The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy

Kaestner, Erik; Balachandra, Akshara R.; Bahrami, Naeim; Reyes, Anny; Lalani, Sanam J.; Macari, Anna Christina; Voets, Natalie L.; Drane, Daniel L.; Paul, Brianna M.; Bonilha, Leonardo; McDonald, Carrie R.

doi:10.1016/j.nicl.2019.102125

Cited by 32 publications

(37 citation statements)

References 89 publications

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“…The rich information provided by MRI can also be exploited to calculate the brain connectome, i.e., a matrix of functional or structural connectivity between pairs of regions of gray matter. Once again, these connectomes have mostly been created for the cerebrum without including the cerebellum (Uddin et al, 2019;Kaestner et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

Palesi

Lorenzi

Casellato

et al. 2020

Front. Cell. Neurosci.

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Section: Introductionmentioning

confidence: 99%

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

Palesi

Lorenzi

Casellato

et al. 2020

Front. Cell. Neurosci.

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“…Nevertheless, one can hypothesize that patients with lesions affecting higher‐order and multiple demand networks may present with executive dysfunction. Several reports have shown associations between cortical and subcortical morphology and cognitive variables, as well as interregional connections based on resting‐state fMRI or diffusion MRI tractography 46–48 . Connectome‐level assessments were also related to cognitive phenotypes, in both ETE and TLE 49–51 .…”

Section: Key Application Areas Of Network Biomarkersmentioning

confidence: 97%

“…Network biomarker candidates in temporal lobe epilepsy (TLE). A, Multivariate techniques associating structural connectome to cognitive profiles (left 49 ) and machine learning techniques predicting language impairment (right 46 ). B, Connectome measures for the prediction of seizure focus laterality(left 128 )and associations between connectome architecture and subregional measures of hippocampal pathology (right 16 ).…”

Section: Key Application Areas Of Network Biomarkersmentioning

confidence: 99%

“…Few studies have employed multivariate associative techniques to relate connectome features to cognitive scores, using regularization and cross‐validation to reduce overfitting 49,51 . Recent studies evaluated the potential of connectome features in out‐of‐sample machine learning prediction of cognitive impairment, including memory and language 46,50 . One such study predicted memory impairments by combining structural connectome and hippocampal volumetric features, 50 achieving high sensitivity and moderate specificity, thus introducing the potential of connectome features as biomarkers of cognitive dysfunction.…”

Section: Key Application Areas Of Network Biomarkersmentioning

confidence: 99%

“…A logical next step should be their incorporation into multicentric trials aimed at establishing their utility for surgical decision‐making. Recently developed network markers are still lagging behind, both in terms of validation and biomarker assessment, despite emerging approaches to evaluate their predictive power for various outcomes, including cognitive impairment, 46,50 postoperative seizure freedom, 82,91 and profiling of epileptogenic tissue 84 . In what follows, we outline several key avenues that we consider helpful for future DRE biomarker development and validation efforts.…”

Section: Conclusion and Recommendations For Future Biomarker Discoverymentioning

confidence: 99%

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Connectome biomarkers of drug‐resistant epilepsy

Larivière

Bernasconi

et al. 2020

Epilepsia

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Drug‐resistant epilepsy (DRE) considerably affects patient health, cognition, and well‐being, and disproportionally contributes to the overall burden of epilepsy. The most common DRE syndromes are temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal epilepsy related to cortical malformations. Both syndromes have been traditionally considered as "focal," and most patients benefit from brain surgery for long‐term seizure control. However, increasing evidence indicates that many DRE patients also present with widespread structural and functional network disruptions. These anomalies have been suggested to relate to cognitive impairment and prognosis, highlighting their importance for patient management. The advent of multimodal neuroimaging and formal methods to quantify complex systems has offered unprecedented ability to profile structural and functional brain networks in DRE patients. Here, we performed a systematic review on existing DRE network biomarker candidates and their contribution to three key application areas: (1) modeling of cognitive impairments, (2) localization of the surgical target, and (3) prediction of clinical and cognitive outcomes after surgery. Although network biomarkers hold promise for a range of clinical applications, translation of neuroimaging biomarkers to the patient's bedside has been challenged by a lack of clinical and prospective studies. We therefore close by highlighting conceptual and methodological strategies to improve the evaluation and accessibility of network biomarkers, and ultimately guide clinically actionable decisions.

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Characterizing white matter connectome abnormalities in patients with temporal lobe epilepsy using threshold‐free network‐based statistics

Chu,

Imhoff‐Smith,

Nair

et al. 2024

Brain and Behavior

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IntroductionEmerging evidence illustrates that temporal lobe epilepsy (TLE) involves network disruptions represented by hyperexcitability and other seizure‐related neural plasticity. However, these associations are not well‐characterized. Our study characterizes the whole brain white matter connectome abnormalities in TLE patients compared to healthy controls (HCs) from the prospective Epilepsy Connectome Project study. Furthermore, we assessed whether aberrant white matter connections are differentially related to cognitive impairment and a history of focal‐to‐bilateral tonic–clonic (FBTC) seizures.MethodsMulti‐shell connectome MRI data were preprocessed using the DESIGNER guidelines. The IIT Destrieux gray matter atlas was used to derive the 162 × 162 structural connectivity matrices (SCMs) using MRTrix3. ComBat data harmonization was applied to harmonize the SCMs from pre‐ and post‐scanner upgrade acquisitions. Threshold‐free network‐based statistics were used for statistical analysis of the harmonized SCMs. Cognitive impairment status and FBTC seizure status were then correlated with these findings.ResultsWe employed connectome measurements from 142 subjects, including 92 patients with TLE (36 males, mean age = 40.1 ± 11.7 years) and 50 HCs (25 males, mean age = 32.6 ± 10.2 years). Our analysis revealed overall significant decreases in cross‐sectional area (CSA) of the white matter tract in TLE group compared to controls, indicating decreased white matter tract integrity and connectivity abnormalities in addition to apparent differences in graph theoretic measures of connectivity and network‐based statistics. Focal and generalized cognitive impaired TLE patients showcased higher trend‐level abnormalities in the white matter connectome via decreased CSA than those with no cognitive impairment. Patients with a positive FBTC seizure history also showed trend‐level findings of association via decreased CSA.ConclusionsWidespread global aberrant white matter connectome changes were observed in TLE patients and characterized by seizure history and cognitive impairment, laying a foundation for future studies to expand on and validate the novel biomarkers and further elucidate TLE's impact on brain plasticity.

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The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy

Cited by 32 publications

References 89 publications

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

Connectome biomarkers of drug‐resistant epilepsy

Characterizing white matter connectome abnormalities in patients with temporal lobe epilepsy using threshold‐free network‐based statistics

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