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Introduction
Major depressive disorder (MDD) is among the most prevalent and disabling psychiatric disorders globally, with significant social and economic burden. According to the World Health Organization, MDD is a leading cause of disability worldwide and a major contributor to the global burden of disease(1). Despite its prevalence, effective treatment remains elusive for a substantial subset of patients, particularly those who do not respond to first-line pharmacological therapies. This condition, commonly referred to as treatment-resistant depression (TRD), affects approximately 30% of individuals diagnosed with MDD, leading to chronicity, functional impairment and increased suicide risk. Historically, antidepressant development has been guided by the monoaminergic hypothesis, which posits that deficits in neurotransmitters such as serotonin, norepinephrine and dopamine underlie the pathophysiology of depression. While this framework has led to the development of multiple classes of antidepressants, it fails to account for the variability in treatment response and symptomatology observed across patients. Consequently, recent research has shifted focus towards more integrative models that incorporate neural circuitry, neuroplasticity, neuroinflammation and genetic vulnerability. Advances in neuroimaging, particularly functional magnetic resonance imaging (fMRI), have revealed that MDD is associated with aberrant patterns of connectivity within and between large-scale brain networks. These networks, which coordinate complex cognitive and emotional functions, may exhibit hypo- or hyperconnectivity in MDD, contributing to the disorder’s clinical heterogeneity. Disruption in neuroplastic mechanisms further exacerbates functional disintegration across these networks. This review explores the current understanding of brain network dysfunction in MDD, assesses the implications for treatment resistance, and highlights the potential for personalized medicine approaches.
Methodology
This narrative literature review was conducted by examining peer-reviewed articles published in the last ten years from databases including PubMed, UpToDate and Consensus AI. The search strategy included keywords such as “major depressive disorder”, “brain networks”, “functional connectivity”, “neuroimaging”, “treatment resistance” and “personalized psychiatry”. Studies selected included meta-analyses, systematic reviews, randomized controlled trials (RCTs) and original research articles, with a particular emphasis on studies employing advanced neuroimaging modalities. These included functional MRI (fMRI), structural MRI, diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS) and positron emission tomography (PET). Articles not available in English or not focused on adult populations were excluded. Case reports and anecdotal studies were also excluded to ensure methodological robustness.
Results
Seven main large-scale brain networks have been implicated in MDD. These networks comprise functionally connected regions that contribute to various domains of cognition and emotion. The networks include the Default Mode Network (DMN), Salience Network (SN), Central Executive Network (CEN), Affective Network, Ventral Reward Network (RN), Sensorimotor Network (SMN) and Central Autonomic Network (CAN). Together, these systems support self-referential thinking, emotional regulation, cognitive control, reward processing, motor activity and autonomic functions. Dysfunctions in these networks manifest as the core symptoms of MDD, such as rumination, anhedonia, psychomotor retardation, and dysregulation of mood(2,3).
Default Mode Network (DMN)
The DMN is engaged during rest and is associated with internally directed processes, including self-referential thinking, mind-wandering, and the retrieval of autobiographical memories. Anatomically, the DMN includes the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), precuneus, inferior parietal lobule, lateral temporal cortex and hippocampal formation. Subcortical components such as the thalamus, caudate and amygdala also interface with this network.
In MDD, particularly in cases of treatment-resistant depression, the DMN shows both decreased intra-network functional connectivity (FC) and abnormal inter-network interactions(4). Studies report diminished connectivity between anterior and posterior DMN regions, such as between the medial PFC and PCC/precuneus. Additionally, reduced FC between the parahippocampal gyrus and the left inferior parietal lobule, as well as between the hippocampus and middle cingulum, has been identified. These alterations correlate with persistent negative self-focus and maladaptive rumination, hallmarks of depressive symptomatology.
Conversely, certain studies report increased functional connectivity within the DMN, suggesting heterogeneity in connectivity patterns across patient subgroups. For instance, increased connectivity between the PCC and insula or between the mPFC and middle frontal gyrus may reflect compensatory mechanisms or divergent MDD subtypes. Notably, hyperactivity in anterior DMN regions, such as the anterior cingulate cortex (ACC), has been linked to excessive rumination and self-criticism(5).
Salience Network (SN)
The SN plays a critical role in detecting salient stimuli and coordinating the switch between the DMN and CEN. Key structures include the anterior insula, dorsal anterior cingulate cortex (dACC), amygdala, ventral striatum and thalamus. The SN supports the integration of sensory, emotional and cognitive information, enabling the prioritization of relevant stimuli for adaptive behavior.
In major depressive disorder, SN dysfunction is characterized by aberrant functional connectivity that undermines the network’s regulatory role. Reduced connectivity between the dACC and limbic structures, such as the hippocampus and amygdala, contributes to emotional blunting and diminished emotional insight. Additionally, hyperconnectivity between the anterior insula and somatosensory cortices has been observed, which may underlie increased somatic preoccupation and bodily symptoms in depression. The SN also shows reduced dynamic functional connectivity, impairing its ability to flexibly regulate other networks based on changing contextual demands(6).
