Integrating intracranial stimulation with brain connectivity in language impairment and recovery
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M4 (Mezzanine Level)
Poster No:
10
Submission Type:
Abstract Submission
Authors:
Ludovico Coletta1, Paolo Avesani1, Luca Zigiotto2, Martina Venturini3, Luciano Annicchiarico3, Laura Vavassori4, Sharna Jamadar5, Emma Liang5, Justine Hansen6, Bratislav Misic7, Sam Ng8, Hugues Duffau8, Silvio Sarubbo3
Institutions:
1Fondazione Bruno Kessler, Trento, Trento, 2S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sanitari, Trento, Trentino, 3Department of Neurosurgery, “S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sani, Trento, Trento, 4Center for Mind/Brain Sciences – CIMeC, University of Trento, Trento, Trento, 5University of Monash, Melbourne, Victoria, 6McGill University, Montreal, QC, 7Montreal Neurological Institute, Montreal, Quebec, 8Institute of Functional Genomics, University of Montpellier, Montpellier, Occitania
First Author:
Co-Author(s):
Luca Zigiotto
S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sanitari
Trento, Trentino
S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sanitari
Trento, Trentino
Martina Venturini
Department of Neurosurgery, “S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sani
Trento, Trento
Department of Neurosurgery, “S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sani
Trento, Trento
Luciano Annicchiarico
Department of Neurosurgery, “S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sani
Trento, Trento
Department of Neurosurgery, “S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sani
Trento, Trento
Silvio Sarubbo
Department of Neurosurgery, “S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sani
Trento, Trento
Department of Neurosurgery, “S. Chiara” University-Hospital, Azienda Provinciale per i Servizi Sani
Trento, Trento
Introduction:
Neurological conditions such as stroke and glioma are major causes of disability worldwide, accounting for millions of deaths per year and long-lasting cognitive impairments1. Recent studies show that brain network disruption can predict survival rates in glioma and model cognitive impairment in stroke2,3. However, a similar connectivity framework to model cognitive functioning after surgery is currently lacking.
Here, we introduce a novel method that integrates direct electrical brain stimulation (DES) with MRI-driven functional network mapping to recover the white matter substrates causally implicated in language production.
Here, we introduce a novel method that integrates direct electrical brain stimulation (DES) with MRI-driven functional network mapping to recover the white matter substrates causally implicated in language production.
Methods:
Recent evidence suggests that spontaneous hemodynamic oscillations in the white matter map into distributed functional brain networks with distinct neurophysiological underpinnings4-6.
In line with these observations, we used white matter DES points causing transient speech arrest, semantic or phonological aphasia (N=297 patients, 486 stimulations) in combination with resting-state fMRI via lesion network mapping7,8 (N=1'000 control subjects) to derive functional networks in the white matter.
To validate our approach, we tested whether DES derived networks are predictive of unseen stimulation points via cross validation, i.e by repeating the network aggregation by iteratively leaving one point out. We also compared hemodynamic activity with metabolic fluctuations using FDG-fPET imaging9.
We used all the white matter regions exceeding the 90th percentile of the correctly classified DES points – in combination with the corresponding cortical DES-derived networks7– as set of waypoints and terminations to filter the whole brain tractography reconstructions of an independent cohort of control subjects (N=753, Human Connectome Project, Fig. 1). In doing so, we recovered the causal anatomical substrates subtending cognitive functioning (Fig. 1).
We obtained one aggregated volumetric map for each functional category, with voxels' values reflecting the importance of a given white matter region for the overall network's architecture (Fig.1). These normative volumetric maps were then applied to model aphasia severity in stroke patients and longitudinal language recovery in glioma patients.
In line with these observations, we used white matter DES points causing transient speech arrest, semantic or phonological aphasia (N=297 patients, 486 stimulations) in combination with resting-state fMRI via lesion network mapping7,8 (N=1'000 control subjects) to derive functional networks in the white matter.
To validate our approach, we tested whether DES derived networks are predictive of unseen stimulation points via cross validation, i.e by repeating the network aggregation by iteratively leaving one point out. We also compared hemodynamic activity with metabolic fluctuations using FDG-fPET imaging9.
We used all the white matter regions exceeding the 90th percentile of the correctly classified DES points – in combination with the corresponding cortical DES-derived networks7– as set of waypoints and terminations to filter the whole brain tractography reconstructions of an independent cohort of control subjects (N=753, Human Connectome Project, Fig. 1). In doing so, we recovered the causal anatomical substrates subtending cognitive functioning (Fig. 1).
