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Schraegle WA, Hsu DA, Almane DN, Gundlach C, Stafstrom CE, Seidenberg M, Jones JE, Hermann BP. Neighborhood disadvantage and intellectual development in youth with epilepsy. Epilepsy Behav 2023; 149:109492. [PMID: 37951133 DOI: 10.1016/j.yebeh.2023.109492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 11/13/2023]
Abstract
RATIONALE Recent cross-sectional investigations have demonstrated an adverse impact of socioeconomic disadvantage on cognition and behavior in youth and adults with epilepsy. The goal of this study is to investigate the impact of disadvantage on prospective intellectual development in youth with epilepsy. METHOD Participants were youth, aged 8-18 years, with recent onset epilepsy (n = 182) and healthy first-degree cousin controls (n = 106). The Wechsler Abbreviated Scale of Intelligence (WASI) was administered at baseline and 2 years later. The Neighborhood Atlas identified each family's Area Deprivation Index via state deciles and national percentiles. WASI data were analyzed by mixed group by time ANOVAs followed by regression analysis to identify other baseline predictors of time 2 outcomes. RESULTS Youth with epilepsy demonstrated significant interactions between group and time for both verbal (F = 4.02, df = 1,215, p =.05) and nonverbal (F = 4.57, df = 1,215, p =.04) reasoning, demonstrating that disadvantage was associated with slower cognitive development compared to advantaged youth with epilepsy. Similar interactions were not observed for controls. CONCLUSIONS In youth with new and recent onset epilepsies, neighborhood-level disadvantage is associated with a negative impact on the development of verbal and nonverbal reasoning skills.
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Affiliation(s)
- William A Schraegle
- Departments of Neurology and Pediatrics, Dell Medical School, The University of Texas, Austin, TX, USA; Comprehensive Pediatric Epilepsy Center, Dell Children's Medical Center, Austin, TX, USA
| | - David A Hsu
- Department of Neurology, University of Wisconsin, School of Medicine and Public Health, USA
| | - Dace N Almane
- Department of Neurology, University of Wisconsin, School of Medicine and Public Health, USA
| | - Carson Gundlach
- Department of Neurology, University of Wisconsin, School of Medicine and Public Health, USA
| | | | - Michael Seidenberg
- Department of Psychology, Rosalind Franklin University of Medicine and Science, USA
| | - Jana E Jones
- Department of Neurology, University of Wisconsin, School of Medicine and Public Health, USA
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin, School of Medicine and Public Health, USA.
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Denis C, Dabbs K, Nair VA, Mathis J, Almane DN, Lakshmanan A, Nencka A, Birn RM, Conant L, Humphries C, Felton E, Raghavan M, DeYoe EA, Binder JR, Hermann B, Prabhakaran V, Bendlin BB, Meyerand ME, Boly M, Struck AF. T1-/T2-weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy. Ann Clin Transl Neurol 2023; 10:2149-2154. [PMID: 37872734 PMCID: PMC10647008 DOI: 10.1002/acn3.51653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/29/2022] [Accepted: 06/09/2022] [Indexed: 10/25/2023] Open
Abstract
Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.
