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Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence

Gleichgerrcht, Ezequiel; Kaestner, Erik; Hassanzadeh, Reihaneh; Roth, Rebecca W; Parashos, Alexandra; Davis, Kathryn A; Bagić, Anto; Keller, Simon S; Rüber, Theodor; Stoub, Travis; Pardoe, Heath R; Dugan, Patricia; Drane, Daniel L; Abrol, Anees; Calhoun, Vince; Kuzniecky, Ruben I; McDonald, Carrie R; Bonilha, Leonardo
Despite decades of advancements in diagnostic MRI, 30-50% of temporal lobe epilepsy (TLE) patients remain categorized as "non-lesional" (i.e., MRI negative or MRI-) based on visual assessment by human experts. MRI- patients face diagnostic uncertainty and significant delays in treatment planning. Quantitative MRI studies have demonstrated that MRI- patients often exhibit a TLE-specific pattern of temporal and limbic atrophy that may be too subtle for the human eye to detect. This signature pattern could be successfully translated into clinical use via artificial intelligence (AI) advances in computer-aided MRI interpretation, thereby improving the detection of brain "lesional" patterns associated with TLE. Here, we tested this hypothesis by employing a three-dimensional convolutional neural network (3D CNN) applied to a dataset of 1,178 scans from 12 different centers. 3D CNN was able to differentiate TLE from healthy controls with high accuracy (85.9% ± 2.8), significantly outperforming support vector machines based on hippocampal (74.4% ± 2.6) and whole-brain (78.3% ± 3.3) volumes. Our analysis subsequently focused on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI- patients from this cohort were accurately identified as TLE 82.7% ± 0.9 of the time, an encouraging finding since clinically these were all patients considered to be MRI- (i.e., not radiographically different than controls). The saliency maps from the CNN revealed that limbic structures, particularly medial temporal, cingulate, and orbitofrontal areas, were most influential in classification, confirming the importance of the well-established TLE signature atrophy pattern for diagnosis. Indeed, the saliency maps were similar in MRI+ and MRI- TLE groups, suggesting that even when humans cannot distinguish more subtle levels of atrophy, these MRI- patients are on the same continuum common across all TLE patients. As such, AI can identify TLE lesional patterns and AI-aided diagnosis has the potential to greatly enhance the neuroimaging diagnosis of TLE and redefine the concept of "lesional" TLE.
PMID: 39842945
ISSN: 1460-2156
CID: 5802322

A corollary discharge circuit in human speech

Khalilian-Gourtani, Amirhossein; Wang, Ran; Chen, Xupeng; Yu, Leyao; Dugan, Patricia; Friedman, Daniel; Doyle, Werner; Devinsky, Orrin; Wang, Yao; Flinker, Adeen
When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals; however, in humans, its exact origin and temporal dynamics remain unknown. We report electrocorticographic recordings in neurosurgical patients and a connectivity analysis framework based on Granger causality that reveals major neural communications. We find a reproducible source for corollary discharge across multiple speech production paradigms localized to the ventral speech motor cortex before speech articulation. The uncovered discharge predicts the degree of auditory cortex suppression during speech, its well-documented consequence. These results reveal the human corollary discharge source and timing with far-reaching implication for speech motor-control as well as auditory hallucinations in human psychosis.
PMCID:11648673
PMID: 39625978
ISSN: 1091-6490
CID: 5780132

Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language

Hong, Zhuoqiao; Wang, Haocheng; Zada, Zaid; Gazula, Harshvardhan; Turner, David; Aubrey, Bobbi; Niekerken, Leonard; Doyle, Werner; Devore, Sasha; Dugan, Patricia; Friedman, Daniel; Devinsky, Orrin; Flinker, Adeen; Hasson, Uri; Nastase, Samuel A; Goldstein, Ariel
Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, neuroscience researchers haven't kept up with the quick progress in LLM development. Here, we utilized several families of transformer-based LLMs to investigate the relationship between model size and their ability to capture linguistic information in the human brain. Crucially, a subset of LLMs were trained on a fixed training set, enabling us to dissociate model size from architecture and training set size. We used electrocorticography (ECoG) to measure neural activity in epilepsy patients while they listened to a 30-minute naturalistic audio story. We fit electrode-wise encoding models using contextual embeddings extracted from each hidden layer of the LLMs to predict word-level neural signals. In line with prior work, we found that larger LLMs better capture the structure of natural language and better predict neural activity. We also found a log-linear relationship where the encoding performance peaks in relatively earlier layers as model size increases. We also observed variations in the best-performing layer across different brain regions, corresponding to an organized language processing hierarchy.
PMCID:11244877
PMID: 39005394
ISSN: 2692-8205
CID: 5676342

