Hesheng Liu

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.

Autism Spectrum Disorder (ASD) presents a substantial global challenge, yet no pharmacological treatments effectively target its core symptoms, especially in individuals with severe cognitive or adaptive impairments. This double-blind, sham-controlled, randomized clinical trial assessed accelerated intermittent theta burst stimulation (iTBS) targeting the personalized fronto-parietal network (FPN) in ASD. Participants (6–30 years) were randomized in a 2:1 ratio to active or sham iTBS (three daily sessions, 1800 pulses/session) over 12 weeks (324k pulses) alongside behavioral training. The primary outcome was defined as the response rate, charaterized by a ≥ 1-point reduction in ADOS-2 SA at week 12. Of 132 individuals screened, 67 were randomized (mean age 10.04 ± 4.22 years; 88.1% male; all with cognitive/adaptive delays), with 59 completing the study. Active iTBS resulted in a significantly higher response rate (55% vs. 29%) and higher symptom improvement than sham (cohen’s d = −0.53), with mild local pain in only 5% of iTBS group. In the profound autism subgroup, the active group exhibited language improvement alongside amelioration of core symptoms. These findings suggest that prolonged, accelerated FPN-targeted iTBS is a safe and efficacious intervention for severe ASD, offering a promising therapeutic approach. Registration ClinicalTrials.gov Identifier: NCT05890846

Sleep is an essential biological process that restores the brain. Although numerous advances have been made in uncovering the circuits regulating the sleep-wake cycle, the sleep-related dynamic reconfiguration of brain functional connectome topography remains unknown. Here, we collected simultaneous electroencephalogram and functional magnetic resonance imaging data, along with multiple behavioral assessments, from 130 healthy adults during nocturnal sleep. This rich dataset enabled us to construct a large-scale, sleep-state-dependent suite of system-level brain atlases and to establish their associations with sleep quality and pressure. We found that the action-mode network acts as a highly dynamic system that contracts as sleep deepens, most prominent in subcortical structures. Similar contractions were observed in humans and macaques under anesthesia, reflecting cross-species homologues of network reorganization during low-arousal states. Furthermore, the topological reconfiguration of the action-mode network is a stable biological marker that distinguishes sleep stages, reflects sleep quality, and predicts sleep pressure. Together, our work identifies the action-mode network as a dynamic core that reconfigures across the sleep-wake cycle, potentially serving as a neuro-target for intervening sleep disorders and tracking brain restoration.

This study investigates age-related changes in diffusion-relaxation coupling within basal ganglia nuclei using a novel single-scan multi-TE diffusion-weighted imaging (SMT-DWI) approach, with a focus on microstructural alterations associated with regional iron deposition patterns during normal brain aging. Fifty-seven healthy participants (10–73 years) underwent optimized SMT-DWI at 3T, simultaneously acquiring multi-b-value (0/500/1000 s/mm²) and multi-TE (53/71/89 ms) data in 7:58 minutes (1.5 × 1.5 × 4 mm³ resolution). Apparent diffusion coefficient (ADC) and R2 maps were generated through mono-exponential fitting, with coupling quantified via TE-dependent ADC (kADC/TE) and b-value-dependent R2 (kR2/b) metrics. Iron-related tissue properties were assessed using R2 at b = 0 (R2b = 0) as a surrogate. The SMT-DWI method achieved excellent image quality (e.g., SNR = 63.8 under the most unfavorable conditions of b = 1000 s/mm² and TE = 89 ms) with good anatomical delineation. Young subjects exhibited positive coupling (ADC increasing with TE, R2 rising with b-value) across basal ganglia, while older adults showed progressive inversion to negative coupling that correlated strongly with age after accounting for sex as a covariant (sex was not a significant predictor; all p > 0.05). This transition was the most pronounced in iron-rich putamen (p < 0.001, R2b = 0 = 0.0208 ± 0.0027 ms−¹), globus pallidus (p < 0.001, R2b = 0 = 0.0276 ± 0.0030 ms−¹), and substantia nigra (p < 0.001, R2b = 0 = 0.0241 ± 0.0023 ms−¹), while the caudate maintained stable coupling (p > 0.05) with lower iron levels (R2b = 0 = 0.0169 ± 0.0012 ms−¹). All four regions demonstrated significant negative correlations between coupling metrics and R2b = 0 (r = -0.38 to -0.75, all p < 0.01), consistent with a role for iron-mediated microstructural changes in driving the observed coupling shifts. The age-dependent inversion from positive to negative diffusion-relaxation coupling reflects regionally heterogeneous microstructural alterations in basal ganglia. Our method provides sensitive detection of compartment-sensitive microstructural alterations, offering new insights into normal brain aging and a potential biomarker for neurodegenerative risk assessment.

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.