dialogues in Clinical Neuroscience. 12(4):489-501. PubMed PMID: 21319494
Dabrowski K. (1969). Higher emotions and objectivity in evaluation. Annals of Medical Psychology. 2(5), 589-613. French. PubMed PMID: 5374913.
Dabrowski K. (1959) On positive disintegration: An outline of the theory concerning the psychological development of man through unbalanced states, nervous states, neuroses and psychoses. Annals of Medical Psychology. 117(2), 643-68. French. PubMed PMID: 13813649.
Dabrowski K. (1961). The principal dynamisms of disintegration at multiple levels. Annals of Medical Psychology. 119(1), 225-34. French. PubMed PMID: 13719256.
Dabrowski K. (1960). Remarks on typology based on the theory of positive disintegration. Annals of Medical Psychology, 118(2). 401-6. French. PubMed PMID: 13719255.
Dabrowski, K & Piechowski, M.M. (1977). Theory of levels of emotional development: Vol. 1. Multilevelness and positive disintegration. Oceanside, NY: Dabor Science.
Decety, J. and Ickes, W. (2009). The Social Neuroscience of Empathy. MIT Press: Cambridge.
Desco, M., Navas-Sanchez, F.J., Sanchez-González, J., Reig, S., Robles, O., Franco, C.,
Guzmán-De-Villoria, J.A., García-Barreno, P., and Arango, C. (2011). Mathematically gifted adolescents use more extensive and more bilateral areas of the fronto-parietal network than controls during executive functioning and fluid reasoning tasks. Neuroimage. 57(1). 281-92. doi: 10.1016/j.neuroimage.2011.03.063. PubMed PMID: 21463696.
Dicke, U. and Roth, G. (2016). Neuronal factors determining high intelligence. Philosophical Transactions of the Royal Society B: Biological Sciences. 371(1685), 2015-80.
Eklund, K., Tanner, N., Stoll, K., and Anway, L. (2015). Identifying emotional and behavioral risk among gifted and nongifted children: A multi-gate, multi-informant approach. School Psychology Quarterly, 30(2), 197.
Fonseca, C. (2011). Emotional coaching: A lesson in intensity. Parenting for High Potential. 8(1), 24-27.
Fonseca, C. (2015). Emotional intensity in gifted students: Helping kids cope with explosive feelings. Prufrock Press: Waco, TX.
Filler, A. (2009). The History, Development and Impact of Computed Imaging in Neurological Diagnosis and Neurosurgery: CT, MRI, and DTI”. Nature Precedings. http://dx.doi.org/10.1038/npre.2009.3267.5
Gignac, G. E., and Bates, T. C. (2017). Brain volume and intelligence: The moderating role of intelligence measurement quality. Intelligence. 64. 18–29
Gong, Q.Y., Sluming, V., Mayes, A., Keller, S., Barrick, T., Cezayirli, E., Roberts, N., (2005). Voxel-based morphometry and stereology provide convergent evidence of the importance of medial prefrontal cortex for fluid intelligence in healthy adults. Neuroimage.25. 1175–1186.
Haier, R. J. (2009). Neuro-intelligence, neuro-metrics and the next phase of brain imaging studies. Intelligence. 37(2). 121-133.
Haier, R.J., Jung, R.E., Yeo, R.A., Head, K., Alkire, M.T. (2004). Structural brain variation and general intelligence. Neuroimage. 23(1). 425-33. PubMed PMID: 15325390.
Haier, R.J., Siegel, Jr., B.V., MacLachlan, A., Soderling, E., Lottenberg, S., and Buchsbaum, M.S. (1992). Regional glucose metabolic changes after learning a complex visuospatial/motor Task: a positron emission tomographic study. Brain Results. 570(1-2). 134-43. PubMed PMID: 1617405.
Haier, R. J., and Jung, R. E. (2008). Brain imaging studies of intelligence and creativity: What is the picture for education?. Roeper Review. 30(3), 171-180.
