Maps of paramagnetic and diamagnetic components of magnetic susceptibility can provide insights into the distribution of iron and myelin during brain aging. Postmortem validation is essential to ensure that these maps accurately reflect invivo biological processes. In this study, we applied the APART-QSM method for susceptibility separation to insitu (intracranial) postmortem MRI data from 47 subjects (ages 31-91) to investigate how age affects magnetic susceptibility components, comparing the results with previously reported invivo associations. Linear regression was used to assess age-related associations with susceptibility values in 17 deep gray matter (DGM) and white matter (WM) regions. Diamagnetic susceptibility showed a consistent age-related decline in DGM basal ganglia regions, which appeared to result from a shared underlying factor across these areas. Based on the assumption that fractional anisotropy (FA) reflects myelin integrity, we also investigated the correlation between FA and diamagnetic susceptibility in WM regions. A negative correlation was found, suggesting a potential myelin contribution to the diamagnetic component. Consistent with invivo analyses, the putamen's paramagnetic and total QSM susceptibility values demonstrated a strong age association in our postmortem condition, and it was the only region in which susceptibility values increased linearly with age. Finally, the analysis of exvivo putamen tissue samples revealed a moderate association between paramagnetic susceptibility and iron concentration, supporting iron's biological contribution to MRI paramagnetic susceptibility maps of the putamen. The results enhance the biological interpretability of MRI data and promote cross-validation between imaging and direct tissue analysis, with implications for both clinical and research applications related to aging and neurodegenerative diseases.

Normalization is widely used in electroencephalogram (EEG)-based multivariate pattern classification (MVPC) to reduce magnitude differences across trials and subjects. However, the spatial normalization method as applied to EEG channel-based brain maps has been rarely investigated in EEG-based decoding tasks like event-related potential (ERP) experiments. Meanwhile, the effectiveness of spatial normalization across diverse experimental paradigms remains unclear. This study evaluated the impact of spatial normalization on decoding accuracy using the support vector machine (SVM). The analysis included nine experimental paradigms, with seven binary ERP paradigms, one four-class facial expression paradigm, and one sixteen-class orientation paradigm. Results showed that spatial normalization significantly improved the between-subjects decoding accuracy (Cohen’s d = 1 . 39 , p < 0 . 001 ) but did not enhance the within-subjects decoding accuracy. Additionally, the morphological fidelity of the difference wave was preserved after spatial normalization, as evidenced by the high similarity between the normalized and original ERP difference waves across the seven binary paradigms. We validated our findings across diverse experimental paradigms and demonstrated that spatial normalization effectively enhances between-subjects decoding accuracy using SVM while preserving the temporal consistency of ERP, offering a generalizable preprocessing approach for EEG-based cognitive, clinical, and brain–computer interface (BCI) applications. • The effect of spatial normalization was evaluated on the EEG decoding performance using SVM. • Nine diverse paradigms (seven binary, two multi-category) were examined in the between and within-subjects conditions. • Spatial normalization enhanced between-subjects decoding accuracy but not within-subjects decoding accuracy. • Spatially normalized data preserved the morphological fidelity of the difference waves as evidenced by seven binary paradigms.

Abstract Direct communication between the hippocampus and cerebellum has been shown via coactivation and synchronized neuronal oscillations in animal models. Further, the cerebello-hippocampal circuit has been under-investigated in the human brain and may be impacted by sex steroid hormones. The cerebellum and hippocampus are dense with estradiol and progesterone receptors relative to other brain regions. Females experience up to a 90% decrease in ovarian estradiol production after the menopausal transition. Postmenopausal women show lower cerebello-cortical functional connectivity (FC) compared to reproductive aged females. Further, sex hormones are established modulators of both memory function and synaptic organization in the hippocampus in non-human animal studies. However, investigation of the cerebello-hippocampal (CB-HP) circuit has been limited to animal studies and small homogeneous samples of young adults as it relates to spatial navigation. Here, we investigate the CB(right)-HP(left) circuit in 138 adult humans (53% female) from 35-86 years of age, to define its FC patterns, and investigate its associations with behavior, hormone levels, and sex differences therein. We demonstrated robust FC patterns between the right CB and left HP in this sample. We predicted and found negative relationships between age and CB-HP FC. As expected, estradiol levels exhibited positive relationships with CB-HP connectivity. We found lower CB-HP FC with higher levels of progesterone. We provide the first characterization of the CB-HP circuit across middle and older adulthood and demonstrate that connectivity is sensitive to sex steroid hormone levels. This work provides the first clear CB-HP circuit functional mapping in the human brain and serves as a foundation for future work in neurological and psychiatric diseases.

