Aberrant synaptic transmission and maladaptive plasticity are fundamental mechanisms that underlie the pathophysiology of psychiatric disorders. The balance between excitatory and inhibitory neurotransmission is crucial for the stability of neural networks, cognitive flexibility, and emotional regulation. The disruption of this equilibrium, particularly within glutamatergic, GABAergic, dopaminergic, and serotonergic systems, has been implicated in various psychiatric conditions, including SCZ, ASD, depression, and bipolar disorder (Zhang J. et al., 2024). Further, glutamatergic signaling, primarily mediated by N-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, regulates synaptic strength, learning, and memory (Stockwell et al., 2024). In SCZ, the hypofunction of NMDA receptors has become a central hypothesis, resulting in cortical disinhibition, impaired gamma oscillations, and cognitive deficits. Studies have shown that the hypofunction leads to a decrease in excitatory input to inhibitory interneurons, particularly parvalbumin-positive (PV) cells, causing an imbalance between excitation and inhibition at the network level and resulting in abnormal cortical synchrony (Roach et al., 2025). In ASD, deficits in NMDA receptor-mediated signaling lead to abnormal synaptic maturation and altered sensory processing. Additionally, excessive excitation or insufficient inhibition during critical developmental periods may disrupt functional connectivity. GABAergic dysfunction exacerbates this imbalance, as impaired inhibitory tone compromises network oscillations critical for attention and working memory (Jiang et al., 2022; Zhao et al., 2022; Tumdam et al., 2024). Concurrent disruptions in dopaminergic and serotonergic neurotransmission influence reward processing, mood, and social behavior, resulting in shared neurochemical foundations among various disorders. Further, dopamine dysregulation within mesocorticolimbic circuits plays a role in both the positive and negative symptoms of SCZ, while serotonergic abnormalities affect affective stability and stress reactivity in mood disorders (Hahn et al., 2025; Peng et al., 2025). In addition to receptor-level dysfunction, disruptions in synaptic plasticity, such as long-term potentiation (LTP) and long-term depression (LTD), hinder adaptive learning and circuit remodeling. These changes in neurotransmitter signaling and synaptic dynamics indicate a failure in the brain’s capacity to optimize information processing, resulting in the cognitive, emotional, and behavioral disruptions typical of psychiatric disorders (Appelbaum et al., 2023; Mäki-Marttunen et al., 2024). Comprehending these interconnected systems provides essential insights for targeted interventions that aim to restore synaptic balance and enhance functional neural plasticity.

The dysregulation of intracellular signaling pathways is a significant molecular mechanism involved in the pathophysiology of psychiatric disorders. These pathways function as integrative hubs that convert extracellular neurotransmitter activity into enduring changes in gene expression, synaptic plasticity, and neuronal survival (Chen Y. et al., 2024). Prominent signaling cascades include cAMP-PKA-CREB, mitogen-activated protein kinase/extracellular signal-regulated kinases (MAPK/ERK), phosphoinositide 3-kinase-protein kinase B (PI3K-Akt), mammalian target of rapamycin (mTOR), and Wnt, each contributing uniquely and interdependently to neuronal homeostasis and adaptive responses (Lima Caldeira et al., 2019). The cAMP-PKA-CREB pathway modulates transcriptional programs that are critical for synaptic plasticity, learning, and mood regulation. For example, impaired CREB activation is linked to cognitive deficits, anhedonia, and changes in stress responsiveness, especially in the context of depression and SCZ (Balu and Coyle, 2018; Sahay et al., 2024b). Likewise, the MAPK/ERK cascade, responsible for activity-dependent synaptic remodeling and dendritic spine dynamics, exhibits abnormal activation patterns in mood and psychotic disorders, resulting in maladaptive neuroplastic changes (Li et al., 2023). The PI3K-Akt-mTOR pathway regulates protein synthesis and cellular metabolism, connecting intracellular energy balance with synaptic growth and resilience. Additionally, the dysregulation of this pathway leads to compromised neurotrophic signaling and cellular stress responses, as evidenced in bipolar disorder and ASD (Vanderplow et al., 2021; Chen Y. et al., 2024). Moreover, Studies have shown that the Wnt signaling pathway, essential for neuronal differentiation and synapse formation, shows altered expression and downstream signaling in SCZ, indicating its role in neurodevelopmental vulnerability (Sahay et al., 2024a). A significant aspect of these cascades is their interaction with neurotrophic signaling, especially the brain-derived neurotrophic factor-tropomyosin receptor kinase B (BDNF-TrkB) pathway, which is crucial for neuronal growth, survival, and synaptic strength. Decreased BDNF expression or compromised TrkB receptor activation disrupts downstream CREB and Akt signaling, resulting in synaptic destabilization and mood dysregulation. These intracellular signaling networks function as a precise molecular interface linking environmental stimuli, neurotransmitter systems, and genomic regulation (Fan et al., 2025). Furthermore, dysregulation affects neuronal adaptability, leading to persistent structural and functional abnormalities in psychiatric disorders. This presents potential targets for molecular and pharmacological interventions designed to restore signaling homeostasis and improve neuroplasticity.

Ion channels are crucial in regulating neuronal excitability, synaptic transmission, and network synchronization, which are processes vital for cognition, emotion, and behavior. Studies have demonstrated that ion channel dysfunction, referred to as channelopathies, has been increasingly associated with the pathophysiology of psychiatric disorders, especially those marked by mood instability, psychosis, and cognitive impairment (Imbrici et al., 2013). Further, voltage-gated sodium (Na⁺), potassium (K⁺), calcium (Ca²⁺), and chloride (Cl⁻) channels are essential for the generation and propagation of action potentials, as well as for maintaining the excitatory-inhibitory balance in neural circuits. Alterations in genetic and functional aspects of these channels can impair neuronal firing precision, synaptic integration, and oscillatory synchrony, resulting in extensive network instability (Palmisano V. F. et al., 2024; Qiu et al., 2025). Additionally, mutations in voltage-gated Ca²⁺ channels, such as CACNA1C and CACNA1H, have been significantly linked to bipolar disorder and SCZ, indicating a connection between altered intracellular calcium signaling, dysregulated neurotransmitter release, and impaired synaptic plasticity (Allen et al., 2024; Han, 2025). Moreover, abnormalities in K⁺ channel genes, including KCNQ2, KCNH2, and KCNN3, are associated with hyperexcitability, mood dysregulation, and heightened vulnerability to psychosis due to their effects on neuronal repolarization and rhythmic activity. However, variants in Na⁺ and Cl⁻ channels contribute to disrupted excitability and altered gamma oscillations, which are neural signatures linked to deficits in working memory and perceptual coherence in SCZ (Alam et al., 2023; Nakanishi et al., 2023). These channelopathies collectively disrupt the temporal coordination of neural networks, leading to abnormal signaling patterns that contribute to fundamental psychiatric symptoms (Verriello et al., 2025). Thus, examining the role of specific ion channel dysfunctions in altering electrophysiological dynamics offers essential insights into psychiatric pathophysiology and reveals potential therapeutic avenues for reestablishing circuit stability and cognitive-emotional regulation.

