Applications of Functional MRI in Psychopharmacological Research

At the time of writing this chapter, most of the applications of fMRI methods have been experimental—for example, helping investigators map areas of functional activation in response to cognitive and affective tasks. Similarly, several novel and innovative uses of imaging methods are seen in the literature, and the field has evolved considerably since the previous edition of this textbook. A few of these advances will be reviewed in this section and are also included in Table 10-2.

Neural activation in response to tasks

Measurement of neural activation patterns in response to specific tasks, using fMRI, has been particularly useful in elucidating the neural mechanisms contributing to the development of psychiatric disorders. Thus, specific tasks can be constructed that target specific emotional or cognitive behaviors that are altered in a given disorder. This research strategy allows delineation of neural pathways involved in the generation of specific symptoms and behaviors. A few examples of commonly used paradigms are discussed below.

Mood/self-referential paradigms

As an example, a core feature of depression involves negative bias and anhedonia, suggesting specific alterations in neural pathways mediating salience, self-reference, and reward. Accordingly, several fMRI studies reported failed activation of dorsomedial prefrontal cortex (BA 10) in depressed patients in response to positive words or pictures (Le Bastard et al. 2003; Mitterschiffthaler et al. 2003). Interestingly, activation of this region is also seen in tasks requiring self-referencing in healthy subjects (Craik et al. 1999; Fossati et al. 2003). A depression-specific pattern has been described using an emotional "Go/No-Go task" that identified blunted responses in reward circuits in response to neutral words but exaggerated responses in self-referential areas of the rostral cingulate and medial frontal cortex in response to sad words (Elliott et al. 2002). Such sensitive tasks can plausibly be used as outcome measures in the evaluation of the efficacy of antidepressant treatments.

Facial expression-processing paradigms

Another task frequently used across a range of disorders involves the presentation of faces with different emotional expressions, such as angry, sad, fearful, happy, and neutral. Such faces can be either overtly presented or may be hidden behind a mask, depending on the study hypothesis. With overt presentation, subjects typically perform a behavioral task related to classifying some aspect of the faces. Application of such tasks in fMRI studies has produced a large amount of data regarding the neural basis of emotional processing in healthy subjects, reporting robust activation of the amygdala in response to emotional faces (Whalen et al. 1998). Of note, patients with depression, anxiety disorders, and PTSD exhibit increased amygdala responses to the presentation of fearful or angry faces (Rauch et al. 2000; Shin et al. 2005; Whalen et al. 2002). The paradigm has also proven useful to evaluate treatment effects of antidepressant drugs (Sheline et al. 2001), as well as temperamental or genetic contributions to emotional processing (Hariri et al. 2002; Stein et al. 2007). Similar strategies have identified antidepressant-induced changes with other tasks (Fu et al. 2007).

Cognitive "working memory" paradigms

Another common task used to identify neurocognitive changes is the so-called n-back task. The n-back task is a working memory task that reflects prefrontal cortical function. The n-back task has been used in fMRI studies to identify altered neural circuits in a variety of disorders, including schizophrenia and depression (Harvey et al. 2005; Meyer-Lindenberg and Weinberger 2006). The "n-back" working memory test (Owen et al. 2005) involves visual presentation of letter stimuli at previously chosen intervals and epochs (e.g., 2-second interval for 30-second epochs). The baseline (control) condition is usually a 0-back condition in which subjects are required to press a button with the right index finger when the stimulus (e.g., the letter "x") appears. In the experimental condition (1-, 2-, or 3-back), subjects are required to press a button if the presented stimulus is the same as a stimulus presented n trials previously (n = 1, n = 2, or n = 3). The level of task difficulty and the condition are varied in a previously specified order throughout the scan time. Subject performance during scanning in regard to accuracy (number of target stimuli correctly identified) and response time (RT) is usually recorded. Interestingly, findings vary across disorders, suggesting differing impacts of underlying pathophysiology on networks mediating these behaviors. For example, increased prefrontal activity is seen in depressed patients relative to control subjects performing this task, an effect amplified by task difficulty (Harvey et al. 2005). On the other hand, several studies have reported that schizophrenic patients demonstrate deficits in activation of the prefrontal cortex during this task, thought to reflect alterations in dopamine functioning. Normalization of neural activation patterns by antipsychotic drugs is associated with response to treatment. The n-back task has also been useful in identifying genetic contributions to schizophrenia risk (Meyer-Lindenberg and Weinberger 2006). Bookheimer et al. (2000) used a verbal memory paradigm, in which patients memorized unrelated pairs of words during scanning, to study hippocampal activation in patients at risk of developing Alzheimer's disease. Not only did the carriers of the apolipoprotein epsilon 4 (ApoE-i4)++ allele (higher risk of dementia) show greater hippocampal activation, but this baseline activation pattern predicted longitudinal cognitive decline.