Central Executive Network (CEN)
The CEN is implicated in high-level cognitive processes, including working memory, decision-making, goal-directed behavior and cognitive flexibility. It primarily comprises the dorsolateral prefrontal cortex (DLPFC), posterior parietal cortex, and supplementary motor area.
In major depressive disorder, the CEN exhibits reduced functional connectivity both within the network and with other brain systems. Hypoconnectivity between the DLPFC and the superior parietal lobule is associated with impaired executive function, attentional deficits and increased susceptibility to maladaptive cognitive patterns. Importantly, this disruption diminishes the CEN’s ability to exert top-down control over limbic circuits, exacerbating emotional dysregulation(6).
Other networks implicated in MDD
The Affective Network, encompassing the orbitofrontal cortex, subgenual ACC, amygdala and ventromedial prefrontal cortex, shows hyperconnectivity in major depressive disorder. This abnormal synchronization may contribute to emotional hypersensitivity, affective lability and persistent negative affect.
The Ventral Reward Network (RN), responsible for processing reward-related stimuli, including the nucleus accumbens and ventral tegmental area, exhibits reduced functional connectivity in MDD. This dysfunction underpins anhedonia, decreased motivation and impaired reward learning.
The Sensorimotor Network (SMN) includes the primary motor and sensory cortices. Hypoconnectivity in this network correlates with psychomotor retardation, a symptom frequently observed in major depressive disorder and treatment-resistant depression. Functional disintegration within the SMN is often reported by caregivers as reduced physical responsiveness or slowed movement.
The Central Autonomic Network (CAN), which includes the insula, anterior cingulate cortex and brainstem nuclei, modulates physiological states such as heart rate variability and stress response. Alterations in this network may underlie the autonomic dysregulation seen in MDD, including sleep disturbances and gastrointestinal symptoms(6).
Clinical implications of network dysfunction
Recognition of large-scale network dysfunction in major depressive disorder has led to the identification of neurobiologically defined subtypes or “biotypes” of depression. In a landmark study by Drysdale et al. (2017)(7), four distinct biotypes were delineated based on patterns of functional connectivity. Each biotype exhibited unique connectivity signatures and symptom clusters, such as anxiety, anhedonia, or psychomotor retardation(8).
Crucially, these biotypes showed differential responses to interventions like repetitive transcranial magnetic stimulation (rTMS). For example, patients in biotype 1, characterized by fronto-amygdala hypoconnectivity, had a significantly higher response rate to rTMS compared to those in biotype 2, marked by widespread hypoconnectivity. These findings underscore the potential for network-based biomarkers to guide treatment selection and improve outcomes(9).
Emerging treatments such as esketamine and psychedelic-assisted psychotherapy also appear to modulate network connectivity. Esketamine reduces hyperactivity in the DMN and increases global functional connectivity, particularly enhancing connectivity between prefrontal regions and the executive striatum(10). These changes correlate with rapid antidepressant effects in treatment-resistant depression(11,12).
Moreover, preliminary evidence suggests that cognitive-behavioral therapy (CBT) and mindfulness-based interventions may normalize dysfunctional connectivity in the DMN and SN(13). For instance, CBT has been shown to increase functional connectivity between the DLPFC and limbic regions, enhancing cognitive control over affective processes.
Connectivity dysfunction can also predict antidepressants response(14).
Discussion
Major depressive disorder is increasingly understood as a disorder of brain network dysregulation, involving complex interactions among cognitive, emotional and somatic systems. This paradigm shift moves beyond the neurotransmitter-centric view and highlights the importance of circuit-level dysfunction in shaping symptom expression and treatment response.
The heterogeneity of functional connectivity abnormalities across patients provides a compelling rationale for personalized psychiatry. By integrating neuroimaging biomarkers with clinical assessments, clinicians can better stratify patients and tailor interventions to individual neurobiological profiles. However, several challenges remain. Advanced imaging techniques are costly and not routinely available in most clinical settings. Additionally, standardization of data acquisition and analysis is necessary to translate research findings into practical tools.
Future research should aim to validate network-based biomarkers in large, diverse cohorts and develop accessible imaging protocols. Integrating machine learning algorithms may enhance the predictive power of connectivity data, facilitating clinical decision-making. Importantly, ethical considerations regarding privacy, data ownership and equitable access to personalized treatments must also be addressed.
Conclusions
Major depressive disorder involves widespread disruptions in functional connectivity across several large-scale brain networks. These disruptions contribute to the core symptoms of depression and underlie the heterogeneity of treatment response. Advances in neuroimaging have enabled the identification of connectivity-based subtypes of MDD, offering a framework for personalized interventions. Although practical barriers to implementation exist, ongoing technological and methodological innovations hold promise for integrating network neuroscience into routine psychiatric care. A comprehensive understanding of brain network dynamics in major depressive disorder may ultimately transform diagnosis, treatment and prevention strategies, improving outcomes for millions of patients worldwide.
Autor corespondent: Delia Nicolai E-mail: delia.nicolai@gmail.com
CONFLICT OF INTEREST: none declared.
FINANCIAL SUPPORT: none declared.
This work is permanently accessible online free of charge and published under the CC-BY.