Neuroimmune signaling and chronic low-grade inflammation are significant factors influencing brain function and the pathophysiology of major psychiatric disorders, especially depression and SCZ. The central nervous system (CNS), previously deemed immunoprivileged, is now acknowledged as an active immunological organ (Nusslock et al., 2024; Feng et al., 2025). In the CNS, complex interactions among neurons, glia, and peripheral immune mediators influence neural signaling and behavior. For instance, dysregulated cytokine signaling, microglial activation, and neuroinflammatory feedback loops contribute to changes in synaptic plasticity, neurotransmission, and network connectivity (Tastan and Heneka, 2024). A study was conducted in subsets of patients with MDD and SCZ, where elevated levels of proinflammatory cytokines, including interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α), are consistently observed (Tastan and Heneka, 2024). Additionally, cytokines either traverse the blood-brain barrier (BBB) or are synthesized by resident immune cells, thereby modulating neurotransmitter synthesis, especially serotonin, dopamine, and glutamate, and affecting receptor sensitivity and synaptic vesicle dynamics (Nava et al., 2025; Nayak et al., 2025). Moreover, chronic inflammatory signaling activates microglia, resulting in excessive synaptic pruning, oxidative stress, and disruption of neurotrophic pathways like BDNF-TrkB, which impairs neuronal survival and connectivity. In SCZ, abnormal microglial-mediated synaptic elimination during crucial neurodevelopmental periods has been associated with cortical thinning and cognitive impairments (Hartmann et al., 2024; Toader et al., 2025). Similarly, in depression, peripheral inflammation modifies hypothalamic-pituitary-adrenal (HPA) axis function, resulting in increased glucocorticoid levels and exacerbating immune dysregulation. Chronic inflammation influences metabolic signaling, mitochondrial function, and endothelial integrity, resulting in compromised neurovascular coupling and BBB permeability (Yin et al., 2024). However, neuroinflammatory cascades demonstrate bidirectional interactions with neural circuits, establishing feedback loops that perpetuate maladaptive network states and the chronicity of symptoms. The acknowledgment of immune-mediated influence on neural function has led to the investigation of immunomodulatory therapies, such as anti-cytokine agents, microglial inhibitors, and lifestyle modifications, as supplementary approaches for treatment-resistant depression and psychosis (Huang et al., 2025; Nava et al., 2025). Thus, these findings establish neuroimmune dysregulation as a mechanistic link between peripheral immune activity and central neurotransmission, emphasizing inflammation as a crucial therapeutic target in precision psychiatry.

Mitochondria are essential for sustaining neuronal function through the integration of energy metabolism, synaptic activity, calcium homeostasis, and redox balance. Recent findings suggest that mitochondrial dysfunction and disrupted metabolic signaling play significant roles in the pathophysiology of psychiatric disorders, such as SCZ, bipolar disorder, MDD, and others (Pinto Payares et al., 2024; Zong et al., 2024). Neurons are energy-dependent cells that primarily utilize oxidative phosphorylation to support action potential generation, neurotransmitter release, and synaptic plasticity. Disruptions in mitochondrial bioenergetics result in inadequate ATP production, modifications in the NAD⁺/NADH ratio, and heightened generation of reactive oxygen species (ROS), all of which undermine neuronal excitability and the fidelity of signaling (Nunes et al., 2025). Additionally, oxidative stress causes additional damage to mitochondrial DNA (mtDNA), lipids, and proteins, leading to a self-reinforcing cycle of dysfunction that worsens synaptic and circuit-level instability. Further, metabolic signaling pathways, including AMP-activated protein kinase (AMPK), mTOR, and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), concurrently integrate cellular energy status with neuroplastic processes and stress adaptation (Rakshe et al., 2024; Zong et al., 2024). The dysregulation of these pathways negatively affects neuronal resilience and leads to changes in glutamate metabolism, excitotoxicity, and an abnormal balance between excitatory and inhibitory signals. Furthermore, impaired mitochondrial trafficking and fission–fusion dynamics disrupt local energy supply at synaptic terminals, thereby undermining activity-dependent synaptic remodeling (Zaninello et al., 2024). Moreover, mitochondrial dysfunction interacts with neuroinflammatory and neuroimmune processes, thereby amplifying oxidative and metabolic stress. These alterations collectively result in deficits in neuronal excitability, cognitive performance, and mood regulation (Ye et al., 2025). Thus, examining the complex interactions among mitochondrial bioenergetics, oxidative stress, and neural signaling provides essential mechanistic insights into psychiatric pathophysiology and highlights mitochondrial metabolism as a potential therapeutic target for reestablishing cellular and network homeostasis.

The integrity of synaptic architecture, both structural and functional, is crucial for effective neuronal communication, cognitive processing, and emotional regulation. Changes in dendritic spine density, synaptogenesis, and synaptic pruning, especially during early neurodevelopmental phases, are critical pathological mechanisms associated with various psychiatric disorders, such as SCZ, ASD, and MDD (Howes et al., 2025). Emerging studies have shown that dendritic spines serve as the main locations for excitatory synaptic transmission and exhibit dynamic remodeling during development and in response to experience-dependent plasticity. Further, disruption of spine formation and elimination regulation undermines the delicate equilibrium between synaptic stability and flexibility essential for adaptive learning and memory (Akhgari et al., 2024). SCZ is characterized by postmortem and imaging studies that consistently demonstrate reduced dendritic spine density in the prefrontal cortex and hippocampus. This reduction is indicative of excessive or mistimed synaptic pruning during adolescence, potentially influenced by overactivation of complement-mediated microglial processes (Howes and Onwordi, 2023). In contrast, ASD is frequently linked to heightened spine density and disrupted synaptic pruning, resulting in excessive excitatory connectivity and suboptimal network integration. Molecular regulators, including BDNF, DISC1, SHANK proteins, and the mTOR signaling pathway, are pivotal in the coordination of synaptogenesis and pruning (Zhang Y. et al., 2024; Kesidou et al., 2025). Their dysregulation leads to structural and functional disorganization of cortical microcircuits. The architectural changes disrupt neuronal synchrony and oscillatory coordination among various brain regions, resulting in impairments in working memory, attention, and social cognition (Soto-Icaza et al., 2024). Additionally, atypical connectivity patterns, namely spanning hyperconnectivity in local circuits to hypoconnectivity in long-range networks were disturb the hierarchical structure of brain communication, which is a characteristic of psychiatric disorders (Xia et al., 2024). Thus, exploring the relationship between molecular and developmental disruptions and their effects on synaptic architecture is essential for elucidating disease mechanisms. This understanding can inform potential therapeutic strategies aimed at synaptic remodeling and restoring connectivity, ultimately enhancing cognitive and emotional outcomes in psychiatric disorders.

