Biology subjects

Chang, E. F.

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

49 records · Page 3Linked to original sources

bioRxiv2021

Dissociable oscillatory networks support gain and loss processing in human orbitofrontal cortex

Human neuroimaging and animal studies have linked neural activity in orbitofrontal cortex (OFC) to valuation of positive and negative outcomes. Additional evidence shows that neural oscillations, representing the coordinated activity of neuronal ensembles, support information processing in both animal and human prefrontal regions. However, the role of OFC neural oscillations in reward-processing in humans remains unknown, partly due to the difficulty of recording oscillatory neural activity from deep brain regions. Here, we examined the role of OFC neural oscillations (<30Hz) in reward processing by combining intracranial OFC recordings with a gambling task in which patients made economic decisions under uncertainty. Our results show that power in different oscillatory bands are associated with distinct components of reward evaluation. Specifically, we observed a double dissociation, with a selective theta band oscillation increase in response to monetary gains and a beta band increase in response to losses. These effects were interleaved across OFC in overlapping networks and were accompanied by increases in oscillatory coherence between OFC electrode sites in theta and beta band during gain and loss processing, respectively. These results provide evidence that gain and loss processing in human OFC are supported by distinct low-frequency oscillations in networks, and provide evidence that participating neuronal ensembles are organized functionally through oscillatory coherence, rather than local anatomical segregation.

neuroscience↗

bioRxiv2020

Naturalistic affective behaviors decoded from spectro-spatial features of multi-day human intracranial recordings

Task-based studies have uncovered distributed neural networks that support emotions, but little is known about how these networks produce affective behaviors in non-laboratory, ecological settings. We obtained continuous intracranial electroencephalography (iEEG) recordings from the emotion network in 11 patients with epilepsy during multi-day hospitalizations. We coded naturalistic affective behaviors (spontaneous expressions of positive or negative affect) from 116 hours of time-locked video recordings obtained over multiple days from subjects hospital rooms and utilized data driven classifiers to determine whether we could decode naturalistic affective behaviors from the neural data. Results indicated that binary within-subject random forest models could decode positive and negative affective behaviors from affectless behaviors (behaviors lacking valence) with up to 93% accuracy. Across the emotion network, positive and negative affective behaviors were associated with increased high frequency activity and decreased lower frequency activity. The anterior insula, amygdala, hippocampus, and anterior cingulate cortex (ACC) made strong contributions to affective behaviors in general. In a subset of subjects, three-state decoders distinguished among the positive, negative, and affectless behaviors using the spectro-spatial features from the emotion network. This study demonstrates that multi-day, highly resolved iEEG recordings in cortical and deep brain structures can reveal the circuit-level physiology of affective behaviors. By measuring behavior in an ecologically valid setting, our findings provide novel insights into the spatially distributed dynamics of local neural populations underlying naturalistic affective behaviors. SignificancePrevious neuroimaging and neurophysiological studies have identified a distributed network that supports emotions, but much remains unknown about how this network produces affective behaviors in ecological settings. We used intracranial electroencephalography recordings from the emotion network to decode naturalistic affective behaviors--spontaneous expressions of positive and negative affect that occurred during multi-day hospital stays--from neural data in patients with epilepsy. Our results complement prior neuroscientific studies of emotion and offer novel insights into the spectral and spatial dynamics of the emotion network that characterize naturalistic affective behaviors. The present study suggests intracranial electroencephalography can uncover new details about emotion network physiology and help to expand current neuroanatomical frameworks of human emotions.

neuroscience↗

bioRxiv2020

Topography of speech-related acoustic and phonological feature encoding throughout the human core and parabelt auditory cortex

Speech perception involves the extraction of acoustic and phonological features from the speech signal. How those features map out across the human auditory cortex is unknown. Complementary to noninvasive imaging, the high spatial and temporal resolution of intracranial recordings has greatly contributed to recent advances in our understanding. However, these approaches are typically limited by piecemeal sampling of the expansive human temporal lobe auditory cortex. Here, we present a functional characterization of local cortical encoding throughout all major regions of the primary and non-primary human auditory cortex. We overcame previous limitations by using rare direct recordings from the surface of the temporal plane after surgical microdissection of the deep Sylvian fissure between the frontal and temporal lobes. We recorded neural responses using simultaneous high-density direct recordings over the left temporal plane and the lateral superior temporal gyrus, while participants listened to natural speech sentences and pure tone stimuli. We found an anatomical separation of simple spectral feature tuning, including tuning for pure tones and absolute pitch, on the superior surface of the temporal plane, and complex tuning for phonological features, relative pitch and speech amplitude modulations on lateral STG. Broadband onset responses are unique to posterior STG and not found elsewhere in auditory cortices. This onset region is functionally distinct from the rest of STG, with latencies similar to primary auditory areas. These findings reveal a new, detailed functional organization of response selectivity to acoustic and phonological features in speech throughout the human auditory cortex.

neuroscience↗

bioRxiv2020

Low-frequency neural tracking of speech envelope reflects evoked responses to acoustic edges

