Naturalistic paradigms offer a powerful tool to investigate human brain function, but it remains difficult to link rich, continuous movie content to distributed brain activity in an interpretable way. In this study, I use a multimodal large language model (Gemini) as an automated "semantic annotator" to bridge naturalistic movie stimuli, brain responses, and cognitive performance. Using the Human Connectome Project movie-watching dataset, I segmented the film into 293 overlapping clips, prompting Gemini to rate each clip on 11 psychologically interpretable dimensions. Simultaneously, I extracted clip-wise BOLD activation patterns from the fMR images in 360 cortical ROIs. In this way, the AI and the brain effectively "watch" the same movies in parallel. For each brain ROI, I then fit linear regression models to predict clip-to-clip variation in movie-evoked responses from these features. Gemini-derived features robustly predicted movie-evoked responses in temporal, medial parietal, and lateral frontal association cortex, but explained little variance in unimodal somatosensory, dorsal parietal, insular, and piriform regions. Feature-weight maps reflected known functional specializations, and features with the largest global influence overlapped with the most explainable ROIs. Partial least squares analysis revealed that individual differences in resting-state connectivity strength and semantic explainability covaried along an asymmetric intrinsic axis: strongly integrated sensory-opercular systems at rest were associated with poorer AI predictability, whereas a smaller set of dorsal and medial association regions showed enhanced alignment. Finally, regional AI explainability in medial parietal and left perisylvian association areas was positively related to specific cognitive abilities. Together, these findings demonstrate that interpretable features from AI models provide a simple and scalable framework for quantifying AI-derived semantic predictability in naturalistic settings, offering a practical framework for utilizing artificial models as semantic references to probe human neural processing and individual differences.
Deciphering how the human brain matures and reorganizes across the lifespan remains a central challenge in developmental neuroscience. Understanding the complex developmental processes is essential for elucidating the biological bases of cognition and behavior, as well as the mechanisms underlying aging and neurodegenerative diseases. Neuroimaging has enabled the mapping of nonlinear, age-related changes in brain morphology, microstructure, and connectivity from gestation to senescence. However, we lack a unified understanding of interplay across multimodal neuroimaging measures, structure-function coupling, and the cellular and molecular drivers of network reorganization. In this review, we synthesize current evidence to provide a multiscale MRI-derived account of structural and functional brain development across the human lifespan, highlight key conceptual and methodological gaps, and outline priorities for future research.
Understanding movement encoding within human cortical circuits has been essential for advancing brain computer interfaces (BCIs). However, there are limited minimally invasive, high resolution neurorecording methods sensitive enough to detect single-trial movement-correlated neural activity. Functional ultrasound imaging (fUSI) provides submillimeter spatial resolution of deep cortical tissue with high sensitivity and, when paired with acoustically transparent skull implants, enables transcutaneous recording of human neurovascular changes. Prior studies have used fUSI in participants with acoustically transparent skull implants for on-off task mapping and decoding. Here, we demonstrate fUSI's ability to reliably resolve multi-body-part and single digit movement encoding within the primary sensorimotor cortex in a participant with an acoustically transparent skull implant. We obtained fine-grained mappings of individual effector representation that were consistent with classic somatotopy for both multi-body-part and single digit movement. We were able to resolve single-trial event-related activity, enabling single-trial decoding of both conditions. Analysis of voxels important for decoding suggested differential encoding of single digit movement information across the different Brodmann areas. Finally, we show that these patterns can be approximated across different sessions, allowing for cross session decoding. These results establish that fUSI can reliably delineate somatotopically organized motor representations at submillimeter resolution, bridging a critical gap between invasive electrophysiology and noninvasive hemodynamic imaging in a human subject.
Quantitative MRI has characterised early human brain development primarily through measures of water mobility, leaving other biophysical properties of maturing tissue unexplored. Here we show that radiofrequency electrical conductivity, reflecting ionic composition, membrane density and extracellular geometry in addition to water content, can be extracted retrospectively and at scale from the phase of conventional MRI acquisitions, revealing a distinct dimension of early brain tissue maturation and state. Applying electrical property tomography to 888 neonatal MRI sessions (784 subjects, 26-45 weeks post-menstrual age) alongside infant and childhood data, we find that whole-brain conductivity declines steeply during the perinatal period and remains independently associated with age after adjustment for mean diffusivity and ventricular volume, confirming that it represents a distinct developmental signal that is not reducible to tissue water content alone. Preterm birth was associated with elevated conductivity at term-equivalent age, particularly in deep grey matter. In neonates with hypoxic-ischaemic encephalopathy (n = 25), conductivity was elevated while mean diffusivity was reduced within the same tissue compartments, a dissociation indicating that these measures capture mechanistically distinct aspects of the tissue response to injury. These findings establish radiofrequency electrical properties as a new biophysical window on perinatal brain maturation and its perturbation by injury, extending quantitative MRI beyond measures of water mobility.