Neuronal oscillations establish the temporal framework that synchronizes communication among brain regions, thereby enhancing perception, cognition, and emotional regulation. Oscillatory dynamics, especially in the gamma (30–100 Hz) and theta (4–8 Hz) frequency bands, play a vital role in synchronizing neural ensembles and facilitating higher-order cognitive and affective processes (Shi et al., 2025). Disruptions in rhythmic patterns have been consistently associated with psychiatric disorders, including SCZ, MDD, and anxiety. In SCZ, diminished gamma-band power and disrupted phase synchronization indicate dysfunction of PV⁺ interneurons and NMDA receptor hypofunction, leading to desynchronized cortical activity and cognitive deficits, such as impairments in working memory and perceptual distortions (Marín, 2024; De Pieri et al., 2025). Further, abnormalities in theta oscillations, crucial for hippocampal-prefrontal communication and memory encoding, lead to deficits in attention, emotional regulation, and episodic memory in MDD and anxiety disorders. Functional neuroimaging and electrophysiological studies indicate that disrupted thalamo-cortical and fronto-limbic synchronization is the basis for these oscillatory abnormalities, resulting in impaired integration among sensory, cognitive, and affective networks (Tsai et al., 2023; Kragel et al., 2025; Tang et al., 2025). In depression, reduced fronto-limbic coherence is associated with emotional dysregulation and rumination, whereas heightened amygdala-prefrontal coupling in anxiety states sustains fear processing and hypervigilance. The thalamus functions as a pacemaker for cortical oscillations and is essential for maintaining rhythmic integrity; its dysregulation leads to global desynchronization and abnormal signal propagation across networks (Huang et al., 2023; Onofrj et al., 2023). Additionally, disruptions in oscillatory signaling indicate underlying synaptic and circuit-level dysfunctions and serve as a mechanistic link between cellular pathology and network-level communication deficits. Emerging neuromodulatory interventions, including transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS), aim to restore oscillatory synchrony (Wang Q. et al., 2024; Dai et al., 2025). Thus, focusing these approaches hold therapeutic potential for reestablishing functional connectivity and enhancing cognitive and affective outcomes in psychiatric disorders.

The functional organization of the brain is determined by recurrent circuit motifs that integrate sensory input, emotional valence, and cognitive control. The disruption of these motifs, which regulate the flow of information between cortical and subcortical structures, represents a fundamental pathophysiological mechanism associated with psychiatric disorders (Tekin and Cummings, 2002). Additionally, disruption of specific circuit motifs propagates molecular abnormalities to systems-level dysfunction through altered neural oscillations and impaired interregional communication. Cortico–striatal and thalamo–cortical loops rely on precisely timed inhibitory and excitatory signaling to generate gamma- and theta-band oscillations that support cognitive control, working memory, and sensory integration (Buzsáki and Wang, 2012; Lisman and Jensen, 2013). Perturbations in interneuron function or synaptic plasticity reduce oscillatory coherence, leading to desynchronized network activity observed in electrophysiological and neuroimaging studies of psychiatric and neurodegenerative disorders. Importantly, pharmacological or genetic restoration of upstream molecular defects normalizes oscillatory dynamics and circuit coupling, supporting a causal link between molecular dysregulation and network-level impairment rather than simple association (Cho et al., 2006; Gonzalez-Burgos et al., 2015).

The cortico-striatal-thalamo-cortical (CSTC) loops, amygdala-prefrontal circuits, and hippocampal-cortical networks have been consistently associated with the development of distinct but overlapping symptom domains, including compulsivity, mood dysregulation, and cognitive dysfunction (Hudgins et al., 2024; Choe et al., 2025). The CSTC circuitry, responsible for mediating goal-directed behavior, action selection, and inhibitory control, plays a crucial role in obsessive-compulsive disorder (OCD) and SCZ. In OCD, hyperactivity in the orbitofrontal and anterior cingulate cortices, along with dysregulated striatal gating, results in excessive thalamo-cortical feedback, contributing to the emergence of intrusive thoughts and compulsive behaviors (Shephard et al., 2021). In SCZ, altered dopaminergic modulation within the CSTC pathway disrupts the accuracy of reward and salience processing, contributing to psychosis and disorganized cognition. Additionally, the amygdala-prefrontal circuitry, critical for emotional appraisal and regulation, is significantly disrupted in depression and PTSD (Cui et al., 2024; Li Y. et al., 2025). The hypoactivation of the ventromedial prefrontal cortex (vmPFC) and hyperreactivity of the amygdala disrupt top-down inhibitory control, leading to sustained negative affect, increased stress reactivity, and maladaptive fear responses. In PTSD, abnormal connectivity among the amygdala, hippocampus, and medial prefrontal cortex contributes to intrusive memories and deficits in extinction learning (Haris et al., 2023; Davis and Hamner, 2024). Further, the hippocampal-cortical network integrates episodic memory and contextual processing, displaying both structural and functional deficits in various psychiatric disorders. The emerging evidence suggest that reduced hippocampal volume, impaired theta-gamma coupling, and diminished communication with the prefrontal cortex are linked to memory impairment and cognitive inflexibility in depression and SCZ (Černousova and Patrono, 2025). These circuit-level perturbations demonstrate the emergence of psychiatric symptoms from dysfunction within distributed and interconnected neural systems. Thus, comprehending these motifs establishes a mechanistic framework that connects molecular and synaptic abnormalities to observable behavioral phenotypes. It also underscores potential avenues for circuit-targeted therapies, such as neuromodulation, cognitive interventions, and precision pharmacology, intended to restore functional connectivity and network homeostasis.

Recent developments in neuroimaging and network neuroscience have shifted the perspective on psychiatric disorders from localized abnormalities to disruptions in large-scale brain connectivity. Functional connectivity, characterized by the temporal correlation of neural activity across various regions, offers a systems-level approach for investigating the disruption of neural communication in mental disorders (Xia et al., 2024). Graph-theoretic modeling has become an effective method for quantifying disruptions by depicting the brain as a complex network of nodes (brain regions) and edges (functional connections). For example, healthy brains demonstrate an organization with small-world properties, which are marked by efficient integration among modules and specialized local processing, thereby optimizing information transfer and cognitive performance (Akın et al., 2024). Psychiatric disorders are characterized by topological reorganization, hub vulnerability, and modular segregation, which together diminish global efficiency and network resilience. Alterations in key network hubs, including the precuneus, anterior cingulate cortex, insula, and dorsolateral prefrontal cortex, have been observed in SCZ, MDD, and bipolar disorder (Chen et al., 2025). These hubs function as integrative relay centers between cognitive and affective systems, exhibiting reduced centrality and weakened connectivity. This results in inefficient information flow and impaired coordination among networks. Increased modular segregation, characterized by diminished inter-network connectivity and improved intra-network coherence, further leads to cognitive rigidity and maladaptive emotional processing (Chan et al., 2024; Wu et al., 2024). In contrast, hyperconnectivity in specific subnetworks, like the default mode network (DMN), may lead to heightened self-referential processing and rumination associated with depression. Additionally, graph-theoretic analyses reveal a disrupted rich-club organization, characterized by preferential connectivity among highly connected hubs, suggesting a deterioration of hierarchical communication crucial for adaptive cognition (Barreiros et al., 2024; Zheng et al., 2024). The identified topological abnormalities are associated with symptom severity, cognitive deficits, and treatment response, highlighting their clinical significance. Further, network topology provides mechanistic insights into the emergent properties of psychiatric dysfunction, where molecular and synaptic disruptions aggregate to modify global brain organization (Choi et al., 2025; Mo et al., 2025). Thus, the integration of graph theory with multimodal imaging and computational modeling establishes a robust framework for the identification of network biomarkers, patient stratification, and the guidance of circuit-level interventions designed to restore connectivity efficiency and network homeostasis in psychiatric disorders.

