Applying a Cognitive Neuroscience Perspective to Disruptive Behavior Disorders: Implications for Schools

Abstract

A cognitive neuroscience perspective seeks to understand behavior, in this case disruptive behavior disorders (DBD), in terms of dysfunction in cognitive processes underpinned by neural processes. While this type of approach has clear implications for clinical mental health practice, it also has implications for school-based assessment and intervention with children and adolescents who have disruptive behavior and aggression. This review articulates a cognitive neuroscience account of DBD by discussing the neurocognitive dysfunction related to emotional empathy, threat sensitivity, reinforcement-based decision-making, and response inhibition. The potential implications for current and future classroom-based assessments and interventions for students with these deficits are discussed.

Trial registration: ClinicalTrials.gov NCT00104039.

Conflict of interest statement

The authors report no competing interests.

Figures

Figure 1.

In the Expression Multi-morph Task (A; Marsh et al., 2008), participants are shown faces expressing different emotions at differing intensities. Participants indicate the gender of the face displayed. The amygdala (B) is more responsive to fear and sad expressions relative to neutral expressions.

Figure 2.

In the Looming Task (A; Coker-Appiah et al., 2013), threatening and neutral images are presented, either going from small to large and appear to approach the participant or going from large to small and appear to recede from the participant. Looming and threatening images activate the basic threat system (B).

Figure 3.

In the Passive Avoidance Task (A; White, Tyler et al., 2016), objects are presented to participants. If participants respond to the object, they receive rewarding (A1) or punishing (A2) feedback. If participants do not respond, they receive no feedback (A3). Over time, responding to an object results in a net gain or loss; participants must learn which objects to respond to in order to maximize reward. A network of brain regions mediates choosing to make optimal (B) or avoid sub-optimal(C) choices.

Figure 4.

During the Go/No-Go task (A; see Durston et al., 2002 for a similar task design), participants are instructed to respond to one stimulus class (i.e., Spiderman) and to not respond to another stimulus class (i.e., Green Goblin). Successful response inhibition activates an inhibition network which includes inferior frontal gyrus (IFG) and pre-supplementary motor area (pre-SMA).

Figure 5.

This schematic of the theoretical approach depicts four neural loci, the corresponding four cognitive functions that these neural systems mediate, and the symptom sets particularly associated with dysfunction in each of these four neurocognitive systems. It also sketches environmental variables that may be particularly related to maladaptive development of these systems; e.g., prior trauma is likely to increase acute threat responsiveness while neglect may disrupt the development of systems implicated in reinforcement-based decision-making and response control (Sheridan & McLaughlin, 2014). It is assumed that the development of each of these neurocognitive systems is under potentially independent genetic influences but the specifics of these influences remain largely unknown. Note that dysfunction in all of these systems is thought to increase the risk for conduct problems more generally defined. Note also that dysfunctions within systems implicated in acute threat response, reinforcement-based decision-making, and response inhibition are seen as risk factors for the development of substance abuse (De Bellis et al., 2013; Nigg et al., 2006) as well as a consequence of prior substance abuse (Ganzer, Broning, Kraft, Sack, & Thomasius, 2016).