Personalized connectome fingerprints: Their importance in cognition from childhood to adult years

Munsell, Brent C.; Gleichgerrcht, Ezequiel; Hofesmann, Eric; Delgaizo, John; McDonald, Carrie R.; Marebwa, Barbara; Styner, Martin; Fridriksson, Julius; Rorden, Christopher; Focke, Niels K.; Gilmore, John H.; Bonilha, Leonardo

doi:10.1016/j.neuroimage.2020.117122

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…For the SVC model, support vector coefficients were used to select the ROI variables that had the largest influence on group classification accuracy. For the DLC model, the backtracking technique ( Hazlett et al, 2017 , Munsell et al, 2020 Nov 1 ) was used to select the ROI variables that had the largest influence on group classification accuracy. Values were averaged across the 1000 iterations of model training .…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Gleichgerrcht¹,

Munsell²,

Alhusaini³

et al. 2021

NeuroImage: Clinical

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Highlights Machine learning and artificial intelligence have gained popularity for medical applications. We applied support vector machine (SV) and deep learning (DL) in termporal lobe epilepsy (TLE) Structural and diffusion-based models showed similar classification accuracies. Diffusion-based models to diagnose TLE performed better or similar compared to models to lateralize TLE. Models for patients with hippocampal sclerosis were more accurate than models that stratified non-lesional patients.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Gleichgerrcht¹,

Munsell²,

Alhusaini³

et al. 2021

NeuroImage: Clinical

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“…There is increasing interest in understanding the brain’s structure–function relationship in light of inter-individual variability. Recent evidence has identified anatomical variations that are linked to differences in cognition and clinical outcomes (Forkel et al 2020 ; Harrison et al 2020 ; Johnson et al 2020 ; Taebi et al 2020 ; Munsell et al 2020 ; Wang et al 2021 ). The neurosurgical literature is also increasing our understanding through mapping cognitive–anatomical variability in single case series during pre-, post-, and intrasurgical imaging assessments (e.g., Vanderweyen et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

White matter variability, cognition, and disorders: a systematic review

et al. 2021

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Inter-individual differences can inform treatment procedures and—if accounted for—have the potential to significantly improve patient outcomes. However, when studying brain anatomy, these inter-individual variations are commonly unaccounted for, despite reports of differences in gross anatomical features, cross-sectional, and connectional anatomy. Brain connections are essential to facilitate functional organization and, when severed, cause impairments or complete loss of function. Hence, the study of cerebral white matter may be an ideal compromise to capture inter-individual variability in structure and function. We reviewed the wealth of studies that associate cognitive functions and clinical symptoms with individual tracts using diffusion tractography. Our systematic review indicates that tractography has proven to be a sensitive method in neurology, psychiatry, and healthy populations to identify variability and its functional correlates. However, the literature may be biased, as the most commonly studied tracts are not necessarily those with the highest sensitivity to cognitive functions and pathologies. Additionally, the hemisphere of the studied tract is often unreported, thus neglecting functional laterality and asymmetries. Finally, we demonstrate that tracts, as we define them, are not correlated with one, but multiple cognitive domains or pathologies. While our systematic review identified some methodological caveats, it also suggests that tract–function correlations might still be a promising tool in identifying biomarkers for precision medicine. They can characterize variations in brain anatomy, differences in functional organization, and predicts resilience and recovery in patients.

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“…8,9 There is a critical need to quantify the extent of the epileptogenic tissue and measure the impact of planned surgery to predict seizure outcomes more accurately. 10,11 Several studies have investigated both structural and functional networks in epilepsy, [10][11][12][13][14][15][16][17][18][19][20][21] but there is a need for more studies that correlate the 2 types of networks, especially using iEEG. [22][23][24][25] Given that structural connectivity (SC) constraints functional connectivity (FC), and FC modulates SC through plasticity mechanisms, these relationships may offer valuable insights into the underlying mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have investigated both structural and functional networks in epilepsy, 10-21 but there is a need for more studies that correlate the 2 types of networks, especially using iEEG. 22-25 Given that structural connectivity (SC) constraints functional connectivity (FC), and FC modulates SC through plasticity mechanisms, these relationships may offer valuable insights into the underlying mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

Sinha,

Duncan,

Diehl

et al. 2023

Neurology

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Background and objectives: Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain’s structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship impacts the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control following surgery. Method: We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using pre-surgery functional data from intracranial EEG (iEEG) recordings, pre- and post-surgery structural data from T1-weighted MRI, and pre-surgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using Spearman rank correlation and analyzed this structure-function coupling at two spatial scales: (i) global iEEG network level and (ii) individual iEEG electrode contacts using “virtual surgeries”. We retrospectively predicted post-operative seizure freedom, by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model. Result: We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free subjects compared to those with seizure recurrence (p=0.002, d=0.76, AUC=0.78 [95%CI 0.62, 0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p=0.007, d=0.96, AUC=0.73 [95%CI 0.58, 0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 [95%CI 0.67, 0.94]. These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 [95%CI 0.82, 1.0], accuracy of 92%, sensitivity of 93%, and specificity of 91%. Conclusion: Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into pre-surgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery. Classification of Evidence: This is a Class IV retrospective case series showing that structure-function mapping may help determine outcome from surgical resection for treatment-resistant focal epilepsy.

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Personalized connectome fingerprints: Their importance in cognition from childhood to adult years

Cited by 11 publications

References 28 publications

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

White matter variability, cognition, and disorders: a systematic review

Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

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