Biology subjects

Chang, E. F.

Publications and source records attributed to Chang, E. F..

At least 19 recordsLinked to original sources

bioRxiv2026

Decoding Chronic Pain States from Distributed Intracranial Recordings

Chronic pain engages distributed cortical and subcortical circuits, and large-scale intracranial recordings in humans offer a valuable opportunity to characterize its neural signatures. Here, we recorded multi-day stereoelectroencephalography (sEEG) from six participants with refractory chronic neuropathic pain, each implanted with sEEG electrodes spanning dozens of cortical and subcortical structures. Using simultaneous chronic pain ratings, we decoded spontaneous high versus low pain states within individuals (median area under the curve = 0.72; five of six participants performed above chance). Pain-predictive signals were broadly distributed and highly participant-specific. However, mapping the spatial distribution of pain-predictive features revealed preferential representation within canonical macroscale networks: beta-band activity in the default mode network and high-gamma activity in the salience network. These results demonstrate that intracranial recordings can capture distributed, network-organized representations of spontaneous chronic pain states.

neuroscience↗

bioRxiv2026

Opposing effects of slow and fast theta synchrony on working memory in the human hippocampal-orbitofrontal network

Working memory (WM) enables us to maintain and manipulate information over time, but how the brain organizes sequential information locally and across networks remains unclear. Recent work suggests that slow and fast theta oscillations serve different roles in memory, yet their distinct contributions to sequential WM are unknown. Based on evidence that the hippocampus (HC) and orbitofrontal cortex (OFC) support sequential WM and that slower theta cycles provide optimal temporal windows for organizing items in WM, we predicted that these regions would coordinate via slow theta dynamics. We analyzed intracranial EEG from the HC, OFC, and amygdala (AMY) in 21 neurosurgical patients (7 female, 13-54 years of age; M {+/-} SD, 30 {+/-} 11.2 years) performing a delayed match-to-sample WM task. We assessed phase locking between regions, phase-amplitude coupling within regions, and neuronal phase coding for slow (~1-4.5 Hz) and fast (~4.5-8 Hz) theta oscillations. We found significant slow and fast theta synchrony between all regions, but identical anatomical pathways produced opposing behavioral effects depending on oscillatory frequency, particularly during higher cognitive demand. Slow theta synchrony was associated with faster response times (RTs), while fast theta synchrony between HC and OFC hindered both accuracy and RTs. Unexpectedly, AMY modulated RT through demand-dependent slow theta synchrony, where AMY-OFC synchrony predicted faster RTs during maintenance and HC-AMY synchrony predicted faster RTs during higher cognitive demand. Sustained coupling between slow theta oscillations and high-frequency broadband activity within each region suggests that local organization coincides with beneficial network behavioral effects. These results establish a frequency-opponent mechanism in which theta oscillation frequencies determine whether HC-OFC circuits facilitate or impair sequential WM.

neuroscience↗

bioRxiv2026

Molecular dynamics of Brodmann Area 22 in development and autism

Challenges in verbal communication are a prominent feature of autism. However, gene regulatory programs in speech-related cortical regions remain poorly characterized. In parallel, it remains unclear whether the heterogeneous genetic factors underlying autism converge on shared neurobiological mechanisms. To address these gaps, we generated paired transcriptomic and epigenomic data from post-mortem human brain tissue across 100 donors. Here, we show that transcriptional differences in the speech-related Brodmann Area 22 in individuals with neurodevelopmental conditions, including autism, are strongest among those with a known genetic diagnosis. A similar but attenuated signature is observed in those without a genetic diagnosis. These transcriptional differences are most pronounced in neurons, with glutamatergic L4/5 intratelencephalic neurons affected across multiple modalities. Finally, multimodal analysis implicates altered RFX3-dependent networks as a central hub in autism, particularly among L4/5 intratelencephalic neurons in non-verbal individuals. Together, our study identifies regulatory architecture linking chromatin state, transcriptional output, and variation in verbal ability in autism.