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Affiliation(s)
- Colin Denis
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Kevin Dabbs
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Veena A. Nair
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Jedidiah Mathis
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Dace N. Almane
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | | | - Andrew Nencka
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Rasmus M. Birn
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Lisa Conant
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Colin Humphries
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Elizabeth Felton
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Manoj Raghavan
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Edgar A. DeYoe
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Jeffrey R. Binder
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Bruce Hermann
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Vivek Prabhakaran
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Mary E. Meyerand
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Mélanie Boly
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Aaron F. Struck
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- William S. Middleton Veterans Administration HospitalMadisonWisconsinUSA
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Schraegle WA, Slomowitz RF, Gundlach C, Hsu DA, Almane DN, Stafstrom CE, Seidenberg M, Jones JE, Hermann BP. Disadvantage and Neurocognitive Comorbidities in Childhood Idiopathic Epilepsies. Epilepsia 2023. [PMID: 36965077 DOI: 10.1111/epi.17581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/27/2023]
Abstract
OBJECTIVE To characterize the relationship between neighborhood disadvantage on cognitive function as well as clinical, sociodemographic, and family factors in children with new onset idiopathic epilepsy and healthy controls. METHODS Research participants were 288 children aged 8-18 years with recent onset epilepsy (CWE, n = 182; mean age = 12.2 ± 3.2 years), healthy first-degree cousin controls (HC, n = 106; mean age = 12.5 ± 3.0), and one biological or adopted parent per child (n=279). All participants were administered a comprehensive neuropsychological battery (reasoning, language, memory, executive function, motor function, and academic achievement). Family residential addresses were entered into the Neighborhood Atlas to determine each family's Area Deprivation Index (ADI), a metric used to quantify income, education, employment, and housing quality. A combination of parametric (ANOVA) and non-parametric (χ2 ) tests examined the effect of ADI by group (epilepsy and controls) across cognitive, academic, clinical, and family factors. RESULTS Disadvantage (ADI) was equally distributed between groups (p = .63). For CWE, high disadvantage was associated with lower overall intellectual quotient (p =.04), visual naming/expressive language (p = .03), phonemic (letter) fluency (p <.01), passive inattention (omission errors) (p = .03), delayed verbal recall (p = .04) and dominant fine motor dexterity and speed (p < .01). Cognitive status of the HC group did not differ by level of disadvantage (p = .40). CWE exhibited greater academic difficulties in comparison to HC (p < .001), which were exacerbated by disadvantage in CWE (p = .02), but not HC (p < .05). High disadvantage was associated with a threefold risk for academic challenges prior to epilepsy onset (odds ratio = 3.31, p = .024). SIGNIFICANCE Socioeconomic hardship (increased neighborhood disadvantage) exerts a significant adverse impact on the cognitive and academic status of youth with new and recent onset epilepsies, an impact that needs to be incorporated into etiological models of the neurobehavioral comorbidities of epilepsy.
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Affiliation(s)
- William A Schraegle
- Departments of Neurology and Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, Texas, USA
- Comprehensive Pediatric Epilepsy Center, Dell Children's Medical Center, Austin, Texas, USA
| | - Rebecca F Slomowitz
- Comprehensive Pediatric Epilepsy Center, Dell Children's Medical Center, Austin, Texas, USA
| | - Carson Gundlach
- Department of Neurology, University of Wisconsin School of Medicine and Public Health
| | - David A Hsu
- Department of Neurology, University of Wisconsin School of Medicine and Public Health
| | - Dace N Almane
- Department of Neurology, University of Wisconsin School of Medicine and Public Health
| | | | - Michael Seidenberg
- Department of Psychology, Rosalind Franklin University of Medicine and Science
| | - Jana E Jones
- Department of Neurology, University of Wisconsin School of Medicine and Public Health
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health
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Rivera Bonet CN, Hwang G, Hermann B, Struck AF, J Cook C, A Nair V, Mathis J, Allen L, Almane DN, Arkush K, Birn R, Conant LL, DeYoe EA, Felton E, Maganti R, Nencka A, Raghavan M, Shah U, Sosa VN, Ustine C, Prabhakaran V, Binder JR, Meyerand ME. Neuroticism in temporal lobe epilepsy is associated with altered limbic-frontal lobe resting-state functional connectivity. Epilepsy Behav 2020; 110:107172. [PMID: 32554180 PMCID: PMC7483612 DOI: 10.1016/j.yebeh.2020.107172] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/11/2020] [Accepted: 05/11/2020] [Indexed: 11/18/2022]
Abstract