Deciphering cellular microenvironments at atlas scale remains challenging because molecular identity, spatial context, and platform heterogeneity are tightly coupled. Here we present CellNiche, a scalable contrastive-learning framework that identifies and characterizes cellular microenvironments from spatial omics data using cell-centric spatial-proximity subgraphs. CellNiche combines spatial co-localization and molecular co-expression cues to learn microenvironment-aware embeddings. Across spatial omics datasets from multiple platforms (>10 million cells in total), scaling experiments show improved representations with more training data and competitive clustering and embedding-quality performance with efficient computation. In a multi-sample human non-small-cell lung cancer (NSCLC) cohort, CellNiche identifies conserved and sample-specific tumor and immune microenvironments and captures localized spatial transitions. In four independent mouse brain atlases, CellNiche integrates 293 slices into a unified virtual brain map for cross-atlas annotation transfer and spatial refinement.

Unlocking the mind: brain mapping for the exploratory quest of functional and cognitive networks in awake neurosurgery.

Perinatal mood and anxiety disorders (PMADs) are among the most common and consequential complications of pregnancy. The perinatal period is also characterized by profound hormonal fluctuations and large-scale brain plasticity. However, the mechanisms linking these neurobiological changes to psychiatric risk are poorly understood. Prospective, clinically informed studies are needed to identify quantitative biomarkers and clarify pathways linking perinatal neurobiology to PMADs risk. This report describes the design of a prospective, longitudinal cohort study integrating multimodal neuroimaging, biofluid sampling, and deep clinical phenotyping to enable precision characterization of neurobiological trajectories of PMADs risk. Twenty-five individuals at elevated risk for PMADs will be recruited prior to conception and followed across six in-person timepoints spanning the menstrual cycle, pregnancy, and early postpartum, with additional remote follow-ups through the first postpartum year. Data collection includes high-resolution structural MRI, functional brain mapping using multi-echo resting-state fMRI, diffusion MRI, arterial spin labeling, ultra-high field MR-based techniques for measuring glutamate (GluCEST and 1 HMRS), biofluid sampling, and comprehensive clinical, behavioral, and cognitive assessments. Structured clinical interviews assess categorical diagnoses while dimensional symptom measures capture heterogeneity and transdiagnostic features of perinatal psychopathology. Longitudinal analyses will model nonlinear trajectories of brain and symptom change across the perinatal period as well as evaluate whether preconception network features and menstrual cycle-related brain changes are associated with subsequent perinatal symptom emergence. This cohort study establishes a longitudinal, multimodal framework for investigating neurobiological changes across the transition to pregnancy in individuals at elevated risk for PMADs. By anchoring pregnancy-related brain changes to preconception and menstrual cycle-related variability within the same individuals, this study is designed to evaluate associations between preconception hormone sensitivity, pregnancy-induced neuroplasticity, and PMADs risk. The resulting dataset will provide a deeply phenotyped longitudinal resource for investigating brain-behavior relationships across the perinatal period. Findings are expected to inform future larger-scale studies aimed at advancing mechanistic understanding of PMADs, improving individualized risk stratification, and supporting development of personalized preventive and neuromodulatory interventions.