Tuberculous brain abscess is a rare manifestation of CNS tuberculosis, typically occurring in patients with significant immunosuppression. These lesions may present as solitary ring-enhancing masses and can radiographically resemble neoplastic processes, creating diagnostic and management challenges. A 43-year-old man with well-controlled human immunodeficiency virus (HIV) infection and a remote history of treated pulmonary tuberculosis presented with progressively worsening unilateral headaches. MRI demonstrated a small, lobulated, peripherally enhancing posterior temporal lobe mass adjacent to dominant-hemisphere language cortex without surrounding edema, raising concern for a neoplastic lesion. Given the progressive symptoms and eloquent location, the patient underwent awake craniotomy with language mapping and gross-total resection. Histopathological evaluation revealed a chronic abscess with granulomatous inflammation, and acid-fast staining and polymerase chain reaction confirmed Mycobacterium tuberculosis, establishing the diagnosis of tuberculous brain abscess. The patient recovered without new neurological deficits and showed clinical improvement at follow-up. Tuberculous brain abscess can occur even in patients without overt immunodeficiency, including those with well-controlled HIV infection. Because imaging findings may overlap with neoplastic lesions and systemic infectious features may be absent, maintaining a broad differential diagnosis and pursuing timely tissue confirmation are essential to guide appropriate management and avoid delays in treating either infection or malignancy. https://thejns.org/doi/10.3171/CASE26178.
Individuals with psychiatric disorders frequently experience comorbid cardiometabolic conditions, complicating treatment and worsening health outcomes. Both psychiatric and cardiometabolic disorders have been individually associated with alterations in brain structure. Yet, it remains unclear whether these associations stem from a shared genetic basis that underlies their frequent co-occurrence. We analyzed genome-wide association summary statistics from large international consortia of individuals of European ancestry, including psychiatric disorder GWAS with case-control sample sizes ranging from ~18,000 to ~158,000 cases, cardiometabolic disease GWAS with up to ~242,000 cases, and cortical morphology GWAS from UK Biobank comprising ~39,000 individuals. We applied complementary multivariate, causal, and mediation genetic analyses to disentangle genetic factors underlying brain alterations and comorbidity. Here we show that patterns of genetic overlap differ across disorders. Schizophrenia exhibits substantial polygenic overlap with cortical thickness and type 2 diabetes, despite low genetic correlation. In contrast, attention-deficit/hyperactivity disorder (ADHD) is more strongly correlated with cardiometabolic disease but shows limited overlap with cortical morphology. Notably, cortical surface area partly mediates the genetic association between ADHD and type 2 diabetes. Pathway analyses highlight metabolic stress processes in ADHD as well as neurodevelopmental and immune processes in schizophrenia. These findings indicate that psychiatric-cardiometabolic comorbidity arises through both shared and disorder-specific genetic pathways. This work clarifies the genetic architecture of multimorbidity and highlights opportunities for trait-targeted prevention strategies in psychiatry. Many people with psychiatric disorders also experience physical health problems, such as heart disease or diabetes. It remains unclear whether these links are due to shared genetic factors. In this study, we used large genetic datasets from hundreds of thousands of participants to investigate how psychiatric disorders, brain structure, and cardiometabolic diseases are genetically connected. We applied statistical methods to identify shared genetic influences and to explore whether one trait may partly influence another. We found that schizophrenia and ADHD show distinct genetic patterns: schizophrenia shares more genetic factors with brain structure and diabetes, while ADHD is more strongly linked to metabolic pathways. These findings highlight that different mental disorders may involve different biological routes, which could inform prevention and treatment strategies in the future.