Reward-processing paradigms

Reward-processing paradigms are commonly used in studying substance use and craving, but also in studying anhedonia in both depression and schizophrenia. In addiction studies, typically the patient, while lying in a scanner, is presented with multiple contexts associated with drug abuse, and activation of reward-processing circuitry is studied. Zink et al. (2006) used fMRI to study activation of the basal ganglia (a key component of reward circuitry) of healthy volunteers in response to salient stimuli with high motivational relevance. Figure 10-5 illustrates differential activation patterns in the ventral striatum in response to wins versus monetary losses during a gambling task, as measured with fMRI. The concept of "intertemporal discounting" is often used to describe choosing between discounted immediate reward to delayed reward at a rate that is much higher in value. Patients with impulse-control disorders such as pathological gambling show considerable variation in their tendency to use intertemporal discounting (Dixon et al. 2006). A recent study using fMRI reported that increased activation of paralimbic cortex was associated when choosing smaller/earlier rewards while fronto/parietal activation was seen when making the larger/later (opposite) type of choice (McClure et al. 2004). Studies such as these might help us develop brain activation-based biomarkers of complex disorders such as pathological gambling and substance abuse. A recent innovation in this field has been the development of "hyperscanning"—a method by which a person in an fMRI scanner at one location can interact with another person in another scanner using World Wide Web-based connectivity (Montague et al. 2002). Thus, the brain structures that are activated during social interactions could be studied using this technology.

FIGURE 10-5. Ventral striatum activation for wins versus monetary losses during a gambling task, as visualized with functional magnetic resonance imaging (fMRI).

Copyright @ American Psychiatric Publishing, Inc., or American Psychiatric Association, unless otherwise indicated in figure legend, All rights reserved.

Source. Image courtesy of Giuseppe Pagnoni, Ph.D. Summary of neural activation paradigms

Copyright @ American Psychiatric Publishing, Inc., or American Psychiatric Association, unless otherwise indicated in figure legend, All rights reserved.

Source. Image courtesy of Giuseppe Pagnoni, Ph.D. Summary of neural activation paradigms

In conclusion, these examples illustrate the potential of fMRI activation studies in probing brain-behavior relationships in psychiatric disorders, owing to the flexibility of fMRI paradigm design as well as the large variety of standardized tasks and possibility to develop novel tasks.

Neuroimaging genomics

Despite the substantial advances in the pharmacotherapy of psychiatric disorders, response rates of patients remain unsatisfactory. For example, only 40%-70% of depressed patients adequately respond to 6 weeks of treatment with a single antidepressant drug. The factors that determine whether or not a patient will favorably respond to a specific drug are poorly understood, and there are no established strategies for clinicians to decide whether a patient might benefit from psychotherapy rather than pharmacotherapy (Binder and Holsboer 2006). Similarly, there is considerable individual variability in the response to antipsychotic drugs in patients with schizophrenia, which remains unexplained, and some but not other patients experience debilitating side effects (Reynolds et al. 2006). In concert with the rapid advances in the field of molecular genetics, there has been increasing recognition that genes influence individual differences in treatment response, as studied in the field of pharmacogenetics (see Chapter 3, "Genetics and Genomics"). Neuroimaging techniques have substantial potential to further the field of pharmacogenetics. There has been a surge of studies in recent years using neuroimaging to describe genetically determined differences in brain structure and function. Research strategies include neuroimaging studies in twin populations, unaffected individuals with high familial risk for a specific disorder (i.e., healthy siblings of patients), and groups of individuals with different variants of functional polymorphisms in genes involved in brain development or behavior (Hariri et al. 2006; Meyer-

Lindenberg and Weinberger 2006; Peper et al. 2007). Such studies not only advance our understanding of individual differences in cognitive and emotional behaviors, and the mechanisms involved in mental disorders, but also elucidate genetically determined variations in neural systems that are targeted by specific treatments. For example, one recent study found that the functional Val66Met polymorphism in the brain-derived neurotrophic factor (BDNF) gene moderates the association between depression and hippocampal volume loss. Carriers of the Met allele had smaller hippocampal volumes than subjects who were homozygous for the Val allele (Frodl et al. 2007). These findings suggest that the Met allele conveys increased risk for smaller hippocampi and susceptibility to depression. Because antidepressants stimulate neurogenesis in the hippocampus, carriers of the Met allele may be particularly responsive to antidepressant drugs.