The shift from categorical diagnostic frameworks to dimensional models of psychopathology indicates an increasing acknowledgment that psychiatric symptoms stem from distributed network dysfunctions instead of isolated regional lesions or neurotransmitter irregularities. Network neuroscience offers a comprehensive framework for connecting neural circuitry with behavioral dimensions, including cognition, emotion, and motivation, which are fundamental constructs related to mental health and disease (Pan et al., 2024; Stevanovic et al., 2024; Lett et al., 2025). Alterations in large-scale brain networks demonstrate transdiagnostic patterns that align with symptom dimensions common to multiple disorders, rather than adhering strictly to diagnostic boundaries. Cognitive deficits, prevalent in SCZ, MDD, and bipolar disorder, are linked to impaired connectivity within and between the frontoparietal control network and the dorsolateral prefrontal cortex, which are critical for executive functioning, working memory, and cognitive flexibility (Yang J. et al., 2024). Abnormalities in emotion-related networks, such as the amygdala-prefrontal and salience networks, contribute to affective instability, hypervigilance, and impaired emotional regulation seen in anxiety and mood disorders. Dysregulated interactions between the DMN and salience network contribute to maladaptive self-referential processing and rumination in depression (Willinger et al., 2024; Langhammer et al., 2025). Additionally, aberrant fronto-striatal connectivity disrupts reward valuation and motivation in anhedonia and apathy across various disorders. The findings endorse a continuum-based framework wherein the severity and combination of symptoms indicate graded changes in network topology, connectivity strength, and dynamic flexibility, rather than categorical distinctions (Derosiere et al., 2025). Advanced computational and connectomic methods, including graph theory, dynamic causal modeling, and machine learning (ML), facilitate the identification of individualized network signatures that predict specific symptom dimensions and treatment responsiveness. Further, functional connectivity patterns within the prefrontal-limbic circuitry have been demonstrated to predict outcomes of antidepressant and cognitive behavioral therapy, whereas fronto-striatal dynamics are indicative of motivational enhancement following dopaminergic modulation (Jiao et al., 2025; Liu et al., 2025). These insights contribute to a precision psychiatry framework in which network-level biomarkers act as objective indicators of symptom expression, disease progression, and treatment response. Dimensional models informed by network neuroscience provide a mechanistic and clinically relevant understanding of psychiatric disorders by connecting cognition, emotion, and motivation to dynamic network interactions.

While SCZ, MDD, and bipolar disorder share similar abnormalities in networks at a network level (e.g., prefrontal-limbic, salience, and default) there is also considerable heterogeneity between these disorders regarding their developmental trajectories, regions of vulnerability, and molecular mechanisms. For instance, SCZ is considered to be a neurodevelopmental disorder, with genetic risk factors present during childhood as well as immune insults occurring prenatally, in addition to excessive synaptic pruning occurring during early childhood, producing significant disruptions to the thalamo-cortical and fronto-striatal networks. Each of these network disruptions is associated with (1) decreased functioning of NMDA receptors, (2) altered signaling of parvalbumin-positive interneurons, and (3) an imbalance between excitation and inhibition in these networks; collectively, these disruptions lead to deficits in cognitive control and working memory (Homayoun and Moghaddam, 2007; Rapoport et al., 2012). Moreover, compared to other forms of depressive disorders, MDD has a much less homogeneous pattern of presentation and many times is related to periods of stress throughout life, from adolescence or into adulthood. The MDD phenotype(s) are characterized by hyper-connectivity within the default mode network and decreased functional connectivity between prefrontal regulatory circuits and limbic structures (for example, the amygdala and hippocampus). These circuit-level abnormalities develop from the alteration of monoaminergic signaling, neurotrophic support (for example BDNF), mutations in epigenetic regulation and low-grade chronic inflammation, which may lead to dysregulation of emotion(s) and result in poor emotional resilience (Castrén and Rantamäki, 2010; Nestler, 2014; Kaiser et al., 2015). In contrast, bipolar disorder is somewhere in between, as it presents both genetically or developmentally vulnerable individuals who have episodes of large periods of network instability across their lives (both unipolar circuitry and bipolar). Studies involving functional imaging have shown state-dependent differences in the prefrontal-limbic or fronto-striatal circuits of individuals with bipolar disorder, with patterns of connectivity and oscillatory dynamic differences depending upon whether the person is at a manic or depressive phase. At the molecular level, bipolar disorder is associated with alterations in intracellular signalling pathway regulation (for example, GSK3β and calcium signalling), dysfunction in the mitochondria, and alterations in circadian rhythms (Strakowski et al., 2005; Beaulieu et al., 2008; McClung, 2013; Insel, 2014). These three areas of dysregulation help to create the oscillatory and state-dependent nature of the network dysregulation patterns seen in bipolar disorder (Table 2). Based on these observations, we suggest that (i) shared network motifs offer a transdiagnostic basis for symptom dimensions; (ii) however, the timing of development, susceptibility of regional circuits to disease, and molecular pathways associated with a disorder all play an important role in shaping clinical phenotype. Therefore, when developing a model of potential treatments using network-based approaches, it is necessary to consider the unique features of an individual disorder to inform intervention strategies that are tailored to the specific disorder.

Psychiatric disorders exhibit a high degree of polygenicity, where risk is influenced by the cumulative impact of multiple common genetic variants, each contributing a small effect size that collectively modifies neural signaling, synaptic plasticity, and circuit organization (Ayano et al., 2025). Genome-wide association studies (GWAS) have identified numerous susceptibility loci across disorders including SCZ, bipolar disorder, MDD, and ASD. These studies demonstrate significant enrichment in genes related to synaptic structure, neurotransmission, and intracellular signaling pathways. Key genes, including CACNA1C, GRIN2B, and DISC1, are crucial in the regulation of neuronal excitability, calcium signaling, and synaptic plasticity (Astorino et al., 2025). For instance, variants in CACNA1C, encoding the α1C subunit of L-type voltage-gated calcium channels, affect intracellular calcium dynamics essential for neurotransmitter release and activity-dependent gene expression via CREB and MAPK/ERK signaling pathways. The dysregulation of CACNA1C expression or channel kinetics is associated with impaired synaptic integration, abnormal emotional processing, and disrupted prefrontal-limbic connectivity in bipolar disorder and SCZ (Datta et al., 2024; Boller et al., 2025). Further, polymorphisms in GRIN2B, which encodes the NR2B subunit of the NMDA receptor, influence glutamatergic transmission, LTP, and the excitatory-inhibitory balance, thereby contributing to cognitive and perceptual disturbances associated with psychotic and developmental disorders (Tumdam et al., 2024). The DISC1 gene is implicated in various neuropsychiatric disorders, including SCZ. Its role in neuronal development and synaptic function has garnered significant research interest. The gene, initially identified in a Scottish family exhibiting a high prevalence of psychiatric disorders, regulates neurodevelopmental processes, including neuronal migration, axonal growth, and synapse formation, via its interaction with signaling pathways, such as cAMP-PKA and PI3K-Akt-mTOR (Gutiérrez-Rodríguez et al., 2025; Volgin et al., 2025). Functional variants and structural rearrangements in DISC1 disrupt mitochondrial trafficking, cytoskeletal organization, and synaptic connectivity, resulting in modified cortical circuitry and cognitive impairment. Pathway-level analyses indicate that polygenic risk converges on signaling networks related to calcium homeostasis, glutamate receptor trafficking, and neurotrophic regulation, specifically the BDNF-TrkB and Wnt pathways (Norkett et al., 2020; Schirò et al., 2022). The widespread effects of these variants highlight that psychiatric vulnerability arises not from isolated gene mutations but from disruptions at the network level within interconnected molecular pathways that regulate synaptic signaling and neurodevelopment. The integration of polygenic risk scores with transcriptomic and functional imaging data facilitates the mechanistic mapping of gene networks to brain connectivity, providing a biologically informed framework for precision psychiatry and genetically guided therapeutic interventions (Wang M. et al., 2024; Bracher-Smith and Escott-Price, 2025).