Haier, R.J., Schroeder, D.H., Tang, C., Head, K., and Colom, R. (2010). Gray matter correlates of cognitive ability tests used for vocational guidance. BMC Research Notes. 3(206). doi: 10.1186/1756-0500-3-206. PubMed PMID: 20649948.
Hülür, G., Gasimova, F., Robitzsch, A., and Wilhelm, O. (2018). Change in fluid and crystallized intelligence and student achievement: the role of intellectual engagement. Child Development. 89(4), 1074-1087.
Jung, R.E., and Haier, R.J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behavioral Brain Science. 30(2). 135-54; discussion 154-87. PubMed PMID:17655784.
Jung, R. E., Haier, R. J., Vartanian, O., Bristol, A. S., & Kaufman, J. C. (2013). Creativity and intelligence: Brain networks that link and differentiate the expression of genius. In Neuroscience of creativity. O. Vartanian, A. Bristol, J. Kaufman (Eds.) 233-254.
Jauk, E., Benedek, M., Dunst, B., Neubauer, A. C. (2013). The relationship between intelligence and creativity: New support for the threshold hypothesis by means of empirical breakpoint detection. Intelligence, 41. 212-221.
Karwowski, M., Dul, J., Gralewski, J., Jauk, E., Jankowska, D. M., Gajda, A., … Benedek, M. (2016). Is creativity without intelligence possible? A necessary condition analysis. Intelligence, 57, 105-117
Kanazawa, S. (2010). Evolutionary psychology and intelligence research. American Psychologist, 65(4), 279.
Karama, S., Ad-Dab’bagh, Y., Haier, R. J., Deary, I. J., Lyttelton, O. C., Lepage, C., Evans, A. & Brain Development Cooperative Group. (2009). Erratum to “Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds”. Intelligence, 37(4), 432-442.
Katja-Pässler, K., Beinicke, A., & Benedikt, H. (2015). Interests and intelligence: A meta-analysis. Intelligence, 50, 30-51.
Keith, T. Z., & Reynolds, M. R. (2010). Cattell–Horn–Carroll abilities and cognitive tests: What we’ve learned from 20 years of research. Psychology in the Schools. 47(7), 635-650.
Kievit, R. A., Davis, S. W., Griffiths, J., Correia, M. M., & Henson, R. N. (2016). A watershed model of individual differences in fluid intelligence. Neuropsychologia, 91, 186-198.
Koziol, L., Budding, D., and Chidekel, D. (2010). Adaptation, expertise and giftedness: Towards an understanding of corticol, subscorticol, and cerebellar network contributions. The Cerebellum. 9(4). 499-529.
Lachaux, J. P., Fonlupt, P., Kahane, P., Minotti, L., Hoffmann, D., Bertrand, O., & Baciu, M. (2007). Relationship between task‐related gamma oscillations and BOLD signal: New insights from combined fMRI and intracranial EEG. Human brain mapping. 28(12), 1368-1375.
Lee, K.H., Choi, Y.Y., Gray, J.R., Cho, S.H., Chae, J.H., Lee, S., and Kim, K. (2006). Neural correlates of superior intelligence: stronger recruitment of posterior parietal cortex. Neuroimage. 29(2). 578-86. PubMed PMID: 16122946.
Liu, T., Shi, J., Zhang, Q., Zhao, D., and Yang, J. (2007) Neural mechanisms of auditory sensory processing in children with high intelligence. Neuroreport. 18(15). 1571-5. PubMed PMID:17885604.
McDaniel, M. A. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence. 33(4), 337-346.
McGrew, K. S. (2005). The Cattell-Horn-Carroll theory of cognitive abilities: Past, present, and future. In D. P. Flanagan, J. L. Genshaft, & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (pp.136–182). New York: Guilford.
Miller, E. (1994). Intelligence and brain myelination: A hypothesis. Personality and Individual Differences. 17(6). 803-832.
Navas‐Sánchez, F. J., Alemán‐Gómez, Y., Sánchez‐Gonzalez, J., Guzmán‐De‐Villoria, J. A., Franco, C., Robles, O., Arango, C., and Desco, M. (2014). White matter microstructure correlates of mathematical giftedness and intelligence quotient. Human Brain Mapping. 35(6), 2619-2631.
