A double-blind study assessing the impact of orbitofrontal theta burst stimulation on goal-directed behavior

A molecularly defined orbitofrontal cortical neuron population controls compulsive-like behavior, but not inflexible choice or habit

Habits are familiar behaviors triggered by cues, not outcome predictability, and are insensitive to changes in the environment. They are adaptive under many circumstances but can be considered antecedent to compulsions and intrusive thoughts that drive persistent, potentially maladaptive behavior. Whether compulsive-like and habit-like behaviors share neural substrates is still being determined. Here, we investigated mice bred to display inflexible reward-seeking behaviors that are insensitive to action consequences. We found that these mice demonstrate habitual response biases and compulsive-like grooming behavior that was reversible by fluoxetine and ketamine. They also suffer dendritic spine attrition on excitatory neurons in the orbitofrontal cortex (OFC). Nevertheless, synaptic melanocortin 4 receptor (MC4R), a factor implicated in compulsive behavior, is preserved, leading to the hypothesis that Mc4r+ OFC neurons may drive aberrant behaviors. Repeated chemogenetic stimulation of Mc4r+ OFC neurons triggered compulsive and not inflexible or habitual response biases in otherwise typical mice. Thus, Mc4r+ neurons within the OFC appear to drive compulsive-like behavior that is dissociable from habitual behavior. Understanding which neuron populations trigger distinct behaviors may advance efforts to mitigate harmful compulsions.

Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.