In the area of schizophrenia research, it has been shown that a genetic variation (Val[108/158]Met) of catechol-O-methyltransferase (COMT), an important enzyme that degrades cortical dopamine at the synapse, is associated with deficits in working memory and prefrontal cortical activation in response to a working memory task. Specifically, carriers of the Val allele exhibit more deficits and less prefrontal cortical activation compared with subjects homozygous for the Met allele, likely reflecting low synaptic dopamine availability due to greater COMT activity, potentially increasing risk of developing schizophrenia (Meyer-Lindenberg and Weinberger 2006). Interestingly, the same polymorphism predicts effects of antipsychotic treatment on prefrontal cortical function in schizophrenic patients, as measured by fMRI, contributing to individual differences in treatment response (Bertolino et al. 2004). Based on such findings, COMT inhibitors are being investigated in the treatment of cognitive symptoms in schizophrenia.

Of course, in studying the effects of genes on neural responses to drugs, factors that interact with genes in shaping brain structure and function—for example, environmental factors across development—must be considered as well. In another example, it has been found that a functional polymorphism in the promoter region of the serotonin transporter gene—5-HTTLPR polymorphism (SCL6A4)—moderates the relationship between stress, including child maltreatment, and depression (Caspi et al. 2003; Kaufman et al. 2004; Kendler et al. 2005). Carriers of the short (S) allele had increased depression risk in relation to stress, whereas subjects homozygous for the long (L) allele were resilient even in the context of severe stress. Remarkably, S allele carriers demonstrate increased amygdala activation as well as decreased functional connectivity between amygdala and inhibitory prefrontal cortical regions in response to emotional stimuli, compared with L/L allele carriers (Hariri et al. 2002; Heinz et al. 2005; Pezawas et al. 2005). Of note, both the SCL6A4 polymorphism and developmental stress have been associated with treatment response to serotonergic drugs (Nemeroff et al. 2003; Reimold et al. 2007). Accordingly, neuroimaging is evolving as a valuable tool to evaluate gene-environment-drug interactions at the neural level. Such studies, taken together, have the potential to yield diagnostic markers to guide treatment decisions and identify targets for the prevention of manifest disorders in subjects at risk.

Default or resting-state functional imaging

Most studies reviewed so far have used challenges or probes that elicit neural activation and blood flow responses that could be captured by fMRI or PET scanners. Several authors have consistently found evidence for the existence of a "default" neural network that preferentially shows greater activity during restful or passive cognitive states (Buckner and Vincent 2007; Gusnard et al. 2001). High activity of these regions (posterior cingulate; inferior parietal and medial frontal cortices) during periods of wakeful rest and passive self-reflection have led some to hypothesize that this activity may "consolidate the past, stabilize brain ensembles, and prepare us for the future" (Buckner and Vincent 2007, p. 1066). Abnormalities in the functional connectivity of this network in psychiatric disorders such as schizophrenia (Bluhm et al. 2007), depression (Greicius et al. 2007), dementia (Rombouts et al. 2005), autism (Cherkassky et al. 2006), and multiple sclerosis (Lowe et al. 2002) have been reported. It is possible that further research into these default networks might yield key diagnostic or pathological information about various psychiatric disorders.

Application of virtual reality-based techniques to functional imaging

Most of the recently available challenge tasks used in functional imaging are based on tasks or tests standardized for clinical testing. A major criticism of this approach has been that it might not be reflective of real-life situations. Consequently, some authors use virtual reality (VR)-based techniques to overcome this criticism. Ability to remember places and navigate through a VR-based city (similar to spatial memory testing in rodents) has been used in an fMRI environment to study activity of parietal and temporal cortices (Spiers and Maguire 2006).

Statistical modeling methods

A complex array of data is now available using functional and structural imaging methods in multiple states of activation within subjects and between groups, necessitating the use of elegant multivariate and multifactorial models (such as independent component analysis). A detailed description of these techniques is beyond the scope of this chapter and has been undertaken elsewhere (Pearlson and Calhoun 2007). Thus, using the related approach of structural equation modeling, it has been possible to compare functional activation changes across multiple scans and states: depressed subjects versus control subjects, treatment responders versus nonresponders, medication responders versus cognitive-behavioral therapy responders, and so forth (Chen et al. 2007). Use of such models has provided Chen and colleagues with the opportunity to identify and classify depression phenotypes at the level of neural systems, with future implications for evidence-based treatment selection.

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