We obtained one aggregated volumetric map for each functional category, with voxels' values reflecting the importance of a given white matter region for the overall network's architecture (Fig.1). These normative volumetric maps were then applied to model aphasia severity in stroke patients and longitudinal language recovery in glioma patients.
Results:
We found that DES driven functional white matter networks are predictive of unseen subcortical stimulations with both leave-one-DES-point and leave-one-patient-out scheme (94, 94, and 96% accuracy for semantics, phonology, and speech articulation, respectively).
After accounting for spatial autocorrelation and correcting for multiple comparisons, we found that 80% of the regions (32/40) showed a statistical trend (0.1 < p-value > 0.05) or a statistically significant (p-value < 0.05) correlation between the spatial organization of spontaneous resting state fMRI brain oscillations and FDG-fPET-derived glucose metabolism in the white matter.
With respect to the clinical analyses, we found that both the volume of overlap between the stroke lesion and the volumetric normative maps for semantic and phonological processing as well as total lesion size significantly correlate with symptom severity expressed by a low aphasia quotient score, upper panel of Fig. 2). However, the normative volumetric maps yielded more reliable predictions than total lesion size (quantile regression, upper panel of Fig. 2). Similarly, we found that the volume of overlap between the tumor cavity and the normative volumetric maps to be the best predictor of longitudinal language recovery (lower panel of Fig. 2), outperforming all other predictors – demographical, clinical, and genetic variables.
After accounting for spatial autocorrelation and correcting for multiple comparisons, we found that 80% of the regions (32/40) showed a statistical trend (0.1 < p-value > 0.05) or a statistically significant (p-value < 0.05) correlation between the spatial organization of spontaneous resting state fMRI brain oscillations and FDG-fPET-derived glucose metabolism in the white matter.
With respect to the clinical analyses, we found that both the volume of overlap between the stroke lesion and the volumetric normative maps for semantic and phonological processing as well as total lesion size significantly correlate with symptom severity expressed by a low aphasia quotient score, upper panel of Fig. 2). However, the normative volumetric maps yielded more reliable predictions than total lesion size (quantile regression, upper panel of Fig. 2). Similarly, we found that the volume of overlap between the tumor cavity and the normative volumetric maps to be the best predictor of longitudinal language recovery (lower panel of Fig. 2), outperforming all other predictors – demographical, clinical, and genetic variables.
Conclusions:
We provide robust evidence suggesting that our novel multimodal derivation of DES derived structural networks can be leveraged as a tool to effectively model lesion-induced impairments in stroke and glioma patients, highlighting its translational potential.
Brain Stimulation:
Direct Electrical/Optogenetic Stimulation 1
Language:
Speech Production
Learning and Memory:
Neural Plasticity and Recovery of Function
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Keywords:
Data analysis
FUNCTIONAL MRI
Neoplastic Disease
Neurological
Tractography
White Matter
Other - Direct Electrical Stimulation
1|2Indicates the priority used for review
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2. Salvalaggio A, Pini L, Gaiola M, et al. White Matter Tract Density Index Prediction Model of Overall Survival in Glioblastoma. JAMA Neurol. Published online September 25, 2023. doi:10.1001/jamaneurol.2023.3284
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4. Huang Y, Wei PH, Xu L, et al. Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter. Nat Commun. 2023;14(1):3414. doi:10.1038/s41467-023-39067-3
5. Li J, Wu GR, Li B, et al. Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter. Commun Biol. 2021;4(1):1417. doi:10.1038/s42003-021-02952-y
6. Zu Z, Choi S, Zhao Y, et al. The missing third dimension—Functional correlations of BOLD signals incorporating white matter. Sci Adv. 2024;10(4):eadi0616. doi:10.1126/sciadv.adi0616
7.Coletta L, Avesani P, Zigiotto L, et al. Integrating direct electrical brain stimulation with the human connectome. Brain. 2024;147(3):1100-1111. doi:10.1093/brain/awad402
8.Fox MD. Mapping Symptoms to Brain Networks with the Human Connectome. N Engl J Med. 2018;379(23):2237-2245. doi:10.1056/NEJMra1706158
9. Jamadar SD, Ward PGD, Liang EX, Orchard ER, Chen Z, Egan GF. Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study. Cereb Cortex. 2021;31(6):2855-2867. doi:10.1093/cercor/bhaa393