What does the phrase all part of the cortex contribute equally to complex behaviors such as learning mean?

Introduction

As a teacher, all children need to be challenged and nurtured in order to profit from your instruction. Instruction that is above or below the maturity level of a child's brain is not only inappropriate; it can also lead to behavior problems in your classroom. Inappropriate behaviors — such as avoidance, challenging authority and aggression towards other students — can be explained by a failure to match instruction to the brain maturity of your students.

You should also know that all brain functions do not mature at the same rate. A young child with highly advanced verbal skills may develop gross and fine motor control more slowly and have trouble learning to write clearly. Another child may be advanced physically but not know how to manage his/her social skills. Others may be cognitively advanced but show emotional immaturity.

For all of these reasons, it is important to understand how our brains mature as well as the differences that may be present at each stage of "normal" development.

Do's and don'ts

The recommendations below are supported by evidence.

Do:

• Be aware of developmental differences among your students. These differences have implications for behaviors that students display in your classroom. 
• Understand that normal development varies widely within the same age and the same grade. Our educational system is set up for the convenience of teaching large numbers of children in a grade-level classroom. The age for entrance into a particular grade is not necessarily linked to brain maturity for all children. Although you do not determine which children are in your class, you should be sensitive to the variety of developmental levels presented in your classroom. 
• Be aware that children who are born prematurely may not be at the same developmental level as others of their chronological age. Children who are born more than 8 weeks early may not catch up to their peers until they are 3 or 4 years old. Although premature children over the age of 4 are often indistinguishable from children who were not premature, there may be prematurely born children who continue to show delays. Be aware of this possibility when discussing a child's progress with his/her parents.
• Be aware that childhood illnesses — such as ear infections, asthma, severe allergies, frequent hospitalizations, etc. — or family disruption caused by death or divorce may impact a child's development. A child with a history of these difficulties may benefit from specific accommodations, including:

  - Sitting at the front of the class.
  - Adjusting his/her pace of school work.
  - Receiving a more overt display of understanding and encouragement by his/her teacher.

  In addition, it is often helpful to provide children who have chronic illnesses and/or physical limitations alternate activities and to help their peers to understand the reason for offering these different activities.

• Be aware that a healthy brain likes to learn, and children learn best when they are exposed to a variety of ideas, experiences, skills and materials. In the early years, children like to explore and learn using several senses or multiple skills at the same time.

  Activities that pair motor and auditory skills can encourage the development of both pathways.

  A child who has difficulty with writing and other fine motor skills benefits from lacing cards, mazes and tracing. These activities actually help students develop the visual-motor areas of their brains.

  When a child talks through a difficult visual problem, it can help him/her learn. In other cases, a child whose language skills are delayed may benefit from tasks that don't require language.

• Be aware that brain systems do not all develop at the same time or at the same rate. A child may show advanced development in one area and be delayed in another. For example, a child may read early but be physically clumsy.

  Brain development also does not occur in a straight line. Some skills may develop earlier than other skills. Also, precocious ability does not necessarily last. It is possible for a child to be accelerated in reading or verbal skills in kindergarten but show average ability by third or fourth grade.

Don't:

• Don't assume that a child has a disability just because his/her learning is delayed. Be aware that the development of cognitive and other skills is often uneven. 
• Don't assume that delays a child is showing today will get better over time. If a child does not improve his/her progress, it is important to gather more information and then refer the child for further evaluation if indicated. 
• Don't adopt a one-size-fits-all approach. Experienced teachers vary skills and activities for different students within a grade. Some of this variability works because of the different life experiences of children and some works because of differences in brain maturity. But, for either reason, variety is a good thing. 
• Don't place children in groups based solely on age. For some children, learning to read is a struggle. Many are not ready to learn to read until they are seven years old, while others are ready at age four. (This may be particularly true for boys.) Social maturity does not correlate with other learning skills. Both social and learning characteristics need to be addressed separately to determine appropriate placement. 
• Don't judge ability based on physical appearance. It's very important not to judge children based on their physical appearance. Children who are taller and/or more physically mature may not be cognitively advanced. And children with cerebral palsy often have average to above average ability despite significant problems with motor and speech production.

What do we know about brain maturation?

Children learn in different ways. And although the maturity of the brain is an important factor when it comes to learning differences, the real story is more complicated than that. The way children learn depends on age, level of development and brain maturity. Learning differences are also related to genetics, temperament and environment, but in this module we will focus on how and when the brain matures.

Before birth

Different brain structures mature at different rates and follow different paths, but maturation begins long before birth. As a fetus grows, nerve cells (neurons) travel to their eventual locations within the brain. The survival of any one neuron is not guaranteed. There is competition among neurons for limited space and those that do not find a home — a place where they can live and thrive — are pruned back and destroyed. It is not yet known why some neurons find a home and others do not, but after a neuron settles down it continues to grow and develop within its region of the brain.

When pruning does not happen or is incomplete, disorders in learning and/or behavior can be the result.

Development of the brain from 25 days to 9 months:

Preschool years

At birth, both motor and sensory systems of the brain are already up and running. A newborn infant has enough motor control to feed and to move away from painful or other unpleasant stimuli. Although visual and auditory systems are present at birth, they continue to develop in the first few months of life as the brain reacts to the environment (Carlson, 2014).

In healthy children, motor and sensory systems continue to develop during toddlerhood and the preschool years. Auditory and visual skills improve during this time too. Since brain development after birth is influenced by inputs from the environment, and because those inputs are unique to each child, every human brain is unique.

Note: Inputs from the environment are not always a good thing. Children born prematurely are thought to associate the initial noise and clatter around them as painful. Research indicates that a quiet environment allows these children to catch up as their neurons make connections (Rothbart et al., 2003).

Although the age at which a child is ready to learn a specific skill becomes hard-wired as the brain develops, learning itself is also environmentally determined. For example, a child is ready to learn to read when his or her auditory system is developmentally ready to distinguish one sound from another. But if reading instruction is not provided, or if the child's parents do not enrich the environment by reading to him or her, learning to read will be delayed.

Conversely, a child whose auditory system is not ready when reading instruction is provided will also be delayed in learning to read.

