The common and persistent cognitive deficits that accompany psychiatric disease represent a major, yet underrecognized, source of global clinical, economic, and social burden across disorders [1], with many major psychiatric disorders ranking among the top causes of long-term functional impairment [2]. Furthermore, cognitive impairment is a core and transdiagnostic feature, encompassing deficits in attention, processing speed, working memory, executive functioning, and learning [1,3]. Data suggest that cognitive deficits are often present early in illness onset, are observable across childhood, adulthood, and older age, and persist despite psychiatric symptom remission [3-6]. In schizophrenia and bipolar disorder, impairments in executive functioning and verbal memory can be characterized as “moderate to severe” and may be detectable prior to first episode as well as during euthymic mood states [7]. In unipolar mood, anxiety, and trauma-related disorders, deficits in cognitive control and processing speed reliably persist following clinical recovery [1,3,8]. Importantly, the magnitude of cognitive impairment shows only weak-to-modest correlations with symptom severity, supporting the view that cognition represents a partially independent dimension of psychopathology [9-11]. Cognitive deficits are robust predictors of real-world functioning—including employment, social functioning, and independent living—and are a primary driver of psychosocial disability across depression and transdiagnostic psychiatric populations [12-14].

Among cognitive domains, deficits in executive function or cognitive control exert particularly important prognostic and treatment-modifying effects across psychiatric disorders [15,16]. Baseline executive impairment predicts poorer response to pharmacologic and psychotherapeutic interventions, slower and less complete functional recovery, increased risk of relapse and chronicity, higher all-cause mortality, and elevated suicide risk [6,15-22]. Despite these effects, standard psychiatric treatments, including antidepressants, antipsychotics, mood stabilizers, and evidence-based psychotherapies, primarily target emotional or behavioral symptoms and do not reliably improve cognition [23,24]. Large randomized trials and network meta-analyses indicate that, with few exceptions, commonly prescribed treatments yield minimal or no clinically meaningful improvement in objective cognitive outcomes. Consequently, many patients achieve symptomatic remission while continuing to experience persistent cognitive and functional disability, contributing to the well-documented gap between clinical (symptomatic) recovery and real-world (functional) recovery. Collectively, these findings identify cognitive impairment as a central, enduring, and functionally consequential determinant of disability across psychiatric conditions [15]. The transdiagnostic nature of executive functioning deficits, their disproportionate contribution to economic and social burden, and their limited responsiveness to existing treatments highlight cognition as a critical unmet target for clinical intervention and public health impact [15,16].

Neuroplasticity-based digital cognitive interventions (DCIs) have emerged as brief, safe, accessible, and scalable adjunctive treatments that provide structured training to ameliorate cognitive deficits in psychiatric populations. (Neuroplasticity-based DCIs are computerized training programs designed to drive experience-dependent changes in specific neural systems through repeated, targeted practice, often with adaptive difficulty and immediate feedback. Treatment effects may reflect neural target engagement rather than simple task repetition or general engagement.). We and others have demonstrated that DCIs targeting cognitive control functions, even in the aging brain, can improve cognitive performance (eg, inhibition, semantic clustering, and executive organization) and reduce depressive symptoms in individuals with major depressive disorder (MDD) [25-28]. These findings support the hypothesis that engagement of cognitive control mechanisms may yield downstream clinical benefits in MDD [29]. However, despite growing interest, the field of DCIs has reached a critical inflection point: the neurobiological mechanisms through which DCIs exert their effects remain poorly specified, inadequately tested, and frequently confounded with the more general experience of treatment engagement itself.

This review synthesizes emerging evidence on DCIs through a multimethod, multimodal lens to clarify mechanisms of action and optimize intervention design (Figure 1). We review the current landscape of DCIs and highlight key design features hypothesized to drive neuroplasticity, including adaptivity, task specificity, and engagement strategies. We suggest that dismantling methodology, adapted from early studies of psychotherapeutic interventions and applied to DCI clinical trials, may isolate the mechanisms that do and do not drive neuroplasticity. By mapping these elements onto National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) constructs, we suggest candidate operationalizations of mechanistic hypotheses (Table 1). Building on this, we discuss experimental approaches, such as dismantling designs and adaptive trials, that can isolate active ingredients. Finally, we propose an integrated framework that combines behavioral, neural, and clinical endpoints to guide the rigorous review, development, and investigation of mechanistically informed, precision-targeted DCIs.

The inability to disentangle engagement from mechanism represents a major barrier to scientific progress. Without isolating the “active ingredients” of DCIs, it is difficult to interpret null findings, optimize intervention design, or personalize treatment. Most DCI studies operationalize engagement using objective usage metrics (eg, number of logins, duration of use, and task completion) and examine associations between these metrics and clinical outcomes [37]. However, engagement is rarely manipulated independently of intervention content, nor is it experimentally dissociated from the hypothesized therapeutic mechanism. As a result, observed improvements may reflect nonspecific effects of sustained engagement, novelty, or expectancy rather than selective modulation of a theoretically defined cognitive process. This limitation substantially constrains causal inference and impedes the development of mechanistically precise and generalizable interventions; thus, underlying mechanisms related to clinical improvement with DCIs remain unclear [38-40].