Author Correction: Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns

Goldstein, Ariel; Grinstein-Dabush, Avigail; Schain, Mariano; Wang, Haocheng; Hong, Zhuoqiao; Aubrey, Bobbi; Nastase, Samuel A; Zada, Zaid; Ham, Eric; Feder, Amir; Gazula, Harshvardhan; Buchnik, Eliav; Doyle, Werner; Devore, Sasha; Dugan, Patricia; Reichart, Roi; Friedman, Daniel; Brenner, Michael; Hassidim, Avinatan; Devinsky, Orrin; Flinker, Adeen; Hasson, Uri
PMID: 39353920
ISSN: 2041-1723
CID: 5739352

The impact of COVID-19 on people with epilepsy: Global results from the coronavirus and epilepsy study

Vasey, Michael J; Tai, Xin You; Thorpe, Jennifer; Jones, Gabriel Davis; Ashby, Samantha; Hallab, Asma; Ding, Ding; Andraus, Maria; Dugan, Patricia; Perucca, Piero; Costello, Daniel J; French, Jacqueline A; O'Brien, Terence J; Depondt, Chantal; Andrade, Danielle M; Sengupta, Robin; Datta, Ashis; Delanty, Norman; Jette, Nathalie; Newton, Charles R; Brodie, Martin J; Devinsky, Orrin; Cross, J Helen; Sander, Josemir W; Hanna, Jane; Besag, Frank M C; Sen, Arjune; ,
OBJECTIVE:To characterize the experience of people with epilepsy and aligned healthcare workers (HCWs) during the first 18 months of the COVID-19 pandemic and compare experiences in high-income countries (HICs) with non-HICs. METHODS:Separate surveys for people with epilepsy and HCWs were distributed online in April 2020. Responses were collected to September 2021. Data were collected for COVID-19 infections, the effect of COVID-related restrictions, access to specialist help for epilepsy (people with epilepsy), and the impact of the pandemic on work productivity (HCWs). The frequency of responses for non-HICs and HICs were compared using non-parametric Chi-square tests. RESULTS:Two thousand one hundred and  five individuals with epilepsy from 53 countries and 392 HCWs from 26 countries provided data. The same proportion of people with epilepsy in non-HICs and HICs reported COVID-19 infection (7%). Those in HICs were more likely to report that COVID-19 measures had affected their health (32% vs. 23%; p < 0.001). There was no difference between non-HICs and HICs in the proportion who reported difficulty in obtaining help for epilepsy. HCWs in non-HICs were more likely to report COVID-19 infection than those in HICs (18% vs 6%; p = 0.001) and that their clinical work had been affected by concerns about contracting COVID-19, lack of personal protective equipment, and the impact of the pandemic on mental health (all p < 0.001). Compared to pre-pandemic practices, there was a significant shift to remote consultations in both non-HICs and HICs (p < 0.001). SIGNIFICANCE/CONCLUSIONS:While the frequency of COVID-19 infection was relatively low in these data from early in the pandemic, our findings suggest broader health consequences and an increased psychosocial burden, particularly among HCWs in non-HICs. Planning for future pandemics should prioritize mental healthcare alongside ensuring access to essential epilepsy services and expanding and enhancing access to remote consultations. PLAIN LANGUAGE SUMMARY/CONCLUSIONS:We asked people with epilepsy about the effects of COVID-19 on their health and healthcare. We wanted to compare responses from people in high-income countries and other countries. We found that people in high-income countries and other countries had similar levels of difficulty in getting help for their epilepsy. People in high-income countries were more likely to say that their general health had been affected. Healthcare workers in non-high-income settings were more likely to have contracted COVID-19 and have the care they deliver affected by the pandemic. Across all settings, COVID-19 associated with a large shift to remote consultations.
PMID: 39225433
ISSN: 2470-9239
CID: 5687772

