Jianxun Ren

Personalized functional imaging using fMRI is a paradigm shift in neuroscience but demands extensive data per participant. Functional near-infrared spectroscopy (fNIRS) is a cost-effective alternative, yet its ability to capture individual-specific functional architecture with adequate precision remains unproven. To assess fNIRS for personalized mapping, we charted functional organization in nine individuals using task-based and resting-state fNIRS alongside fMRI. Each participant contributed 11.5 hours of imaging data, including 4 hours of resting-state and 4.2 hours of task-based (motor, working memory, language) fNIRS data, and 3.3 hours of matched fMRI for cross-validation. Results show fNIRS reliably identifies individual-specific resting-state networks, requiring only 20 minutes of data for stable characterization. Additionally, fNIRS captures subject-specfic activation patterns in sensorimotor and higher-order domains, including atypical right-lateralized language dominance. These personalized fNIRS profiles align strongly with fMRI, offering comparable reliability at far lower cost. fNIRS proves a reliable, scalable, and cost-efficient tool for personalized brain mapping.

Neuromodulation approaches can target specific brain functional circuits to treat various brain disorders, and resting-state functional magnetic resonance imaging is increasingly being used to map functional circuits in individual patients for target localization. However, this strategy faces three major hurdles for clinical translation: time-intensive resting-state functional magnetic resonance imaging processing, insufficient robustness for clinical use and a lack of automated targeting solutions. Here we cover the use of a generic, rapid, robust and automated platform for personalized functional circuit-guided target planning across different neuromodulation techniques, termed Unified Platform for Neuromodulation with Individualized Target Estimation (UNITE). UNITE is computationally efficiency and robust and uses DeepPrep, a recently developed end-to-end preprocessing pipeline that achieves a tenfold speedup and improved robustness compared with state-of-the-art clinical pipeline. Using DeepPrep's preprocessed data, UNITE maps functional circuits and localizes personalized targets through automated targeting algorithms. Currently, UNITE offers three built-in transcranial magnetic stimulation-targeting algorithms for post-stroke functional rehabilitation and one focused ultrasound stimulation-targeting algorithm for Parkinson's disease. Notably, it is designed to be adapted for other brain disorders and neuromodulation applications. To ensure quality and usability, UNITE generates a data quality control report and a visual verification report of the identified targets, with target coordinates exported in multiple formats for compatibility with different neuronavigation systems. Overall, this platform enables personalized neuromodulation target localization within 30 min on a local graphics processing unit-equipped workstation. A basic level of computer science proficiency, along with neuroimaging experience, is sufficient for its use.

Lesion network mapping (LNM) is a powerful tool for linking distributed brain lesions to symptom-relevant networks, but similar LNM maps across unrelated symptoms have raised concerns about its statistical validity and specificity. Here we introduce robust LNM (rLNM), a statistically principled framework that combines two complementary controls: an anatomy-preserving spatial null model to account for connectome-derived hub bias shared across symptoms, and hypothesis-driven contrasts to resolve selective specificity among related symptoms. rLNM effectively controls both false positives and negatives. Across 10 lesion datasets (384 lesions) and two task-fMRI meta-analyses (339 experiments), rLNM recovers distinct symptom-specific networks (mean r = 0.044), which show significantly reduced similarity to the connectome hub pattern ( r = 0.12 vs. 0.33 in conventional LNM). In 56 patients with neurosurgical lesions, rLNM predicts postoperative language decline at the single-patient level. rLNM thus resolves these concerns and lays a unified statistical foundation for network-symptom inference.

In September 2025, the Organization for Human Brain Mapping (OHBM) organised its first virtual satellite conference in collaboration with the OHBM Sustainability and Environmental Action Special Interest Group (SEA-SIG). This three-day gathering of neuroimagers from around the world was an online complement to the in-person meeting. It had a similar content structure, featuring a half-day educational course, a keynote address from the 2025-2026 OHBM Council Chair Professor Michael Breakspear, three symposia sessions, and an OHBM Special Interest Groups showcase. Here, we summarise the content of this first-of-its-kind event, the benefits for accessibility and sustainability, and provide an outlook for future OHBM accessible and sustainable conference practices.

Determining the functional organization of the auditory cortex (AC) has been difficult with conventional task-based approaches due to the broad responsiveness of auditory subregions to various acoustic properties. Moreover, most studies have investigated functional organization of AC with static methods, although brain has shown to be organized into dynamic networks. Here, we investigated dynamically varying coactivation patterns of the local networks in the auditory cortex (AC) determined from 7T fMRI data. Dynamic AC patterns successfully captured interindividual variability, as indicated by significantly higher variability between than within individuals for the AC pattern occurrence rates and spatial topographies. The coactivation patterns shared similarities between resting-state and auditory-task data, as indicated by the group-level similarity of 0.84 and individual-level similarity of 0.71 in the spatial topographies. Furthermore, the occurrence rates of AC patterns identified in the task data, using pattern templates derived from resting-state data, correlated with specific task contrast regressors. Our results suggest that the AC function can be characterized by a set of dynamically varying coactivation patterns. These patterns are consistently observed during resting state and auditory stimulation, and they become synchronized with auditory inputs. These findings enhance our understanding of the relationship between spontaneous and stimulus-driven activity in the AC and support the development of more time-efficient paradigms for studying its functional organization.