With recent advances in anatomical and functional brain mapping, Alice in Wonderland syndrome (AIWS), a perceptual distortion disorder, has received increased attention. We report the case of a 67‐year‐old man with bipolar I disorder (manic episode), AIWS, and delusional parasitosis (Ekbom syndrome). The patient exhibited diverse perceptual distortions across visual, auditory, and tactile modalities. In addition to derealization and depersonalization, he showed a distorted sense of time. Single‐photon emission computed tomography (SPECT) revealed a transient but significant decrease in blood flow in the right posterior cingulate region, accompanied by relatively increased blood flow in the bilateral occipital regions during the episode.

Understanding the mechanisms of brain function and dysfunction is at the core of the neuroscience mission. However, the field's grasp of causal relationships between brain properties has been hindered by a focus on single modalities that neglects the complex interplay between the features found at different neural scales. Progress in neuroinformatics and the increasing availability of open datasets have helped overcome this limitation by facilitating the contextualization of brain maps against cellular, metabolic and network features. Despite the rapid uptake of data contextualization methods proposing that quantification of spatial similarity between brain maps may shed light on pathways of structure-function coupling, development and disease, their potential pitfalls have received little attention. In the context of neuroimaging research, these limitations include reliance on often small-sample and non-representative reference datasets, repeated use of the same brain maps across studies, and problems with intermodal and interindividual alignment. Applying data contextualization without considering these limitations can lead to circular reasoning, overfitting and correlational overreach, and limits the interpretation of findings to the properties of the source data. Here we provide a Roadmap of practical guidelines operating at the level of study design, analysis pipelines and interpretation of findings to encourage the development of best practices in data contextualization. A more informed use of brain map correlation approaches will improve mechanistic investigations and our understanding of causal relationships between brain properties.

Autism spectrum disorder (ASD) is a heterogeneous developmental disconnection syndrome. Identifying circuit deficits is crucial for understanding ASD etiology, yet the involvement of multiple brain regions and genetic variations complicates this analysis. Here, using an AI-powered mapping platform, BM-auto (Brain Mapping with Auto-ROI correction), to analyze a Thy1-YFP reporter, we show that different ASD-associated mutations cause distinct circuit abnormalities but share common deficits in the piriform cortex, a region regulating olfactory discrimination and social behavior patterns. We analyzed the whole-brain distribution of the Thy1-YFP reporter in three ASD mouse models (Tbr1+/-, Nf1+/-, and Vcp+/R95G). YFP signals revealed altered axonal projections and structural connectivity. We also found that Thy1-YFP+ cell numbers varied across brain regions, revealing deficits in the differentiation or maintenance of projection neurons. While each mutation caused unique connectivity alterations, sensory regions-including the visual, somatosensory, and piriform cortices-were recurrently affected. However, effects on the visual and somatosensory cortices varied between models. The piriform cortex was the only region consistently impaired, showing reduced YFP signals and fewer Thy1-YFP+ neurons across all three models. Furthermore, all three mutants exhibited common olfactory discrimination impairments. Manipulating piriform cortex activity altered social behavior patterns, highlighting its role in ASD-linked circuit dysfunction. These findings underscore the vulnerability of sensory regions-especially the piriform cortex-to ASD-related mutations, strengthening the notion that altered sensory experiences are common in ASD.

Image segmentation and registration of carp brain tissue slices oriented to brain atlas construction.

Experimental evaluation of an integrated focused ultrasound and electroencephalography approach for developing activation-informed neuroimaging.

Interpreting brain-behavior relationships through the lens of anatomical parcellations or functional networks is commonplace in human brain mapping. However, statistical approaches for testing whether brain-behavior associations are stronger (i.e., enriched) within a region of interest remain underdeveloped. Here, we propose a permutation-based approach for network enrichment testing using ordinal dominance curves (NETDOM). In simulation studies, we demonstrate that NETDOM properly controls the type I error rate-unlike other prominent enrichment methods-while exhibiting increased statistical power when enrichment occurs in a subset of in-network locations. Using data from two large-scale neurodevelopmental cohorts, we illustrate that NETDOM effectively detects enriched associations between structural and functional brain measures and neurocognitive performance.