The ability to read is also enhanced by the development of the auditory cortex and the development of skills involved in remembering what is taught and applying that knowledge to real problems.

Note: A key predictor of reading readiness is a child's ability to understand rhyming (Semrud-Clikeman, 2006). This ability translates into skills in understanding how sounds differ and in turn predicts a child's success with phonics instruction.

At every stage of development, it is important to give children age-appropriate tasks. But, be careful when you combine tasks. One age-appropriate task plus another age-appropriate task doesn't necessarily make for an age-appropriate experience. For example:

In the early grades, children learn how to coordinate fine motor skills and visual skills. They are able to copy letters and figures they see. Although this simple task is automatic for you, it takes a lot of concentration for them. Therefore, a child should not be asked to copy items from the blackboard and solve problems at the same time unless the act of copying has become automatic.

Early elementary years

During the early elementary years, fibers continue to grow between neurons and the white matter of the brain (also called myelin). The growing neural networks of connected neurons and fibers are essential to the transmission of information throughout the brain. As the brain matures, more and more fibers grow and the brain becomes increasingly interconnected. These interconnected networks of neurons are very important to the formation of memories and the connection of new learning to previous learning.

As neural networks form, the child learns both academically and socially. At first, this learning is mostly rote in nature. As skills become more automatic, the child does not have to think as hard about what he or she is learning or doing, and brain resources are freed up to be used for complex tasks that require more and more attention and processing. Skills in reading, mathematics and writing become more specialized and developed.

Late elementary to middle school years

The late elementary and middle school years

From late elementary school into middle school, inferential thinking becomes more emphasized in schools, while rote learning is de-emphasized. This shift in focus is supported by the increased connectivity in the brain and by chemical changes in the neuronal pathways that support both short and long term memory. These chemical changes can continue for hours, days and even weeks after the initial learning takes place (Gazzaniga, & Magnun, 2014). Learning becomes more consolidated, as it is stored in long-term memory.

Where does learning occur?

During the early elementary years, the child develops motor skills, visual-motor coordination, reasoning, language, social understanding and memory. As learning is consolidated into neural networks, concepts combine into meaningful units that are available for later use. An ability to generalize and abstract begins at this stage and continues into adulthood. Also during this time, the child learns about perspective-taking and social interaction. The ability to understand one's social place is crucial for the development of appropriate relationships with other people. These skills are closely tied to development of the tracts of the right hemisphere as well as in the areas of the brain that are tied to emotional processing (also called the limbic system) (Semrud-Clikeman, 2007). (A tract is a pathway that connects one part of the brain with another, usually consisting of myelin-insulated axons. Tracts are known collectively as white matter.)

During the later elementary years and early middle school years, the child's brain activity is mostly in the posterior regions where the areas for auditory, visual and tactile functioning intersect. This intersection is called the association area of the brain and generally contains information that has been learned and is now stored. This is the information that is commonly measured on achievement tests and verbally based ability tests.

The frontal lobes begin to mature more fully in middle school. The maturation continues through high school and adulthood (Semrud-Clikeman & Ellison, 2009). The frontal lobes are a more recent evolutionary development in brains and allow humans to evaluate and adapt their behavior based on past experience. The frontal lobes are also thought to be where social understanding and empathy reside (Damasio, 2008).

The refined development of the frontal white matter tracts begins around age 12 and continues into the twenties. This region of the brain is crucial for higher cognitive functions, appropriate social behaviors and the development of formal operations. These tracts develop in an orderly fashion and experience appears to contribute to further development.

If you are teaching adolescents, you should emphasize inferential thinking as well as metacognition. For some adolescents, brain development matches our educational expectations. For others, the two do not coincide and there is a mismatch between biology and education. In this case, the adolescent is unable to obtain the maximum benefit from instruction and is often unable to understand more advanced ideas. Although learning problems may be due to immaturity, they may indicate more serious learning or attentional problems.

As connecting tracts in the frontal lobes become more refined, adolescents are expected to "think" about their behaviors and to change these behaviors. Unfortunately, this is the time when adolescents are more risk-prone and impulsive than adults. Some of this tendency is linked to changes in hormonal development as well as in brain changes.

White matter tracts

The figure below shows the white matter tracts in a mature brain. Notice the colored areas that reveal the tracts from front to back of the brain, allowing for good communication both from front to back as well as from right to left.

Brain changes in the frontal lobe continue at a fast pace during adolescence and the healthy individual becomes better able to control more primitive methods of reacting (such as fighting or being verbally aggressive) in favor of behaviors that are adaptive. Adolescents and young adults start to see the world through the eyes of others and they become better at relating to other people.

Their progress toward more independence can be an exciting but also daunting task. When the transition to more adult behavior is problematic, the difficulty may be due to brain maturation. That's where a teacher can help.

Some adolescents need more structure; others need more freedom. A teacher is in a unique place to help parents and adolescents to understand these boundaries and to tailor their guidance to each situation. Schools are also beginning to recognize that smaller groupings and more contact with adults helps, too. These changes are very appropriate and in tune with the social and emotional needs of adolescents — as well as brain maturation — that are occurring at this crucial time.

Summary

In each stage of development, it is important for teachers to understand the relationship between neurological development and learning. This understanding is particularly important when there is a mismatch between development and educational expectations. The mismatch may be due to brain maturational differences or it can be due to a developmental disability. Research has found differences in brain structure, activation and development in children with learning disabilities (Aylward, E. H., et al., 2003; Maisog et al., 2008; Shaywitz, 2004), attention deficit hyperactivity disorder (Siedman et al., 2006; Swanson, et al., 2007) and in mood disorders (Konarski, et al., 2008; Pliszka, 2005). Further research is needed in all of these areas.

Myths about brain development and learning

Myth: You can train certain parts of the brain to improve their functioning.

Fact: This has been an attractive and sometimes lucrative idea for many entrepreneurs. However, it is not possible to target a specific brain region and teach just to that part of the brain. The brain is highly connected. Neurons in the brain learn remember and forget, but they do not do so in isolation. Skills need to be broken down into their component parts and these parts can be taught. However, we do not totally understand how this learning takes place nor do we know exactly "where" in the brain that learning is stored. Evidence from victims of stroke and head injury show that injury to the brain of one individual may not result in the same loss in the brain of another person (Goeggel, 2012). Brains are like fingerprints — although there are commonalities, there are differences that make each brain unique.