The amplitude envelope of speech is crucial for accurate comprehension. Considered a key stage in speech processing, the phase of neural activity in the theta-delta bands (1 - 10 Hz) tracks the phase of the speech amplitude envelope during listening. However, the mechanisms underlying this envelope representation have been heavily debated. A dominant model posits that envelope tracking reflects entrainment of endogenous low-frequency oscillations to the speech envelope. Alternatively, envelope tracking reflects a series of evoked responses to acoustic landmarks within the envelope. It has proven challenging to distinguish these two mechanisms. To address this, we recorded magnetoencephalography while participants listened to natural speech, and compared the neural phase patterns to the predictions of two computational models: An oscillatory entrainment model and a model of evoked responses to peaks in the rate of envelope change. Critically, we also presented speech at slowed rates, where the spectrotemporal predictions of the two models diverge. Our analyses revealed transient theta phase-locking in regular speech, as predicted by both models. However, for slow speech we found transient theta and delta phase-locking, a pattern that was fully compatible with the evoked response model but could not be explained by the oscillatory entrainment model. Furthermore, encoding of acoustic edge magnitudes was invariant to contextual speech rate, demonstrating speech rate normalization of acoustic edge representations. Taken together, our results suggest that neural phase locking to the speech envelope is more likely to reflect discrete representation of transient information rather than oscillatory entrainment.

neuroscience↗

bioRxiv2020

Human microglia upregulate cytokine signatures and accelerate maturation of neural networks

Microglia are the resident macrophages of the brain that emerge in early development and play vital role disease states, as well as in normal development. Many fundamental questions about microglia diversity and function during human brain development remain unanswered, as we currently lack cellular-resolution datasets focusing on microglia in developing primary tissue, or experimental strategies for interrogating their function. Here, we report an integrative analysis of microglia throughout human brain development, which reveals molecular signatures of stepwise maturation, as well as human-specific cytokine-associated subtype that emerges around the onset of neurogenesis. To demonstrate the utility of this atlas, we have compared microglia across several culture models, including cultured primary microglia, pluripotent stem cell-derived microglia. We identify gene expression signatures differentially recruited and attenuated across experimental models, which will accelerate functional characterization of microglia across perturbations, species, and disease conditions. Finally, we identify a role for human microglia in development of synchronized network activity using a xenotransplantation model of human microglia into cerebral organoids.

developmental biology↗

bioRxiv2020

Biomarkers of Depression Symptoms Defined by Direct Intracranial Neurophysiology

Quantitative biological substrates of depression remain elusive. We carried out this study to determine whether application of a novel computational approach to high spatiotemporal resolution direct neural recordings may unlock the functional organization and coordinated activity patterns of depression networks. We identified two subnetworks conserved across the majority of individuals studied. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings identify distributed circuit activity associated with depression, link neural activity with functional connectivity profiles, and inform strategies for personalized targeted intervention.

neuroscience↗

bioRxiv2019

Machine translation of cortical activity to text with an encoder-decoder framework

A decade after the first successful attempt to decode speech directly from human brain signals, accuracy and speed remain far below that of natural speech or typing. Here we show how to achieve high accuracy from the electrocorticogram at natural-speech rates, even with few data (on the order of half an hour of spoken speech). Taking a cue from recent advances in machine translation and automatic speech recognition, we train a recurrent neural network to map neural signals directly to word sequences (sentences). In particular, the network first encodes a sentence-length sequence of neural activity into an abstract representation, and then decodes this representation, word by word, into an English sentence. For each participant, training data consist of several spoken repeats of a set of some 30-50 sentences, along with the corresponding neural signals at each of about 250 electrodes distributed over peri-Sylvian speech cortices. Average word error rates across a validation (held-out) sentence set are as low as 7% for some participants, as compared to the previous state of the art of greater than 60%. Finally, we show how to use transfer learning to overcome limitations on data availability: Training certain components of the network under multiple participants data, while keeping other components (e.g., the first hidden layer) \"proprietary,\" can improve decoding performance--despite very different electrode coverage across participants.

neuroscience↗

bioRxiv2018

Intelligible speech synthesis from neural decoding of spoken sentences

The ability to read out, or decode, mental content from brain activity has significant practical and scientific implications1. For example, technology that translates cortical activity into speech would be transformative for people unable to communicate as a result of neurological impairment2,3,4. Decoding speech from neural activity is challenging because speaking requires extremely precise and dynamic control of multiple vocal tract articulators on the order of milliseconds. Here, we designed a neural decoder that explicitly leverages the continuous kinematic and sound representations encoded in cortical activity5,6 to generate fluent and intelligible speech. A recurrent neural network first decoded vocal tract physiological signals from direct cortical recordings, and then transformed them to acoustic speech output. Robust decoding performance was achieved with as little as 25 minutes of training data. Naive listeners were able to accurately identify these decoded sentences. Additionally, speech decoding was not only effective for audibly produced speech, but also when participants silently mimed speech. These results advance the development of speech neuroprosthetic technology to restore spoken communication in patients with disabling neurological disorders.