Cerebral amyloid angiopathy (CAA) commonly co-occurs with Alzheimer's disease (AD), yet the molecular changes that accompany vascular [Formula: see text]-amyloid deposition in human tissue remain incompletely defined. Herein, we use a novel imaging approach that combines matrix-assisted laser desorption/ionization imaging mass spectrometry (IMS) with immunofluorescence microscopy on the same sections of postmortem human frontal cortex to map the lipid microenvironment of leptomeningeal vasculature in cases with and without CAA. Autofluorescence-guided regions-of-interest were imaged by IMS in both negative and positive ion modes and registered to post-IMS-acquired microscopy images. Immunofluorescence microscopy using markers for collagen IV, [Formula: see text]-smooth muscle actin ([Formula: see text]SMA), and thiazine red enabled automated segmentation of total, amyloid-positive, and amyloid-negative vasculature regions. A CAA index, the ratio of amyloid-positive area to total vasculature area in a region imaged by IMS, was used to define vasculature and classify each case into having CAA, or CAA-present, and not having CAA, or CAA-absent. An interpretable machine learning approach (XGBoost models with Shapley additive explanations for interpretation) was trained on pixel-level spectra and identified lipid signatures of vascular identity shared across groups as well as class-specific marker candidates that distinguished CAA-present from CAA-absent vasculature. CAA-absent vessels were characterized by higher contributions from phosphatidylserines (e.g., long-chain polyunsaturated PS species). Univariate differences were inconsistent between the two groups, but multivariate models in negative mode yielded stable discriminatory features. These results define spatial lipid correlates of vascular amyloid pathology in the human brain and establish a multimodal framework for mechanistically linking lipid metabolism, vascular integrity, and CAA in AD.
Brain white matter undergoes structural and functional alterations linked to late-life cognitive decline, but the cellular and molecular basis of its selective vulnerability remains incompletely defined. Here, in naturally aged mice, we demonstrate that senescent and disease-associated microglia (DAM) phenotypes converge in hippocampal-adjacent white matter, particularly in the fimbria. Using regional gene expression profiling, immunolabeling, GeoMx digital spatial profiling and CosMx spatial molecular imaging, we identify an aged brain-exclusive microglial population concentrated in white matter that expresses DAM genes together with a 'SenBrain' senescence gene signature, including galectin-3 (GAL3/Lgals3). Single-cell spatial trajectory analyses suggest that multiple cell fate transitions may give rise to this aged, proinflammatory, senescent- and DAM-linked state. Pharmacogenetic or pharmacological senotherapeutic interventions reduced white matter GAL3+ DAM abundance and restored a more youthful microglial organization in aged fimbria. These findings identify a senescence- and DAM-enriched microglial state as a prominent and partially reversible feature of aged brain white matter.
The zona incerta (ZI) is a deep brain region originally described by Auguste Forel as an "immensely confusing area about which nothing can be said." Despite the elusive nature of this structure, mounting evidence supports the role of the ZI and surrounding regions across a diverse range of brain functions and as a candidate target for neuromodulatory therapies. Using in vivo diffusion MRI and data-driven connectivity, we identify a topographic organization between the ZI and neocortex. Specifically, our methods identify a rostral-caudal gradient predominantly connecting the frontopolar and ventral prefrontal cortices with the rostral ZI, and the primary sensorimotor cortices with the caudal ZI. Moreover, we demonstrate how clustering and gradient approaches build complementary evidence including facilitating the mapping of a central region of the ZI, connected with the dorsal prefrontal cortex. These results were shown to be replicable across multiple datasets and at the individual subject level, building evidence for the important role of the ZI in mediating frontal lobe-associated tasks, ranging from motor to cognitive to emotional control. Finally, we consider the impact of this topographic organization on the refinement of neuromodulatory targets. These results pave the way for an increasingly detailed understanding of ZI substructures, and considerations for in vivo targeting of the ZI for neuromodulation.
Microglia regulate brain health and disease through diverse, dynamic activation states, but capturing this continuous heterogeneity at scale remains challenging. We developed an imaging and analysis framework to map activation landscapes of human iPSC-derived microglia (iMG) at single-cell resolution. High-content imaging combined a hypothesis-driven immunofluorescence (IF) panel targeting NF-κB, ASC, and CD45 with a discovery-oriented cell painting (CP) assay. Phenotypes were quantified using handcrafted and representation-learning features. To classify cells, we applied Gaussian mixture models (GMMs), enabling soft probabilistic assignments that capture transitional states. Compared with graph-based methods such as Leiden, GMMs achieved similar performance while providing more interpretable descriptions of microglial heterogeneity. Deep-learning features from the targeted IF panel were most informative, yielding high classification accuracy and strong correlation with biological readouts, including NLRP3 inflammasome activation. This platform offers a scalable approach to quantify microglial states and provides a scalable platform for discovering compounds that modulate microglial phenotypes.