Advancing intraoperative brain mapping: First-in-human evaluation of flexible high-density electrocorticography grids for epilepsy surgery.

The term generative model is widely used in human neuroimaging; however, its meaning is often left implicit. Prompted by observations and discussions at the 2025 Organization for Human Brain Mapping (OHBM) Annual Meeting, we surveyed members of the neuroimaging community to examine how generative models are defined, used, and evaluated in practice. Responses revealed some agreement on functional criteria — such as a model’s ability to simulate data — alongside marked disagreement about whether specific, widely used methods should be considered generative models. Evaluative priorities also varied across respondents, though out-of-sample generalization and interpretability were consistently emphasized. In light of these findings, we propose a pragmatic working definition of generative modeling, and a short set of reporting commitments intended to make generative claims easier to interpret and evaluate.

Aim of the study was to propose a practical method for T2*-relaxometry of fetal brain white matter using prenatal MRI, adapted to real-world clinical conditions and the specific limitations of prenatal imaging. Material and Methods . A multi-echo echo-planar imaging (EPI) sequence was used to acquire a series of images with different echo times within a single breath-hold. T2* values were calculated using ROI (region of interest) analysis of manually annotated white matter regions, with exponential fitting of the signal decay. The method was adapted to the constraints of prenatal MRI, including fetal motion, the influence of amniotic fluid, dielectric artifacts at 3T, and the need to balance resolution with scan time. Results . The method yielded reproducible T2* values in fetuses during the second and third trimesters: 182 ± 11 ms at 1.5T and 153 ± 13 ms at 3T, both higher than those in adults (66.2 ± 2.5 and 51.8 ± 2.9 ms, respectively). Agreement between two T2* calculation methods was high (intraclass correlation coefficient 0.955, p &lt; 0.0001). The method shows promise for assessing fetal hypoxia. Conclusions. With appropriate scanning and post-processing settings, T2*-relaxometry can be successfully applied to prenatal MRI. The proposed solutions overcome key limitations and pave the way to further clinical and research use of the method.

Advancing neural interfaces requires large-scale, high-density recording technologies capable of capturing full-spectrum neural activity across cortical and subcortical regions. Here, we present a scalable approach to integrate neural electrodes with advanced application-specific integrated circuits (ASICs). Specifically, we custom-designed an ASIC with 5,376 simultaneous channels, each sampling at 20 kS/s and enabling >1.3 Gb/s total data streaming throughput. The ASIC incorporates in-pixel amplification, time-division multiplexed ADCs, and on-chip stimulation capabilities, ensuring precise signal acquisition with minimal power consumption while maintaining a low noise level of 5.5 μVrms. We further developed an interconnect strategy using gold bump bonding, which allows for high-density integration of the flexible probe and rigid chip. We demonstrate the capacity of this platform through the integration with a flexible μECoG array. The resulting device allows for the high-resolution mapping of in vivo field potentials on the cortical surfaces of rat brains, supported by the precise localization of evoked sensory activities. These results prove an effective approach towards highly integrated neural interfaces with applications in brain-computer interfaces, neuroprosthetics, and large-scale functional brain mapping.

Although large field-of-view two-photon microscopy (LF-TPM) is a powerful neuroimaging tool, low signal-to-noise (SNR) poses challenges for high-speed imaging. Increasing the average laser power improves the SNR, but the thermal effect of high laser power on the cortex is not well studied. Further, capturing the curvature of the cortex requires creating an image stack, which also reduces the temporal resolution. Solving these problems would enable mesoscopic mapping with LF-TPM. We aim to study the temperature dynamic of the mouse cortex as a function of the field of view and the average laser power and demonstrate the feasibility of relatively high illumination power. Combining the higher illumination intensity and the curved scanning, we aim to showcase the capability of the LF-TPM system in both stimulated and spontaneous mesoscopic mapping. We developed a combined thermal imaging and LF-TPM system to measure the spatial-temporal dynamics of heat during TPM imaging. We used an electrically tunable lens to vary the focusing depth and track the cortical curvature as a function of the medial distance. We then used the optimized system to image functional activations and resting-state functional connectivity patterns across both hemispheres. The steady-state maximum cortical temperature declines as the FOV increases. For a FOV and 388mW average laser power on the cortical surface, the cortical temperature stayed below 40°C. Using 360mW average power and a curved imaging surface, the custom LF-TPM system can map bilateral hind paw stimulation responses with a single trial and spontaneous bilateral functional connectivity. Concurrent thermal and LF-TPM imaging enables a quantitative optimization of laser power and SNR in LF-TPM systems. We demonstrated the feasibility of bilateral brain mapping using LF-TPM. These findings will help expand the utility of LF-TPMs for mesoscopic brain mapping applications.

