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Timing and location of speech errors induced by direct cortical stimulation

Kabakoff, Heather; Yu, Leyao; Friedman, Daniel; Dugan, Patricia; Doyle, Werner K; Devinsky, Orrin; Flinker, Adeen
Cortical regions supporting speech production are commonly established using neuroimaging techniques in both research and clinical settings. However, for neurosurgical purposes, structural function is routinely mapped peri-operatively using direct electrocortical stimulation. While this method is the gold standard for identification of eloquent cortical regions to preserve in neurosurgical patients, there is lack of specificity of the actual underlying cognitive processes being interrupted. To address this, we propose mapping the temporal dynamics of speech arrest across peri-sylvian cortices by quantifying the latency between stimulation and speech deficits. In doing so, we are able to substantiate hypotheses about distinct region-specific functional roles (e.g. planning versus motor execution). In this retrospective observational study, we analysed 20 patients (12 female; age range 14-43) with refractory epilepsy who underwent continuous extra-operative intracranial EEG monitoring of an automatic speech task during clinical bedside language mapping. Latency to speech arrest was calculated as time from stimulation onset to speech arrest onset, controlling for individual speech rate. Most instances of motor-based arrest (87.5% of 96 instances) were in sensorimotor cortex with mid-range latencies to speech arrest with a distributional peak at 0.47 s. Speech arrest occurred in numerous regions, with relatively short latencies in supramarginal gyrus (0.46 s), superior temporal gyrus (0.51 s) and middle temporal gyrus (0.54 s), followed by relatively long latencies in sensorimotor cortex (0.72 s) and especially long latencies in inferior frontal gyrus (0.95 s). Non-parametric testing for speech arrest revealed that region predicted latency; latencies in supramarginal gyrus and in superior temporal gyrus were shorter than in sensorimotor cortex and in inferior frontal gyrus. Sensorimotor cortex is primarily responsible for motor-based arrest. Latencies to speech arrest in supramarginal gyrus and superior temporal gyrus (and to a lesser extent middle temporal gyrus) align with latencies to motor-based arrest in sensorimotor cortex. This pattern of relatively quick cessation of speech suggests that stimulating these regions interferes with the outgoing motor execution. In contrast, the latencies to speech arrest in inferior frontal gyrus and in ventral regions of sensorimotor cortex were significantly longer than those in temporoparietal regions. Longer latencies in the more frontal areas (including inferior frontal gyrus and ventral areas of precentral gyrus and postcentral gyrus) suggest that stimulating these areas interrupts a higher-level speech production process involved in planning. These results implicate the ventral specialization of sensorimotor cortex (including both precentral and postcentral gyri) for speech planning above and beyond motor execution.
PMCID:10948744
PMID: 38505231
ISSN: 2632-1297
CID: 5640502

Temporal dynamics of neural responses in human visual cortex

Groen, Iris I A; Piantoni, Giovanni; Montenegro, Stephanie; Flinker, Adeen; Devore, Sasha; Devinsky, Orrin; Doyle, Werner; Dugan, Patricia; Friedman, Daniel; Ramsey, Nick; Petridou, Natalia; Winawer, Jonathan
Neural responses to visual stimuli exhibit complex temporal dynamics, including sub-additive temporal summation, response reduction with repeated or sustained stimuli (adaptation), and slower dynamics at low contrast. These phenomena are often studied independently. Here, we demonstrate these phenomena within the same experiment and model the underlying neural computations with a single computational model. We extracted time-varying responses from electrocorticographic (ECoG) recordings from patients presented with stimuli that varied in contrast, duration, and inter-stimulus interval (ISI). Aggregating data across patients from both sexes yielded 98 electrodes with robust visual responses, covering both earlier (V1-V3) and higher-order (V3a/b, LO, TO, IPS) retinotopic maps. In all regions, the temporal dynamics of neural responses exhibit several non-linear features: peak response amplitude saturates with high contrast and longer stimulus durations; the response to a second stimulus is suppressed for short ISIs and recovers for longer ISIs; response latency decreases with increasing contrast. These features are accurately captured by a computational model comprised of a small set of canonical neuronal operations: linear filtering, rectification, exponentiation, and a delayed divisive normalization. We find that an increased normalization term captures both contrast- and adaptation-related response reductions, suggesting potentially shared underlying mechanisms. We additionally demonstrate both changes and invariance in temporal response dynamics between earlier and higher-order visual areas. Together, our results reveal the presence of a wide range of temporal and contrast-dependent neuronal dynamics in the human visual cortex, and demonstrate that a simple model captures these dynamics at millisecond resolution.SIGNIFICANCE STATEMENTSensory inputs and neural responses change continuously over time. It is especially challenging to understand a system that has both dynamic inputs and outputs. Here we use a computational modeling approach that specifies computations to convert a time-varying input stimulus to a neural response time course, and use this to predict neural activity measured in the human visual cortex. We show that this computational model predicts a wide variety of complex neural response shapes that we induced experimentally by manipulating the duration, repetition and contrast of visual stimuli. By comparing data and model predictions, we uncover systematic properties of temporal dynamics of neural signals, allowing us to better understand how the brain processes dynamic sensory information.
PMID: 35999054
ISSN: 1529-2401
CID: 5338232

