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Hofstra Horizons Research

A Look Toward the Future of Treating Psychiatric Disorders

Keith Shafritz, PhD, Professor of Psychology, Hofstra University

A great challenge for the fields of psychiatry and clinical psychology is establishing an accurate diagnosis, followed by the initiation of a treatment program that is successful for each individual client or patient. For many patients, however, determining the appropriate diagnosis is fraught with difficulty, and initial attempts at treatment are sometimes ineffective. For example, approximately one-third of patients with schizophrenia or related psychotic disorders do not see symptom improvement even after trying two different medication treatments. Furthermore, symptoms of many disorders overlap, often making it exceedingly difficult to differentiate one potential diagnosis from another. For example, ritualistic and repetitive behaviors are core symptoms of both obsessive-compulsive disorder (OCD) and autism spectrum disorder (ASD), and engaging in these behaviors can impair social interactions. However, whether a clinician determines that such behaviors are due to OCD or ASD will have implications for treatment and services offered to the families.

Similarly, inattention and impulsivity can result from several different conditions, including attention-deficit/hyperactivity disorder (ADHD) and anxiety. However, recommended treatments for these two disorders could not be more different from one another. For medication management of ADHD, the person may be prescribed a low dose of a stimulant drug, such as amphetamine, while for anxiety, the person may be prescribed a drug such as Prozac or Zoloft. Behavioral-based treatments for ADHD and anxiety also differ dramatically.

Diagnosis of mental illness is established through careful observation by clinicians and the reporting of symptoms during a clinical interview …

Currently, diagnosis of mental illness is established through careful observation by clinicians and the reporting of symptoms during a clinical interview, often accompanied by symptom checklists completed by patients or caregivers. To aid in this process, two classification systems have emerged: the Diagnostic and Statistical Manual for Mental Disorders, published by the American Psychiatric Association and currently in its fifth edition (DSM-5), and the International Classification of Diseases, published by the World Health Organization and currently in its 10th edition (ICD-10). These books provide a way for clinicians and researchers to classify different disorders based upon the presenting symptoms. However, they do not indicate how to treat the different disorders, nor do they provide specific guidelines as to how to measure the various symptoms. Rather, the books provide a series of criteria for each disorder, and then clinicians and researchers create questionnaires and clinical observation systems that are used to assess for the presence or absence of symptoms and to establish the severity of those symptoms within each individual.

A common theme emerging from such a method for establishing diagnosis is that differentiating one diagnosis from another can be highly subjective. Two (or more) clinicians can observe the same person and establish two (or more) different diagnoses. It is difficult to determine which of these diagnoses is correct, or whether the person truly has more than one condition. Therefore, the National Institutes of Health has recently established a research priority to create a different way of classifying and diagnosing mental illness. This program, known as the Research Domain Criteria (RDoC), attempts to combine genetics, neuroscience, and behavior, in an effort to better classify and understand mental illnesses (Insel et al., 2010). By combining aspects of a person’s biology with their thoughts and behaviors, it may be possible to tailor specific treatment programs to specific patients and improve the overall success of treatments for mental illness.

Consistent with this approach, my research program uses a combination of brain imaging and behavioral testing to better understand the underlying brain basis for mental disorders and their associated treatments. For the past several years, I have focused my efforts on two clusters of disorders, ASD and psychosis spectrum disorders, which includes schizophrenia and related conditions.

Figure 1: Examples of individual task trials from the Emotional Face Go/No-Go task. Part (A) on the top shows the first few images from a group of trials of the Go condition, while Part (B) on the bottom shows the first few images from a group of trials of the fear No-Go condition. The times listed under each screen are the presentation times in milliseconds (ms).
Figure 1: Examples of individual task trials from the Emotional Face Go/No-Go task. Part (A) on the top shows the first few images from a group of trials of the Go condition, while Part (B) on the bottom shows the first few images from a group of trials of the fear No-Go condition. The times listed under each screen are the presentation times in milliseconds (ms).