Nestor, P.G., Nakamura, M., Niznikiewicz, M., Levitt, J.J., Newell, D.T., Shenton, M.E., and McCarley, R.W. (2015). Attentional Control and Intelligence: MRI Orbital Frontal Gray Matter and Neuropsychological Correlates. Behavioral Neurology. doi:10.1155/2015/354186. PubMed PMID: 26101457
Nestor, P.G., Nakamura, M., Niznikiewicz, M., Thompson, E., Levitt, J.J., Choate, V., Shenton, M.E., and McCarley, R.W. (2013). In search of the functional neuroanatomy of sociality:
MRI subdivisions of orbital frontal cortex and social cognition. Social Cognition and Affective Neuroscience. 8(4). 460-67. doi: 10.1093/scan/nss018. PubMed PMID: 22345366.
O’Boyle, M.W., Cunnington, R., Silk, T.J., Vaughan, D., Jackson, G., Syngeniotis, A., and Egan G.F. (2005). Mathematically gifted male adolescents activate a unique brain network during mental rotation. Brain Research/Cognitive Brain Research. 25(2). 583-7. PubMed PMID: 16150579.
Ohtani, T., Nestor, P.G., Bouix, S., Saito, Y., Hosokawa, T., and Kubicki, M. (2014). Medial frontal white and gray matter contributions to general intelligence. PLoS One. 9(12). doi:10.1371/journal.pone.0112691. PubMed PMID: 25551572.
Oishi, K., Faria, A.V., Hsu, J., Tippett, D., Mori, S., and Hillis, A.E. (2015). Critical role of the right uncinate fasciculus in emotional empathy. Annals of Neurology. 77(1): 68-74. doi: 10.1002/ana.24300. PubMed PMID: 25377694.
Olson, I.R., Heide, R.J., Alm, K.H., and Vyas, G. (2015). Development of the uncinate fasciculus: Implications for theory and developmental disorders. Developmental Cognitive Neuroscience. 14. 50-61. doi: 10.1016/j.dcn.2015.06.003. PMID: 26143154.
Penke, L., Maniega, S. M., Bastin, M. E., Hernández, M. V., Murray, C., Royle, N. A., Starr, J.M., Wardlaw, J.M., and Deary, I. J. (2012). Brain white matter tract integrity as a neural foundation for general intelligence. Molecular psychiatry, 17(10), 1026-39.
Piechowski, M. M. (1991). Emotional development and emotional giftedness. In N. Colangelo & G. A. Davis (Eds.), Handbook of gifted education. Boston: Allyn & Bacon.
Pietschnig, J, Penke, L, Wicherts, J., Zeiler, M, and Voracek, M. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?”. Neuroscience & Biobehavioral Reviews. 57: 411–432. doi: 10.1016/j.neubiorev.2015.09.017
Raichle, M. (2010). The brain’s dark energy. Scientific American. 302(3). 44-49.
Reeck, C., Ames, D. R., and Ochsner, K. N. (2016). The social regulation of emotion: An integrative, cross-disciplinary model. Trends in cognitive sciences, 20(1), 47-63.
Ritchie, S. J., Booth, T., Hernández, M. D. C. V., Corley, J., Maniega, S. M., Gow, A. J., Royle, N., Pattie, A., Karama, S., Starr, J., and Bastin, M. E. (2015). Beyond a bigger brain: Multivariable structural brain imaging and intelligence. Intelligence. 51, 47-56.
Rinn, A. N., Mullet, D. R., Jett, N., & Nyikos, T. (2018). Sensory processing sensitivity among high-ability individuals: A psychometric evaluation of the highly sensitive person scale. Roeper Review, 40(3), 166-175.
Rinn, A. N., & Majority, K. L. (2018). The Social and Emotional World of the Gifted. In Pfieffer, S. (ed) Handbook of Giftedness in Children (pp. 49-63). Springer, Cham.