Epigenetic regulation serves as a vital link among genetic predisposition, environmental influences, and neural function, facilitating the dynamic adjustment of gene expression without modifying the DNA sequence itself. Evidence suggests that DNA methylation, histone modifications, and miRNA-mediated post-transcriptional regulation are significant mechanisms involved in the dysregulation of neural signaling, synaptic plasticity, and behavioral abnormalities in psychiatric disorders (Ohi et al., 2025; Peña, 2026). For instance, DNA methylation, which generally takes place at cytosine-phosphate-guanine (CpG) dinucleotides, influences chromatin accessibility and gene transcription. Aberrant methylation patterns in synaptic and neurodevelopmental genes, including BDNF, RELN, GAD1, and GRIN2B, have been observed in SCZ, MDD, and bipolar disorder, frequently resulting in transcriptional repression of genes critical for synaptic integrity and neuronal communication (Saada and Stern, 2025; Yu et al., 2025). Additionally, environmental stressors, such as early-life adversity and chronic stress, can lead to lasting hypermethylation of glucocorticoid receptor and neurotrophic factor promoters, consequently impairing the regulation of the HPA axis and synaptic resilience (Rizavi et al., 2023). Histone modifications, especially acetylation and methylation, impact chromatin structure and transcriptional dynamics by modifying DNA accessibility to transcription factors. Reduced histone acetylation at the promoters of plasticity-related genes correlates with diminished expression of synaptic proteins and cognitive impairment (Mai et al., 2025). In contrast, inhibition of histone deacetylases (HDACs) has been demonstrated to counteract these effects, leading to the restoration of synaptic strength and behavioral flexibility in preclinical models (Kilgore et al., 2010; Gräff et al., 2012). In addition to these mechanisms, miRNAs regulate gene expression by targeting messenger RNAs for degradation or translational repression (Garayo-Larrea et al., 2024). For example, dysregulated miRNAs, including miR-132, miR-137, and miR-124, are associated with neuronal differentiation, synaptogenesis, and the expression of neurotransmitter receptors. miR-137, identified as a risk locus for SCZ, modulates genes related to calcium signaling and vesicular transport, thereby connecting molecular regulation to deficits in network-level communication (Pergola et al., 2024; Adly et al., 2025). These epigenetic modifications collectively integrate environmental and developmental signals, influencing neural circuitry and behavior. Their reversible nature presents significant therapeutic potential; pharmacological interventions aimed at epigenetic enzymes or miRNA pathways may correct abnormal gene expression patterns and synaptic function, facilitating the advancement of mechanism-based treatments in precision psychiatry.

Neurodevelopment is regulated by meticulously timed molecular and cellular mechanisms that coordinate neuronal differentiation, synaptogenesis, and circuit refinement. Perturbations during critical periods of brain maturation, characterized by increased plasticity and sensitivity to environmental input, can have significant and lasting impacts on brain structure and function, increasing the risk of psychiatric disorders (Khodosevich and Sellgren, 2023; Vivi and Di Benedetto, 2024). Disruptions in early-life signaling, such as abnormal neurotransmitter dynamics, neurotrophic imbalances, inflammatory activation, and hormonal dysregulation, can impede the sequential development of excitatory and inhibitory circuits critical for cognitive, emotional, and social processing (Caballero et al., 2021). Further, abnormal glutamatergic and GABAergic signaling during perinatal and adolescent stages disrupts the establishment of excitatory-inhibitory balance, which impairs cortical maturation and prefrontal-limbic connectivity, which are key features of SCZ, ASD, and mood disorders (Hollestein et al., 2023). Additionally, altered BDNF-TrkB signaling during synaptic refinement phases can disrupt dendritic spine stability and synaptic pruning, resulting in hyperconnectivity or inefficient circuit integration seen in neurodevelopmental psychopathologies (Hernández-del Caño et al., 2024). Early-life stress, infection, or nutritional deficiencies induce inflammatory and endocrine responses that alter neurodevelopmental trajectories through epigenetic programming and microglial activation, resulting in enduring impacts on neuronal excitability and synaptic organization (Rahman and McGowan, 2022). Disruption during critical periods affects the development of extensive networks, including the default mode and salience networks, which are essential for advanced cognitive functions and emotional regulation. The timing of perturbation is crucial in determining vulnerability; disruptions during early gestation can impair neurogenesis and migration, while adolescent disturbances typically influence myelination, synaptic pruning, and the maturation of neurotransmitter receptors (Azarias et al., 2025; Banihashemi et al., 2025). Longitudinal imaging and transcriptomic studies indicate that delayed or excessive pruning, atypical myelination, and impaired synaptic stabilization are factors contributing to network inefficiency and cognitive decline in psychiatric disorders (Hansen et al., 2022). The findings indicate that mental disorders, while frequently clinically evident during adolescence or early adulthood, may stem from subtle variations in developmental timing that predispose individuals to later dysfunction. Thus, exploring the impact of early-life signaling abnormalities on persistent circuit vulnerabilities provides essential insights for developing preventive strategies and early interventions that aim to restore neurodevelopmental trajectories and improve neural resilience in at-risk populations.