neuroscience↗

bioRxiv2026

Scalable high-fidelity human vascularized cortical assembloids recapitulate neurovascular co-development and cell specialization

Human cortical development involves the coupling of neurogenesis and cerebrovascular growth. However, interactions between neural and vascular cells are largely missing in most brain organoids, which are crucial models for studying neurodevelopment and disease. Here, we establish vascularized cortical assembloids (vCAs) by fusing mesoderm-derived vascular organoids (VOs) with cortical organoids (COs). vCAs self-assemble lumenized networks of endothelial cells, pericytes, and perivascular fibroblasts that acquire blood-brain barrier (BBB) specialization and arteriovenous specification in vitro. Single-cell RNA-sequencing and immunofluorescence imaging revealed that vascularization improves neuroepithelial architecture, reduces hypoxia and apoptosis, expands cortical progenitor pools, and enhances neuronal maturation and connectivity compared with COs. Moreover, atlas-level integration with human neurodevelopmental tissue and cross-protocol benchmarking demonstrate superior transcriptional concordance, especially for vascular cell and glial populations, relative to existing approaches. This scalable and reproducible platform improves fidelity and throughput for modeling human neurovascular co-development and enables systematic studies across brain regions and diseases using engineered or patient-derived iPSCs.

developmental biology↗

bioRxiv2025

Sensorimotor Theta Oscillations Coordinate Speech Movements

Fluent speech depends on precisely timed motor commands that coordinate rapid transitions between successive articulatory gestures. Using direct cortical recordings, we identified a prominent sensorimotor theta oscillation (6-10 Hz) that supports this coordination. During articulation, premotor speech circuits exhibited enhanced theta phase coherence, with elevated population activity near theta troughs. The oscillations frequency remained remarkably stable across varying speech rates and cognitive states, consistent with an intrinsically generated rhythm. Vocal-tract kinematics revealed pulse-like movements at 6-10 Hz, tightly coupled to cortical theta phase. At a mesoscopic scale, theta cycles structured sequential sensorimotor activations encoding articulatory gestures, with syllable identity optimally decodable following theta troughs. These findings identify theta oscillations as an intrinsic timing mechanism that coordinates the distributed and synergistic motor control underlying fluent speech.

neuroscience↗

bioRxiv2025

High-Fidelity Neural Speech Reconstruction through an Efficient Acoustic-Linguistic Dual-Pathway Framework

Reconstructing speech from neural recordings is crucial for understanding speech coding and developing brain-computer interfaces (BCIs). However, existing methods trade off acoustic richness (pitch, prosody) for linguistic intelligibility (words, phonemes). To overcome this limitation, we propose a dual-path framework to concurrently decode acoustic and linguistic representations. The acoustic pathway uses a long-short term memory (LSTM) decoder and a high-fidelity generative adversarial network (HiFi-GAN) to reconstruct spectrotemporal features. The linguistic pathway employs a transformer adaptor and text-to-speech (TTS) generator for word tokens. These two pathways merge via voice cloning to combine both acoustic and linguistic validity. Using only 20 minutes of electrocorticography (ECoG) data per subject, our approach achieves highly intelligible synthesized speech (mean opinion score = 4.0/5.0, word error rate = 18.9%). Our dual-path framework reconstructs natural and intelligible speech from ECoG, resolving the acoustic-linguistic trade-off.

