Integrating direct electrical stimulation with brain connectivity predicts lesion-induced language impairment and recovery
Bibliographic record
Abstract
Neurological conditions account for millions of deaths per year and induce long-lasting cognitive impairments. The disruption of structural brain networks predicts the emergence of cognitive impairments in stroke cases, but the role of the white matter in modeling longitudinal behavioral trajectories in glioma patients is understudied. We analyzed 486 intracranial brain stimulations from 297 patients (age range 37–40, male ratio 53–64% depending on the functional categories) along with functional and structural brain connectivity data from over 1750 healthy individuals, to create a network mapping method able to identify the neural substrate causally involved in language production. We tested the validity of our procedure by (i) quantifying the spatial correspondence between white matter metabolic and hemodynamic spontaneous activity, measured via resting-state functional Magnetic Resonance Imaging and [18 F]-fluorodeoxyglucose functional Positron Emission Tomography (respectively); (ii) predicting unseen intracranial stimulations points; (iii) modeling the severity of stroke-induced aphasia (n = 105) and the longitudinal recovery of language abilities in glioma patients (n = 42, 3 timepoints). We show that spontaneous white matter hemodynamic oscillations map into metabolic fluctuations. We also demonstrate that the integration of patient-specific intracranial stimulation points and normative human connectivity data (i) is predictive of unseen stimulation points; (ii) provides better estimates than total lesion volume in predicting the severity of stroke-induced aphasia symptoms; (iii) models post-operative language recovery trajectories better than state-of-the-art clinical measures in glioma patients. This work presents a data-driven and neurobiologically grounded tool for modeling cognitive and neurological impairments in terms of network disruption, demonstrating improved precision over existing approaches. Coletta et al. integrate patient-specific intracranial stimulations in the white matter with normative brain connectivity data. Findings reveal that intracranial driven network mapping is an accurate predictor of both neurological impairments in stroke patients and post-operative language recovery trajectories in glioma patients. Understanding how connections in the brain can affect the recovery of language skills after stroke or surgery for brain tumors is of critical importance. Here, we developed a method that combines non-invasive brain scans with direct brain stimulation to identify the exact brain connections important for language production. Our findings reveal a brain map that could predict the severity of language problems after a stroke and how well people recovered their language skills following surgery for brain tumors, better than traditional ways of predicting language problems. We created an effective way to predict and track language difficulties caused by damage in the brain. This could help doctors plan better treatments and support for people recovering from brain injuries or surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".