For clarity, we define active ingredients as the discrete features of a DCI that are hypothesized to causally engage neural mechanisms. This contrasts with nonspecific aspects such as mere time-on-task or expectancy. Identifying these ingredients requires isolating them experimentally. For example, one could compare a full-feature DCI (with rewards and variable difficulty) to a version that is identical except for rewards. Differences in brain and behavioral outcomes between conditions would then implicate reward as an active ingredient.

Little is known about how reward contingencies, reward schedules, and reward deployment—core features of digital design—engage well-characterized dopaminergic systems implicated in motivation, learning, and cognitive control [45,46]. From a neuroscience perspective, dopamine is not only central to reward processing but also plays critical roles in both synaptic plasticity and the updating of cognitive control processes in the prefrontal cortex [47,48]. Dopamine may be particularly important for strategic processing under conditions of uncertainty and adaptive demand [49,50]. Performance-contingent and variably deployed rewards modulate phasic dopamine signaling within mesolimbic and frontostriatal circuits, reinforcing action-outcome associations and regulating learning rates and effort allocation [49,51]. Consequently, reward schedules embedded within cognitively demanding tasks may influence both adherence to the tasks and the efficiency of cognitive skill acquisition and generalization [52]. For example, when behavior modification is the desired outcome, devices such as Fitbit (Google) or Apple Watches deliver many and varied real-time metrics to reinforce users’ awareness of progress toward goals, while continuous performance monitoring enables immediate cues for action and opportunities for proximate and frequent rewards [53,54].

Disrupted reward processing, related to blunted striatal responsivity and reduced dopaminergic signaling, is prevalent in many psychiatric disorders and is closely linked to anhedonia, diminished motivation, impaired cognitive flexibility, and poor functional outcomes [55,56]. These abnormalities suggest that reward deployment may not act only as a critical facilitator of engagement and learning but may also represent an “active ingredient” of DCIs. Reward exposure alone does not produce durable cognitive or clinical improvement in the absence of task demands that directly engage targeted neural systems (such as cognitive control), suggesting that motivation without mechanism is insufficient for sustained change [57,58]. Consistent with this, commercial and “serious” video games reliably improve task-specific perceptual and attentional skills, yet these gains rarely transfer to broader cognitive or functional outcomes [59,60]. This limited transfer likely reflects inadequate engagement of higher-order cognitive control with rewarded gameplay, rather than insufficient task engagement or persistence [60].

Dismantling design requires precise classification of intervention elements. Components of DCIs may include the cognitive task itself (eg, working memory and inhibitory control), pacing or scheduling features (eg, adaptive difficulty, dose, and duration), and reward structures (eg, points and badges). Disentangling these components allows investigators to test which features drive neural and behavioral improvements, rather than improvements due to engagement or placebo effects.

Each intervention component selected for systematic removal within the dismantling design should be explicitly linked to a priori mechanistic hypotheses and prespecified, clinically relevant downstream outcomes. For each component, hypotheses should specify (1) the targeted neural and cognitive systems, (2) the expected form of neuroplastic change within those systems, and (3) the predicted behavioral or cognitive consequences. It is hypothesized that component removal will result in attenuated, altered, or absent effects on these mechanistic and clinical outcomes, thereby enabling causal inferences about the hypothesized active ingredients. Primary outcomes in dismantling studies of DCIs should prioritize direct assessment of hypothesized neural and cognitive mechanisms relevant to the target disorder, rather than relying exclusively on global symptom measures. Neurobiological (eg, network-level activation and connectivity) and cognitive (eg, executive control, learning efficiency) outcomes should be complemented by gold-standard clinical assessments to establish clinical relevance.

For example, the dynamic difficulty adjustment is hypothesized to engage frontoparietal executive control networks by continuously increasing cognitive demands, thereby recruiting overlapping neuromodulatory systems and reducing reliance on automatized or rote responding [77]. Sustained engagement of these systems is expected to promote experience-dependent plasticity within cognitive control circuits [78], resulting in measurable improvements in cognitive flexibility, adaptive control, and related disorder-relevant cognitive functions [79]. Removal of adaptive difficulty would be predicted to yield reduced engagement of these networks, diminished neuroplastic change, weaker cognitive effects, and functional outcomes. Differential outcomes following the removal or alteration of this component may then provide preliminary evidence for its mechanistic role.