Myth: You are born with certain abilities and these do not change over time.

Fact: At one time, people believed that the brain developed into its full form by the age of three, and that what developed afterwards was just a matter of refinement. In fact, we now know that the brain is plastic — it changes with experience and development. Evidence shows that rather than ending development at the age of 5, or even 12, brain development continues into one's twenties. For some adolescents, the maturation of the frontal lobes may not end until age 25. For others, frontal-lobe maturity may be reached by the age of 18 or 19. For this reason, some adolescents may require additional time before they are ready for college, while others are ready at an earlier age.

Facts about brain development and learning

A child with a learning disability will always have the disability.

While a child with a learning disability, or with attention deficit hyperactivity disorder (ADHD), may show continuing problems in these areas, there are treatments that may help the child compensate for the problems. (These interventions are discussed in other parts of this module.) The brain changes with experience and the direct teaching of appropriate skills is the most important aspect of learning for children with special needs. Shaywitz (2004) reports success in teaching compensation skills to children with severe dyslexia beginning at an early age and continuing throughout school. Gross-Glenn (1989) found that adults with an early history of dyslexia, who had learned to read, had developed different pathways compared to those without such a history. The evidence from this research indicates that new pathways can be formed with intervention. Although these pathways are not as efficient as those generally utilized for these tasks, they can function adequately. Response To Intervention is a method that can help tailor an intervention to a child's needs (Fiorello, Hale, & Snyder, 2006).

The environment can improve a child's ability.

The environment can increase ability or it can lower it. A child with average ability in an enriched environment may well accomplish more than a bright child in an impoverished environment. Although it is heartening to believe that enrichment can be effective at any point, recent research indicates that early enrichment is more beneficial than later enrichment. The brain grows in spurts, particularly in the 24th to 26th week of gestation, and between the ages of one and two, two and four, middle childhood (roughly ages 8 to 9) and adolescence (Semrud-Clikeman & Ellison, 2009). These brain growth spurts are roughly commensurate with Piaget's stages of development. They coincide with periods of fast learning of language and motor skills in the one to four year old child; concrete operations in middle childhood; and formal operations in adolescents. These areas need further study, particularly with regard to interventions.

Skills such as working memory, planning, organization and attention develop over time with brain maturation and with practice.

• Working memory is the ability to keep information in mind while solving a problem. For young children, teachers need to give directions one at a time. For late elementary school children, directions can be given in a limited series of steps. For children with difficulty in this area, it is helpful to have them repeat the directions to make sure they recall what is asked of them. Listing steps on the blackboard can also be helpful. Problems in working memory can be linked to difficulties with distractibility and/or attention.

• Executive functions are those skills that allow a person to evaluate what has happened, to review what was done, and to change course to an alternative or different response (Diamond, 2006). Executive function skills allow children to understand what has happened previously and to change their behavior to fit new situations. Teachers can help with executive function development by including exercises that ask "what do you think may happen next in the story?" or they can provide story maps.

• Planning and organization is the ability to plan and organize is a skill that develops along with the brain's ability to consolidate information. These skills develop slowly and with experience and development. Teachers can assist in developing these abilities by initially asking the child to think about the steps needed to complete a project. Teaching the child how to analyze a problem is also helpful — what do you need to do first? What do you need to do next? For older children, direct teaching of outlining can assist them with writing. The use of day planners and calendars can also help students plan for the completion of longer assignments.

Skills

Working memory

Do you ever go to a telephone book to look up a number and remember it just long enough to dial it? That's an example of working memory. If you get distracted between looking up the number and dialing the number, you will forget it. In order for something in working memory to be stored, it must be rehearsed and practiced. For a young child, this is particularly difficult because attention and distractibility significantly impact working memory. In addition, working memory is generally a frontal lobe function and for younger children the frontal lobe is not as well developed as in older children. Therefore, asking a young child to do more than one, or at the most two things at a time will not be successful — their brains are simply not ready. For elementary school children, working memory improves as the brain matures. Most children in elementary school are able to follow up to four directions at one time. For those who are younger, it is possible to practice one direction at a time or to have the child repeat the directions — practicing these skills improves performance. For adolescents, working memory may fail due to distractions. To improve the functioning of working memory it is helpful to make sure the person is listening to you. In addition, even for a fully developed working memory, the memory buffer is sensitive to overload. If a student is asked to do (or remember) too many things at once, he/she will not be able to process this information. Similarly, in a lecture format, information needs to be provided both visually and orally in order for sufficient material to make it into the working memory buffer. The use of lists, rehearsals and day planners have all been found to be helpful in remembering information that would otherwise overload working memory (Diamond, & Lee, 2011).

Executive functions

Evidence suggests that these skills primarily reside in the frontal lobes and develop over time. Although young children have some ability to improve their executive functioning skills based on feedback from teachers and parents, executive functions improve with age. Older children become more adept with these skills and use them more flexibly. It is interesting to note that executive functions are negatively affected by lack of focus, and children with ADHD frequently have difficulty with executive functions.

Recent research also indicates that when material is emotionally charged in a negative way (such as the pressure to learn something for a test, or the pressure of being called on by the teacher and made to answer a question), executive functioning decreases. This happens to some degree in every child, but it is particularly true for children with ADHD (Castellanos, Songua-Barke, & Milham, 2006).

When you are asking any child to perform a task that requires concentration and planning, it is important to provide as much scaffolding as possible for the child in order for him/her to profit from instruction. With maturity, executive functioning is related to appropriate behavior in a variety of situations.

Posner's model

In Posner's model of attention, both posterior and anterior regions of the brain form a complex network that includes subcortical structures such as the caudate nucleus for processing attention-related activities (Posner, & Rothbart, 2007). In this model, there are three networks believed to be involved: alerting, orienting and executive.

