Substance use disorders (SUDs) are a critical public health challenge characterized by high relapse rates, with existing treatments often proving inadequate. The focus of this review is to provide an update on the current state of transcranial magnetic stimulation (TMS) as a therapeutic intervention for SUDs and discuss neuroimaging-guided TMS practices. This review explores the neurobiology underlying SUDs, emphasizing the roles of the prefrontal cortex, striatal circuits, and dopaminergic pathways, and examines the theory that TMS modulates neurocircuitry to impact addiction-related behaviors. We discuss TMS procedural aspects and provide a comparative analysis of TMS protocols, focusing on repetitive, deep, single-pulse, paired-pulse, and a more recent approach, theta burst stimulation. We review recent randomized clinical trials (RCTs) to demonstrate reductions in cravings and use across SUDs as well as highlight the need for standardized protocols. We emphasize the power of combining neuroimaging techniques to show functional connectivity changes in the brain and identify potential biomarkers predictive of SUD treatment response, an unexplored area of discussion. With these topics, this review highlights the potential of TMS as a versatile and effective therapeutic modality for SUDs, especially when combined with neuroimaging. Key findings emphasize the necessity for future research to address methodological challenges, such as standardizing protocols and optimizing stimulation parameters. The integration of neuroimaging provides insights into functional connectivity changes, enabling enhanced precision and individualized treatment strategies. By validating TMS approaches and incorporating multimodal techniques, this field can advance toward a more robust clinical utility in addressing the complex neurocircuitry of addiction-related behaviors underlying SUDs.

Schizophrenia is a serious cognitive disorder characterized by disruptions in neurotransmission, a process requiring the coordination of multiple kinase-mediated signaling events. Evidence suggests that the observed deficits in schizophrenia may be due to imbalances in kinase activity that propagate through an intracellular signaling network. Specifically, 3′-5′-cyclic adenosine monophosphate (cAMP)-associated signaling pathways are coupled to the activation of neurotransmitter receptors and modulate cellular functions through the activation of protein kinase A (PKA), an enzyme whose function is altered in the frontal cortex in schizophrenia. In this study, we measured the activity of PKA in human postmortem anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC) tissue from schizophrenia and age- and sex-matched control subjects. No significant differences in PKA activity were observed in male and female individuals in either brain region; however, correlation analyses indicated that PKA activity in the ACC may be influenced by tissue pH in all subjects and by age and tissue pH in females. Our data provide novel insights into the function of PKA in the ACC and DLPFC in schizophrenia.

In bioinformatics, pathway analyses are used to interpret biological data by mapping measured molecules with known pathways to discover their functional processes and relationships. Pathway analysis has become an essential tool for interpreting large-scale omics data, translating complex gene sets into actionable experimental insights. However, issues inherent to pathway databases and misinterpretations of pathway relevance often result in “pathway fails,” where findings, though statistically significant, lack biological applicability. For example, the Tumor Necrosis Factor (TNF) pathway was originally annotated based on its association with observed tumor necrosis, while it is multifunctional across diverse physiological processes in the body. This review broadly evaluates pathway analysis interpretation, including embedding-based, semantic similarity-based, and network-based approaches to clarify their ideal use-case scenarios. Each method for interpretation is assessed for its strengths, such as high-quality visualizations and ease of use, as well as its limitations, including data redundancy and database compatibility challenges. Despite advancements in the field, the principle of “garbage in, garbage out” (GIGO) shows that input quality and method choice are critical for reliable and biologically meaningful results. Methodological standardization, scalability improvements, and integration with diverse data sources remain areas for further development. By providing critical guidance with contextual examples such as TNF, we aim to help researchers align their objectives with the appropriate method. Advancing pathway analysis interpretation will further enhance the utility of pathway analysis, ultimately propelling progress in systems biology and personalized medicine.

Schizophrenia is a devastating neuropsychiatric disorder associated with the dysregulation of glutamate and dopamine neurotransmitter systems. The adenosine system is an important neuroregulatory system in the brain that modulates glutamate and dopamine signaling via the ubiquitously expressed adenosine receptors; however, adenosine A₁ and A_2A receptor (A₁R and A_2AR) mRNA expression is poorly understood in specific cell subtypes in the frontal cortical brain regions implicated in this disorder. In this study, we assayed A₁R and A_2AR mRNA expression via qPCR in enriched populations of pyramidal neurons, which were isolated from postmortem anterior cingulate cortex (ACC) tissue from schizophrenia (n = 20) and control (n = 20) subjects using laser microdissection (LMD). A₁R expression was significantly increased in female schizophrenia subjects compared to female control subjects (t₍₁₃₎ = −4.008, p = 0.001). A₁R expression was also significantly decreased in female control subjects compared to male control subjects, suggesting sex differences in basal A₁R expression (t₍₁₇₎ = 2.137, p = 0.047). A significant, positive association was found between dementia severity (clinical dementia rating (CDR) scores) and A_2AR mRNA expression (Spearman’s r = 0.424, p = 0.009). A_2AR mRNA expression was significantly increased in unmedicated schizophrenia subjects, suggesting that A_2AR expression may be normalized by chronic antipsychotic treatment (F_(1,14) = 9.259, p = 0.009). Together, these results provide novel insights into the neuronal expression of adenosine receptors in the ACC in schizophrenia and suggest that receptor expression changes may be sex-dependent and associated with cognitive decline in these subjects.

The disruption of brain energy metabolism, leading to alterations in synaptic signaling, neural circuitry, and neuroplasticity, has been implicated in severe mental illnesses such as schizophrenia, bipolar disorder, and major depressive disorder. The therapeutic potential of ketogenic interventions in these disorders suggests a link between metabolic disturbances and disease pathology; however, the precise mechanisms underlying these metabolic disturbances, and the therapeutic effects of metabolic ketogenic therapy, remain poorly understood. In this study, we conducted an in silico analysis of transcriptomic data to investigate perturbations in metabolic pathways in the brain across severe mental illnesses via gene expression profiling. We also examined dysregulation of the same pathways in rodent or cell culture models of ketosis, comparing these expression profiles to those observed in the disease states. Our analysis revealed significant perturbations across all metabolic pathways, with the greatest perturbations in glycolysis, the tricarboxylic acid (TCA) cycle, and the electron transport chain (ETC) across all three disorders. Additionally, we observed some discordant gene expression patterns between disease states and ketogenic intervention studies, suggesting a potential role for ketone bodies in modulating pathogenic metabolic changes. Our findings highlight the importance of understanding metabolic dysregulation in severe mental illnesses and the potential therapeutic benefits of ketogenic interventions in restoring metabolic homeostasis. This study provides insights into the complex relationship between metabolism and neuropsychiatric disorders and lays the foundation for further experimental investigations aimed at appreciating the implications of the present transcriptomic findings as well as developing targeted therapeutic strategies.