Neuroticism, a core personality trait characterized by a tendency towards experiencing negative affect, has been reported to be higher in people with temporal lobe epilepsy (TLE) compared with healthy individuals. Neuroticism is a known predictor of depression and anxiety, which also occur more frequently in people with TLE. The purpose of this study was to identify abnormalities in whole-brain resting-state functional connectivity in relation to neuroticism in people with TLE and to determine the degree of unique versus shared patterns of abnormal connectivity in relation to elevated symptoms of depression and anxiety. Ninety-three individuals with TLE (55 females) and 40 healthy controls (18 females) from the Epilepsy Connectome Project (ECP) completed measures of neuroticism, depression, and anxiety, which were all significantly higher in people with TLE compared with controls. Resting-state functional connectivity was compared between controls and groups with TLE with high and low neuroticism using analysis of variance (ANOVA) and t-test. In secondary analyses, the same analytics were performed using measures of depression and anxiety and the unique variance in resting-state connectivity associated with neuroticism independent of symptoms of depression and anxiety identified. Increased neuroticism was significantly associated with hyposynchrony between the right hippocampus and Brodmann area (BA) 9 (region of prefrontal cortex (PFC)) (p < 0.005), representing a unique relationship independent of symptoms of depression and anxiety. Hyposynchrony of connection between the right hippocampus and BA47 (anterior frontal operculum) was associated with high neuroticism and with higher depression and anxiety scores (p < 0.05), making it a shared abnormal connection for the three measures. In conclusion, increased neuroticism exhibits both unique and shared patterns of abnormal functional connectivity with depression and anxiety symptoms between regions of the mesial temporal and frontal lobe.
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Affiliation(s)
| | - Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, United States of America
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Cole J Cook
- Department of Medical Physics, University of Wisconsin-Madison, United States of America
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, United States of America
| | - Jedidiah Mathis
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Dace N Almane
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Karina Arkush
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Rasmus Birn
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Medical Physics, University of Wisconsin-Madison, United States of America; Department of Psychiatry, University of Wisconsin-Madison, United States of America
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Edgar A DeYoe
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America; Department of Biophysics, Medical College of Wisconsin, United States of America
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Andrew Nencka
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Umang Shah
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Veronica N Sosa
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Vivek Prabhakaran
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Neurology, University of Wisconsin-Madison, United States of America; Department of Radiology, University of Wisconsin-Madison, United States of America
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, United States of America; Department of Biophysics, Medical College of Wisconsin, United States of America
| | - Mary E Meyerand
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Medical Physics, University of Wisconsin-Madison, United States of America; Department of Radiology, University of Wisconsin-Madison, United States of America
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Hermann B, Conant LL, Cook CJ, Hwang G, Garcia-Ramos C, Dabbs K, Nair VA, Mathis J, Bonet CNR, Allen L, Almane DN, Arkush K, Birn R, DeYoe EA, Felton E, Maganti R, Nencka A, Raghavan M, Shah U, Sosa VN, Struck AF, Ustine C, Reyes A, Kaestner E, McDonald C, Prabhakaran V, Binder JR, Meyerand ME. Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy. Neuroimage Clin 2020; 27:102341. [PMID: 32707534 PMCID: PMC7381697 DOI: 10.1016/j.nicl.2020.102341] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 06/10/2020] [Accepted: 07/03/2020] [Indexed: 01/14/2023]
Abstract
This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P < 0.05), fewer patient (P < 0.001) and parental years of education (P < 0.05), greater racial diversity (P < 0.05), and greater number of lifetime generalized seizures (P < 0.001). The three groups also differed in an orderly manner across total intracranial (P < 0.001) and bilateral cerebellar cortex volumes (P < 0.01), and rate of bilateral hippocampal atrophy (P < 0.014), but minimally in regional measures of cortical volume or thickness. In contrast, large-scale patterns of cortical-subcortical covariance networks revealed significant differences across groups in global and local measures of community structure and distribution of hubs. Resting-state fMRI revealed stepwise anomalies as a function of cluster membership, with the most abnormal patterns of connectivity evident in the generalized impairment group and no significant differences from controls in the cognitively intact group. Overall, the distinct underlying cognitive phenotypes of temporal lobe epilepsy harbor systematic relationships with clinical, sociodemographic and neuroimaging correlates. Cognitive phenotype variations in patient and familial education and ethnicity, with linked variations in total intracranial volume, raise the question of an early and persisting socioeconomic-status related neurodevelopmental impact, with additional contributions of clinical epilepsy factors (e.g., lifetime generalized seizures). The neuroimaging features of cognitive phenotype membership are most notable for disrupted large scale cortical-subcortical networks and patterns of functional connectivity with bilateral hippocampal and cerebellar atrophy. The cognitive taxonomy of temporal lobe epilepsy appears influenced by features that reflect the combined influence of socioeconomic, neurodevelopmental and neurobiological risk factors.
