Transcranial magnetic stimulation (TMS) is a widely used neuromodulation treatment for depression, but its mechanisms are poorly understood. Indirect clinical evidence suggests that TMS enhances plasticity within the prefrontal cortical target site and engages downstream networks. However, establishing causal mechanisms to help optimize the large stimulation parameter space has been challenging. Using an optogenetic model of accelerated intermittent theta burst stimulation (prelimbic [PL]-aiTBS) that drives rapid antidepressant-like effects, we examined cell type-specific effects on synapse-related gene expression, increased spine density, and increased excitatory currents in prefrontal intratelencephalic projection neurons. Whole-brain c-Fos immunolabeling, fiber photometry, chemogenetic, and projection-specific optogenetic manipulations revealed that PL-aiTBS activates a fronto-insular network that is necessary and sufficient for its antidepressant-like behavioral effects. Finally, we validate a key role for fronto-insular connectivity and TMS-evoked responses in the human insula using intracortical stereo-electroencephalogram (EEG) and resting-state fMRI. These results establish a fronto-insular circuit as a critical mediator of the antidepressant effects of aiTBS.

Task fMRI1 and electrophysiology2 have revealed distributed, linked cortical patches with shared category preferences (e.g., faces, objects, places)1,3-5, smaller than cytoarchitectonic areas. Resting-state functional connectivity (RSFC) similarly showed that somato-cognitive action network (SCAN) nodes interleave with effectors (foot, hand, mouth), subdividing the precentral gyrus6. Here, using multiple precision functional mapping (PFM) modalities (RSFC, task, lags), we discovered that most of association cortex is organized like face processing and SCAN, with small, discrete patches interconnected into chains. Such patch-chains densely tile prefrontal cortex but are largely absent from primary cortex. Cortico-striatal connectivity is organized such that patches of the same chain connect to the same striatal location. Within chains, infra-slow fMRI signals are ordered in time. RSFC-defined chains align with task fMRI localizers (e.g., visual, motor, pain). Chains are absent at birth and emerge in the first year of life, suggesting their formation is at least partially experience-driven. Cytoarchitectonic areas are subdivided by patches, and patches in the same chain are distributed across different cytoarchitectures. Chains represent parallel ordered processing streams that are separated by information domain and behavioral goals, not cytoarchitectonics. Functional subdivision of architectonics into smaller patches, interlinked to form cross-architecture chains, enable greater parallelization and flexible specialization of processing.

Depression is driven by dysfunction in discrete neural circuits, but a deeper understanding of the underlying molecular and synaptic mechanisms is needed to guide the development of therapeutics. Here, we decipher the mechanisms of action of the fast-acting antidepressant ketamine to enable the identification of G protein-coupled receptor (GPCR) antidepressant targets. We find that the behavioral effects of ketamine rely on mu-opioid receptors (MORs), which are enriched in somatostatin-expressing interneurons (Sst+ INs) in the medial prefrontal cortex (mPFC). Chronic stress drives presynaptic hypertrophy of mPFC Sst+ INs and excessive inhibition of pyramidal neurons, which is rescued by ketamine. Motivated by these findings, we use RNA sequencing to identify mPFC Sst+ IN-enriched GPCRs and validate the antidepressant potential of promising targets. Synergistic targeting of multiple GPCRs enables potent antidepressant-like responses with reduced side effects. Together, these findings reveal a general approach to identifying therapeutic GPCR targets for brain disorders.

For decades, psychiatric neuroimaging has searched for biomarkers of depression and other disorders, but they remain elusive in clinical practice. While the last 5 years have seen rapid progress, other large-scale correlative studies have found only small, unreliable links between brain measures and clinical symptoms. Growing evidence suggests that such limitations are not just about sample size but depend critically on how models represent data. This review traces a recent shift away from univariate methods to multivariate/multiview approaches that learn more effective representations of biological and symptom measures by flexibly learning multimodal latent representations. First, we review how linear multiview embedding methods have revealed reproducible biological depression subtypes but do not perform well in small samples or samples enriched for mild symptoms. Then, we consider newer work exploring more sophisticated representations for neuroimaging data, including deep-learning and graph-based representations, and multimodal extensions that uncover complex latent patterns that single-modality studies miss. Then, we review recent developments in foundation models, which, once trained on large corpora, can "transfer learn" readily to small clinical cohorts, potentially bringing the advantages of large-scale learning to small, privacylimited data. Finally, we highlight emerging representation tools that treat the brain as a dynamic, stateful multivariate process. Taken together, these advances point to a future in which the value of neuroimaging will be determined not only by ever-larger sample sizes but also by data quality and by how well our algorithms capture the distributed, multimodal, and evolving nature of psychiatric disorders.

Precision functional mapping (PFM) enables the individual-level characterization of brain network organization but requires substantially more and higher-quality fMRI data than is standard. Despite the growing use of PFM, the objective criteria for data sufficiency and the quality needed to ensure interpretable and replicable individual-level results remain unclear. Here, we introduce the network similarity index (NSI), an objective measure of the extent to which functional connectivity (FC) patterns express the large-scale network structure required for PFM. The NSI captures low-spatial-frequency, coherent network organization and denoising fidelity, and it aligns closely with blinded expert assessments of PFM usability. The NSI also accounts for the variability in the rate at which FC becomes reliable across individuals. This NeuroResource provides an open source framework for NSI-based data quality evaluation and models linking NSI values with expert-judged PFM suitability. This framework can inform expected returns from additional data collection, thus enabling principled decisions about data sufficiency and replication in precision fMRI research.