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals

Chen, Junbo; Chen, Xupeng; Wang, Ran; Le, Chenqian; Khalilian-Gourtani, Amirhossein; Jensen, Erika; Dugan, Patricia; Doyle, Werner; Devinsky, Orrin; Friedman, Daniel; Flinker, Adeen; Wang, Yao
OBJECTIVE/UNASSIGNED:This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training. APPROACH/UNASSIGNED:We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes, by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train both subject-specific models using data from a single participant as well as multi-patient models exploiting data from multiple participants. MAIN RESULTS/UNASSIGNED:The subject-specific models using only low-density 8x8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC=0.817), over N=43 participants, outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N=39) led to further improvement (PCC=0.838). For participants with only sEEG electrodes (N=9), subject-specific models still enjoy comparable performance with an average PCC=0.798. The multi-subject models achieved high performance on unseen participants, with an average PCC=0.765 in leave-one-out cross-validation. SIGNIFICANCE/UNASSIGNED:The proposed SwinTW decoder enables future speech neuroprostheses to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. Importantly, the generalizability of the multi-patient models suggests the exciting possibility of developing speech neuroprostheses for people with speech disability without relying on their own neural data for training, which is not always feasible.
PMCID:10980022
PMID: 38559163
ISSN: 2692-8205
CID: 5676302

A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations

Zada, Zaid; Goldstein, Ariel; Michelmann, Sebastian; Simony, Erez; Price, Amy; Hasenfratz, Liat; Barham, Emily; Zadbood, Asieh; Doyle, Werner; Friedman, Daniel; Dugan, Patricia; Melloni, Lucia; Devore, Sasha; Flinker, Adeen; Devinsky, Orrin; Nastase, Samuel A; Hasson, Uri
Effective communication hinges on a mutual understanding of word meaning in different contexts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We developed a model-based coupling framework that aligns brain activity in both speaker and listener to a shared embedding space from a large language model (LLM). The context-sensitive LLM embeddings allow us to track the exchange of linguistic information, word by word, from one brain to another in natural conversations. Linguistic content emerges in the speaker's brain before word articulation and rapidly re-emerges in the listener's brain after word articulation. The contextual embeddings better capture word-by-word neural alignment between speaker and listener than syntactic and articulatory models. Our findings indicate that the contextual embeddings learned by LLMs can serve as an explicit numerical model of the shared, context-rich meaning space humans use to communicate their thoughts to one another.
PMID: 39096896
ISSN: 1097-4199
CID: 5696672

Do germline genetic variants influence surgical outcomes in drug-resistant epilepsy?