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Affiliation(s)
- Bruce Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Cole J Cook
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Camille Garcia-Ramos
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Veena A Nair
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jedidiah Mathis
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA
| | - Charlene N Rivera Bonet
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dace N Almane
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Karina Arkush
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Edgar A DeYoe
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Rama Maganti
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Andrew Nencka
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Umang Shah
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Veronica N Sosa
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Anny Reyes
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Erik Kaestner
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Carrie McDonald
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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Hwang G, Hermann B, Nair VA, Conant LL, Dabbs K, Mathis J, Cook CJ, Rivera-Bonet CN, Mohanty R, Zhao G, Almane DN, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Humphries CJ, Raghavan M, DeYoe EA, Bendlin BB, Prabhakaran V, Binder JR, Meyerand ME. Brain aging in temporal lobe epilepsy: Chronological, structural, and functional. Neuroimage Clin 2020; 25:102183. [PMID: 32058319 PMCID: PMC7016276 DOI: 10.1016/j.nicl.2020.102183] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 12/03/2019] [Accepted: 01/13/2020] [Indexed: 10/26/2022]
Abstract
The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.
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Affiliation(s)
- Gyujoon Hwang
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
| | - Bruce Hermann
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Veena A Nair
- Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Lisa L Conant
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Kevin Dabbs
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jed Mathis
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Cole J Cook
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Rosaleena Mohanty
- Electrical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Gengyan Zhao
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Dace N Almane
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew Nencka
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Aaron F Struck
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Rasmus Birn
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Rama Maganti
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Manoj Raghavan
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Edgar A DeYoe
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Vivek Prabhakaran
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Mary E Meyerand
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
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Almane DN, Jones JE, McMillan T, Stafstrom CE, Hsu DA, Seidenberg M, Hermann BP, Oyegbile TO. The Timing, Nature, and Range of Neurobehavioral Comorbidities in Juvenile Myoclonic Epilepsy. Pediatr Neurol 2019; 101:47-52. [PMID: 31122836 PMCID: PMC6752993 DOI: 10.1016/j.pediatrneurol.2019.03.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 03/05/2019] [Accepted: 03/10/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND Accumulating evidence suggests that considerable cognitive and psychiatric comorbidity is associated with juvenile myoclonic epilepsy, for which the etiology remains controversial. Our goal was to comprehensively characterize the status of multiple neurobehavioral comorbidities in youth with new- or recent-onset juvenile myoclonic epilepsy, before effects of chronic seizures and medications. METHODS A total of 111 children aged eight to 18 years (41 new- or recent-onset juvenile myoclonic epilepsy and 70 first-degree cousin controls) underwent neuropsychological assessment (attention, executive, verbal, perceptual, speed), structured review of need for supportive academic services, parent reports of behavior and executive function (Child Behavior Checklist and Behavior Rating Inventory of Executive Function), and formal structured psychiatric interview and diagnosis (Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version). RESULTS Children with juvenile myoclonic epilepsy performed worse than controls across all tested cognitive domains (F(1,105) = 3.85, P < 0.01), utilized more academic services (47% versus 19%, P = 0.002), had more parent-reported behavioral problems and dysexecutive function with lower competence (P < 0.001), and had a higher prevalence of current Axis I diagnoses (attention-deficit/hyperactivity disorder, depression, and anxiety; 54% versus 23%, P = 0.001). Academic and psychiatric problems occurred antecedent to epilepsy onset compared with comparable timeline in controls. CONCLUSION Comprehensive assessment of cognitive, academic, behavioral, and psychiatric comorbidities in youth with new- or recent-onset juvenile myoclonic epilepsy reveals a pattern of significantly increased neurobehavioral comorbidities across a broad spectrum of areas. These early evident comorbidities are of clear clinical importance with worrisome implications for future cognitive, behavioral, and social function. It is important for health care providers to avoid delays in intervention by assessing potential comorbidities early in the course of the disorder to optimize their patients' social, academic and behavioral progress.
