Transcranial magnetic stimulation, a non-invasive neuromodulation technique, uses magnetic pulses to influence neural activity in specific brain regions. Since receiving FDA approval in 2008 for major depressive disorder, TMS has grown in clinical utility, particularly for patients unresponsive to traditional pharmacological and psychotherapeutic interventions. Historically, determining the target site for TMS has relied on surface anatomical landmarks on the scalp, which serve as proxies for underlying brain structures. While pragmatically sound and easily accessible for widespread clinical use, this traditional approach lacks customization to the patient’s unique brain circuitry, potentially limiting therapeutic gains.

The innovation introduced by this study lies in leveraging functional magnetic resonance imaging (fMRI) to identify individualized treatment targets based on resting-state functional connectivity. This imaging modality measures synchronized activity patterns amongst disparate brain regions while subjects are at rest. By parsing these connectivity networks, the research team pinpointed precise loci within the brain circuits implicated in depression, thereby refining the spatial accuracy of stimulation. This neuroimaging foundation enables a more tailored intervention that accounts for the heterogeneity of depression at the circuit level.

Of particular note is the application of accelerated TMS (aTMS), which compresses multiple treatment sessions into a single day, thereby shortening the overall treatment course from several weeks to a mere week. This intensification not only improves patient convenience but may enhance neurobiological receptivity to stimulation by delivering more frequent pulses within a condensed timetable. The study set out to compare the clinical outcomes of aTMS when targets were defined by fMRI connectivity versus the established scalp-based targeting.

The trial enrolled 40 adult participants with moderate to severe treatment-resistant major depression, spanning a broad age range of 22 to 80 years. Each individual underwent pre-treatment fMRI scanning to delineate functional connectivity profiles. Subsequently, subjects were randomized to receive aTMS directed either at their individualized connectivity-based target or the conventional scalp-based target. Crucially, both patients and clinical raters were blinded to group assignments to mitigate bias.

One month post-treatment assessments revealed that the group receiving connectivity-guided aTMS demonstrated significantly greater alleviation of depressive symptoms compared to their scalp-based counterparts. These improvements were quantified using the Montgomery-Åsberg Depression Rating Scale (MADRS), a gold-standard clinician-administered instrument that sensitively captures changes in depression severity. Furthermore, the response rate — defined by clinically meaningful symptom reduction — was markedly higher in the connectivity group, with 80% responding versus 60% in the traditional targeting group, underscoring the potential clinical advantage of imaging-informed intervention.

This research builds upon prior explorations led by Joseph Taylor and colleagues, including investigations into imaging-based modulation of anxiety circuits within depressive populations, as recently reported in Molecular Psychiatry. These cumulative findings lend prospective support to the concept that precision neuroimaging can transcend theoretical neuroscience and play a direct role in augmenting therapeutic outcomes.

Taylor emphasizes the significance of closing the gap between neuroimaging research and tangible clinical benefit. Historically, the complexity and additional cost associated with imaging have created barriers to its routine clinical adoption for TMS guidance. This study represents a crucial step toward justifying such investment by empirically demonstrating a quantifiable benefit above conventional practice, a vital incentive for healthcare providers and payers considering integration of this technology.

Despite the promising results, the authors acknowledge particular study limitations, including the modest sample size and single-center study design, which may affect generalizability. They advocate for larger, multi-site trials to validate these early findings and explore durability of treatment effects over extended follow-up periods. Broadening the participant demographics will also be essential to ascertain the utility of connectivity-based targeting across diverse patient populations and varying clinical subtypes of depression.

This trial’s implications extend beyond depression, suggesting that functional brain imaging could inform individualized treatment strategies for a range of psychiatric disorders treatable by neuromodulation, including anxiety, obsessive-compulsive disorder, and post-traumatic stress disorder. As aTMS and neuroimaging technologies continue to evolve and become more accessible, the integration of connectivity-guided targeting holds promise for ushering in a new era of personalized psychiatry grounded in neurobiological precision.

