can study the fibers that connect these regions using a technique called diffusion tensor imaging (DTI). DTI is similar to fMRI but it measures the diffusion of water molecules in brain tissue. DTI provides a high-resolution image of the density and volume of white matter, myelinated axons that connect brain regions (Image 3.3). By measuring the structural integrity of this tissue, scientists can estimate the connectivity between brain regions (Baribeau & Anagnostou, 2015; Emsell, Van Hecke, & Tournier, 2016).
For example, Wu and colleagues (2019) used DTI to compare the brains of school-age children with and without ADHD. The researchers were especially interested in white matter tracts that connect the frontal lobes of the brain to other regions responsible for attention and behavioral control. They found that children with ADHD showed lower cell density and volume in the white matter that connects these brain regions, especially in the right hemisphere. Their finding is important because reduced connectivity could underlie many ADHD symptoms.
Thomas Schultz
Neuroimaging studies involving children and adolescents often yield inconsistent results. One reason for these inconsistent findings is that studies differ in their recruitment of participants, methods of data collection, and resolution of neuroimaging. Subtle differences in the studies’ methods can yield divergent results. For example, Bouziane and colleagues (2019) also used DTI to compare the brains of children with and without ADHD. Surprisingly, they found no differences in connectivity between youths with and without the disorder. Unlike other studies, none of the children with ADHD in their study had used medication in the past. The researchers suggested that medication, rather than ADHD, might partially explain the reduced connectivity seen in children with ADHD in other research.
Another reason for inconsistent results is that children show enormous variability in their brain volumes and rates of brain development. For example, total brain volume can differ by as much as 20% based on factors such as children’s age and gender. Researchers wishing to identify structural abnormalities in the brains of children with specific disorders need to carefully control for these factors (Sadock & Sadock, 2015).
Perhaps the most important reason for the inconsistent findings is that disorders in children and adolescents rarely have single causes that can be traced to specific brain regions. For example, ADHD appears to be caused by a complex relationship between biological, psychological, and social–cultural factors. It would be a mistake to think that a specific brain abnormality would account for all (or even most) cases of ADHD or any other disorder. Instead, it is likely that early differences in brain structure interact with environmental experiences to produce symptoms (Nusslock, 2018).
Review
A case study provides a detailed description of a person, small group, or phenomenon. Case studies focus on idiographic assessment, that is, unique abilities, behaviors, or experiences. Case studies are especially useful to describe new disorders or treatments.
A survey is used to describe larger groups of individuals. Surveys focus on nomothetic assessment, that is, how people generally think, feel, or act. Surveys that rely on random selection reflect the larger population.
Commonly used neuroimaging techniques include MRI, fMRI, and DTI. These techniques provide images of the brain’s structure, functioning, and connectivity, respectively.
How Do Psychologists Predict Behavior?
Correlational Research
Surveys can also be used to predict children’s behavior or developmental outcomes. Some researchers are interested in identifying possible risk factors for ADHD. For example, the genes that children inherit from their parents, complications during pregnancy or delivery, and exposure to illnesses or toxins in early childhood can increase the likelihood that children will develop the disorder. Other researchers focus on protective factors that might lower a child’s likelihood of developing symptoms. For example, mothers who receive prenatal medical care, who avoid alcohol and other drugs during pregnancy, and who manage stress after delivery may lower their child’s risk for ADHD. Still other researchers explore children’s prognosis or developmental outcomes. For example, some children with ADHD may develop academic problems, depression, or substance use disorders later in life. Correlational studies allow researchers to explore possible relationships between variables like these (Kazdin, 2017).
Researchers usually quantify the magnitude of the association between two variables using a correlation coefficient. The Pearson product–moment correlation coefficient (r) is the most commonly used statistic. It reflects the linear relationship between two variables. Correlation coefficients range from 1.0 to –1.0.
The strength of association is determined by the absolute value of the number. Coefficients near 1.0 or –1.0 indicate strong covariation; if we know the value of one variable, we can predict the other with accuracy. Coefficients near 0 indicate weak or absent covariation; the value of one variable does not tell us much about the value of the other.
The direction of the association is determined by the sign of the coefficient. Positive values indicate a direct association between variables; as one variable increases, the other increases. Negative values indicate an inverse association; as one variable increases, the other decreases.
Humphreys and colleagues (2013) used correlation coefficients to investigate the association between children’s ADHD symptoms and their social functioning. They asked parents to rate the severity of their children’s ADHD symptoms, the degree of stress that they experienced caring for their children at home, and the quality of their children’s relationships with peers. The researchers found a positive correlation (r = .32) between children’s ADHD symptoms and parenting stress at home: the greater children’s symptoms, the higher their parents’ stress. In contrast, the researchers found a negative correlation (r = –.31) between children’s ADHD symptoms and the quality of their peer interactions: the greater children’s symptoms, the fewer friends they had at school.
Correlations ≠ Causality
Correlational studies allow us to identify associations between variables, but they do not allow us to say that one variable caused the other. Correlations do not imply causality for two reasons.
First, a correlation between two variables does not tell us the direction of the relationship between the two variables. It is tempting to conclude that children’s behavior problems increase their parents’ levels of stress. However, it is also possible that the opposite could be true. Parents who are experiencing high levels of stress might be more likely to lose their temper toward their children and increase their children’s disruptive behavior (Figure 3.2).
Second, correlational studies do not rule out alternative explanations for covariation. A third variable might explain both the severity of children’s behavior problems and their parents’ levels of stress. For example, parental divorce, unemployment, or illness might adversely affect both children’s behavior and their parents’ stress levels.
Cross-Sectional and Longitudinal Studies
There are two types of correlational designs that are especially relevant to researchers who study childhood disorders: (1) cross-sectional and (2) longitudinal (Kazdin, 2017).
In a cross-sectional study, researchers examine the association between variables at the same point in time. For example, Humphreys and colleagues (2013) assessed the relationship between children’s ADHD symptoms and mood. They found a significant, positive correlation between these variables: children with more ADHD symptoms also experienced more symptoms of depression. However, the researchers could not determine the direction of this relationship because the variables were assessed at the same point in time.
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