Multidimensional Associations between Cognition and Connectome Organization in Temporal Lobe Epilepsy

Rodríguez‐Cruces, Raúl; Bernhardt, Boris C.; Concha, Luis

doi:10.1101/675884

Cited by 8 publications

(12 citation statements)

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“…In this study, we utilized the a2005s atlas and a2009s atlas for network node definition because they are two of the most commonly used surface parcelllation schemes in previous brain network studies (e.g., Buchanan et al, 2020; Rodríguez-Cruces et al, 2020; Seibert et al, 2011; Zhang et al, 2019). However, these two atlases are relatively coarser (68 and 148 regions, respectively), which may be insufficient to represent local characteristics of finer-level brain regions.…”

Section: Methodsmentioning

confidence: 99%

Surface-based Single-subject Morphological Brain Networks: Effects of Morphological Index, Brain Parcellation and Similarity Measure, Sample Size-varying Stability and Test-retest Reliability

Wang

et al. 2021

Preprint

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Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ∼70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.

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Section: Methodsmentioning

confidence: 99%

Surface-based Single-subject Morphological Brain Networks: Effects of Morphological Index, Brain Parcellation and Similarity Measure, Sample Size-varying Stability and Test-retest Reliability

Wang

et al. 2021

Preprint

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“…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 . Although this points to sensitivity of network features in charting cognition, further work is needed to bench test predictors for specificity and generalizability.…”

Section: Key Application Areas Of Network Biomarkersmentioning

confidence: 96%

“…Furthermore, findings are often based on single‐site data or within‐sample correlations, not evaluating out‐of‐sample predictions of discovered features. 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 .…”

Section: Key Application Areas Of Network Biomarkersmentioning

confidence: 99%

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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“…In TLE, MRI morphometry combined with clustering identified subgroups of patients with distinct patterns of mesiotemporal atrophy that did not spatially overlap. 36 Leveraging individual variability, these techniques may further refine clinical predictors, such as cognitive profiles 37,38 and postsurgical seizure outcome. 36 In FCD type II, recent data identified tissue classes with distinct structural, functional, and histopathological profiles within lesions and across patients.…”

Section: Lesion Detection and Disease Biotyping Techniquesmentioning

confidence: 99%