The alerting network lets a person know that something different is occurring. The orienting network orients the person to an event — where the event is, what the event is, etc. The executive network coordinates input of information and determines appropriate actions and reactions. Right frontal lobe dysfunctions are related to deficits in the alerting network, bilateral posterior dysfunctions are consistent with deficits in the orienting network, and left caudate nucleus dysfunctions correspond to deficits in the executive network.

Similar to Posner's theory, Corbetta and Shulman (2002) suggest that networks in various parts of the brain are involved in attentional functions. They say that the anterior of the brain is involved in selecting or detecting items to be attended to and preparing goal-driven behavior. The second system is in the temporal-parietal region and the lower frontal regions of the right hemisphere. It is this system that is specialized for the selection of relevant stimuli particularly when an event is unexpected. This second network pays attention to environmental events that are important because they are either rare or surprising. As such, this system would be a protective system to channel attention to particularly threatening or rewarding stimuli.

For further recommendations for skills also see Lynn Meltzer's "Executive Function in Education: From Theory to Practice" (Meltzer, 2011).

Interventions

1. Reward good behaviors quickly and as frequently as possible. Please refer to the module on giving praise.
2. Follow through with consequences. When a child breaks the established rules, warn once. If the behavior continues, follow through with the promised consequence immediately.
3. For excessive activity:
   1. Use activity as a reward. Alternate a seat-based activity with a more physical activity. For example, send the child to the office with a note for the secretary or give an activity that removes the child from the situation.
   2. Solicit active responses. Examples include talking, moving or organizing responses.
   3. Do not try to reduce physical activity.
   4. Encourage non-disruptive movement.
   5. Allow students to stand while doing seatwork.
4. Positively reinforce effort as well as success. For example, tell the child how well he/she is working.
5. Give clear, concise instructions.
6. Have a child repeat directions to you aloud.
7. Reinforce directions with a visual reminder when appropriate. For example, provide a list on the blackboard of what is expected and the approximate amount of time that each step should take.
8. Allow limited choice of tasks, topics and activities.
9. Use a child's interest whenever possible in designing activities or introducing material.
10. Match a child's learning ability and preferred method of response.
11. Allow alternate response modes (computer, taped assignments) with every assignment.
12. Provide a predictable routine in your class.
13. Encourage the use of color coded folders or other forms of personal organizers.
14. Make tasks as interesting as possible.
15. Allow children to work with partners.
16. Alternate high and low interest tasks.
17. Give targeted children priority seating close to the teacher.
18. Increase or provide novelty at later stages of the task to keep the child motivated.
19. Decrease the length of the tasks you assign.
20. Break up tasks into smaller parts.
21. Have tasks arranged so that children complete smaller parts after longer parts.
22. For every task students tend not to prefer, engage in two preferred tasks. Let students know that this will happen.
23. Give fewer math or spelling problems. For example, have the child do only the odd or even problems. Or put fewer problems (words on one page).
24. Use distributed (rather than mass) practice for problems beginning a task.
25. Increase structure and/or add emphasis to relevant parts of a task or assignment.
26. Ask a child to repeat directions.
27. Use written directions.
28. Set realistic standards for acceptable work.
29. Point out topic sentences, headings, etc. to improve task completion.
30. Use lists and assignment organizers.
31. Substitute verbal or motor responses for written responses.
32. Have a child work on easier parts of a task before tackling the more difficult ones.
33. Underline key words in directions.
34. Allow quiet play.
35. Encourage note taking for older children in high school.
36. Reward short intervals of patient waiting.
37. Don't assume that impulsive behaviors are aggressive.
38. Cue the child to upcoming difficult times when extra control is needed.
39. Bring distracters or toys that are quiet and absorbing.
40. Encourage after school activities.
41. Develop the child's sense of confidence and responsibility.
42. Encourage targeted children to play with children who can serve as positive role models.
43. Model good behavior.
44. Reward good behavior.

Special populations

There are few direct studies of differences in brain development between girls and boys, and few to none on ethnicity. However, there are a number of studies looking at differences in brain structure and functioning in children with learning disabilities (LDs), autistic spectrum disorder or ADHD. Findings shed light on the difficulties that can arise when brain development does not go according to plan.

The next paragraphs briefly review the literature on gender differences, learning disabilities and ADHD. The review is not exhaustive, as research in this area is ongoing. It continues to contribute to our understanding of how the brain matures and give us ideas about interventions that can be used to alleviate problems.

Gender

Although there are few studies looking at gender differences in young girls and boys, it has been found that adult women have a larger corpus callosum (a bundle of myelinated fibers connecting the two hemispheres) than men (Semrud-Clikeman, Fine, & Bledsoe, 2009). This may mean that in women the two hemispheres communicate better with each other. In addition, there are indications that women have their skills spread throughout the brain, while males tend to have their skills in specific regions of the brain. It is not clear whether these differences are universally present. As a result, much more research is needed.

Learning disabilities

More and more we are learning that children with learning disabilities have brains that are different. Using magnetic resonance imaging (MRI), many studies have found that the brain area involved in matching sounds and letters is compromised in children with dyslexia (Maisog, Einbinder, Flowers, Turkeltaub, & Eden, 2008). These smaller brain areas correlate with poorer performance on tests of reading achievement, word attack and rapid naming ability of letters, numbers and objects (Gabrieli, 2009). The corpus callosum has also been found to differ in children with dyslexia. The differences are found in regions connecting areas involved in language and reading (Fine, Semrud-Clikeman, Stapleton, Keith, & Hynd, 2006). These differences appear to be due to decreased rates of pruning during the fifth and seventh month of gestation (Paul, 2011).

Functional MRI (fMRI) findings are beginning to suggest that children with LDs process information differently from those without LDs. Frontal brain regions are more efficient in fluent adult readers compared to children who are beginning to read (Schlaggar, 2003). As a child develops, the left frontal region becomes more active. But, fluent reading appears to be related to this region too. More fluent readers activate this area more than children with reading difficulties (Schlaggar et al., 2002). Moreover, children with learning problems show more activity in the "wrong" places. For example, their parietal and occipital areas are more active, and they show more activity in the right hemisphere than the left. In contrast, children without learning problems activate the frontal regions and the left hemisphere with less activation in the right hemisphere.