neuroscience↗

bioRxiv2025

Electrocorticographic Detection of Speech Networks in Glioma-infiltrated Cortex

Direct cortical stimulation (DCS) is the clinical gold standard for identifying functional cortex in the human brain, which is essential for the safe removal of brain lesions. Defining the electro-physiological properties of DCS- positive cortical regions may facilitate the identification of critical language regions, thereby permitting safe glioma resections in communities without access. Leveraging a multicenter electrophysiologic dataset of DCS- positive language regions spatially matched with subdural arrays, we analyzed regions identified as functionally critical (DCS+) versus functionally non-critical (DCS-) during intraoperative language mapping. In IDH-mutant gliomas, DCS+ regions exhibited significantly greater speech-related neural activity and enhanced encoding and decoding of linguistic and semantic features. We demonstrate that resting-state classifiers distinguish DCS+ from DCS- regions in IDH-mutant tumors. Task-based and resting-state electrophysiologic distinctions were pathology-specific and not present in IDH-wildtype glioblastomas. These findings may accelerate DCS mapping by guiding surgeons to priority regions, improving efficiency, and patient outcomes.

neuroscience↗

bioRxiv2025

Aperiodic neural dynamics define a novel signature of glioma-induced excitation-inhibition dysregulation

Diffuse gliomas remodel neuronal circuits with prognostic and therapeutic significance for patients. Electrophysiologic measures of cortical excitability hold promise for monitoring disease progression and evaluating therapeutic responses. The power law exponent (aperiodic slope) reflects the balance between excitatory and inhibitory activity within neuronal networks, a critical aspect of normal brain function often disrupted in neurological conditions. Despite its potential, the significance of the aperiodic slope in glioma-infiltrated tissue and its underlying cellular processes has not been fully investigated. Here, we integrate multi-modal electrophysiological analysis with transcriptomic profiling to analyze the aperiodic slope in both normal and glioma-infiltrated cortex. We determine that glioma infiltration induces a flattening of the aperiodic slope, indicating a shift toward excitation dominance that varies according to tumor subtype and correlates with impairments in semantic naming. Single-nucleus RNA sequencing revealed that cortical regions with flat aperiodic slope exhibit transcriptional programs enriched in glutamatergic signaling, membrane depolarization, and excitatory synaptic transmission. The aperiodic slope responds to pharmacologically induced changes in cortical inhibition during propofol administration, a GABAA agonist. Our results establish the aperiodic slope as a robust biomarker of glioma-associated excitation-inhibition imbalance, with potential applications in tumor classification and treatment monitoring.

cancer biology↗

bioRxiv2025

Dynamics of auditory word form encoding in human speech cortex

When we hear continuous speech, we perceive it as a series of discrete words, despite the lack of clear boundaries in the acoustic signal. The superior temporal gyrus (STG) encodes phonetic elements like consonants and vowels, but how it extracts whole words as perceptual units remains unclear. Using high-density cortical recordings, we investigated how the brain represents auditory word forms--integrating acoustic-phonetic, prosodic, and lexical features--while participants listened to spoken narratives. Our results show that STG neural populations exhibit a distinctive reset in activity at word boundaries, marked by a brief, sharp drop in cortical activity. Between these resets, the STG consistently encodes distinct acoustic-phonetic, prosodic, and lexical information, supporting the integration of phonological features into coherent word forms. Notably, this process tracks the relative elapsed time within each word, independent of its absolute duration, providing a flexible temporal scaffolding for encoding variable word lengths. We observed similar word form dynamics in the deeper layers of a self-supervised artificial speech network, suggesting a potential convergence with computational models. Additionally, in a bistable word perception task, STG responses were aligned with participants perceived word boundaries on a trial-by-trial basis, further emphasizing the role of dynamic encoding in word recognition. Together, these findings support a new dynamical model of auditory word forms, highlighting their importance as perceptual units for accessing linguistic meaning.

neuroscience↗

bioRxiv2025

Towards individualized deep brain stimulation: A stereoencephalography-based workflow for unbiased neurostimulation target identification