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Affiliation(s)
- Dace N Almane
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison Wisconsin
| | - Jana E Jones
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison Wisconsin
| | - Taylor McMillan
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison Wisconsin
| | - Carl E Stafstrom
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore Maryland
| | - David A Hsu
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison Wisconsin
| | | | - Bruce P Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison Wisconsin
| | - Temitayo O Oyegbile
- Department of Pediatrics and Neurology, Georgetown University, Washington District of Columbia.
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8
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Cook CJ, Hwang G, Mathis J, Nair VA, Conant LL, Allen L, Almane DN, Birn R, DeYoe EA, Felton E, Forseth C, Humphries CJ, Kraegel P, Nencka A, Nwoke O, Raghavan M, Rivera-Bonet C, Rozman M, Tellapragada N, Ustine C, Ward BD, Struck A, Maganti R, Hermann B, Prabhakaran V, Binder JR, Meyerand ME. Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project. Brain Connect 2019; 9:174-183. [PMID: 30398367 DOI: 10.1089/brain.2018.0600] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using these data, the effective connectivity of the default mode network (DMN) in patients with left TLE compared with healthy controls was investigated using spectral dynamic causal modeling (spDCM) of resting-state functional magnetic resonance imaging data. Group comparisons were made using two parametric empirical Bayes (PEB) models. The first level of each PEB model consisted of each participant's spDCM. Two different second-level models were constructed: the first comparing effective connectivity of the groups directly and the second using the Rey Auditory Verbal Learning Test (RAVLT) delayed free recall index as a covariate at the second level to assess effective connectivity controlling for the poor memory performance of left TLE patients. After an automated search over the nested parameter space and thresholding parameters at 95% posterior probability, both models revealed numerous connections in the DMN, which lead to inhibition of the left hippocampal formation. Left hippocampal formation inhibition may be an inherent result of the left temporal epileptogenic focus as memory differences were controlled for in one model and the same connections remained. An excitatory connection from the posterior cingulate cortex to the medial prefrontal cortex was found to be concomitant with left hippocampal formation inhibition in TLE patients when including RAVLT delayed free recall at the second level.