ASD is a neurodevelopmental disorder characterized by impairments in social interactions and/or social communication, and the presence of repetitive, stereotyped behaviors or a restricted (i.e., limited) set of interests. The number of cases of ASD diagnosed in the past decade has skyrocketed, primarily due to a broadening of diagnostic criteria, along with better awareness of the disorder. Schizophrenia and the related psychosis spectrum are characterized by the presence of hallucinations, which are visual or auditory perceptions that are not there in reality, and delusions, which are thoughts that are unrealistic, such as believing you are the leader of a country, you are being pursued, or your thoughts are being controlled by outside forces. Schizophrenia is also marked by “cognitive” symptoms, including impairments in working (i.e., “short-term”) memory, planning, and inhibiting impulsive responses.

Common to both ASD and schizophrenia is a deficit in executive functioning, which refers to a collection of mental processes that allows for flexible, adaptive behaviors. These executive functions include planning, inhibiting inappropriate or ongoing responses, generating the most fitting response, monitoring performance, making appropriate adjustments when errors occur, and allocating attentional resources where needed. Collectively, these mental processes allow us to alter our behavior according to current needs or the social rules and conventions that govern behavior. Therefore, executive functions have also been referred to as our cognitive control mechanism.

A rather substantial number of behavioral tests exist to examine executive functioning, some testing one specific aspect of executive function and others testing multiple components. One of the most common tests to examine response inhibition is the Go/ No-Go task, in which participants are instructed to press a response button (on a keyboard or response box) for each stimulus they see appearing on a computer screen. However, when they see a predesignated target stimulus, the person must withhold their ongoing response. In a common variant of this task, English letters are presented one at a time and participants are instructed to press a button for all letters except the letter X. Other versions of the test use more complex stimuli, such as emotionally expressive faces. In the Emotional Face Go/No-Go, participants are instructed to press the response button for all faces except for those depicting a specified emotion – happiness, for example. These more complex versions of the Go/No-Go task allow us to examine decision-making in social or emotional contexts.

A major advance in the fields of psychology, neuroscience, and psychiatry is the ability to noninvasively track activity throughout the entire brain while a person completes one or more behavioral tasks.

Another common test of response inhibition requires decisions that are inconsistent with a standard (or “prepotent”) way of thinking or behaving, which measures a construct known as “response conflict.” In one version of this test, a geometric shape is presented either on the left- or right-hand side of a computer screen. Participants are given a response box with two buttons, one on the left and one on the right. They are instructed to either press the response button that matches the side on which the shape is presented or that is on the opposite side of the shape. For the “opposite side” task, participants must overcome the prepotent tendency to press the button corresponding to the same side as the shape.

A major advance in the fields of psychology, neuroscience, and psychiatry is the ability to noninvasively track activity throughout the entire brain while a person completes one or more behavioral tasks. Using strong magnetic fields, functional magnetic resonance imaging (fMRI) is a technology that detects subtle changes in blood flow to regions of the brain that are used during a mental activity. By comparing the amount of blood flow during one mental activity with that during an alternate activity, we can determine which areas of the brain are used more for that specified mental activity. What makes fMRI particularly attractive as a research tool, compared with alternative methods for examining brain function such as positron emission tomography, is that fMRI poses very little danger to participants and involves no radiation exposure. Therefore, fMRI is well-suited for use in children and adults, and people can be scanned repeatedly without adverse side effects.

Using a type of MRI scanning called diffusion tensor imaging (DTI), MRI technology also allows researchers to examine the integrity of axon pathways, which are the connections between brain regions that communicate messages from one region to another. These axon pathways, also known as the brain’s white matter, have been referred to as the information superhighway of the brain. By examining the integrity of these pathways, we can infer how well specific brain regions are communicating with one another.

Figure 2: Brain activations during the Emotional Go/No-Go task. Areas in yellow and orange show regions with increases in brain activity. Brain region labels refer to the areas of activity appearing just below the labels. Adapted from Shafritz et al. (2015
Figure 2: Brain activations during the Emotional Go/No-Go task. Areas in yellow and orange show regions with increases in brain activity. Brain region labels refer to the areas of activity appearing just below the labels. Adapted from Shafritz et al. (2015).
Figure 3: Brain activations during the Response Conflict task that correlated with amount of symptom reduction after 12 weeks of antipsychotic medication treatment in patients experiencing their first episode of psychosis. Adapted from Shafritz et al. (2018).
Figure 3: Brain activations during the Response Conflict task that correlated with amount of symptom reduction after 12 weeks of antipsychotic medication treatment in patients experiencing their first episode of psychosis. Adapted from Shafritz et al. (2018).