Activation of the brain is more diffuse when children are beginning to learn to read. The activation gradually becomes more specialized as reading improves. Similarly, when asked to read single words, normal readers show left hemispheric activation, whereas those with dyslexia show more right hemispheric activation (Breier, et al., 2002; Papincolaou, 2003).

Brain regions in the left hemisphere and temporal region have been found to be more active in good readers compared to those who had compensated for their dyslexia and were able to read adequately (Raizada, Tsao, Liu, Holloway, Ansari, & Kuhl, 2010). In addition, Gabrieli (2003) found that improvements were found in activation following remediation of auditory processing ability. It is not yet clear whether these changes continue over time; further study is needed to understand possible brain response to remediation. This finding is important because activation of the left hemisphere, a region specialized for language functions, plays an important role in reading while the right hemisphere has generally been implicated for processing of novel stimuli. Since children with learning disabilities activate the right hemisphere when they read, this seems to indicate that they find reading to be a more novel task than a learned task.

Early reading uses visual-perceptual processes generally located in the posterior portion of the brain. As the reading process becomes more automatized, the frontal systems become more active. Thus, the progression from simple letter and word calling to actual reading comprehension requires a maturation of neural pathways linking the back of the brain to the front (Shaywitz, 2004). Changes from right hemispheric processing to left hemispheric processing have also been found to occur with improvement in reading skills and improvement in language functioning. Such changes are not found for children with dyslexia, and their reading skill does not become automatic and effortless. Additional research is progressing in learning disabilities in older students.

Attention deficit hyperactivity disorder

There have been several studies of the possible structural differences between children with and without attention deficit hyperactivity disorder (ADHD) (Bledsoe, Semrud-Clikeman, Pliszka, 2009; Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Cherkasova, & Hechtman, 2009; Shaw, Eckstrand, Sharp, Blumenthal, Lerch, Greenstein, ... & Rapoport, 2007). A study of total brain volume found a five percent smaller volume in the brains of the group with ADHD compared to a control group. This difference in volume was not related to age, height, weight, or IQ. Another structure of interest has been the caudate nucleus. The caudate nucleus is located in the center of the brain and is associated with the neurotransmitter dopamine. The caudate has been found to be smaller in children with ADHD, possibly indicating less availability of dopamine — the neurotransmitter that assists with focusing of attention and impulse control (Semrud-Clikeman et al., 2006). Volumetric studies have also found smaller frontal lobe volumes in children with ADHD particularly the white matter volume of the frontal lobe. Differences have also been noted in the white matter in the posterior regions of the brain particularly for those children who did not respond to stimulant medication such as Ritalin (Hale, Reddy, Semrud-Clikeman, Hain, Whitaker, Morley, ... & Jones, 2011).

3-D illustration of the Caudate

Coronal slice showing white matter

The finding of reduced white matter volume in the right frontal and posterior regions of the brain, as well as caudate asymmetry differences, suggests that systems commonly associated with sustained attention are different for children with ADHD. This finding may help to explain the difficulty children with ADHD have in more advanced attentional functions, such as self-regulation and executive function. Reduced white-matter volume leads to less communication between the frontal and posterior areas. The posterior region of the brain is responsible for accessing information from previous situations while the frontal region of the brain applies this knowledge to the current situation at hand. When there is not enough communication between these two centers, the child will have difficulty either accessing previously learned information or applying it correctly to the new situation. This corresponds to the finding that a child with ADHD has difficulty applying knowledge (or rules) even though he/she may be able to tell you the rule.

A fairly new avenue of investigation is the gene X environment interaction to help understand the etiology and course of ADHD. Nigg et al. (2010) reviewed the literature and found that psychosocial factors contribute to attentional difficulty. For example, a child may do adequately if family stresses are lower. However, if family disruptions (divorce, contentious parenting) occur, significant impairment may ensue. ADHD has a relatively high heritability meaning that it tends to run in families. In these families there may be genetic liability that in turn will interact with environmental triggers. Thus, when working with families with a history of ADHD, it is important for educators to provide information as appropriate and to be aware of these vulnerabilities.

The development of fewer connections between brain areas may well impact the efficiency of these connections — resulting in a poorer level of functioning but not a total loss of function (Fair, Nagel, Bathula, Dias, Mills, ... & Nigg, 2010; Makris, Buka, Biederman, Papadimitriou, Hodge, Valera, ... & Seidman, 2008; Nigg, 2006). Functional neuroimaging, which allows one to view what the brain is doing when the person is completing a task, showed lowered activation in the regions of the frontal lobe and caudate nucleus when the child is asked to inhibit a response. (Not respond when he/she would like to respond) (Pliszka et al., 2006). Less activation may well indicate fewer connections being made between neural networks and poorer attention to detail. Additional study is needed in this area to more fully understand differences that may be present in children with ADHD and those without.

fMRI axial slice image

Autistic spectrum disorders

Children with autism have been found to have larger heads than the general population (Verhoeven, De Cock, Lagae, & Sunaert, 2010). It has been found that the brains of toddlers with autism are 10 percent larger than same-aged peers, with the largeness of the head decreasing with age. They continue, however, to be larger than matched aged peers throughout life (Anagnostou, & Taylor, 2011). Interestingly, there is no difference in head size at birth (Keller, Kana, & Just, 2007) and the brain growth that later occurs may be due to early overgrowth of neurons, glial cells and a lack of synaptic pruning. Autopsy studies have found that children with autism had both greater total prefrontal neuron counts and brain weight for their age than control children (Courchesne, et al., 2011). Findings have suggested that the extra tissue that causes the increase in size is not well utilized or organized — thus resulting in poorer skill development (Aylward et al., 2002). Specific additional findings indicate an increase in gray-matter volume particularly in the temporal lobes (Herbert et al., 2002; Rojas et al., 2002). Using structural MRI analyses, Courchesne et al. (2003) found smaller amounts of white matter compared to gray matter in toddlers and adolescents. Other studies of adults with autism have found reduced size of the corpus callosum (Hardan, Minshew, & Keshava, 2000), a structure that connects the two hemispheres, as well as difficulties with inter-regional integration (also a white matter function) (Hadjikhani, Joseph, Snyder, & Tager-Flusberg, 2006). Some studies have suggested that the larger brain, higher white matter volume and disrupted gray matter cellular columns may contribute difficulty that a person with autism has in integrating information and generalizing this information to new situations (Schultz et al., 2000). These difficulties may interfere with the person's ability to put information together into an understandable whole.

fMRI autism vs. healthy control activation pattern

MRI autism vs. healthy control volume comparison

The amygdala, anterior cingulate and hippocampus are part of the limbic system — the emotional part of the brain. The amygdala is important in emotional arousal, as well as processing social information. The hippocampus allows for the short-term and eventual long-term storage of information while the anterior cingulate works as a type of central executive, directing attention where it is most required.