The topology of spiking neural networks (SNNs) plays an important role in determining their dynamic representation ability, recognition performance, and biological interpretability in speech recognition. However, most existing SNN reservoirs are constructed using random, regular, or manually designed connectivity patterns, which may not reflect the functional organization of the human brain during speech perception. In this study, we propose a task-state fMRI-constrained SNN framework for speech recognition. Human fMRI data acquired during naturalistic English audiobook listening are used offline to derive a task-state whole-brain functional topology, which serves as a biologically inspired structural prior for the recurrent connectivity of the SNN reservoir. Because the fMRI and downstream isolated-digit recognition tasks use different speech paradigms, this topology is interpreted as a general speech-listening prior rather than a digit-specific neural representation. The Schaefer-400 cortical parcellation is used to define 400 whole-brain functional nodes, all of which are retained to preserve distributed cortical interactions during speech listening. Within this topology, 7 SomMotB_Aud parcels are identified as auditory core nodes and analyzed as an embedded auditory circuit. Compared with resting-state fMRI, task-state fMRI shows enhanced functional connectivity among these auditory nodes, indicating task-related auditory-circuit activation. The resulting 400-node task-state topology is mapped onto the recurrent connectivity of the SNN reservoir. This mapping is regarded as a topology-constrained computational abstraction rather than a direct model of biological information transmission. During recognition, speech spike trains are the only external input, while fMRI data are used only for offline topology construction. Experimental comparisons with baseline SNNs show that the proposed topology improves recognition performance and biological interpretability. Resting-state topology comparison, auditory-core contribution analysis, threshold-sensitivity analysis, and statistical testing are further used to evaluate robustness. These findings suggest that speech-evoked whole-brain functional organization may provide an effective topology prior for biologically inspired speech recognition models.
Adolescence is a critical period for brain development, impacting social, cognitive, and emotional functions. While hypothesis-driven studies have linked multiple person characteristics to brain development, the driving force behind differential brain development remains unclear. We applied unsupervised clustering to multimodal neuroimaging data from early adolescents in the Adolescent Brain Cognitive Development (ABCD) study. Clustering analyses were conducted separately for resting-state functional MRI (rs-fMRI), structural MRI (sMRI), and diffusion MRI (dMRI), and replicated in two independent samples of 2666 individuals each. Longitudinal trajectories over a two-year follow-up period were examined. Associations with sociodemographic and family-related factors were assessed. Two clusters were identified in rs-fMRI data across both independent samples. One cluster, comprising approximately 9%-10% of individuals, showed functional brain differences at baseline, along with altered neurodevelopmental trajectories over 2 years. These functional differences were associated with lower socioeconomic status, family instability, and stronger cultural/family values. In contrast, no significant clustering emerged from structural MRI and diffusion MRI data. Our findings suggest that sociodemographic factors are closely associated with early adolescent brain function and development, underscoring the need to consider social environment in neurodevelopmental models and prevention strategies.
Recent advances in human neuroimaging combined with machine learning have enabled identification of neural signatures representing various internal states, providing a promising framework for developing objective biomarkers. However, no study has investigated neural signatures that can reliably identify and distinguish itch and pain. Such neural signatures were explored in the present study using functional MRI (fMRI) and support vector machine (SVM). We measured brain activity in 33 healthy participants under cowhage-induced itch, mustard oil-induced pain, and control conditions using fMRI. We made seed-based functional connectivity images (R-images), where seed brain regions were the posterior cingulate cortex (PCC) and bilateral anterior insular cortex (aIC). We conducted a cross-validated and bootstrapped SVM using R-images to identify key brain regions with weights that were important to identify and distinguish itch and pain (threshold to identify these regions: p < 0.05). These neural signatures of itch and pain were applied to test sets of R-images to examine classification performance (Itch vs. Control or Pain). These signatures showed excellent classification capability, in particular when combining multiple signatures (area under the curve of receiver operating characteristic curve: > 0.9, accuracy: > 90%). This is the first neuroimaging study to explore neural signatures that can reliably detect and distinguish itch and pain using machine learning. Our approach using seed-based functional connectivity images combined with cross-validated and bootstrapped SVM demonstrated high classification performance. The present study serves as a proof-of-concept demonstrating the feasibility of this approach to develop brain-based biomarkers for assessing itch and pain. This is the first study to identify neural signatures of itch and pain. These signatures reveal distinct brain network patterns representing itch and pain, enabling reliable detection and differentiation of these two sensations based on brain activity. These signatures hold strong potential for the development of objective assessments of itch and pain.