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Affiliation(s)
- Cole J Cook
- 1 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Gyujoon Hwang
- 1 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jedidiah Mathis
- 2 Department of Radiology, Froedtert and Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Veena A Nair
- 3 Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lisa L Conant
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Linda Allen
- 5 Department of Neurology, Froedtert Hospital, Milwaukee, Wisconsin
| | - Dace N Almane
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rasmus Birn
- 1 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.,7 Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Edgar A DeYoe
- 2 Department of Radiology, Froedtert and Medical College of Wisconsin, Milwaukee, Wisconsin.,8 Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Felton
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Courtney Forseth
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Colin J Humphries
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Peter Kraegel
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew Nencka
- 8 Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Onyekachi Nwoke
- 9 School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manoj Raghavan
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Charlene Rivera-Bonet
- 10 Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
| | - Megan Rozman
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Neelima Tellapragada
- 3 Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Candida Ustine
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - B Douglas Ward
- 8 Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Aaron Struck
- 5 Department of Neurology, Froedtert Hospital, Milwaukee, Wisconsin
| | - Rama Maganti
- 5 Department of Neurology, Froedtert Hospital, Milwaukee, Wisconsin
| | - Bruce Hermann
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- 3 Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin.,6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin.,10 Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jeffrey R Binder
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin.,7 Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Mary E Meyerand
- 1 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.,3 Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin.,11 Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
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9
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Hwang G, Dabbs K, Conant L, Nair VA, Mathis J, Almane DN, Nencka A, Birn R, Humphries C, Raghavan M, DeYoe EA, Struck AF, Maganti R, Binder JR, Meyerand E, Prabhakaran V, Hermann B. Cognitive slowing and its underlying neurobiology in temporal lobe epilepsy. Cortex 2019; 117:41-52. [PMID: 30927560 DOI: 10.1016/j.cortex.2019.02.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 12/06/2018] [Accepted: 02/23/2019] [Indexed: 11/17/2022]
Abstract
Cognitive slowing is a known but comparatively under-investigated neuropsychological complication of the epilepsies in relation to other known cognitive comorbidities such as memory, executive function and language. Here we focus on a novel metric of processing speed, characterize its relative salience compared to other cognitive difficulties in epilepsy, and explore its underlying neurobiological correlates. Research participants included 55 patients with temporal lobe epilepsy (TLE) and 58 healthy controls from the Epilepsy Connectome Project (ECP) who were administered a battery of tests yielding 14 neuropsychological measures, including selected tests from the NIH Toolbox-Cognitive Battery, and underwent 3T MRI and resting state fMRI. TLE patients exhibited a pattern of generalized cognitive impairment with very few lateralized abnormalities. Using the neuropsychological measures, machine learning (Support Vector Machine binary classification model) classified the TLE and control groups with 74% accuracy with processing speed (NIH Toolbox Pattern Comparison Processing Speed Test) the best predictor. In TLE, slower processing speed was associated predominantly with decreased local gyrification in regions including the rostral and caudal middle frontal gyrus, inferior precentral cortex, insula, inferior parietal cortex (angular and supramarginal gyri), lateral occipital cortex, rostral anterior cingulate, and medial orbital frontal regions, as well as three small regions of the temporal lobe. Slower processing speed was also associated with decreased connectivity between the primary visual cortices in both hemispheres and the left supplementary motor area, as well as between the right parieto-occipital sulcus and right middle insular area. Overall, slowed processing speed is an important cognitive comorbidity of TLE associated with altered brain structure and connectivity.
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Affiliation(s)
- Gyujoon Hwang
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Kevin Dabbs
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Lisa Conant
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Veena A Nair
- Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jed Mathis
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dace N Almane
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew Nencka
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Rasmus Birn
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Manoj Raghavan
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Edgar A DeYoe
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Aaron F Struck
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Rama Maganti
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Elizabeth Meyerand
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Vivek Prabhakaran
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Bruce Hermann
- Neurology, University of Wisconsin-Madison, Madison, WI, USA.
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10
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Hwang G, Nair VA, Mathis J, Cook CJ, Mohanty R, Zhao G, Tellapragada N, Ustine C, Nwoke OO, Rivera-Bonet C, Rozman M, Allen L, Forseth C, Almane DN, Kraegel P, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Conant LL, Humphries CJ, Hermann B, Raghavan M, DeYoe EA, Binder JR, Meyerand E, Prabhakaran V. Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning. Brain Connect 2019; 9:184-193. [PMID: 30803273 PMCID: PMC6484357 DOI: 10.1089/brain.2018.0601] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.