Computational psychiatry provides a mechanistic framework for understanding the relationship between neural signaling disruptions and the emergence of aberrant cognition and behavior through the application of quantitative models of brain function. This approach is centered on Bayesian brain models, predictive coding, and hierarchical inference frameworks, which view the brain as a probabilistic inference machine that consistently updates internal models to predict sensory inputs and reduce uncertainty (Qela et al., 2025). Perception, cognition, and emotion arise from the interplay between bottom-up sensory information and top-down prior expectations. Psychiatric disorders can be conceptualized as disorders of inference and prediction, wherein abnormalities in neural signaling affect the precision weighting of sensory information relative to prior knowledge (Keller and Sterzer, 2024). In SCZ, impaired NMDA receptor function and cortical disinhibition may result in the aberrant assignment of salience to irrelevant stimuli, leading to delusions and hallucinations via disrupted predictive coding. In depression, maladaptive priors that reflect negative expectations skew hierarchical inference, leading to enduring pessimism and a failure to revise beliefs in light of positive evidence (Keller and Sterzer, 2024; Bottemanne et al., 2025). In anxiety, heightened precision of threat-related priors increases prediction errors in the amygdala-prefrontal circuitry, thereby maintaining hypervigilance and anticipatory fear. Hierarchical Bayesian inference-based computational models correlate dysfunctions with quantifiable neural dynamics, including changes in cortical oscillations, synaptic gain modulation, and network connectivity, thereby integrating molecular, circuit-level, and behavioral data (Hess et al., 2025). Moreover, in depression, computational models describe maladaptive priors and impaired belief updating that bias hierarchical inference toward pessimistic expectations and reduced reward sensitivity, while in anxiety disorders excessive precision of threat-related priors amplifies prediction errors within amygdala–prefrontal circuits, sustaining hypervigilance and avoidance behaviors (Browning et al., 2015; Huys et al., 2015; Kube et al., 2020). These models offer a cohesive framework for the integration of neuroimaging, electrophysiology, and behavioral data, facilitating the individualized characterization of computational phenotypes that surpass conventional diagnostic classifications. Furthermore, computational frameworks possess significant clinical implications; by identifying latent parameters that influence perception, learning, and decision-making, they can forecast treatment responses and inform personalized interventions (Karvelis et al., 2023). Bayesian and predictive coding models reconceptualize psychiatric disorders as disruptions in the brain’s inferential architecture, connecting abnormal neural signaling to distorted mental representations and facilitating the advancement of precision computational psychiatry. Empirical support for this framework comes from computational studies demonstrating that aberrant precision weighting within hierarchical predictive coding models can reproduce key psychotic phenomena, including hallucinations and delusions, when NMDA receptor–dependent synaptic gain is disrupted (Sterzer et al., 2018).

Multimodal data fusion and connectomic modeling are advanced methodologies in neuroscience that facilitate a thorough comprehension of neural signaling hierarchies at molecular, cellular, and systems levels. Integrating complementary modalities, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), positron emission tomography (PET), and single-cell transcriptomics, enables researchers to develop multiscale models that connect molecular activity with dynamic brain networks and behavioral outcomes (Sui et al., 2023; Ghosh et al., 2024). fMRI delivers high spatial resolution for the identification of distributed functional networks, whereas EEG provides millisecond-level temporal precision to detect oscillatory synchronization and transient communication among neural ensembles. Likewise, PET imaging provides quantitative insights into the distribution of neurotransmitter receptors, metabolic activity, and neuroinflammatory processes, thereby connecting molecular signaling with functional dynamics (Lawn et al., 2023; Tuan et al., 2025). Single-cell transcriptomic data reveal cellular heterogeneity and gene expression patterns that underlie network organization, providing a molecular basis for the observed differences at the circuit level. Integrative computational frameworks, such as graph-theoretic modeling, dynamic causal modeling, and ML-based multimodal fusion, are increasingly employed to synthesize heterogeneous data types into coherent representations of neural function (Piwecka et al., 2023; Yao et al., 2025). These models clarify the mechanisms by which disturbances in synaptic signaling or neurotransmitter systems influence mesoscale circuits, leading to significant network changes associated with psychiatric disorders. The integration of fMRI-derived connectivity with PET measures of dopaminergic or glutamatergic activity facilitates the identification of neurochemical correlates associated with aberrant network synchronization in SCZ and depression (Millevert et al., 2023; Varvari et al., 2025). The integration of EEG oscillatory patterns with single-cell transcriptomic signatures elucidates the translation of cellular-level channelopathies or receptor dysregulation into macroscopic network instability. In addition to providing mechanistic insights, multimodal connectomic models possess diagnostic and therapeutic potential by offering individualized network biomarkers that can predict treatment response or disease progression (Jimenez-Marin et al., 2024; Krejcar and Namazi, 2025). In addition, representative multimodal studies have demonstrated the power of this approach, showing that PET-derived neurotransmitter receptor distributions constrain fMRI network organization and EEG oscillatory synchronization, while integration with transcriptomic data reveals how molecular and cellular perturbations propagate hierarchically to circuit- and systems-level dysfunction (Babiloni et al., 2004; Riedl et al., 2014). Advancements in the field indicate that multimodal data fusion is shifting psychiatry from a symptom-focused approach to one centered on circuits and mechanisms. This transition facilitates the mapping of brain-wide signaling hierarchies that support cognition, emotion, and behavior in both health and disease contexts.

The integration of ML and AI into neuroscience and psychiatry has transformed mechanistic discovery and therapeutic innovation by connecting molecular perturbations with systems-level network dysfunction. AI-driven models analyze complex, high-dimensional datasets from multimodal neuroimaging, genomics, electrophysiology, and clinical phenotyping to reveal latent relationships that traditional analytical methods may overlook (Baydili et al., 2025; Bracher-Smith and Escott-Price, 2025). In psychiatric disorders, the heterogeneity and overlapping symptom dimensions complicate diagnosis and treatment. Further, ML facilitates the identification of network phenotypes, which are distinct patterns of connectivity, activity, and molecular signaling that characterize specific pathophysiological subtypes (Chen et al., 2022). Supervised and unsupervised learning algorithms, including convolutional neural networks (CNNs), graph neural networks (GNNs), and manifold learning approaches, are increasingly employed to correlate functional and structural connectivity abnormalities with underlying molecular signatures obtained from transcriptomic, proteomic, and metabolomic data (Tanaka, 2025). These models clarify the role of disruptions in specific neurotransmitter systems, ion channels, or intracellular signaling pathways (such as mTOR, PI3K-Akt, or MAPK/ERK) in the large-scale network disintegration associated with SCZ, MDD, and ASD (Angela Asir et al., 2025). Additionally, AI enhances mechanistic drug discovery through the integration of pharmacogenomic data with network-level representations, enabling the prediction of therapeutic targets and drug-response profiles. Network-based ML models can identify compounds that rectify abnormal connectivity or signaling patterns by simulating their effects across molecular and circuit hierarchies, a methodology referred to as network pharmacology (Zhang P. et al., 2024; Cui et al., 2025). Moreover, traditional diagnostic categories are less effective for classifying patients when using supervised/unsupervised ML methods, including random forests, support vector machines and deep neural networks, to integrate and use neuroimaging, transcriptomic, epigenomic and electrophysiological data. For instance, multimodal classifiers developed by combining functional connectivity with gene expression profiles have produced the ability to distinguish biologically distinct forms of depression and SCZ having different clinical characteristics, such as site of treatment and rate of response, by gathering data from multiple modalities together (Richiardi et al., 2015; Clementz et al., 2016). Similarly, ML based methods have been developed using resting-state fMRI and EEG characteristics, to be able to predict response to antidepressant and antipsychotic medications, highlighting potential for translational research regarding network-defined biomarkers. Additionally, the importance of using a multi-modal approach allows patterns to be identified that provide converging evidence of dysfunction from a variety of molecular, cellular, and multi-cellular levels (Chekroud et al., 2016; Marquand et al., 2016; Kang and Cho, 2020). In this manner signaling fingerprints are a means of identifying a coordinated level of dysregulation across all of these modalities, thus allowing for the identification of upstream drivers, such as neuroinflammatory processes associated with MDD or SCZ, and downstream phenotypes related to neuronal or synaptic dysfunction. Additionally, these fingerprints provide an avenue of stratifying patients based on biological factors that are causally associated with their treatment responses (Woo et al., 2017).