While the field of imaging transcriptomics is evolving rapidly, several methodological challenges persist in functional enrichment analysis. Here, we introduce BrainEnrich, an R package that integrates whole-brain gene expression profiles from the Allen Human Brain Atlas (AHBA) with in vivo imaging-derived phenotypes (IDPs). By offering a suite of flexible association methods, aggregation strategies, comprehensive lists of predefined gene sets, and both competitive and self-contained null models, the package enables researchers to examine the spatial coupling between molecular profiles and IDPs at both group and individual levels. A novel feature of BrainEnrich is its individual-level enrichment analysis, which mapped individual IDPs onto a molecular coordinate framework, capturing the molecular signature of individual IDPs and enabling a deeper exploration of inter-individual variability. Its statistical power was examined through extensive simulation studies based on linear regression with different combinations of test statistics and null models. The results suggest that with appropriate null models, this approach effectively controlled Type 1 error while retaining sensitivity to detect associations between molecular profiles and phenotypic data. Two case studies were performed to demonstrate the utility of the package. In the group-level enrichment analysis, the effect size map of case-control comparison in cortical thickness of major depressive disorder patients was associated with molecular pathways such as synaptic signaling, lipid regulation, and steroid hormone balance, providing candidate molecular annotations for group-level IDPs. A separate case study found that synaptic gene set scores showed nominal associations with multiple cognitive measures, demonstrating its utility for individual-level molecular annotations to explore associations with phenotypic variables. Collectively, BrainEnrich provides a flexible framework for integrating macro-level IDPs with micro-level transcriptomic profiles for molecular contextualization of IDPs.
The network organization of the human brain dynamically reconfigures in response to changing environmental demands, an adaptive process that may be disrupted in a symptom-relevant manner across psychiatric illnesses. Here, in a transdiagnostic sample of participants with (n = 134) and without (n = 85) psychiatric diagnoses, functional connectomes from intrinsic (resting-state) and task-evoked fMRI were decomposed to identify constraints on brain network dynamics across six cognitive states. Hierarchical clustering of 110 clinical, behavioral, and cognitive measures identified participant-specific symptom profiles, revealing four core dimensions of functioning: internalizing, externalizing, cognitive, and social/reward. Brain network dynamics were flattened across cognitive states in individuals with psychiatric illness and could be used to accurately separate dimensional symptom profiles more robustly than both case/control status and primary diagnostic grouping. A key role of inhibitory cognitive control and frontoparietal network interactions was uncovered through systematic model comparison. We provide evidence that brain network dynamics can accurately differentiate the extent that psychiatrically-relevant dimensions of functioning are exhibited across health and disease.
Changes in gene expression have been observed in the aging human brain, but our understanding of the underlying regulatory mechanisms remains limited. To unravel these complexities, we analyzed single-nucleus gene expression, chromatin accessibility, DNA methylation, and three-dimensional (3D) chromatin architecture from human hippocampal tissues spanning the adult lifespan. We identified both linear and nonlinear dynamic gene regulatory programs during aging. Between the ages of 50 to 75, embryonic yolk sac-derived microglia were depleted and replaced by cells resembling peripheral blood monocyte-derived microglia. Hippocampal astrocytes decreased substantially with age, including those regulating synaptic transmission. Across cell types, 3D genome architecture underwent global erosion. Our analysis provides insights for how altered gene regulatory programs promote cell type-specific aging phenotypes in the human brain.
Over the past decade, comparisons between deep neural networks (DNNs) and the human brain have become central to cognitive neuroscience. Early work focused on vision, driven by the success of convolutional neural networks in object recognition, before such comparisons later gained traction in language with the rise of large-scale language models. These comparisons have validated existing hypotheses and generated new ones, challenging views of information processing, connectivity, and computational goals. Despite progress, debates persist over the interpretability and validity of mapping DNNs to brains, underscoring the need for more refined models and methods. Looking ahead, integrating cross-modal insights from vision and language, together with improved modeling and experimental frameworks, promises to advance the mechanistic understanding of cognition.