Marques, Paula; Moloney, Patrick B; Ji, Caihong; Zulfiqar Ali, Quratulain; Ramesh, Archana; Goldstein, David B; Barboza, Karen; Chandran, Ilakkiah; Rong, Marlene; Selvarajah, Arunan; Qaiser, Farah; Lira, Victor S T; Valiante, Taufik A; Devinsky, Orrin; Depondt, Chantal; O'Brien, Terence; Perucca, Piero; Sen, Arjune; Dugan, Patricia; Sands, Tristan T; Delanty, Norman; Andrade, Danielle M
OBJECTIVE:We retrospectively explored patients with drug-resistant epilepsy (DRE) who previously underwent presurgical evaluation to identify correlations between surgical outcomes and pathogenic variants in epilepsy genes. METHODS:Through an international collaboration, we evaluated adult DRE patients who were screened for surgical candidacy. Patients with pathogenic (P) or likely pathogenic (LP) germline variants in genes relevant to their epilepsy were included, regardless of whether the genetic diagnosis was made before or after the presurgical evaluation. Patients were divided into two groups: resective surgery (RS) and non-resective surgery candidates (NRSC), with the latter group further divided into: palliative surgery (vagus nerve stimulation, deep brain stimulation, responsive neurostimulation or corpus callosotomy) and no surgery. We compared surgical candidacy evaluations and postsurgical outcomes in patients with different genetic abnormalities. RESULTS:We identified 142 patients with P/LP variants. After presurgical evaluation, 36 patients underwent RS, while 106 patients were NRSC. Patients with variants in ion channel and synaptic transmission genes were more common in the NRSC group (48 %), compared with the RS group (14 %) (p<0.001). Most patients in the RS group had tuberous sclerosis complex. Almost half (17/36, 47 %) in the RS group had Engel class I or II outcomes. Patients with channelopathies were less likely to undergo a surgical procedure than patients with mTORopathies, but when deemed suitable for resection had better surgical outcomes (71 % versus 41 % with Engel I/II). Within the NRSC group, 40 underwent palliative surgery, with 26/40 (65 %) having ≥50 % seizure reduction after mean follow-up of 11 years. Favourable palliative surgery outcomes were observed across a diverse range of genetic epilepsies. SIGNIFICANCE/CONCLUSIONS:Genomic findings, including a channelopathy diagnosis, should not preclude presurgical evaluation or epilepsy surgery, and appropriately selected cases may have good surgical outcomes. Prospective registries of patients with monogenic epilepsies who undergo epilepsy surgery can provide additional insights on outcomes.
PMID: 39168079
ISSN: 1872-6844
CID: 5680782

Clinical outcomes among initial survivors of cryptogenic new-onset refractory status epilepsy (NORSE)

Costello, Daniel J; Matthews, Elizabeth; Aurangzeb, Sidra; Doran, Elisabeth; Stack, Jessica; Wesselingh, Robb; Dugan, Patricia; Choi, Hyunmi; Depondt, Chantal; Devinsky, Orrin; Doherty, Colin; Kwan, Patrick; Monif, Mastura; O'Brien, Terence J; Sen, Arjune; Gaspard, Nicolas
OBJECTIVE:New-onset refractory status epilepticus (NORSE) is a rare but severe clinical syndrome. Despite rigorous evaluation, the underlying cause is unknown in 30%-50% of patients and treatment strategies are largely empirical. The aim of this study was to describe clinical outcomes in a cohort of well-phenotyped, thoroughly investigated patients who survived the initial phase of cryptogenic NORSE managed in specialist centers. METHODS:Well-characterized cases of cryptogenic NORSE were identified through the EPIGEN and Critical Care EEG Monitoring Research Consortia (CCEMRC) during the period 2005-2019. Treating epileptologists reported on post-NORSE survival rates and sequelae in patients after discharge from hospital. Among survivors >6 months post-discharge, we report the rates and severity of active epilepsy, global disability, vocational, and global cognitive and mental health outcomes. We attempt to identify determinants of outcome. RESULTS:Among 48 patients who survived the acute phase of NORSE to the point of discharge from hospital, 9 had died at last follow-up, of whom 7 died within 6 months of discharge from the tertiary care center. The remaining 39 patients had high rates of active epilepsy as well as vocational, cognitive, and psychiatric comorbidities. The epilepsy was usually multifocal and typically drug resistant. Only a minority of patients had a good functional outcome. Therapeutic interventions were heterogenous during the acute phase of the illness. There was no clear relationship between the nature of treatment and clinical outcomes. SIGNIFICANCE/CONCLUSIONS:Among survivors of cryptogenic NORSE, longer-term outcomes in most patients were life altering and often catastrophic. Treatment remains empirical and variable. There is a pressing need to understand the etiology of cryptogenic NORSE and to develop tailored treatment strategies.
PMID: 38498313
ISSN: 1528-1167
CID: 5640142

Temporal dynamics of short-term neural adaptation across human visual cortex

Brands, Amber Marijn; Devore, Sasha; Devinsky, Orrin; Doyle, Werner; Flinker, Adeen; Friedman, Daniel; Dugan, Patricia; Winawer, Jonathan; Groen, Iris Isabelle Anna
Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventral- and lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses between lower and higher visual brain areas and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences.
PMID: 38815000
ISSN: 1553-7358
CID: 5663772