The unique and intricate pattern of human cortical folding is rooted in fetal neurodevelopmental processes and can now be comprehensively quantified by new neuroimaging-derived measures of sulcal complexity. Here, we provide the first genetic maps of human sulcal complexity. Beginning with large effects of rare variants, we survey nine different neurogenetic syndromes (n=615), detecting visible changes in sulcal complexity on a shared axis of sulcal change coupled to the prenatal timing of sulcation. Turning to common genetic variants, we use genome-wide association studies of complexity scores for 40 sulci in the UK Biobank (n~29,000) to (i) resolve variable heritability across sulci, (ii) reveal both local and remote shared genetic effects with cortical morphology, and (iii) identify complexity-associated genes and their embedding in brain maps of prenatal gene expression. These reference genetic maps uncover multiple new mechanistic pathways for cortical morphogenesis in health and disease.

Personal experiences are encoded and stored in memory engram cells and are crucial for social preference dynamics in future social contexts, yet the neural circuit mechanisms involved are still poorly understood. Here, we develop a mouse model that combines self-experienced single social defeat stress with vicarious social defeat stress, demonstrating a social preference with defeat stress-experienced cagemate and social avoidance toward an aggressor. Basolateral amygdala engram cells (BLAEC) exhibit significant activation, and chemogenetic manipulations confirm their sufficiency and necessity for both social preference and social avoidance behaviors. Virus-based cell-type-specific brain mapping suggests BLAEC project to anterior cingulate cortex (ACC) and these projections are also responsible for modulating social preference dynamics. Distinct projections from BLA-ACC circuit, including ventral/dorsal hippocampus and zona incerta, exert the diverse effects on these behaviors in male mice. Our findings reveal regulation of social preference dynamics by divergent circuits originating from BLAEC, which may contribute to the neurobiological mechanism of social psychopathologies.

Ovarian hormone shifts enable reproduction and are associated with substantial brain plasticity and disease risks. While imaging studies provide (micro)structural insights into brain changes across the ovarian cycle and pregnancy, the high resolution, single-cell map of the brain across reproductive transitions is missing. Here, we performed multiome (gene expression and chromatin accessibility) analysis of the mouse ventral hippocampus (vHIP) across sex, estrous cycle, and peripartum period at single cell resolution. We identify dynamic changes in vHIP cellular composition across the estrous cycle and pregnancy, including in the neural stem cells of the dentate gyrus (DG), enabling hormone-driven neurogenesis. Major gene expression changes are neuronal function-relevant, cell type-specific, and found in excitatory neurons of CA1, CA3, and DG subfields, across sex and reproductive transitions. In contrast, chromatin accessibility changes are more extensive and found across cell types, likely driven by estrogen level shifts in both within-female and between-sex comparisons. We show that chromatin remodeling during the estrous cycle primes the genome for gene expression changes during pregnancy and is also enriched for brain disease-relevant genes. Finally, we reveal a thyroid hormone transporter (Transthyretin, Ttr) gene as the major candidate gene that drives structural and behavioral changes across the estrous cycle and pregnancy. Our study provides an extensive cellular and molecular view of how reproductive transitions shape the brain and opens the possibility to target downstream targets of estrogen, including thyroid hormone signaling, as a treatment option for hormone-sensitive periods in women.