Emerging studies have demonstrated that deep learning frameworks utilizing extensive pharmacological datasets are capable of predicting ligand-receptor interactions, off-target effects, and opportunities for drug repurposing in neuropsychiatric conditions. The integration of functional MRI connectivity fingerprints with transcriptomic atlases has facilitated the identification of compounds that influence specific neurotransmitter pathways associated with dysfunctional brain networks (Lv et al., 2024; Gupta et al., 2025). Further, reinforcement learning and causal modeling approaches are employed to create adaptive treatment strategies that adjust interventions dynamically based on real-time neural feedback. Moreover, AI-driven mechanistic modeling enhances precision in target identification and contributes to biomarker discovery, facilitating personalized treatment based on an individual’s molecular and network profile (Wright and Anticevic, 2024). With the ongoing increase in computational power and multimodal datasets, ML emerges as a transformative element in psychiatry. It provides a data-driven framework for deciphering neural complexity, enhancing drug discovery, and initiating a new phase of precision neurotherapeutics based on mechanistic insights. The current limitations of AI for clinical purposes represent areas where improvement can be achieved. Most of the current research in this area has focused on smaller to moderate sized cohort studies which raise issues of model overfitting and generalization. The variability in data acquisition protocols, preprocessing pipeline design and feature selection across study sites limits reproducibility among study sites (Marek et al., 2022; Varoquaux and Cheplygina, 2022). Most of the current AI models have a “black box” nature causing challenges with respect to the biological interpretability of the data as well as issues with gaining regulatory approval. To overcome these issues and create effective clinical applications of AI, larger and harmonized multi-site cohort studies must occur. Additionally, standardized analytic methods and continued focus on the use of explainable AI will be needed to maintain the biological basis for any findings and create clinically useful solutions (Topol, 2019; Poldrack et al., 2020). Creating longitudinal multi-omics datasets and linking the resulting data to clinical outcomes will be critical to validating the clinical application of the resulting AI models as they will provide a means of establishing causation or predicting disease progression.

Recent advances in precision neuromodulation technologies, including TMS, transcranial direct current stimulation (tDCS), DBS, and emerging closed-loop systems were representing a major step forward in the ability to selectively target and modulate dysfunctional brain circuits with increasing spatial, temporal, and mechanistic specificity (Stagg and Nitsche, 2011; Fox et al., 2012; Lozano et al., 2019; Soleimani et al., 2023). These approaches are grounded in a network-based understanding of psychiatric and neurological disorders, which conceptualizes disease states as arising from maladaptive interactions across distributed neural systems rather than focal lesions. By directly engaging circuit dynamics, modern neuromodulatory strategies offer a powerful means to restore aberrant communication patterns underlying cognitive, affective, and behavioral dysfunction (Bullmore and Sporns, 2009; Fornito et al., 2012). TMS is a non-invasive technique that induces focal electric currents through rapidly changing magnetic fields, enabling modulation of cortical excitability and large-scale network interactions. Extensive evidence demonstrates that TMS can reshape activity within prefrontal–limbic circuits implicated in depression, SCZ, and obsessive-compulsive disorder (Eldaief et al., 2013; Liston et al., 2014; Carmi et al., 2019). Further, rTMS protocols exert frequency- and pattern-dependent effects, with high-frequency stimulation typically enhancing cortical excitability and low-frequency stimulation producing inhibitory effects. These properties allow TMS to normalize hypoactive dorsolateral prefrontal regions in depression or attenuate hyperactive cognitive control and motor networks in obsessive-compulsive disorder (Fitzgerald et al., 2006; Carmi et al., 2019). Importantly, neuroimaging studies indicate that the therapeutic effects of TMS extend beyond the stimulation site, producing coordinated changes in functional connectivity within the default mode, salience, and frontoparietal networks, thereby supporting system-level reorganization rather than isolated focal modulation (Fox et al., 2014; Drysdale et al., 2017). In contrast, tDCS offers a complementary, non-invasive approach by applying weak direct electrical currents to shift neuronal resting membrane potentials and bias ongoing neural activity. Although its effects are subtler than those of TMS, tDCS has been shown to enhance synaptic plasticity, facilitate learning-related processes, and modulate connectivity within executive and affective networks (Stagg and Nitsche, 2011; Chan and Han, 2020). A key advantage of both TMS and tDCS lies in their compatibility with real-time neuroimaging and electrophysiological monitoring, enabling the development of closed-loop neuromodulatory systems. These adaptive frameworks dynamically adjust stimulation parameters based on ongoing neural states, improving precision and potentially reducing interindividual variability in treatment response (Zrenner et al., 2016; Bergmann, 2018; Cappon and Pascual-Leone, 2024). Further, DBS provides a more invasive but highly targeted method for modulating subcortical structures and network hubs implicated in treatment-resistant psychiatric and neurological disorders. Originally developed for movement disorders, DBS has been successfully repurposed for major depressive disorder, obsessive-compulsive disorder, and Tourette’s syndrome, with stimulation targets including the subgenual cingulate cortex, nucleus accumbens, ventral capsule, and ventral striatum (Khosravi, 2025). Unlike open-loop stimulation paradigms, adaptive or closed-loop DBS systems detect pathological network states in real time and deliver stimulation contingently, thereby enhancing therapeutic efficacy while minimizing adverse effects (Guidetti et al., 2025). Advances in ML, connectomics, and computational modeling increasingly inform DBS targeting strategies, allowing stimulation parameters to be personalized based on individual network architecture and disease-specific circuit dysfunction (Ruffini et al., 2009; Fox et al., 2012; Chen S.-Y. et al., 2024). Crucially, neuromodulatory interventions do not exert strictly local effects but instead propagate across anatomically and functionally connected regions, reshaping large-scale network dynamics. Both TMS and DBS have been shown to alter oscillatory synchronization and temporal coordination across distributed circuits (Eldaief et al., 2023; Neumann et al., 2023). Additionally, TMS can entrain cortical oscillations in a frequency-specific manner, enhancing theta- or gamma-band coherence depending on stimulation parameters, while DBS suppresses pathological rhythms such as excessive beta synchrony. These oscillatory effects improve interregional communication and restore information flow, providing a mechanistic link between focal stimulation and global network normalization (Eusebio and Brown, 2007; Thut et al., 2017).

At the molecular and cellular levels, accumulating evidence indicates that these network-level changes are supported by activity-dependent plasticity mechanisms. Repeated neuromodulatory stimulation can induce long-term potentiation or depression-like effects through alterations in calcium signaling, receptor trafficking, and synaptic strength, thereby recalibrating excitation-inhibition balance within affected circuits (McIntyre and Anderson, 2016; Bazzari and Parri, 2019; Yamada and Sumiyoshi, 2023). Concurrent modulation of neurotransmitter systems, including glutamatergic, GABAergic, and dopaminergic signaling that further reshapes synaptic gain, oscillatory coordination, and neuromodulator tone across interconnected regions. Sustained neuromodulatory activity also engages intracellular signaling cascades that regulate gene expression and neurotrophic pathways, including brain-derived neurotrophic factor–mediated mechanisms, supporting synaptic maintenance, structural remodeling, and long-term circuit stabilization (Giordano et al., 2017; Binns et al., 2025). Collectively, these findings position contemporary neuromodulatory interventions as multiscale therapeutic tools that couple molecular and synaptic processes to circuit-level remodeling and systems-level functional recovery. By integrating connectome-informed targeting, adaptive stimulation paradigms, and mechanistic insight across biological scales, precision neuromodulation aligns closely with emerging frameworks of network-based and personalized neuropsychiatric treatment.