Recent studies in my lab have utilized these two aspects of MRI technology to determine underlying brain differences in adolescents with autism when compared with adolescents without a psychiatric diagnosis, often referred to as “neurotypical” adolescents. In one study, my colleagues and I used fMRI to determine whether the brain regions normally active when people make quick decisions about whether or not to respond to emotionally expressive faces are also recruited in adolescents with ASD (Shafritz et al., 2015). Participants completed an emotional face Go/No-Go task with happy, fearful, and neutral faces while being scanned in an MRI machine. While in the scanner, the participants saw emotionally expressive faces presented one at a time on a computer screen that they were able to view by looking at a prismatic mirror placed above their heads. The task was divided into Go and No-Go conditions. During the Go conditions, participants were instructed to press a response button for all faces they saw. During the No-Go conditions, faces of two differing emotions were presented, and participants were instructed, “Do not press for happy faces,” or “Do not press for fearful faces,” in alternating sets of task trials (see Figure 1).

We found brain activity differences between the ASD group and the neurotypical group that provide empirical support for an important theory of ASD suggesting that the disorder is marked by a deficit in social motivation (i.e., the inherent desire to engage in social interactions). Specifically, we found that the nucleus accumbens, a region found deep within the brain and long-known to be involved in reward, became active when neurotypical individuals viewed happy faces, but not when individuals with ASD viewed happy faces. Instead, participants with autism recruited brain regions that are implicated in the basic perceptual processes involved in facial recognition, such as the fusiform gyrus, indicating that people with autism may need to use basic perceptual brain regions to a greater extent than neurotypicals when interpreting facial expression (see Figure 2). Because the participants with autism did not recruit the nucleus accumbens, they may have experienced less pleasure compared with neurotypicals when viewing happy faces, which would be consistent with the social motivation theory.

Our results suggest that if we target therapeutic strategies to the functioning and integrity of these midline brain structures, perhaps we can improve the impairments in executive functioning that are currently quite difficult to treat in people with ASD

In a companion study, my colleagues and I used DTI to examine the integrity and development of axon pathways in individuals with autism (Ikuta et al., 2014). During DTI scanning, participants do not need to engage in a mental activity; rather, they simply lay still in the MRI machine while the machine detects the speed and direction of water molecules moving through the axons in the brain. The movement of these water molecules allows us to determine the integrity of the axon bundles connecting various brain regions. In typical development, the integrity of the axon pathways increases throughout adolescence as the connections between specific brain regions become more established and efficient. We found that in individuals with autism, one of these axon bundles, known as the cingulum bundle, does not exhibit this typical pattern of increased connectivity and efficiency. This pathway runs along the midline of the brain, connecting areas of the frontal lobes to the parietal and temporal lobes that lie behind and below the frontal lobes. Because it connects the front and back of our brains, the cingulum bundle is thought to assist in integrating our perceptions with our actions, which is an important aspect of executive functioning.

The participants in our study also completed a questionnaire, known as the Behavior Rating Inventory of Executive Function (BRIEF), which assesses two distinct domains of executive functioning: behavior regulation and meta-cognition (the ability to assess the contents of your own thoughts). We examined the relationship between scores on this measure with the integrity of the cingulum bundle on a subject-by-subject basis. We found that the integrity of the cingulum bundle was related to executive functioning ability; as the integrity of the cingulum bundle increased, behavior regulation abilities improved. Combined with prior research results showing a relationship between cingulate function, executive functioning, and repetitive behaviors in autism (Shafritz et al., 2008), these results reveal the importance of midline brain structures to the core symptoms of ASD. Our results suggest that if we target therapeutic strategies to the functioning and integrity of these midline brain structures, perhaps we can improve the impairments in executive functioning that are currently quite difficult to treat in people with ASD.

