Biology subjects

Ji, G.-J.

Publications and source records attributed to Ji, G.-J..

4 recordsLinked to original sources

bioRxiv2026

Human brain white matter charts in international populations

Brain charts provide normative references for identifying neurodevelopmental milestones and clinically meaningful deviations. Existing white matter (WM) charts are derived predominantly from European and North American populations, limiting generalizability. Here, we established the first set of population-specific WM charts of 198 morphological and microstructural phenotypes for both Chinese (N=19,215) and Western (N=15,584) populations across the human lifespan. Developmental trajectories and spatial organization of WM maturation and degeneration were broadly conserved across the populations, whereas maturational timing varied across pathways and metrics. The Chinese population showed earlier maturation in several projection and sensorimotor pathways, but more protracted maturation in higher-order association and limbic pathways. Chinese population-matched charts more accurately predicted WM phenotypes and detected more extreme disease-related deviations in brain health in the Chinese population than Western-derived models. Individual deviation patterns further discriminated patients from healthy controls and predicted cognitive performance and plasma biomarkers. Our findings reveal population-specific WM maturation and show that reference-population mismatch can reduce the accuracy and clinical sensitivity of normative brain charts.

neuroscience↗

bioRxiv2026

Non-invasive Neuromodulation Targeting Approach by Mapping Stimulations and Lesions That Modify Visual Memory

Therapeutic brain stimulation is believed to target specific networks, but targeting approaches for memory remain debated. For other symptoms, neuromodulation targets have been localized by mapping connectivity of lesions and stimulation sites to specific symptoms. This approach has yielded networks for global memory, but it remains unclear whether it applies to specific types of memory. Here, we mapped connectivity of stimulation sites, lesions, and atrophy patterns associated with different memory types. We included 544 individuals across three datasets: transcranial magnetic stimulation (N=262), penetrating head trauma (N=169), and ischemic stroke (N=113). We identified a network preferentially connected to lesions and stimulation sites specifically associated with changes in visual memory. Of note, the direction of this effect was inverted depending on whether lesions or stimulation occurred at younger age or an older age, consistent with prior results. This age effect was replicated in an independent dataset of patients with preclinical Alzheimers disease (N=1240). To examine neuromodulation targets, we computed electrical field models for potential TMS sites that overlap with the networks derived from each stimulation or lesion dataset; the resulting targets intersected with established targets that demonstrated efficacy for treating memory impairment - precuneus, cortical-hippocampal network, and dorsolateral prefrontal cortex - with peak intersection at medial posterior parietal lobe, angular gyrus, and left anterior middle frontal gyrus, respectively. Future head-to-head clinical trials are needed to systematically compare these proposed neuromodulation targets against each other. One Sentence SummaryNeuromodulation targets for visual memory diverge by age at the time of injury or stimulation.

neuroscience↗

bioRxiv2026

The diversity of lesion network mapping findings

Lesion network mapping (LNM) has emerged as a popular framework to map the network mechanism of brain disorders using lesions and normative brain connectome (NBC)1. It was first demonstrated in neurological symptoms and was rapidly extended to a broad range of brain disorders in the past decade. A recent study by Van den Heuvel et al. questioned the methodological foundations of LNM2. We here raise concerns regarding their errors and biases in the methodology and visualization. The conclusion of that study--LNM maps circumscribed brain changes mostly to one and the same outcome--is not supported by the data presented.

neuroscience↗

bioRxiv2017

Percent amplitude of fluctuation: a simple measure for resting-state fMRI signal at single voxel level

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxels ALFF by the global mean ALFF. However, this makes the individual voxels ALFF dependent on the global mean. Although Fractional ALFF (fALFF), proposed as a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. We proposed a new standardized, stand-alone, single-voxel metrics for RS-fMRI, namely percent amplitude of fluctuation (PerAF). PerAF is an analog to the percent signal change that has been widely used in the task fMRI communities, which allows it to be a straightforward measurement of BOLD signal fluctuations during resting state. We further conducted a test-retest reliability analysis comparing the relevant metrics, which indicated that PerAF was generally more reliable than the ALFF and fALFF. In a real RS-fMRI application, we further demonstrated that with and without standardization by global mean PerAF yielded prominently different results when comparing eyes open with eyes closed resting conditions, suggesting that future study should provide both with and without global mean standardization. The above results suggest that PerAF is a more reliable, straightforward and promising measurement for voxelwise brain activity-based RS-fMRI studies. For prompting future application of PerAF, we also implemented this method into a user-friendly toolbox REST-PerAF.

neuroscience↗