Parkinson’s disease (PD) is an incurable neurological disorder that often begins insidiously with sleep disturbances and somatic symptoms, progressing to whole-body motor and cognitive symptoms^1,2,3,4,5. Dysfunction of the somato-cognitive action network (SCAN)—which is thought to control action execution^6,7 by coordinating arousal, organ physiology and whole-body motor plans with behavioural motivation—is a potential contributor to the diverse clinical manifestations of PD. To investigate the role of the SCAN in PD pathophysiology and treatments (medications, deep-brain stimulation (DBS), transcranial magnetic stimulation (TMS) and MRI-guided focused ultrasound stimulation (MRgFUS)), we built a large (n = 863), multimodal, multi-intervention clinical imaging dataset. Resting-state functional connectivity revealed that the substantia nigra and all PD DBS targets (subthalamic nucleus, globus pallidus and ventral intermediate thalamus) are selectively connected to the SCAN rather than to effector-specific motor regions. Importantly, PD was characterized by specific hyperconnectivity between the SCAN and the subcortex. We therefore followed six PD cohorts undergoing DBS, TMS, MRgFUS and levodopa therapy using precision resting-state functional connectivity and electrocorticography recording. Efficacious treatments reduced SCAN-to-subcortex hyperconnectivity. Targeting the SCAN instead of effector regions doubled the efficacy of TMS treatments. Focused ultrasound treatment benefits increased when the target was closer to the thalamic SCAN sweet spot. Thus, SCAN hyperconnectivity is central to PD pathophysiology and its alleviation is a hallmark of successful neuromodulation. Targeting functionally defined subcortical SCAN nodes may improve existing therapies (DBS, MRgFUS), whereas cortical SCAN targets offer effective non-invasive or minimally invasive neuromodulation for PD.

Gliomas tend to arise in specific brain regions and may integrate into functional circuits, suggesting they could be regulated by brain activity. However, it remains unclear whether glioma growth is related to system-level brain networks. Analyzing neuroimaging data from three datasets including 1,310 patients with cerebral gliomas, we identified and replicated a functionally connected glioma network, which overlaps with the action-mode network (AMN), somatomotor network (SMN), and action-related subcortical regions. Resting-state functional connectivity (RSFC) of the AMN successfully predicted the location of glioma occurrence in two independent datasets with complex tumor distributions. Remarkably, no patient had a glioma entirely outside the AMN, and over 89% of patients exhibited gliomas with at least 50% overlap with the network. Moreover, the spatial overlap between glioma location and the AMN demonstrated significant prognostic value in survival analyses, with higher AMN-tumor overlap associated with poorer overall survival. Notably, the acetylcholine transporter, a key player in glioma pathogenesis that drives transcriptional reprogramming, showed an expression pattern overlapping with the AMN. Meta-analytic annotations further linked the glioma network to processes of action initiation, execution, and feedback. These findings indicate that gliomas preferentially arise in circuits involved in action and highlight the central role of the AMN in glioma pathophysiology and growth.

Introduction: Cognitive impairment is a common consequence after stroke. Intermittent theta burst stimulation (iTBS) has emerged as a promising cognitive therapy. However, traditional iTBS typically employs lower doses and one-size-fits-all stimulation targets, which may not fully capitalise on the potential of this therapy and warrants further evaluation for both efficacy and safety. This study aims to evaluate the efficacy and safety of high-dose iTBS targeting the individualised frontoparietal cognitive network (FCN) identified by precision functional neuroimaging for post-stroke cognitive impairment (PSCI). Methods and analysis: This is a prospective, double-blind, sham-controlled, parallel-group randomised controlled trial. 60 eligible participants with PSCI will be randomly assigned (1:1) to an active iTBS or a sham-controlled group. The active group will receive high-dose iTBS (3600 pulses/day) at 80% resting motor threshold targeting the left individualised FCN, guided by a real-time neuronavigation system. The sham group will follow identical procedures using a sham coil. Both groups will also undergo conventional computerised cognitive training. The intervention will be administered on workdays over a period of 3 weeks, totalling 15 workdays. The primary outcome is the change in Montreal Cognitive Assessment scores from baseline to immediately post-treatment. Secondary outcomes include long-term change in global cognition, activities of daily living and specific cognitive domains (assessed by a comprehensive neuropsychological battery covering memory, attention, executive function and language), as well as mood. Assessments occur at baseline, post-treatment and 3-month follow-up. Safety outcomes, specifically the number of adverse events related to iTBS, will be monitored and recorded throughout the trial. Ethics and dissemination: This study has been approved by the Medical Ethics Committee of the China Rehabilitation Research Center. The results of this study will be published in peer-reviewed scientific journals and disseminated at academic conferences. Trial registration number: NCT05953415 .

A pervasive dilemma in brain-wide association studies¹ (BWAS) is whether to prioritize functional magnetic resonance imaging (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant). The model explains empirical prediction accuracies well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R ² = 0.89), spanning diverse scanners, acquisitions, racial groups, disorders and ages. For scans of ≤20 min, accuracy increases linearly with the logarithm of the total scan duration, suggesting that sample size and scan time are initially interchangeable. However, sample size is ultimately more important. Nevertheless, when accounting for the overhead costs of each participant (such as recruitment), longer scans can be substantially cheaper than larger sample size for improving prediction performance. To achieve high prediction performance, 10 min scans are cost inefficient. In most scenarios, the optimal scan time is at least 20 min. On average, 30 min scans are the most cost-effective, yielding 22% savings over 10 min scans. Overshooting the optimal scan time is cheaper than undershooting it, so we recommend a scan time of at least 30 min. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-to-whole-brain BWAS. In contrast to standard power calculations, our results suggest that jointly optimizing sample size and scan time can boost prediction accuracy while cutting costs. Our empirical reference is available online for future study design (https://thomasyeolab.github.io/OptimalScanTimeCalculator/index.html).