Background:Repeated stress is a risk factor for developing motivational deficits which are common across a variety of disease states including depression and are particularly resistant to treatment with conventional pharmacotherapies. Amotivation is multifaceted and can be caused by impairments in value learning, reward anticipation, and cost-benefit decision-making. Importantly, not all individuals who experience chronic stress develop motivational symptoms, suggesting there may be neurobiological signatures of resilience. Methods:We developed a novel head-restrained effortful reinforcement task in which anticipatory and consummatory behavior can be tracked. Chronic non-discriminatory social defeat stress combined with behavioral analysis and spatially resolved RNA sequencing were used to determine the transcriptional signatures of stress in the anterior cingulate cortex of mice with varying levels of motivational impairment as well as unstressed controls. Results:While stress led to a general impairment in effortful reward seeking, animals differed in the extent of behavioral deficit, with increased 'susceptibility' marked by a unique set of differentially expressed genes within the anterior cingulate cortex (ACC). By leveraging the spatial component of our data, we were further able to identify altered interactions from inhibitory neurons and astrocytes to excitatory pyramidal cells, which correlated with intact or impaired motivated responding following stress exposure. Conclusions:Chronic psychosocial stress results in divergent effects on motivated behavior and distinct ACC transcriptional signatures that are concentrated in excitatory pyramidal neurons. Cell interaction analysis implicates enhanced inhibitory neuropeptide signaling and reduced astrocytic contact signaling as upstream markers of motivational resilience and point toward ACC hyperexcitability as a targetable feature of stress susceptibility.

Diffusion models excel at generation, but their latent spaces are high dimensional and not explicitly organized for interpretation or control. We introduce ConDA (Contrastive Diffusion Alignment), a plug-and-play geometry layer that applies contrastive learning to pretrained diffusion latents using auxiliary variables (e.g., time, stimulation parameters, facial action units). ConDA learns a low-dimensional embedding whose directions align with underlying dynamical factors, consistent with recent contrastive learning results on structured and disentangled representations. In this embedding, simple nonlinear trajectories support smooth interpolation, extrapolation, and counterfactual editing while rendering remains in the original diffusion space. ConDA separates editing and rendering by lifting embedding trajectories back to diffusion latents with a neighborhood-preserving kNN decoder and is robust across inversion solvers. Across fluid dynamics, neural calcium imaging, therapeutic neurostimulation, facial expression dynamics, and monkey motor cortex activity, ConDA yields more interpretable and controllable latent structure than linear traversals and conditioning-based baselines, indicating that diffusion latents encode dynamics-relevant structure that can be exploited by an explicit contrastive geometry layer.

Classic psychedelics typically act at the serotonin 5-HT2A receptor to profoundly alter brain function and consciousness. Research on these compounds has accelerated. Major strides have been made in understanding their unique mechanisms of action and clinical potential. This Review outlines the state of psychedelic science, spanning cellular mechanisms, systems neuroscience and clinical investigation. We show that preclinical and human research findings converge on two complementary processes: acute neural desynchronization, which destabilizes entrenched network patterns, and subacute neuroplasticity, which opens a window for psychological and behavioral change. We review evidence of therapeutic response across neuropsychiatric indications and consider how this integrates with mechanistic findings. We also explore challenges and opportunities, including discrepancies between preclinical evidence that non-hallucinogenic psychedelic analogs engage putative therapeutic mechanisms, and clinical evidence linking the subjective experience to therapeutic response; the risks inherent to enhanced neuroplasticity; and questions surrounding trial design, scalability and regulatory approval. The growth of psychedelic science and medicine may compel a fundamental rethinking of the relationship between subjective experience and biological change in psychiatry.

Cocaine use disorder (CUD) detrimentally impacts personal health, social relationships, and economic opportunity. Here, we assess CUD-associated shifts in brain dynamics using Network Control Theory and examine how they align with previously identified changes in neurological systems and behavioral profiles of people with CUD. The SUDMEX CONN dataset consists of multi-modal MRI, cocaine use metrics, behavioral measures, and demographics of individuals with CUD (N=132, 71 CUD). We identified recurring brain activity states and used NCT to calculate the transition energy (TE) between pairs of states. ANCOVAs examined global and regional TE associations with drug use group (CUD vs controls (NC)), years of CUD, and risk-taking behaviors. We identified potential mechanisms driving the differences by correlating CUD-related regional TE effects with neurotransmitter/receptor systems. People with CUD had significantly lower global TE and default mode, dorsal attention and limbic network TE compared to non-user controls, particularly in regions enriched for noradrenaline and mu opioid receptors. Longer duration of CUD was associated with more decreased global TE, top-down TE, default mode, control and ventral attention network TE, and regional TE enriched for excitatory neurotransmitters and receptors. People with CUD needed to expend more global and top-down TE to perform better on a risk-taking task (the Iowa Gambling task), an effect which was not found in NCs. Our analysis of whole-brain activity dynamics provides a link between the effects of upstream glutamatergic excitotoxicity and/or opioid receptor dysfunction, and downstream weakening of inhibitory control that is central to CUD. ### Competing Interest Statement The authors have declared no competing interest.