Autopsies of autistic individuals have revealed abnormalities of both the hippocampus and the amygdala including fewer connections and smaller hippocampi. This finding could lead to difficulties in forming new memories or associating emotions with past memories (Carlson, 2014), and may contribute to difficulties seen in people with autism with respect to social reciprocity and social awareness. Structural neuroimaging studies of children with autism show the volume of the amygdala and hippocampus to be enlarged (Groen, Teluij, Buitelaar, & Tendolkar, 2010), although further research is needed in these areas. Some have suggested that the amygdala may be important for mediating physiological arousal and if it is not as active, the person may well not be as motivated for participating in social activities (Murphy, Deeley, Daly, Ecker, O'Brien, Hallahan, & Murphy, 2012).

More recent studies have begun evaluating discrete areas of the brain that may be disrupted in people with autism. An area of the temporal lobe that has been found to be important for recognizing faces has been studied in children with autism. This area has been found to be underactive in people with autism and the degree of under-activation is highly correlated with the degree of social impairment (Schultz et al., 2001). Of additional interest is that this area of the temporal lobe has also been implicated in successful solution of Theory of Mind tasks, skills that are also impaired in people with autism (Castelli et al., 2000; Martin & Weisberg, 2003).

Both the frontal lobes and the upper area of the temporal lobes are important for understanding and perception of social interactions as well as interpretation of facial expressions. The frontal lobes have also been implicated in the ability to take another's perspective — or in social cognition. These areas are intimately connected to the limbic system as well as the temporal lobe areas discussed earlier in this section. Studies of brain metabolism have found reduced activity in these regions of the brain in patients with autism particularly when asked to perform tasks that tap social cognition and perception (Harms, Martin, & Wallace, 2010).

• Center for Excellence in Autism Research at the University of Pittsburgh
• Autism Speaks

References

Anagnostou, E., & Taylor, M. J. (2011). Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here. Mol Autism, 2, 4-4.

Aylward, E. H., Richards, T. L., Berninger, V. W., Nagy, W. E., Field, K. M., Grimme, A. C., ... & Cramer, S. C. (2003). Instructional treatment associated with changes in brain activation in children with dyslexia. Neurology, 61, 212-219.

Aylward, E. H., Minshew, N. J., Field, K., Sparks, B. F., & Singh, N. (2002). Effects of age on brain volume and head circumference in autism. Neurology, 59, 175-183.

Bledsoe, J., Semrud-Clikeman, M., & Pliszka, S. R. (2009). A magnetic resonance imaging study of the cerebellar vermis in chronically treated and treatment-naive children with attention-deficit/hyperactivity disorder combined type. Biological psychiatry, 65, 620-624.

Breier, J., Simos, P. G., & Fletcher, J. M. (2002). Abnormal activation of tempoparietal language areas during phonetic analysis in children with dyslexia. Neuropsychology, 17, 610-621.

Carlson, N.R. (2014). Physiology of Behavior (11 th edition). Bloomington, MN: Pearson.

Castellanos, F. X., Sonuga-Barke, E. J., Milham, M. P., & Tannock, R. (2006). Characterizing cognition in ADHD: beyond executive dysfunction. Trends in cognitive sciences, 10, 117-123.

Castelli, F., Frith, C., Happe, F., & Frith, U. (2002). Autism, Asperger's syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain, 125, 1839-1849.

Cherkasova, M. V., & Hechtman, L. (2009). Neuroimaging in attention-deficit hyperactivity disorder: beyond the frontostriatal circuitry. Canadian journal of psychiatry. Revue canadienne de psychiatrie, 54, 651-664.

Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature reviews neuroscience, 3, 201-215.

Courchesne, E., Mouton, P.R., Calhoun, M.E., Semendeferi, K., Ahrens-Barbeau, C., Hallet, MJ, Barnes, C.C., & Karen Pierce, K. (2011). Neuron number and size in prefrontal cortex of children with autism. Journal of the American Medical Association, 306, 2001-2010.

Damasio, A. (2008). Descartes' error: Emotion, reason and the human brain. Random House.

Diamond, A. (2006). The early development of executive functions. Lifespan cognition: Mechanisms of change, 70-95.

Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in children 4 to 12 years old. Science, 333(6045), 959-964.

Fair, D. A., Posner, J., Nagel, B. J., Bathula, D., Dias, T. G. C., Mills, K. L., ... & Nigg, J. T. (2010). Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder. Biological psychiatry, 68, 1084-1091.

Fine, J. G., Semrud-Clikeman, M., Keith, T. Z., Stapleton, L. M., & Hynd, G. W. (2007). Reading and the corpus callosum: An MRI family study of volume and area. Neuropsychology, 21, 235-241.

Fiorello, C. A., Hale, J. B., & Snyder, L. E. (2006). Cognitive hypothesis testing and response to intervention for children with reading problems. Psychology in the Schools, 43, 835-853.

Gabrieli, J. D. (2009). Dyslexia: a new synergy between education and cognitive neuroscience. Science, 325, 280-283.

Gabrieli, J. (2003). Neuroimaging evidence about the brain basis of dyslexia. Paper presented at the International Dyslexia Association conference in San Diego.

Gazzaniga, M. S., & Mangun, G. R. (2014). Cognitive neuroscience (4 th ed). Boston: MIT Press.

Goeggel, S. B., Berger, S., Hagmann, B. B., & Steinlin, M. (2012). [Traumatic brain injury in children]. Praxis, 101, 317-324.