Advancements in neuromodulation are accompanied by progress in molecular neuroscience, leading to the development of a new generation of synaptic and molecular therapeutics that target the cellular foundations of psychiatric dysfunction (Kargbo, 2024; Howes et al., 2025). Traditional psychotropics have mainly focused on monoaminergic systems; however, their limited effectiveness and delayed therapeutic onset highlight the necessity for more mechanistically informed strategies. Current strategies emphasize the modulation of synaptic and intracellular signaling pathways that govern plasticity, excitatory–inhibitory balance, and neuroimmune interactions (Kavalali and Monteggia, 2025). For instance, metabotropic glutamate receptor (mGluR) modulators are being investigated as viable alternatives to NMDA antagonists, providing a means to precisely regulate glutamatergic transmission while avoiding excitotoxicity. Agents that target mGluR2/3 have shown promise in diminishing hyperglutamatergic signaling and improving cognitive and affective symptoms associated with SCZ and anxiety disorders (McKenzie et al., 2025; Olivares-Berjaga et al., 2025). Further, neurotrophic enhancers, such as agents that upregulate BDNF or activate its receptor TrkB, show potential for restoring synaptic integrity and enhancing resilience against stress-induced neuronal atrophy. Small-molecule TrkB agonists and peptides that mimic BDNF function have demonstrated the ability to increase dendritic spine density, enhance learning, and reduce depressive phenotypes in preclinical studies (Numakawa and Kajihara, 2023). Furthermore, anti-inflammatory and immunomodulatory therapies are increasingly recognized as effective adjunctive treatments in psychiatry, due to the rising acknowledgment of neuroinflammation as a significant contributor to synaptic and circuit dysfunction. Minocycline, nonsteroidal anti-inflammatory drugs, and cytokine inhibitors (e.g., IL-6 and TNF-α antagonists) have demonstrated efficacy in diminishing microglial activation, oxidative stress, and maladaptive synaptic pruning (Panizzutti et al., 2023; Du et al., 2024). Additionally, targeting mitochondrial and metabolic signaling pathways provides a complementary approach to improve neuronal energy efficiency and mitigate oxidative damage (Nunes et al., 2025). These approaches collectively advance beyond mere neurotransmitter replacement to restore the dynamic cellular environment essential for sustaining synaptic signaling and network homeostasis.

The combination of connectomics, neuroimaging, and pharmacology has led to circuit-guided drug development, which aligns molecular specificity with network-level effects. This paradigm aims to connect molecular targets with macroscopic brain function through the utilization of connectomic biomarkers for predicting and monitoring pharmacological responses (Preller et al., 2024; Loiodice et al., 2025). fMRI and PET imaging facilitate the real-time mapping of pharmacological agents’ effects on brain connectivity, thereby enabling the identification of “circuit signatures” indicative of therapeutic efficacy. Modulation of fronto-limbic connectivity correlates with antidepressant response to selective serotonin reuptake inhibitors (SSRIs) and ketamine, whereas restoration of thalamo-cortical synchrony predicts cognitive improvement following dopaminergic treatment in SCZ (Sabaroedin et al., 2022; Saberi et al., 2025). Recent developments in molecular imaging, particularly receptor-specific PET tracers, have improved the quantification of target engagement and the correlation between receptor occupancy and alterations in network activity. This integration facilitates bidirectional translation, encompassing molecular discovery to circuit-level validation and vice versa, thus expediting drug development (Ferrando et al., 2025). Furthermore, computational models that integrate transcriptomic and connectomic datasets facilitate the prediction of drug-target interactions and the identification of network nodes that are most sensitive to molecular perturbation. This strategy is consistent with the concept of network pharmacology, which optimizes therapeutic efficacy by modulating interconnected pathways instead of focusing on single molecular targets (Bi et al., 2025; Li and Kar, 2025). Moreover, psychiatric disorders are fundamentally network-based, and drug development guided by circuit architecture offers a biologically coherent approach to achieving precision in treatment design.

The ultimate challenge in translational psychiatry is the integration of digital phenotyping and biomarker-guided clinical trial design. This approach utilizes advancements in wearable technology, mobile health platforms, and real-time behavioral analytics to enhance the personalization of monitoring and prediction of treatment responses (Alam et al., 2025; Linardon et al., 2025). Conventional clinical assessment instruments frequently depend on subjective symptom reporting, which is characterized by limited temporal resolution and ecological validity. Digital phenotyping employs continuous passive data collection through smartphones, wearables, and biosensors to gather behavioral, physiological, and environmental variables, including sleep patterns, speech dynamics, social activity, and movement (Jilka and Giacco, 2024). High-frequency, multimodal datasets facilitate the identification of digital biomarkers associated with neurobiological states and network-level alterations. Integrating digital measures with neuroimaging, electrophysiology, and genomic data enables researchers to develop multilayered predictive models that can identify early warning signals of relapse, monitor treatment adherence, and dynamically adjust interventions (Choo et al., 2024; Jiang et al., 2024; McGinnis et al., 2024). Alterations in speech prosody or gait dynamics may indicate disruptions in the fronto-striatal network that occur prior to the exacerbation of clinical symptoms. ML algorithms utilized on this data can categorize patient subtypes, forecast treatment responsiveness, and determine optimal therapeutic windows for intervention (Yang M. et al., 2024). Moreover, biomarker-guided clinical trials are essential for advancing precision psychiatry, as they utilize biological and digital markers for inclusion criteria, stratification, and outcome assessment. Neuroimaging biomarkers, including functional connectivity in the default mode and salience networks, function as objective endpoints for assessing treatment efficacy, whereas molecular and genetic profiles inform drug selection and dosing strategies (Preller et al., 2024; Etkin et al., 2025). Adaptive trial designs, which allow treatment protocols to evolve based on real-time biomarker feedback, improve efficiency and individualization in clinical research. A comprehensive translational framework in psychiatry highlights the importance of network-informed, precision-based treatment approaches (Kaizer et al., 2023). Neuromodulation reinstates connectivity, molecular therapeutics rectify signaling and inflammation, while circuit-guided pharmacology synchronizes interventions with network dynamics. The integration of digital phenotyping for real-time personalization with these advancements establishes precision psychiatry based on computational modeling, measurable biomarkers, and individualized neural and molecular profiles (Zhao et al., 2025). The therapeutic landscape in psychiatry is evolving from mere symptom management to a focus on reinstating the biological principles of neural communication, including synchrony, adaptability, and resilience. Translational neuroscience integrates advancements in neurotechnology, molecular biology, and data science, transforming the diagnosis, monitoring, and treatment of psychiatric disorders (Gordon et al., 2024; Yoon et al., 2024). This approach fosters a future of mental healthcare that is predictive, preventive, and personalized.