ObjectivesDeep brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Thus, we have utilized inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for chronic implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for stimulation testing to identify therapeutic targets. Materials and MethodsFourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using sEEG and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. ResultsEffect sizes (Hedges g) of stimulation-induced symptom score changes ranged from -1.5 to +2.39. The standard deviation of sham trial responses was a strong predictor of stimulation response variability, as confirmed by a leave-one-out cross-validated linear regression (R2 = 0.67, permutation p<0.001). Thus, early sham trial data could be used to estimate the variability of stimulation responses for power analysis calculations. We show that approximately 10 sham trials were needed to robustly estimate sham variability. Power analysis (using a paired-t test) showed that for effect sizes [≥] 1.1, roughly 10 trials should be used per stimulation site for sufficiently powered results. ConclusionsThe presented workflow is adaptable to multiple indications and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized and clinically efficacious stimulation sites.

neuroscience↗

bioRxiv2024

Human precentral gyrus neurons link speech sequences from listening to speaking

Speech perception and production are interconnected processes, but the underlying neural mechanisms remain unclear. We investigated this relationship by recording large-scale single-neuron activity in the human brain during a delayed sentence repetition task. Contrary to the traditional view that the precentral gyrus is solely responsible for motor execution, we found that neurons there encoded activity across all task phases of listening, delay, initiation, and speaking. Notably, we discovered "mirror" neurons that activated transiently after hearing and before producing specific speech sounds, and "bridge" neurons that maintained activity between the same speech elements during listening and speaking. Population analysis revealed distinct latent components for each task phase and persistent dynamics for specific sentences. Overall, this study provides novel insights into the neuronal basis of speech processing, emphasizing the intricate interplay between perception, production, and verbal working memory.

neuroscience↗

bioRxiv2024

Convergent representations and spatiotemporal dynamics of speech and language in brain and deep neural networks

Recent studies have explored the correspondence between single-modality DNN models (speech or text) and specific brain networks for speech and language. The key factors underlying these correlations and their spatiotemporal evolution within the brain language network remain unclear, particularly across different DNN modalities. To address these questions, we analyzed the representation similarity between self-supervised learning (SSL) models for speech (Wav2Vec2) and language (GPT-2), against neural responses to naturalistic speech captured via high-density electrocorticography. Our results indicated high prediction accuracy of both types of SSL models relative to neural activity before and after word onsets. It was the shared components between Wav2Vec2.0 and GPT-2 that explained the majority portion of the SSL-brain similarity. Furthermore, we observed distinct spatiotemporal dynamics: both models showed high encoding accuracy 40 milliseconds before word onset, especially in the mid-superior temporal gyrus (mid-STG), which can be explained by the shared contextual components in the SSL models; the Wav2Vec2.0 also peaked at 200 milliseconds after word onset around the posterior STG, which was mainly attributed to the acoustic-phonetic and static semantic information encoded in the SSL models. These results highlight how contextual and acoustic-phonetic cues encoded in DNNs align with spatiotemporal neural activity patterns, suggesting a significant overlap in how artificial and biological systems process linguistic information.

neuroscience↗

bioRxiv2024

Natural speech re-synthesis from direct cortical recordings using a pre-trained encoder-decoder framework

Reconstructing perceived speech stimuli from neural recordings is not only advancing the understanding of the neural coding underlying speech processing but also an important building block for brain-computer interfaces and neuroprosthetics. However, previous attempts to directly re-synthesize speech from neural decoding suffer from low re-synthesis quality. With the limited neural data and complex speech representation space, it is hard to build decoding model that directly map neural signal into high-fidelity speech. In this work, we proposed a pre-trained encoder-decoder framework to address these problems. We recorded high-density electrocorticography (ECoG) signals when participants listening to natural speech. We built a pre-trained speech re-synthesizing network that consists of a context-dependent speech encoding network and a generative adversarial network (GAN) for high-fidelity speech synthesis. This model was pre-trained on a large naturalistic speech corpus and can extract critical features for speech re-synthesize. We then built a light-weight neural decoding network that mapped the ECoG signal into the latent space of the pre-trained network, and used the GAN decoder to synthesize natural speech. Using only 20 minutes of intracranial neural data, our neural-driven speech re-synthesis model demonstrated promising performance, with phoneme error rate (PER) at 28.6%, and human listeners were able to recognize 71.6% of the words in the re-synthesized speech. This work demonstrates the feasibility of using pre-trained self-supervised model and feature alignment to build efficient neural-to-speech decoding model.