Hepatic encephalopathy (HE) is linked to widespread gray matter abnormalities, but it remains unclear whether these changes follow the organizing principles of large-scale brain networks. This study examined the spatial distribution of gray matter abnormalities in HE and their relationships with brain network hubs, neighborhood connectivity, and disease epicenters. In this cross-sectional study, 45 patients with HE and 45 healthy controls underwent high-resolution T1-weighted MRI. Cortical thickness was extracted from 308 cortical regions using the Desikan-Killiany atlas, and volumes were measured from 14 subcortical structures. Group differences were analyzed controlling for age, sex, and total intracranial volume. Connectome-based analyses were based on normative functional and structural connectomes from the Human Connectome Project and assessed hub-related vulnerability, network-neighborhood effects, disease epicenters, and individual-level network patterns. Patients with HE showed widespread gray matter abnormalities in the prefrontal, motor, temporal, and limbic cortices, as well as basal ganglia and thalamus, without significant alignment with normative functional or structural hubs. Structural neighborhood abnormalities were positively correlated with cortical changes (r = 0.58, P spin = 0.004), whereas functional neighborhoods were not. Functional-connectome epicenters were concentrated in the left inferior frontal gyrus, orbitofrontal cortex, and striatum, while structural-connectome epicenters centered on the bilateral superior frontal gyri and left inferior frontal gyrus. Individual analyses revealed heterogeneous epicenter patterns, with prefrontal-related regions repeatedly implicated. These findings suggest that gray matter abnormalities in HE are non-randomly organized, constrained by structural connectivity, and associated with prefrontal-centered disease epicenter networks, providing connectome-based insights into gray matter abnormalities in HE.
Transcutaneous auricular vagus nerve stimulation (taVNS) has shown promise in enhancing cognitive and emotional functions, yet its neural mechanisms remain unclear largely because existing analytical methods cannot characterize multiscale functional connectivity nor reliably infer causal interactions between brain regions in the presence of hemodynamic delays in fMRI signals. To address these limitations, we propose a Multiscale Spatiotemporal Causal Mapping (MSTCM) algorithm that integrates community-aware multiscale functional connectivity with delay-compensated causal inference. This design enables MSTCM to characterize multiscale connectivity structure and infer directed information flow with enhanced robustness. In evaluations using simulated fMRI data, MSTCM significantly outperformed seven existing causal inference algorithms across multiple evaluation metrics, including precision, sensitivity, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC). Applied to resting-state fMRI across four predefined large-scale cortical networks before and after taVNS, MSTCM revealed that taVNS reduced functional coupling between the left lateral sensorimotor cortex (L-LSMC) and the right intraparietal sulcus (R-IPS), increased global efficiency, enhanced causal integration within the salience network (SN), weakened causal connectivity within the dorsal attention network (DAN), and strengthened information flow from DAN to SN. These findings suggest that taVNS may enhance cognitive flexibility and emotional regulation by shifting information processing from exteroceptive toward interoceptive pathways and improving large-scale network efficiency. Consequently, this study provides not only a novel methodological approach but also new neuroimaging evidence supporting the clinical potential of taVNS.
The posterior region of the human superior temporal gyrus and sulcus (pSTG/S) has been observed to play a critical role in a variety of neural functions. The mechanisms that underlie this functional heterogeneity and the extent to which functional subregions overlap are unknown. Leveraging the results of clinically-guided direct cortical stimulation and intracranial recordings in five patients undergoing invasive monitoring for epilepsy surgery, we identify subregions of functional overlap and divergence for auditory category encoding and speech production. Our results showed auditory category encoding sites (VOICE+) distributed across STG/S and primary auditory cortex, while sites that produced disruption to speech production on stimulation (STIM+) were located more posteriorly on STG/S, extending into the parietal lobe. We identified sites common to both functions (OVERLAP) in 4/5 participants. Among channels exhibiting functional overlap, 84.6% were located in the STG/S and, crucially, the majority were localized to the pSTG/S. A Bayes factor analysis revealed evidence in favor of similar auditory category sensitivity between VOICE+ and OVERLAP channels in 2/4 participants. Our findings highlight the particularly flexible nature of the human pSTG/S and provide support for hypotheses that a shared mechanism of dynamic encoding of acoustic information may give rise to the range of functions attributed to this region.