neuroscience↗

bioRxiv2018

Speaker-normalized vowel representations in the human auditory cortex

Humans identify speech sounds, the fundamental building blocks of spoken language, using the same cues, or acoustic dimensions, as those that differentiate the voices of different speakers. The correct interpretation of speech cues is hence uncertain, and requires normalizing to the specific speaker. Here we assess how the human brain uses speaker-related contextual information to constrain the processing of speech cues. Using high-density electrocorticography, we recorded local neural activity from the cortical surface of participants who were engaged in a speech sound identification task. The speech sounds were preceded by speech from different speakers whose voices differed along the same acoustic dimension that differentiated the target speech sounds (the first formant; the lowest resonance frequency of the vocal tract). We found that the same acoustic speech sound tokens were perceived differently, and evoked different neural responses in auditory cortex, when they were heard in the context of different speakers. Such normalization involved the rescaling of acoustic-phonetic representations of speech, demonstrating a form of recoding before the signal is mapped onto phonemes or higher level linguistic units. This process is the result of auditory cortex sensitivity to the contrast between the dominant frequencies in speech sounds and those in their just preceding context. These findings provide important insights into the mechanistic implementation of normalization in human listeners. Moreover, they provide the first direct evidence of speaker-normalized speech sound representations in human parabelt auditory cortex, highlighting its critical role in resolving variability in sensory signals.

neuroscience↗

bioRxiv2018

A speech envelope landmark for syllable encoding in human superior temporal gyrus

Listeners use the slow amplitude modulations of speech, known as the envelope, to segment continuous speech into syllables. However, the underlying neural computations are heavily debated. We used high-density intracranial cortical recordings while participants listened to natural and synthesized control speech stimuli to determine how the envelope is represented in the human superior temporal gyrus (STG), a critical auditory brain area for speech processing. We found that the STG does not encode the instantaneous, moment-by-moment amplitude envelope of speech. Rather, a zone of the middle STG detects discrete acoustic onset edges, defined by local maxima in the rate-of-change of the envelope. Acoustic analysis demonstrated that acoustic onset edges reliably cue the information-rich transition between the consonant-onset and vowel-nucleus of syllables. Furthermore, the steepness of the acoustic edge cued whether a syllable was stressed. Synthesized amplitude-modulated tone stimuli showed that steeper edges elicited monotonically greater cortical responses, confirming the encoding of relative but not absolute amplitude. Overall, encoding of the timing and magnitude of acoustic onset edges in STG underlies our perception of the syllabic rhythm of speech.

neuroscience↗

bioRxiv2018

Myelinated inhibitory axons in human neocortex

Numerous myelinated axons traverse the human neocortex. In a previous paper (Micheva et al., 2016) we showed that in mouse many of these axons belong to local inhibitory neurons, the parvalbumin-positive basket cells. Here, using samples of neurosurgically-excised cortex, we confirm the presence of myelinated inhibitory axons in all layers of human neocortex. As in mouse, these axons have distinctive features, including high neurofilament content, short nodes of Ranvier, and high content of myelin basic protein in their myelin sheath. We further show that, consistent with the known high-energy demands of parvalbumin interneurons, the inhibitory myelinated axons have more mitochondria, as well more 2,3-cyclic nucleotide 3-phosphodiesterase (a protein enriched in the myelin cytoplasmic channels thought to provide access for trophic support from oligodendrocytes). The distinctive features of myelinated inhibitory axons in human cortical grey matter may have important implications for neurological disorders that involve pathologies of myelinated axons.

neuroscience↗

bioRxiv2017

Neural Encoding of Auditory Features during Music Perception and Imagery: Insight into the Brain of a Piano Player

It remains unclear how the human cortex represents spectrotemporal sound features during auditory imagery, and how this representation compares to auditory perception. To assess this, we recorded electrocorticographic signals from an epileptic patient with proficient music ability in two conditions. First, the participant played two piano pieces on an electronic piano with the sound volume of the digital keyboard on. Second, the participant replayed the same piano pieces, but without auditory feedback, and the participant was asked to imagine hearing the music in his mind. In both conditions, the sound output of the keyboard was recorded, thus allowing precise time-locking between the neural activity and the spectrotemporal content of the music imagery. For both conditions, we built encoding models to predict high gamma neural activity (70-150Hz) from the spectrogram representation of the recorded sound. We found robust similarities between perception and imagery - in frequency and temporal tuning properties in auditory areas.\n\nAbbreviations

neuroscience↗

bioRxiv2016

Parallel streams define the temporal dynamics of speech processing across human auditory cortex

To derive meaning from speech, we must extract multiple dimensions of concurrent information from incoming speech signals, including phonetic and prosodic cues. Equally important is the detection of acoustic cues that give structure and context to the information we hear, such as sentence boundaries. How the brain organizes this information processing is unknown. Here, using data-driven computational methods on an extensive set of high-density intracranial recordings, we reveal a large-scale partitioning of the entire human speech cortex into two spatially distinct regions that detect important cues for parsing natural speech. These caudal (Zone 1) and rostral (Zone 2) regions work in parallel to detect onsets and prosodic information, respectively, within naturally spoken sentences. In contrast, local processing within each region supports phonetic feature encoding. These findings demonstrate a fundamental organizational property of the human auditory cortex that has been previously unrecognized.

neuroscience↗