Sandrone S., Bacigaluppi, M., Galloni, M., Cappa S., Moro, A., Catani, M., Filippi, M., Monti, M., Perani, D., Martino, G. (2014). Weighing brain activity with the balance: Angelo Mosso’s original manuscripts come to light. Brain. 137 (Pt 2): 621–33. doi:10.1093/brain/awt091
Schmithorst, V.J., Wilke, M., Dardzinski, B.J., and Holland, S.K. (2005). Cognitive functions correlate with white matter architecture in a normal pediatric population: a diffusion tensor MRI study. Human Brain Mapping, 26(2). 139-47. PubMed PMID:15858815.
Schubert, A. L., Hagemann, D., & Frischkorn, G. T. (2017). Is general intelligence little more than the speed of higher-order processing?. Journal of Experimental Psychology: General. 146(10), 1498.
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., and Giegg, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature. 440(7084), 676-9.
Sternberg, R. J. (2013). Intelligence. John Wiley & Sons Inc: Hoboken, NJ.
Sousa, D. A. (Ed.) (2009). How the Gifted Brain Learns. Corwin Press: Thousand Oaks, CA.
Sousa, D. A. (2016). How the brain learns. Corwin Press: Thousand Oaks, CA.
Thompson, L. and Oehlert, J. (2010) The etiology of giftedness. Learning and Individual Differences. 20. 298-307.
Toga, A. and Thompson, P. (2005). Genetics of brain structure and intelligence. Annual Review of Neuroscience. 28(1), 1-23.
Urben, S., Camos, V., Habersaat, S., & Stephan, P. (2018). Emotional modulation of cognitive control in referred gifted male adolescents: A pilot study. Journal of Clinical Psychiatry and Neuroscience, 1(2). 56-71.
Vandervert, L. R. (2009). The appearance of the child prodigy 10,000 years ago: an evolutionary and developmental explanation. The Journal of Mind and Behavior. 30(1). 15-32.
Venables, P. H., & Raine, A. (2016). The impact of malnutrition on intelligence at 3 and 11 years of age: The mediating role of temperament. Developmental psychology, 52(2), 205.
Vuyk, M. A., Kerr, B. A., & Krieshok, T. S. (2016). From overexcitabilities to openness: Informing gifted education with psychological science. Gifted and Talented International, 31(1), 59-71.
Waisman, I., Leikin, M., Shaul, S., and Leikin, R. (2014). Brain Activity Associated with Translation between Graphical and Symbolic Representations of Functions in Generally Gifted and Excelling in Mathematics Adolescents. International Journal of Science & Mathematics Education, 12(3). 34-51.
Wang, Y. (2012). On abstract intelligence and brain informatics: Mapping cognitive functions of the brain onto its neural structures. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI). 6(4), 54-80.
Webb, J., Gore, J., Amend, E., and DeVries, A. (2007). A Parent’s Guide to Gifted Children. Great Potential Press: Tucson, AZ.
Willerman, L., Schultz, R., Rutledge, J. N., & Bigler, E. D. (1991). In vivo brain size and intelligence. Intelligence. 15(2), 223-228.
Winkler, D., & Voight, A. (2016). Giftedness and overexcitability: Investigating the relationship using meta-analysis. Gifted Child Quarterly, 60(4), 243-257.
Yu, C., Li, J., Liu, Y., Qin, W., Li, Y., Shu, N., Jiang, T., and Li, K. (2008). White matter tract integrity and intelligence in patients with mental retardation and healthy adults. Neuroimage. 40(4):1533-41. doi:10.1016/j.neuroimage.2008.01.063. PubMed PMID: 18353685.
Zhang, L., Gan, J. Q., & Wang, H. (2014). Optimized gamma synchronization enhances functional binding of fronto-parietal cortices in mathematically gifted adolescents during deductive reasoning. Frontiers in human neuroscience, 8(1). 430.
Zeidner, M., and Matthews, G. (2017). Emotional intelligence in gifted students. Gifted Education International, 33(2), 163-182.