In conclusion, the Mass General Brigham team’s randomized controlled trial provides compelling evidence that connectivity-based targeting via functional MRI can substantially enhance the antidepressant impact of accelerated TMS treatment in individuals with refractory depression. This approach offers a paradigm shift toward precision-guided neuromodulation, with the potential to improve patient outcomes and redefine clinical standards for brain stimulation therapies in psychiatric care.

Subject of Research: People

Article Title: Connectivity- versus scalp-based targeting of accelerated TMS for depression: A randomized trial

Web References:

• https://jamanetwork.com/journals/jamapsychiatry/fullarticle/10.1001/jamapsychiatry.2026.1100
• https://www.massgeneralbrigham.org/en/about/neuroscience-institute
• https://www.massgeneralbrigham.org/en/about/complex-psychiatric-care

References:
Taylor, J. et al. “Connectivity- versus scalp-based targeting of accelerated TMS for depression: A randomized trial,” JAMA Psychiatry, DOI: 10.1001/jamapsychiatry.2026.1100

Keywords:
Depression, Transcranial magnetic stimulation, Accelerated TMS, Functional magnetic resonance imaging, Functional connectivity, Neuroimaging-guided neuromodulation, Treatment-resistant depression, Personalized psychiatry, Montgomery-Åsberg Depression Rating Scale

Until recently, early identification of individuals at risk for psychosis relied heavily on clinical interviews and subjective assessments, which often led to delayed diagnosis and treatment. The promise of neuroimaging to provide objective, biological signatures of impending psychotic episodes has long been a scientific aspiration. Ambrosen and colleagues have now demonstrated that measuring the brain’s functional connectivity—a map of synchronized activity across disparate brain regions—provides a robust predictive tool for distinguishing individuals who are in an ultra-high risk (UHR) state from healthy controls.

The study employed resting-state functional magnetic resonance imaging (rs-fMRI) to capture spontaneous brain activity in participants. Resting-state connectivity taps into intrinsic neural network interactions, offering insights into how different regions communicate when the brain is not engaged in any specific task. By analyzing these connectivity profiles on a whole-brain scale, the researchers circumvented the limitations of focusing on isolated brain areas. Instead, they embraced the complexity of neural circuits whose dysregulation is believed to underlie psychosis.

Advanced machine learning algorithms were harnessed to decode the complex connectivity matrices generated from rs-fMRI data. These computational models sifted through the high-dimensional neural data to identify patterns that uniquely characterize the UHR group. The result was a classification framework capable of distinguishing UHR individuals with remarkable accuracy. Such predictive modeling is particularly notable given the heterogeneous nature of psychosis risk, which encompasses a spectrum of cognitive, perceptual, and emotional symptoms.

Moreover, the connectivity patterns were not only predictive of psychosis risk status but also correlated with participants’ level of functioning. Functional outcome measures, often neglected in biomarker research, are crucial clinical endpoints, reflecting an individual’s ability to maintain social relationships, employment, and daily living skills. The finding that whole-brain connectivity could anticipate functional status provides dual clinical utility—early identification and prognostication of functional decline.

The neural circuits implicated in this study span canonical networks involved in cognitive control, sensory processing, and default mode functioning. Disruptions in these networks have been recurrent themes in psychosis research but rarely integrated comprehensively. The current work’s whole-brain approach underscores that it is the dysregulation of network integration and segregation—how brain regions cohere and segregate dynamically—that constitutes the neural fingerprint of psychosis vulnerability.

This study employed rigorous inclusion criteria to define the UHR population, incorporating attenuated psychotic symptoms, brief intermittent psychotic episodes, and a significant functional decline coupled with genetic risk. The meticulous characterization strengthens the translational potential of the findings by ensuring that connectivity signatures are not confounded by diagnostic heterogeneity. Longitudinal follow-up within the cohort further enabled validation of connectivity features in predicting transition to full-blown psychosis and preemptive intervention planning.

One of the unique contributions of the research is the integration of functional connectivity with clinical assessments and neuropsychological testing. This multi-modal approach revealed that certain connectivity disruptions corresponded to specific symptom clusters, such as perceptual abnormalities and executive dysfunction. Such specificity suggests that targeted modulation of network activity, through neuromodulation or cognitive rehabilitation, could tailor therapeutic strategies according to individual connectivity profiles.

