Association of white matter diffusion characteristics and cognitive deficits in temporal lobe epilepsy

Rodríguez‐Cruces, Raúl; Velázquez-Pérez, Leticia; Rodríguez‐Leyva, Ildefonso; Velasco, Ana Luisa; Trejo-Martínez, David; Barragán-Campos, Héctor Manuel; Camacho-Téllez, Vicente; Concha, Luis

doi:10.1016/j.yebeh.2017.11.040

Cited by 56 publications

(64 citation statements)

References 37 publications

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“…Our study joins the expanding field of studies that have attempted to understand the heterogeneity in cognitive ability by subdividing TLE into cognitive phenotypes and characterizing the neuroanatomical correlates of each subtype. This type of phenotyping could enable a personalized medicine approach to patients for predicting cognitive progression and potentially cognitive decline following surgery . Understanding the shared neurobiology within cognitive phenotypes will be a key aspect in tying together the descriptive and predictive elements of this effort as part of ongoing epilepsy‐wide efforts in subdividing patients to improve treatment decision making …”

Section: Discussionmentioning

confidence: 99%

“…This type of phenotyping could enable a personalized medicine approach to patients for predicting cognitive progression and potentially cognitive decline following surgery. [2][3][4][5] Understanding the shared neurobiology within cognitive phenotypes will be a key aspect in tying together the descriptive and predictive elements of this effort as part of ongoing epilepsy-wide efforts in subdividing patients to improve treatment decision making. 52,53…”

Section: Implications For Cognitive Phenotypesmentioning

confidence: 99%

“…Temporal lobe epilepsy (TLE) is a heterogeneous disorder, characterized by an array of clinical and cognitive symptoms . To explain this heterogeneity, studies are beginning to identify TLE cognitive subtypes with similar features . Recently, we have identified four such “cognitive phenotypes” within TLE with unique patterns of structural and microstructural abnormalities on quantitative imaging .…”

Section: Introductionmentioning

confidence: 99%

“…1 To explain this heterogeneity, studies are beginning to identify TLE cognitive subtypes with similar features. [2][3][4] Recently, we have identified four such "cognitive phenotypes" within TLE with unique patterns of structural and microstructural abnormalities on quantitative imaging. 5 Further characterization of these cognitive phenotypes could help to identify those at greatest risk for cognitive progression and/ or postoperative cognitive decline.…”

Section: Introductionmentioning

confidence: 99%

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Identifying the neural basis of a language‐impaired phenotype of temporal lobe epilepsy

et al. 2019

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Objective To identify neuroimaging and clinical biomarkers associated with a language‐impaired phenotype in refractory temporal lobe epilepsy (TLE). Methods Eighty‐five patients with TLE were characterized as language‐impaired (TLE‐LI) or non–language‐impaired (TLE‐NLI) based on comprehensive neuropsychological testing. Structural magnetic resonance imaging (MRI), diffusion tensor imaging, and functional MRI (fMRI) were obtained in patients and 47 healthy controls (HC). fMRI activations and cortical thickness were calculated within language regions of interest, and fractional anisotropy (FA) was calculated within deep white matter tracts associated with language. Analyses of variance were performed to test for differences among the groups in imaging measures. Receiver operator characteristic curves were used to determine how well different clinical versus imaging measures discriminated TLE‐LI from TLE‐NLI. Results TLE‐LI patients showed significantly less activation within left superior temporal cortex compared to HC and TLE‐NLI, regardless of side of seizure onset. TLE‐LI also showed decreased FA in the inferior longitudinal fasciculus and arcuate fasciculus compared to HC. Cortical thickness did not differ between groups in any region. A model that included language‐related fMRI activations within the superior temporal gyrus, age at onset, and demographic variables was the most predictive of language impairment (area under the curve = 0.80). Significance These findings demonstrate a unique imaging signature associated with a language‐impaired phenotype in TLE, characterized by functional and microstructural alterations within the language network. Reduced left superior temporal activation combined with compromise to language association tracts underlies this phenotype, extending our previous work on cognitive phenotypes that could have implications for treatment‐planning or cognitive progression in TLE.

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Section: Discussionmentioning

confidence: 99%

Section: Implications For Cognitive Phenotypesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying the neural basis of a language‐impaired phenotype of temporal lobe epilepsy

et al. 2019

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“…Details of the cognitive evaluation are described elsewhere [18]. In brief, all participants underwent a comprehensive battery of cognitive tests: Wechsler Adult Intelligence Scale (WAIS-IV) and Wechsler Memory Scale (WMS-IV).…”

Section: Cognitionmentioning

confidence: 99%

Multidimensional Associations between Cognition and Connectome Organization in Temporal Lobe Epilepsy

Rodríguez‐Cruces

Bernhardt

Concha

2019

Preprint

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OBJECTIVE. Temporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. The current work sought to identify multidimensional associations between cognitive profiles and structural network signatures in TLE. METHODS. We studied 34 drug-resistant TLE patients and 25 age-and sex-matched healthy controls. All participants underwent a comprehensive neurocognitive battery and multimodal magnetic resonance imaging, allowing for large-scale connectomics as well as morphological evaluation of hippocampal and neocortical regions. Using canonical correlation analysis, we identified multivariate associations between cognitive profiles and structural network signatures. Our approach was complemented by bootstrap-based clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies. RESULTS. Both multivariate techniques provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise in TLE. Cognitive classes presented with an increasing gradient of abnormalities characterized by increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing cognitive dysfunction. Notably, cognitive performance was better indexed by network topological features than by morphometric measures. CONCLUSIONS. Our findings emphasize a close interplay between multidomain cognitive impairment and large-scale network anomalies in TLE, contributing to the understanding of biological factors contributing heterogeneous functional deficits in the condition.

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Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug‐Resistant Temporal Lobe Epilepsy

Miron,

Müller,

Hohmann

et al. 2024

Annals of Neurology

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ObjectiveIn temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug‐resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes.MethodsTLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F‐statistics from comparisons of GM regions within each TLE cognitive phenotype and age‐ and sex‐matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome.ResultsA total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network‐identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15.InterpretationA differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug‐resistant TLE. ANN NEUROL 2024

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Association of white matter diffusion characteristics and cognitive deficits in temporal lobe epilepsy

Cited by 56 publications

References 37 publications

Identifying the neural basis of a language‐impaired phenotype of temporal lobe epilepsy

Identifying the neural basis of a language‐impaired phenotype of temporal lobe epilepsy

Multidimensional Associations between Cognition and Connectome Organization in Temporal Lobe Epilepsy

Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug‐Resistant Temporal Lobe Epilepsy

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