Groen, W., Teluij, M., Buitelaar, J., & Tendolkar, I. (2010). Amygdala and hippocampus enlargement during adolescence in autism. Journal of the American Academy of Child & Adolescent Psychiatry, 49, 552-560.

Gross-Glenn, K., Duara, R., Barker, W. W., & Loewenstein, D. (1991). Positron emission tomographic studies during serial word-reading by normal and dyslexic adults. Journal of Clinical and Experimental Neuropsychology, 13, 531-544.

Hadjikhani, N., Joseph, R. M., Snyder, J., & Tager-Flusberg, H. (2006). Anatomical differences in the mirror neuron system and social cognition network in autism. Cerebral cortex, 16, 1276-1282.

Hale, J. B., Reddy, L. A., Semrud-Clikeman, M., Hain, L. A., Whitaker, J., Morley, J., ... & Jones, N. (2011). Executive impairment determines ADHD medication response: implications for academic achievement. Journal of learning disabilities, 44, 196-212.

Hardan, A. Y., Mishew, N. J., & Keshavan, M. S. (2000). Corpus callosum size in autism. Neurology, 55, 1033-1036.

Harms, M. B., Martin, A., & Wallace, G. L. (2010). Facial emotion recognition in autism spectrum disorders: a review of behavioral and neuroimaging studies.Neuropsychology review, 20, 290-322.

Herbert, M. R., Ziegler, D. A., Deutsch, C. K., O'Brien, L. M., Lange, N., & Bakardjiev, A., et al. (2003). Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain, 126, 1182-1192

Konarski, J.Z., McIntyre, R.S., Kennedy, S.H., Rafi-Tari, S. Soczynska, J.K., & Ketter, T.A. (2008). Volumetric neuroimaging investigations in mood disorders: Bipolar disorder versus major depression disorder. Bipolar Disorders, 10, 1-37.

Maisog, J. M., Einbinder, E. R., Flowers, D. L., Turkeltaub, P. E., & Eden, G. F. (2008). A Meta ‐ analysis of Functional Neuroimaging Studies of Dyslexia. Annals of the New York Academy of Sciences, 1145, 237-259.

Makris, N., Buka, S. L., Biederman, J., Papadimitriou, G. M., Hodge, S. M., Valera, E. M., ... & Seidman, L. J. (2008). Attention and executive systems abnormalities in adults with childhood ADHD: A DT-MRI study of connections. Cerebral Cortex, 18, 1210-1220.

Martin, A., & Weiberg, J. (2003). Neural foundations for understanding social and mechanical concepts. Cognitive Neuropsychology, 20, 575-587.

Meltzer, L. (2011). Executive function in education: From theory to practice. New York: Guilford Press.

Murphy, C. M., Deeley, Q., Daly, E. M., Ecker, C., O'Brien, F. M., Hallahan, B., ... & Murphy, D. G. (2012). Anatomy and aging of the amygdala and hippocampus in autism spectrum disorder: an in vivo magnetic resonance imaging study of Asperger syndrome. Autism Research, 5, 3-12.

Nigg, J.T. (2006). What causes ADHD? Understanding what goes wrong and why. New York: Guilford Press.

Nigg, J.T., Nikolas, M., & Burt, S.A. (2010). Measured gene-by-environment interaction in relation to Attention Deficit/Hyperactivity Disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 863-873.

Papanicolaou, A. C. (2003). Brain imaging in normal and impaired reading: A developmental-educational perspective. Paper presented at the International Dyslexia Association conference in San Diego.

Paul, L. K. (2011). Developmental malformation of the corpus callosum: a review of typical callosal development and examples of developmental disorders with callosal involvement. Journal of Neurodevelopmental Disorders, 3, 3-27.

Pliszka, S. (2005). The neuropsychopharmocology of Attention Deficit Hyperactivity Disorder. Biological Psychiatry, 57, 1385-1390.

Plizska, S., Glahn, D., Semrud-Clikeman, M., Franklin, C., Perez III, R., & Liotti, M., (2006). Neuroimaging of inhibitory control in treatment naïve and chronically treated children with ADHD. American Journal of Psychiatry, 163, 1052-1060.

Posner, M. I., & Rothbart, M. K. (2007). Research on attention networks as a model for the integration of psychological science. Annu. Rev. Psychol., 58, 1-23.

Raizada, R. D., Tsao, F. M., Liu, H. M., Holloway, I. D., Ansari, D., & Kuhl, P. K. (2010). Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math. NeuroImage, 51, 462-471.

Rojas, D. C., Bawn, S. D., Benkers, T. L., Reite, M. L., & Rogers, S. J. (2002). Smaller left hemisphere planum temporale in adults with autistic disorder. Neuroscience Letters, 328, 237-240.

Rothbart, M. K., Ellis, L. K., Rosario Rueda, M., & Posner, M. I. (2003). Developing mechanisms of temperamental effortful control. Journal of personality, 71, 1113-1144.

Schlaggar, B. L. (2003). FMRI and the development of single word reading. Paper presented at the International Dyslexia Association conference in San Diego.

Schlaggar, B. L., Brown, T. T., Lugar, H. M., Visscher, K. M., Miezin, F. M., & Petersen, S. E. (2002). Functional neuroanatomical differences between adults and school-age children in processing of single words. Science, 296, 1476.

Schultz, R. T., Romanski, L., & Tsatsanis, K. (2000). Neurofunctional models of autistic disorder and Asperger's syndrome: Clues from neuroimaging. In A. Klin, F.R. Volkmar, & S.S. Sparrow (Eds.), Asperger's syndrome (pp. 19-209). New York: Plenum Press.

Semrud-Clikeman, M. (2007). Social competence in children and adolescents. New York: Springer.

Semrud-Clikeman, M. (2006). Neuropsychological aspects for evaluating LD. Journal of Learning Disabilities, 38, 563-568.

Semrud-Clikeman, M. & Ellsion, P.A.T. (2009). Child Neuropsychology. New York: Springer.