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Affiliation(s)
- Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Veena A. Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jed Mathis
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Cole J. Cook
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rosaleena Mohanty
- Department of Electrical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Gengyan Zhao
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | - Megan Rozman
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Courtney Forseth
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Dace N. Almane
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Peter Kraegel
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lisa L. Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Colin J. Humphries
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Edgar A. DeYoe
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jeffrey R. Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
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11
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Almane DN, Zhao Q, Rathouz PJ, Hanson M, Jackson DC, Hsu DA, Stafstrom CE, Jones JE, Seidenberg M, Koehn M, Hermann BP. Contribution of Family Relatedness to Neurobehavioral Comorbidities in Idiopathic Childhood Epilepsies. J Int Neuropsychol Soc 2018; 24:653-661. [PMID: 29745359 PMCID: PMC6988642 DOI: 10.1017/s1355617718000243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVES Rates of cognitive, academic and behavioral comorbidities are elevated in children with epilepsy. The contribution of environmental and genetic influences to comorbidity risk is not fully understood. This study investigated children with epilepsy, their unaffected siblings, and controls to determine the presence and extent of risk associated with family relatedness across a range of epilepsy comorbidities. METHODS Participants were 346 children (8-18 years), n=180 with recent-onset epilepsy, their unaffected siblings (n=67), and healthy first-degree cousin controls (n=99). Assessments included: (1) Child Behavior Checklist/6-18 (CBCL), (2) Behavior Rating Inventory of Executive Function (BRIEF), (3) history of education and academic services, and (4) lifetime attention deficit hyperactivity disorder (ADHD) diagnosis. Analyses consisted of linear mixed effect models for continuous variables, and logistic mixed models for binary variables. RESULTS Differences were detected between the three groups of children across all measures (p<.001). For ADHD, academic problems, and executive dysfunction, children with epilepsy exhibited significantly more problems than unaffected siblings and controls; siblings and controls did not differ statistically significantly from each other. For social competence, children with epilepsy and their unaffected siblings displayed more abnormality compared with controls, with no statistically significant difference between children with epilepsy and unaffected siblings. For behavioral problems, children with epilepsy had more abnormality than siblings and controls, but unaffected siblings also exhibited more abnormalities than controls. CONCLUSIONS The contribution of epilepsy and family relatedness varies across specific neurobehavioral comorbidities. Family relatedness was not significantly associated with rates of ADHD, academic problems and executive dysfunction, but was associated with competence and behavioral problems. (JINS, 2018, 24, 653-661).
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Affiliation(s)
- Dace N Almane
- 1Department of Neurology,University of Wisconsin School of Medicine and Public Health,Madison,Wisconsin
| | - Qianqian Zhao
- 2Department of Biostatistics and Medical Informatics,University of Wisconsin School of Medicine and Public Health,Madison,Wisconsin
| | - Paul J Rathouz
- 2Department of Biostatistics and Medical Informatics,University of Wisconsin School of Medicine and Public Health,Madison,Wisconsin
| | - Melissa Hanson
- 1Department of Neurology,University of Wisconsin School of Medicine and Public Health,Madison,Wisconsin
| | - Daren C Jackson
- 1Department of Neurology,University of Wisconsin School of Medicine and Public Health,Madison,Wisconsin
| | - David A Hsu
- 1Department of Neurology,University of Wisconsin School of Medicine and Public Health,Madison,Wisconsin
| | - Carl E Stafstrom
- 3Department of Neurology,Johns Hopkins University School of Medicine,Baltimore,Maryland
| | - Jana E Jones
- 1Department of Neurology,University of Wisconsin School of Medicine and Public Health,Madison,Wisconsin
| | - Michael Seidenberg
- 4Department of Psychology,Rosalind Franklin University of Medicine and Science,North Chicago,Illinois
| | - Monica Koehn
- 5Marshfield Clinic Neurosciences,Marshfield Clinic,Marshfield,Wisconsin
| | - Bruce P Hermann
- 1Department of Neurology,University of Wisconsin School of Medicine and Public Health,Madison,Wisconsin
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