neuroscience↗

bioRxiv2024

Pathological microcircuits initiate epileptiform events in patient hippocampal slices

How seizures begin at the level of microscopic neuronal circuits remains unknown. Advancements in high-density CMOS-based microelectrode arrays can be harnessed to study neuronal network activity with unprecedented spatial and temporal resolution. We use high-density electrophysiology recordings to probe the network activity of human hippocampal brain slices from six patients with mesial temporal lobe epilepsy. Two slices from the dentate gyrus exhibited epileptiform activity in the presence of low magnesium media with kainic acid. Both slices exhibit network oscillations indicative of a reciprocally connected circuit, which is unexpected under normal physiological conditions. Future studies may apply this approach to elucidate the network signals that underlie seizure initiation.

neuroscience↗

bioRxiv2024

Multiscale Cloud-based Pipeline for Neuronal Electrophysiology Analysis and Visualization

Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.

neuroscience↗

bioRxiv2024

The development of aperiodic neural activity in the human brain

The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study (https://osf.io/gsru7), we analyzed intracranial EEG (iEEG) recordings from widespread brain regions in a large developmental cohort. Using task-based (i.e., attention to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93 - 54.00 years, 63 males; n electrodes = 5691), we mapped aperiodic (1/[f]-like) activity, a proxy of neural noise, with steeper slopes indexing less noise and flatter slopes indexing more noise. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in gray matter volume are similarly associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC control during adolescence in the development of attention and memory.

neuroscience↗

bioRxiv2024

Robust Detection of Brain Stimulation Artifacts in iEEG Using Autoencoder-Generated Signals and ResNet Classification

BackgroundIntracranial EEG (iEEG) is crucial for understanding brain function, but stimulation-induced noise complicates data interpretation. Traditional artifact detection methods require manual user input or struggle with noise variability, especially with limited labeled data. ObjectiveWe developed a supervised method to automatically detect stimulation-induced noise in human iEEG recordings using synthetic data generated by Variational Autoencoders (VAEs) to train a ResNet-18 classifier. MethodsMulti-lead iEEG data were collected, preprocessed, and used to train VAEs for generating synthetic clean and noisy signals. The ResNet-18 model was trained on images of spectra generated from these synthetic signals and validated on real iEEG data from five participants. ResultsThe classifier, trained exclusively on synthetic data, demonstrated high accuracy, precision, and recall when applied to real iEEG recordings, with AUC values greater than 0.99 across all participants. ConclusionWe present a novel approach to effectively detect stimulation-induced noise in iEEG, offering a robust solution for improving data interpretation in scenarios with limited labeled data. Additionally, the pre-trained ResNet-18 model is available for the community to use, facilitating further research and application in similar datasets.

neuroscience↗

bioRxiv2024

An enhancer-AAV toolbox to target and manipulate distinct interneuron subtypes

In recent years, we and others have identified a number of enhancers that, when incorporated into rAAV vectors, can restrict the transgene expression to particular neuronal populations. Yet, viral tools to access and manipulate specific neuronal subtypes are still limited. Here, we performed systematic analysis of single cell genomic data to identify enhancer candidates for each of the telencephalic interneuron subtypes. We established a set of enhancer-AAV tools that are highly specific for distinct cortical interneuron populations and striatal cholinergic interneurons. These enhancers, when used in the context of different effectors, can target (fluorescent proteins), observe activity (GCaMP) and manipulate (opto-genetics) specific neuronal subtypes. We also validated our enhancer-AAV tools across species. Thus, we provide the field with a powerful set of tools to study neural circuits and functions and to develop precise and targeted therapy.

neuroscience↗