Multiscale temporal integration organizes hierarchical computation in human auditory cortex

Norman-Haignere, Sam V; Long, Laura K; Devinsky, Orrin; Doyle, Werner; Irobunda, Ifeoma; Merricks, Edward M; Feldstein, Neil A; McKhann, Guy M; Schevon, Catherine A; Flinker, Adeen; Mesgarani, Nima
To derive meaning from sound, the brain must integrate information across many timescales. What computations underlie multiscale integration in human auditory cortex? Evidence suggests that auditory cortex analyses sound using both generic acoustic representations (for example, spectrotemporal modulation tuning) and category-specific computations, but the timescales over which these putatively distinct computations integrate remain unclear. To answer this question, we developed a general method to estimate sensory integration windows-the time window when stimuli alter the neural response-and applied our method to intracranial recordings from neurosurgical patients. We show that human auditory cortex integrates hierarchically across diverse timescales spanning from ~50 to 400 ms. Moreover, we find that neural populations with short and long integration windows exhibit distinct functional properties: short-integration electrodes (less than ~200 ms) show prominent spectrotemporal modulation selectivity, while long-integration electrodes (greater than ~200 ms) show prominent category selectivity. These findings reveal how multiscale integration organizes auditory computation in the human brain.
PMID: 35145280
ISSN: 2397-3374
CID: 5156382

Shared computational principles for language processing in humans and deep language models

Goldstein, Ariel; Zada, Zaid; Buchnik, Eliav; Schain, Mariano; Price, Amy; Aubrey, Bobbi; Nastase, Samuel A; Feder, Amir; Emanuel, Dotan; Cohen, Alon; Jansen, Aren; Gazula, Harshvardhan; Choe, Gina; Rao, Aditi; Kim, Catherine; Casto, Colton; Fanda, Lora; Doyle, Werner; Friedman, Daniel; Dugan, Patricia; Melloni, Lucia; Reichart, Roi; Devore, Sasha; Flinker, Adeen; Hasenfratz, Liat; Levy, Omer; Hassidim, Avinatan; Brenner, Michael; Matias, Yossi; Norman, Kenneth A; Devinsky, Orrin; Hasson, Uri
Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.
PMCID:8904253
PMID: 35260860
ISSN: 1546-1726
CID: 5190382

A cortical network processes auditory error signals during human speech production to maintain fluency

Ozker, Muge; Doyle, Werner; Devinsky, Orrin; Flinker, Adeen
Hearing one's own voice is critical for fluent speech production as it allows for the detection and correction of vocalization errors in real time. This behavior known as the auditory feedback control of speech is impaired in various neurological disorders ranging from stuttering to aphasia; however, the underlying neural mechanisms are still poorly understood. Computational models of speech motor control suggest that, during speech production, the brain uses an efference copy of the motor command to generate an internal estimate of the speech output. When actual feedback differs from this internal estimate, an error signal is generated to correct the internal estimate and update necessary motor commands to produce intended speech. We were able to localize the auditory error signal using electrocorticographic recordings from neurosurgical participants during a delayed auditory feedback (DAF) paradigm. In this task, participants hear their voice with a time delay as they produced words and sentences (similar to an echo on a conference call), which is well known to disrupt fluency by causing slow and stutter-like speech in humans. We observed a significant response enhancement in auditory cortex that scaled with the duration of feedback delay, indicating an auditory speech error signal. Immediately following auditory cortex, dorsal precentral gyrus (dPreCG), a region that has not been implicated in auditory feedback processing before, exhibited a markedly similar response enhancement, suggesting a tight coupling between the 2 regions. Critically, response enhancement in dPreCG occurred only during articulation of long utterances due to a continuous mismatch between produced speech and reafferent feedback. These results suggest that dPreCG plays an essential role in processing auditory error signals during speech production to maintain fluency.
PMID: 35113857
ISSN: 1545-7885
CID: 5153792

Long-term priors influence visual perception through recruitment of long-range feedback

Hardstone, Richard; Zhu, Michael; Flinker, Adeen; Melloni, Lucia; Devore, Sasha; Friedman, Daniel; Dugan, Patricia; Doyle, Werner K; Devinsky, Orrin; He, Biyu J
Perception results from the interplay of sensory input and prior knowledge. Despite behavioral evidence that long-term priors powerfully shape perception, the neural mechanisms underlying these interactions remain poorly understood. We obtained direct cortical recordings in neurosurgical patients as they viewed ambiguous images that elicit constant perceptual switching. We observe top-down influences from the temporal to occipital cortex, during the preferred percept that is congruent with the long-term prior. By contrast, stronger feedforward drive is observed during the non-preferred percept, consistent with a prediction error signal. A computational model based on hierarchical predictive coding and attractor networks reproduces all key experimental findings. These results suggest a pattern of large-scale information flow change underlying long-term priors' influence on perception and provide constraints on theories about long-term priors' influence on perception.
PMID: 34725348
ISSN: 2041-1723
CID: 5037932

Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions

Michelmann, Sebastian; Price, Amy R; Aubrey, Bobbi; Strauss, Camilla K; Doyle, Werner K; Friedman, Daniel; Dugan, Patricia C; Devinsky, Orrin; Devore, Sasha; Flinker, Adeen; Hasson, Uri; Norman, Kenneth A
Humans form lasting memories of stimuli that were only encountered once. This naturally occurs when listening to a story, however it remains unclear how and when memories are stored and retrieved during story-listening. Here, we first confirm in behavioral experiments that participants can learn about the structure of a story after a single exposure and are able to recall upcoming words when the story is presented again. We then track mnemonic information in high frequency activity (70-200 Hz) as patients undergoing electrocorticographic recordings listen twice to the same story. We demonstrate predictive recall of upcoming information through neural responses in auditory processing regions. This neural measure correlates with behavioral measures of event segmentation and learning. Event boundaries are linked to information flow from cortex to hippocampus. When listening for a second time, information flow from hippocampus to cortex precedes moments of predictive recall. These results provide insight on a fine-grained temporal scale into how episodic memory encoding and retrieval work under naturalistic conditions.
PMID: 34518520
ISSN: 2041-1723
CID: 5012282

Gender bias in academia: A lifetime problem that needs solutions

Llorens, Anaïs; Tzovara, Athina; Bellier, Ludovic; Bhaya-Grossman, Ilina; Bidet-Caulet, Aurélie; Chang, William K; Cross, Zachariah R; Dominguez-Faus, Rosa; Flinker, Adeen; Fonken, Yvonne; Gorenstein, Mark A; Holdgraf, Chris; Hoy, Colin W; Ivanova, Maria V; Jimenez, Richard T; Jun, Soyeon; Kam, Julia W Y; Kidd, Celeste; Marcelle, Enitan; Marciano, Deborah; Martin, Stephanie; Myers, Nicholas E; Ojala, Karita; Perry, Anat; Pinheiro-Chagas, Pedro; Riès, Stephanie K; Saez, Ignacio; Skelin, Ivan; Slama, Katarina; Staveland, Brooke; Bassett, Danielle S; Buffalo, Elizabeth A; Fairhall, Adrienne L; Kopell, Nancy J; Kray, Laura J; Lin, Jack J; Nobre, Anna C; Riley, Dylan; Solbakk, Anne-Kristin; Wallis, Joni D; Wang, Xiao-Jing; Yuval-Greenberg, Shlomit; Kastner, Sabine; Knight, Robert T; Dronkers, Nina F
Despite increased awareness of the lack of gender equity in academia and a growing number of initiatives to address issues of diversity, change is slow, and inequalities remain. A major source of inequity is gender bias, which has a substantial negative impact on the careers, work-life balance, and mental health of underrepresented groups in science. Here, we argue that gender bias is not a single problem but manifests as a collection of distinct issues that impact researchers' lives. We disentangle these facets and propose concrete solutions that can be adopted by individuals, academic institutions, and society.
PMID: 34237278
ISSN: 1097-4199
CID: 4933422

Spatiotemporal dynamics between interictal epileptiform discharges and ripples during associative memory processing

Henin, Simon; Shankar, Anita; Borges, Helen; Flinker, Adeen; Doyle, Werner; Friedman, Daniel; Devinsky, Orrin; Buzsáki, György; Liu, Anli
We describe the spatiotemporal course of cortical high-gamma activity, hippocampal ripple activity and interictal epileptiform discharges during an associative memory task in 15 epilepsy patients undergoing invasive EEG. Successful encoding trials manifested significantly greater high-gamma activity in hippocampus and frontal regions. Successful cued recall trials manifested sustained high-gamma activity in hippocampus compared to failed responses. Hippocampal ripple rates were greater during successful encoding and retrieval trials. Interictal epileptiform discharges during encoding were associated with 15% decreased odds of remembering in hippocampus (95% confidence interval 6-23%). Hippocampal interictal epileptiform discharges during retrieval predicted 25% decreased odds of remembering (15-33%). Odds of remembering were reduced by 25-52% if interictal epileptiform discharges occurred during the 500-2000-ms window of encoding or by 41% during retrieval. During encoding and retrieval, hippocampal interictal epileptiform discharges were followed by a transient decrease in ripple rate. We hypothesize that interictal epileptiform discharges impair associative memory in a regionally and temporally specific manner by decreasing physiological hippocampal ripples necessary for effective encoding and recall. Because dynamic memory impairment arises from pathological interictal epileptiform discharge events competing with physiological ripples, interictal epileptiform discharges represent a promising therapeutic target for memory remediation in patients with epilepsy.
PMID: 33889945
ISSN: 1460-2156
CID: 4847522

Learning hierarchical sequence representations across human cortex and hippocampus

Henin, Simon; Turk-Browne, Nicholas B; Friedman, Daniel; Liu, Anli; Dugan, Patricia; Flinker, Adeen; Doyle, Werner; Devinsky, Orrin; Melloni, Lucia
Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain's ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.
PMCID:7895424
PMID: 33608265
ISSN: 2375-2548
CID: 4793972