A critical, often overlooked, parameter in DCIs is the dose of cognitive training, that is, the amount and intensity of practice required to induce meaningful plasticity. Unlike pharmacologic treatments, optimal dosing for DCIs remains poorly specified. Emerging evidence suggests a nonlinear dose-response relationship and further indicates that both insufficient and excessive training may be suboptimal [80]. This pattern is consistent with neuroplasticity models, which show that repeated, paced practice over weeks is required for durable circuit-level change, whereas brief or overly intensive exposure may yield only transient effects [81]. Accordingly, dose should be treated as a core active ingredient in dismantling studies. Systematically varying session length, frequency, and total duration, and including lower-dose comparison arms, can help identify the minimally effective dose needed to produce neural and cognitive change, distinguishing true component effects from differences in exposure [82]. Administering key outcome measures at regular intervals throughout the intervention, rather than solely at the end of the treatment, can also help quantify dose-response curves.

In addition, DCIs uniquely enable the continuous collection of high-resolution digital behavioral data, offering the opportunity to identify novel digital behavioral markers of mechanistic engagement and treatment response. In-game metrics such as adaptive performance slopes, trial-by-trial error monitoring, and intraindividual variability in response times may index dynamic learning processes (ie, “learning to learn”) and real-time deployment of cognitive control. When integrated with neuroimaging and standardized cognitive measures, these digital markers may provide sensitive, scalable indicators of target engagement as well as clinical and cognitive change. Together, this multimodal mechanistic approach will allow investigators to distinguish neural and cognitive effects attributable to specific intervention components from nonspecific influences such as general practice effects or task exposure.

Dismantling-inspired designs use randomized controlled trial structures that systematically compare the full intervention to versions in which specific components are removed. For example, a 2×2 factorial design can independently manipulate adaptive difficulty and feedback, yielding 4 experimental trial conditions: full intervention with adaptive difficulty and feedback, adaptive difficulty only, feedback only, and an active control matched for time and engagement but without adaptive difficulty or feedback. This design, combined with pretests and posttests, allows estimation of the main effects of each component and their interactions, paralleling approaches used in psychotherapy dismantling studies [83] and recent DCI trials in schizophrenia [72].

To establish candidate causal pathways between DCI components and clinical improvement, changes in RDoC-defined constructs hypothesized that mechanisms can be tested as mediators of long-term clinical outcomes. By anchoring intervention elements in RDoC rather than traditional diagnostic categories (eg, MDD only), the dismantling framework tests whether targeting RDoC constructs changes core dysfunctional processes that cut across multiple disorders (eg, cognitive control deficits in depression, anxiety, and attention-deficit/hyperactivity disorder). For instance, repeated-measures or longitudinal mediation models can examine whether DCI-induced increases in dorsolateral prefrontal cortex-anterior cingulate cortex functional connectivity, frontal midline theta, or cognitive control task performance from baseline explain subsequent reductions in depressive symptoms or functional impairment. Mixed effects models allow the decomposition of within-participant (change over time within an individual) and between-participant (individual differences from the group, sample, or population) effects, enabling separation of temporal sequences from stable processes, which are critical for establishing that mechanistic change precedes clinical benefit [84,85]. Structural equation modeling can also be applied to simultaneously test multiple candidate mediators (eg, cognitive control vs reward learning or functional connectivity) to identify which mechanisms are most directly responsible for observed improvements [86]. Prior studies in schizophrenia and depression have successfully applied these approaches, demonstrating that training-induced increases in frontostriatal connectivity or executive control performance mediate improvements in functional outcomes and mood, providing empirical support for the mechanism-to-clinical benefit pathway endorsed by the NIMH’s RDoC criteria [28,72,73].

Identifying moderators of DCI efficacy may enable both within-app personalization and mechanistic specificity. Factors such as age, baseline cognitive performance, symptom severity, genetic polymorphisms, structural or functional imaging, or inflammatory biomarkers can influence both the degree to which participants engage with specific intervention components and the neuroplasticity induced by engaging with DCIs. For example, in late-life depression, individuals with greater baseline cognitive control deficits may show differential recruitment of prefrontal circuits in response to adaptive cognitive training, moderating the magnitude of cognitive gains [28]. In dismantling trials, moderation analyses are typically conducted via interaction terms in mixed effects models or Bayesian hierarchical models, allowing simultaneous estimation of within- and between-individual effects (ie, individual-level and group-level effects, respectively). By integrating baseline neurocognitive and biological markers, researchers can identify subgroups most likely to benefit from particular components (eg, reward modulation, adaptive difficulty, or plateau and graduation criteria).

Such dismantling-informed designs provide a mechanistic map of intervention efficacy, offering an evidence-based process for streamlining DCI by assessing whether removing specific components diminishes cognitive, neural, or clinical outcomes. For example, in the DCI CoNECCT [26,68], if removing adaptive difficulty reduces frontoparietal engagement and blunts cognitive gains, it could be classified as a core contributor, whereas reward function may primarily enhance adherence without directly driving neuroplasticity in target networks [68].