Brain aging in temporal lobe epilepsy: Chronological, structural, and functional

Hwang, Gyujoon; Hermann, Bruce P.; Nair, Veena A.; Conant, Lisa L.; Dabbs, Kevin; Mathis, Jed; Cook, Cole John; Rivera-Bonet, Charlene N.; Mohanty, Rosaleena; Zhao, Gengyan; Almane, Dace; Nencka, Andrew S.; Felton, Elizabeth A.; Struck, Aaron F.; Birn, Rasmus M.; Maganti, Rama; Humphries, Colin; Raghavan, Manoj; DeYoe, Edgar A.; Bendlin, Barbara B.; Prabhakaran, Vivek; Binder, Jeffrey R.; Meyerand, M. Elizabeth

doi:10.1016/j.nicl.2020.102183

Cited by 36 publications

(42 citation statements)

References 41 publications

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“…However, given the limited age range of participants in the current dataset, the number and the patterns of the brain developmental subsystems are unknown. Unlike some recent studies that conducted separate brain age prediction based on imaging modality (Hwang et al, 2020; Truelove-Hill et al, 2020) or clustered imaging features based on their similarity across participants (Lange et al, 2020), we first ran robust regression to extract the coefficients that contained information of developmental pattern of each feature. Then, K-means cluster analysis was conducted based on the coefficients to groups features into clusters with different developmental patterns.…”

Section: Discussionmentioning

confidence: 99%

“…The ICA was also directly conducted on the original imaging features. Unlike these studies (38, 14) and other ones that conducted separate brain age prediction based on imaging modality (24, 55), we first ran robust regression to extract the coefficients that contained information of developmental pattern of each feature. Then, K-means cluster analysis was conducted based on the coefficients to group features into clusters with different developmental patterns.…”

Section: Discussionmentioning

confidence: 99%

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Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Niu

Talyer

Shinohara

et al. 2020

Preprint

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Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices used in previous studies represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. Importantly, this staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. Here we propose an analytic method for computing a multidimensional brain-age index (MBAI) that provides regional age predictions. Using a database of 556 subjects (ages 8-21) that includes psychiatric and neurological patients as well as healthy controls, we conducted robust regression and cluster analyses to identify clusters of imaging features with distinct developmental trajectories. We then built machine-learning models to obtain brain-age predictions from each of the identified clusters to form the MBAI. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age prediction methods. Importantly, brain ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to classify disorder groups (e.g., specific phobia, depression, ADHD) from healthy controls. Compared to unidimensional brain-age indices, we show that the MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns which may serve as biomarkers that may contribute to our understanding of healthy and pathological brain development and to the characterization, diagnosis, and, potentially, treatment of various disorders.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Niu

Talyer

Shinohara

et al. 2020

Preprint

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“…Persistence of seizures leads to decline in verbal and visual memory, which may be associated with progressive hippocampal atrophy and it is possible that uncontrolled seizures will lead to deterioration in extratemporal faculties including executive function, attention, psychomotor speed, and general cognitive function [2][3][4][5][6][7][8][9].…”

Section: Temporal Lobe Epilepsymentioning

confidence: 99%

Default Mode Network Ventral Hub Connectivity Associated with Memory Impairment in Temporal Lobe Epilepsy Surgery

et al. 2020

Preprint

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In patients undergoing surgery for intractable temporal lobe epilepsy, the relationship between the default mode network and patients’ neurocognitive outcome remains unclear. The objective of this study is to employ non-invasive network mapping to identify the relationship between subdivisions of the default mode network and neurocognitive function before and after epilepsy surgery in patients with temporal lobe epilepsy.Twenty-seven medically patients with medically refractory temporal lobe epilepsy were prospectively enrolled and received resting state functional MRI and neuropsychological testing both pre- and post-operatively. Connectivity within the default mode network was modeled and average connectivity within the networks was calculated.Higher pre-operative connectivity in the ventral default mode network hub correlated with impaired baseline performance in a visual memory task. Post-operatively, a decrease in ventral but not dorsal default mode network connectivity was correlated with a deterioration of verbal and logical memory after surgery.Overall, higher connectivity in the ventral default mode network hub was associated with poor memory function in patients with temporal lobe epilepsy both before and after temporal lobe surgery. Pre-operatively, higher ventral connectivity was associated with worse visual function. Post-operatively, decreased connectivity of the ventral and dorsal default mode network was correlated with a greater decrease in logical and verbal memory when compared with the pre-operation baseline. An imbalance in default mode network connectivity towards the ventral stream and more widespread epilepsy networks may be used to predict memory impairments following surgical intervention and may lead to more tailored surgical decision making based on this non-invasive network modeling.

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“…Another similar study on brain age in diverse forms of epilepsy reported that all included types of epilepsy had a trend of accelerated aging (31). Furthermore, a recent study of patients with temporal lobe epilepsy observed that both the structural and functional brain ages were increased compared to the chronological age (32). However, it has yet not been established whether children with RE display the same trend.…”

Section: Introductionmentioning

confidence: 98%

Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study

et al. 2021

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Background: Rolandic epilepsy (RE) is a common pediatric idiopathic partial epilepsy syndrome. Children with RE display varying degrees of cognitive impairment. In epilepsy, age-related neuroanatomic and cognitive changes differ greatly from those observed in the healthy brain, and may be defined as accelerated brain aging. Connectome-based predictive modeling (CPM) is a recently developed machine learning approach that uses whole-brain connectivity measured with neuroimaging data ("neural fingerprints") to predict brain-behavior relationships. The aim of the study will be to develop and validate a CPM for predicting brain age in patients with RE.Methods: A multicenter, cross-sectional study will be conducted in 5 Chinese hospitals. A total of 100 RE patients (including 50 patients receiving anti-epileptic drugs and 50 drug-naïve patients) and 100 healthy children will be recruited to undergo a neuropsychological test using the Wechsler Intelligence Scale.Magnetic resonance images will also be collected. CPM will be applied to predict the brain age of children with RE based on brain functional connectivity. Discussion:The findings of the study will facilitate our understanding of developmental changes in the brain in children with RE and could also be an important milestone in the journey toward developing effective early interventions for this disorder.

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Brain aging in temporal lobe epilepsy: Chronological, structural, and functional

Cited by 36 publications

References 41 publications

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Default Mode Network Ventral Hub Connectivity Associated with Memory Impairment in Temporal Lobe Epilepsy Surgery

Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study

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