In another line of research using fMRI, my colleagues and I examined patterns of brain activity in patients with schizophrenia spectrum disorder who were experiencing their first psychotic episode. We aimed to find brain regions for which the amount of activity during an attention task would distinguish patients whose symptoms would improve by taking antipsychotic medication from patients whose symptoms would not improve (Shafritz et al., 2018). Patients for this study were recruited as part of an ongoing randomized controlled trial taking place at Zucker Hillside Hospital in which patients were assigned to regularly take one of two antipsychotic medications: risperidone (Risperdal) or aripiprazole (Abilify). At the start of their medication treatment, patients were scanned using fMRI while they completed the “response conflict” task described above. We then used their brain activation patterns before treatment to predict their therapeutic response to the antipsychotic medication 12 weeks into treatment. We also compared their brain activation during the task with that of a control group.

We observed that activation in a midline brain region in the frontal lobes, called the anterior cingulate cortex, differed between the control group and the first-episode psychosis patients while they completed the response conflict task. We also found that activation in the anterior cingulate cortex and two additional areas of the brain was strongly associated with the patients’ response to the medication in terms of their symptomatic and functional improvement (see Figure 3). Combined with other recent findings that white-matter integrity can also predict antipsychotic treatment success (Sarpal et al., 2016), our finding can be used to create a standardized brain scanning procedure that in the future may become part of routine care in the treatment of psychosis. This type of screening procedure has the potential to advance the goals of personalized medicine by eliminating the guesswork involved in prescribing medication.

Continuing with research designed to create new ways of predicting functional outcomes in psychiatric disorders, my colleagues and I are currently using fMRI to investigate patterns of brain activation in U.S. armed forces veterans who are experiencing symptoms of post-traumatic stress disorder. We are conducting this series of research studies at the James J. Peters VA Medical Center in Bronx, NY, in collaboration with research teams at the VA New Jersey Health Care System and Rutgers University. Veterans with and without traumatic brain injury, and with and without a history of suicide attempts, are being scanned using fMRI while they complete decision-making tasks, including the Go/No-Go task. We expect to find that impulsive responding during these mental activities will be associated with specific patterns of brain activation that are also associated with a history of suicide attempts. We will then use the observed patterns of brain activation to predict whether patients are at a high risk for suicide attempts. The goal of these research studies will be to create a brain scanning procedure that can assist with determining risk for suicide, so that patients at highest risk will be offered the most intensive therapeutic services available. Because suicide among veterans is highly prevalent, addressing this crisis will hopefully lead to suicide prevention among veterans and in the general population.

Acknowledgments

The research described here has been funded by grants from the National Institute of Mental Health and the United States Department of Veterans Affairs. Thanks to Dr. Donna Lutz for helpful comments.

References

Ikuta, T., Shafritz, K. M., Bregman, J., Peters, B., Gruner, P., Malhotra, A. K., & Szeszko, P. R. (2014). Abnormal cingulum bundle development in autism: A probabilistic tractography study. Psychiatry Research: Neuroimaging, 221, 63-68.

Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., et al. (2010). Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. The American Journal of Psychiatry, 167, 748-751.

Sarpal, D. K., Argyelan, M., Robinson, D. G., Szeszko, P. R., Karlsgodt, K. H., John, M., et al. (2016). Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment. The American Journal of Psychiatry, 173, 69-77.

Shafritz, K. M., Bregman, J. D., Ikuta, T., & Szeszko, P.R. (2015). Neural systems mediating decision-making and response inhibition for social and nonsocial stimuli in autism. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 60, 112-120.

Shafritz, K. M., Dichter, G. S., Baranek, G., & Belger, A. (2008). The neural circuitry mediating executive functioning deficits in autism. Biological Psychiatry, 63, 974-980.

Shafritz, K. M., Ikuta, T., Greene, A., Robinson, D. G., Gallego, J., Lencz, T., DeRossse, P., Kingsley, P. B., & Szeszko, P.R. (2018). Frontal lobe functioning during a simple response conflict task in first-episode psychosis and its relationship to treatment response. Brain Imaging and Behavior, 13, 541-553.