Zheng Wang

Background: Deep brain stimulation (DBS) efficacy for psychiatric disorders remains heterogeneous due to incomplete mechanistic insights and insufficient individualized treatment planning. A biologically grounded computational framework is urgently needed to simulate DBS-related differences for treatment-resistant patients. Methods: Resting-state fMRI data from 11 patients with obsessive-compulsive disorder (OCD) and 11 with major depressive disorder (MDD), scanned in DBS-ON and DBS-OFF states, were used to derive functional connectivity (FC) via network deconvolution. Whole-brain dynamic modeling incorporated stimulation targets, amplitudes, and frequencies to simulate DBS effects in silico. Results: The model recapitulated syndrome-specific DBS-related network patterns in exploratory analyses. OCD stimulation primarily altered frontal-subcortical connectivity, whereas MDD stimulation predominantly affected default mode network hubs ( p < 0.05). Under leave-one-out cross-validation, correlations between predicted and observed clinical outcomes were r = 0.625 in OCD ( p = 0.040) and r = 0.659 in MDD ( p = 0.028). Applying the same FC signatures to simulated FC yielded r = 0.465 in OCD ( p = 0.149) and r = 0.627 in MDD ( p = 0.039). In a proof-of-concept application to a separate OCD cohort ( N = 92), three individuals showed high model-predicted NAc-response profiles and predicted improvement exceeding 35%. Conclusions: This hypothesis-generating framework retrospectively models DBS-related network effects using individualized resting-state FC and virtual perturbations. Linking simulated FC states with empirical DBS-ON/OFF patterns may help characterize how baseline functional architecture shapes DBS-related network responses and inform future individualized DBS modeling.

This review delves into brain imaging genomics, an interdisciplinary field merging brain imaging, genomics, and additional biomarkers with clinical data. The primary aim is to uncover new insights into the brain’s phenotypic, genetic, and molecular characteristics. We emphasize recent advances in genome-wide association studies and transcriptome-wide association studies, especially their integration with MRI-derived phenotypes in humans. These studies are crucial for understanding how various factors influence brain structure and function in normal and pathological states. Furthermore, this review highlights imaging transcriptomics progress in non-human primates, essential for elucidating brain organization and improving animal models evolutionarily to bridge gaps in understanding human disorders. We conclude that brain imaging genomics is set to transform research in neurological and psychiatric disorders, owing to its holistic approach that merges advanced genetic analysis with detailed imaging, will deepen our understanding of the brain, and usher in a new epoch in brain imaging research.

Elucidating how microscale molecular architecture shapes macroscale brain network organization requires integrative frameworks that link transcriptional profiles, structural connectivity, and functional dynamics across scales. However, existing evidence largely relies on cross-modal data acquired from different individuals, limiting causal and mechanistic inference. To overcome this, we performed concurrent awake optogenetic functional MRI (opto-fMRI) and spatial transcriptomics (opto-ST) within the same mice, enabling coupled analysis of neural activity and gene expression in fronto-thalamic and hippo-thalamic networks. Optogenetic stimulation of the medial prefrontal cortex (mPFC) or subiculum (SUB) evoked distinct thalamic BOLD response patterns. Multivariate spatial modeling revealed that energy metabolism and core biochemical processes underlie these GLM-derived activation patterns—a finding further supported by data-driven PCA, which identified enrichments in similar biological processes. These results suggest that conventional linear models predominantly capture linear gene–process relationships, highlighting the need for nonlinear computational approaches in fMRI analysis. Overall, our multi-omics framework reveals that genes regulating metabolic pathways underlie region-specific neurovascular coupling, thereby bridging local transcriptomics and large-scale functional imaging signatures.

Quantifying individual deviations in brain morphology from normative references is useful for understanding neurodiversity and facilitating personalized management of brain health. Here we report Chinese brain normative references using morphological imaging scans of 24,061 healthy volunteers from 105 sites, revealing later peak ages of lifespan neurodevelopmental milestones (1.2–8.9 years) than European/North American populations. We model individual brain deviation scores in 3,932 individuals with different neurological disorders from population references to evaluate three key aspects of brain health assessment using machine learning approaches: estimating disease propensity, predicting cognitive and physical outcomes and assessing treatment effects with distinct disability progression. The norm-deviation scores outperformed raw structural measures in these evaluations. Chinese-specific normative brain references may foster personalized diagnosis and prognosis in neurological diseases, enabling clinically applicable assessments of brain health.

The organization and connectivity of the arcuate fasciculus (AF) in nonhuman primates remain contentious, especially concerning how its anatomy diverges from that of humans. Here, viral‐based genetic labeling and fluorescence micro‐optical sectioning tomography (fMOST) are integrated for single‐neuron tracing with whole‐brain tractography derived from 11.7T diffusion MRI in four macaques. Augmented by human 7.0T MRI spectral embedding analyses, this multimodal framework enabled cross‐species comparative connectomics of the AF. Complemented by spectral embedding analysis of 7.0T MRI in humans, a comparative connectomic analysis of the AF across species is performed. It is demonstrated that the macaque AF originates in the temporal‐parietal cortex, traverses the auditory cortex and parietal operculum, and projects into prefrontal regions. In contrast, the human AF exhibits greater expansion into the middle temporal gyrus and stronger prefrontal and parietal operculum connectivity–divergences quantified by Kullback–Leibler analysis that likely underpin the evolutionary specialization of human language networks. These differences underscore the critical role of AF expansion and differentiation in the evolution of human language capabilities.

Reinforcement learning (RL) enables adaptive behavior across species via reward prediction errors (RPEs), but the neural origins of species-specific adaptability remain unknown. Integrating RL modeling, transcriptomics, and neuroimaging during reversal learning, we discovered convergent RPE signatures - shared monoaminergic/synaptic gene upregulation and neuroanatomical representations, yet humans outperformed macaques behaviorally. Single-trial decoding showed RPEs guided choices similarly in both species, but humans disproportionately recruited dorsal anterior cingulate (dACC) and dorsolateral prefrontal cortex (dlPFC). Cross-species alignment uncovered that macaque prefrontal circuits encode human-like optimal RPEs yet fail to translate them into action. Adaptability scaled not with RPE encoding fidelity, but with the areal extent of dACC/dlPFC recruitment governing RPE-to-action transformation. These findings resolve an evolutionary puzzle: behavioral performance gaps arise from executive cortical readout efficiency, not encoding capacity.