Semrud-Clikeman, M., Fine, J.G., & Bledsoe, J. (2009). Neuroimaging in women. In E. Fletcher-Janzen (Ed.), Neuropsychology of Women (pp. 1-37). Boston, Springer.

Semrud-Clikeman, M., Plizska, S., Liotti, M., Higgins, K., & Lancaster, J. L. (2006). Neuroimaging in children with ADHD, treated and treatment naïve. Neurology, 67, 1023-1027.

Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J. P., Greenstein, D., ... & Rapoport, J. L. (2007). Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proceedings of the National Academy of Sciences, 104, 19649-19654.

Shaywitz, S. (2004). Overcoming dyslexia. New York: Alfred A. Knopf.

Seidman, L. J., Valera, E. M., & Makris, N. (2005). Structural brain imaging of attention-deficit/hyperactivity disorder. Biological psychiatry, 57(11), 1263-1272.

Swanson, J. M., Kinsbourne, M., Nigg, J., Lanphear, B., Stefanatos, G. A., Volkow, N., ... & Wadhwa, P. D. (2007). Etiologic subtypes of attention-deficit/hyperactivity disorder: brain imaging, molecular genetic and environmental factors and the dopamine hypothesis. Neuropsychology review,17, 39-59.

van Zomeren, A. H., & Brouwer, W. H. (1994). Clinical neuropsychology of attention. New York: Oxford University Press.

Verhoeven, J. S., De Cock, P., Lagae, L., & Sunaert, S. (2010). Neuroimaging of autism. Neuroradiology, 52, 3-14.

Glossary

Abstract: Higher-level reasoning or understanding.

Amygdala: An almond-shaped cluster of neurons in the limbic system thought to be involved in processing emotions and memory.

Anterior cingulate: Anterior section of the cingulate cortex. Asymmetry: favoring one side or the other. Also called laterality.

Attention deficit hyperactivity disorder: Mental disorder that consists of behaviors such as impulsivity, hyperactivity and difficulties with inhibition and self-regulation

Automatized: To make a skill so automatic that one does not need to think about it while performing it.

Caudate nucleus: Part of the Basal-Ganglia, the Caudate nucleus is thought to be involved in regulation of movement, learning and memory.

Corpus Callosum: A white matter structure that connects the right and left hemispheres of the cerebral cortex. Thought to contain approximately 250 million axons that allow right and left hemisphere communication.

Dopamine (DA): Part of the catecholamine family of neurotransmitters (epinephrine and norepinephrine), Dopamine is naturally produced in the brain and is thought to be involved in reward-based cognitive functions.

Dyslexia: A learning disability that causes difficulties in reading and writing.

Empathy: The ability to recognize and vicariously experience another person's emotional state.

Executive function: Higher-order cognitive processes that allow one to control organization of thought, and apply context specific rules in order to execute a task successfully.

Formal operations: The skill to think systematically about all of the parts of a problem and to arrive at a reasonable solution.

Frontal lobes: Area of the brain made up by the front portions of right and left hemispheres of the cerebral cortex. These areas are involved in memory, planning, organization, language and impulse control. These areas also have been linked to personality.

Functional magnetic resonance imaging (fMRI): A technique in which neural activity is measured by changes in blood flow. Brighter areas on an fMRI images indicate higher amounts of blood flow and greater activity.

Generalize: To apply a conclusion beyond a specific example.

Glial cells: Cells of the nervous system that provide physical support and nutrition for neurons. Higher cognitive functions: See executive functions.

Hippocampus: Part of the limbic system involved in storing new knowledge.

Impulsive: Behaviors that are not thought out.

Inferential thinking: Reading between the lines, often involves meaning that is implied rather than explicit.

Inhibition: The ability to regulate behavior or impulses.

Inter-regional integration: Neural connections that are similar in location. Language: A system/group of symbols used in verbal and visual communication.

Learning disability: Difficulties in the development of language, reading, mathematical reasoning or other academic undertakings compared to expectations of one's ability. Believed to be neurological in nature.

Left hemisphere: The left side of the cerebral cortex, thought to mediate language and verbal communication.

Limbic system: A multistructural system involved in emotions, memory and physical regulation. Structures such as the amygdala, cingulate gyrus, hippocampus, hypothalamus, ammillary body, nucleus accumbens, orbitofrontal cortex, and thalamus are all structures of the limbic system.

Memory: Ability to store and recall conceptual, social, emotional and physical information.

Metacognition : Thinking about one's own learning, thinking or perception.

Myelinate: The white matter in the brain. It is made up of lipids (fat) that help impulses move more quickly along the nerve.

Myelination: Process during development by which Myelin is formed over the neurons.

Neuronal pathways: These are pathways through which nerve messages travel as they move among the various parts of the brain.

Neurons: Cells that make up the nervous system, they process and transmit signals electrically.

Neurotransmitter: Nervous system chemicals that relay, amplify and modulate electrical signals from one neuron to another neuron.

Perspective-taking: The ability to understand another person's point of view or beliefs. Processing of novel stimuli: Analyzing new information that the brain has not seen before.

Pruning: Process by which brain cells die off in order to make room for more efficient connections between neurons.

Reasoning: Mental process that deals with one's ability to perceive and respond to feelings, thoughts and emotions.

Right hemisphere: The right side of the cerebral cortex, thought to mediate spatial, social and emotional understanding.

Risk-prone: Susceptible to taking chances and making mistakes.

Rote: Learning by memorization.

Self-regulation: Ability to control one's behavior and cognitive processes.

Social understanding: Ability to manage and function in social settings such as peer relationships.

Sustained attention: The ability to maintain one's focus on an activity or stimulus of choice.

Synaptic pruning: When weaker neural connections are thinned and replaced by stronger connections.

Temporal region: The side region of the cerebrum thought to be involved in auditory processing.

Theory of Mind tasks: Tasks that evaluate whether one has the ability to consider another's personal beliefs, needs, desires and intentions.

Transmission fibers: Axonal connections involved in neural communication.

Visual-motor: Coordination of visual and motor processes, like tracing letters.

Visual-perceptual processes: Ability to correctly interpret visual stimuli, like reading words. White matter fibers: Myelinated axons.

White matter volume: Quantified amount of myelinated axons.