From a methodological standpoint, the study leveraged novel analytic frameworks including graph theoretical measures and network control theory to elucidate the topological properties of neural networks in UHR individuals. These frameworks move beyond simple correlational analyses, enabling inference about network resilience, information flow efficiency, and their perturbations in psychosis. Such biophysical interpretations deepen our understanding of the neural underpinnings of psychiatric phenomena.

Notably, the research team addressed challenges related to inter-individual variability by applying normalization and dimensionality reduction techniques to ensure that comparable connectivity features were analyzed across subjects. This approach enhances the reproducibility and generalizability of the findings, critical for the potential deployment of such biomarkers in clinical practice. The study also discusses the implications of scanner differences and head motion artifacts, underscoring the importance of rigorous preprocessing pipelines in neuroimaging studies.

The implications of this research extend beyond psychosis to inform the broader domain of psychiatric neuroscience, where heterogeneity and symptom overlap often blur diagnostic boundaries. Functional connectivity as a transdiagnostic biomarker may enable phenotypic refinement and individualized predictions across mental disorders characterized by network dysfunctions, such as mood disorders and autism spectrum conditions. Therefore, the impact of this study resonates well into future psychiatric research paradigms.

Clinically, the findings suggest that non-invasive neuroimaging could be integrated into routine screening protocols for individuals presenting with subthreshold psychotic symptoms. Early detection facilitated by brain connectivity signatures may prompt timely interventions including psychotherapy, pharmacological treatment, or neuromodulation, potentially altering the disease trajectory. This aligns with the growing emphasis on preventive psychiatry and personalized treatment models, which aim to mitigate the debilitating sequelae of psychosis.

Furthermore, the research opens intriguing avenues for the development of novel therapeutics targeting network connectivity alterations. Pharmacological agents and brain stimulation techniques designed to restore network balance and enhance functional integration may emerge as precision tools to boost cognition and functional capacity in at-risk populations. Understanding the mechanistic pathways that lead from network dysfunction to clinical manifestations remains a priority to translate neuroimaging findings into treatment breakthroughs.

As with all pioneering investigations, the authors acknowledge limitations including sample size constraints and the need for replication in diverse populations to account for genetic, demographic, and environmental heterogeneity. Future studies incorporating multimodal imaging, such as combining structural MRI and diffusion tensor imaging with functional data, may enrich the predictive models. Additionally, expanding longitudinal cohorts will clarify the temporal evolution of connectivity changes relative to symptom onset and remission.

This landmark study by Ambrosen, Kristensen, Glenthøj, and colleagues sets a compelling precedent for integrating functional neuroimaging biomarkers with computational modeling to transform psychiatric diagnostics. The demonstrated ability to predict ultra-high risk for psychosis and levels of functioning using whole-brain connectivity patterns may herald a new era where mental illnesses are understood and treated through the lens of dynamic brain network architecture rather than symptom-based categories.

In conclusion, the convergence of advanced neuroimaging, machine learning, and clinical psychiatry exemplified in this research offers a transformative vision for the future of mental health care. By decoding the brain’s connectivity fingerprints, clinicians may soon possess powerful tools for early identification, personalized intervention, and improved functional outcomes for individuals teetering on the brink of psychosis. This shift toward a biological framework rooted in whole-brain dynamics signals an exciting frontier at the vanguard of neuropsychiatric research.

Subject of Research: Prediction of ultra-high risk for psychosis and functional outcomes using whole-brain functional connectivity

Article Title: Whole-brain functional connectivity predicts ultra-high risk for psychosis status and level of functioning

Article References:
Ambrosen, K.S., Kristensen, T.D., Glenthøj, L.B. et al. Whole-brain functional connectivity predicts ultra-high risk for psychosis status and level of functioning. Schizophrenia (2026). https://doi.org/10.1038/s41537-025-00685-z

Image Credits: AI Generated