Brain network entropy, depression, and quality of life in people with traumatic brain injury and seizure disorders

被引:3
作者
Allendorfer, Jane B. [1 ,2 ,3 ,9 ]
Nenert, Rodolphe [1 ]
Goodman, Adam M. [1 ,3 ]
Kakulamarri, Pranav [1 ]
Correia, Stephen [4 ]
Philip, Noah S. [4 ,5 ]
Lafrance Jr, W. Curt [4 ,5 ,6 ,7 ]
Szaflarski, Jerzy P. [1 ,2 ,3 ,8 ,9 ]
机构
[1] Univ Alabama Birmingham, Dept Neurol, Birmingham, AL USA
[2] Univ Alabama Birmingham, Dept Neurobiol, Birmingham, AL USA
[3] Univ Alabama Birmingham, UAB Epilepsy Ctr, Birmingham, AL USA
[4] VA Providence Healthcare Syst, VA RR&D Ctr Neurorestorat & Neurotechnol, Providence, RI USA
[5] Brown Univ, Dept Psychiat & Human Behav, Providence, RI USA
[6] Brown Univ, Dept Neurol, Providence, RI USA
[7] Rhode Isl Hosp, Div Neuropsychiat & Behav Neurol, Providence, RI USA
[8] Univ Alabama Birmingham, Dept Neurosurg, Birmingham, AL USA
[9] Univ Alabama Birmingham, Civitan Int Res Ctr 312, and, 312 Civitan Int Res Ctr,1719 6th Ave South, Birmingham, AL 35294 USA
关键词
brain network entropy; depression; quality of life; seizure disorders; traumatic brain injury; PSYCHOGENIC NONEPILEPTIC SEIZURES; APPROXIMATE ENTROPY; DIAGNOSIS; EPILEPSY; IMPACT;
D O I
10.1002/epi4.12926
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
ObjectiveTraumatic brain injury (TBI) often precedes the onset of epileptic (ES) or psychogenic nonepileptic seizures (PNES) with depression being a common comorbidity. The relationship between depression severity and quality of life (QOL) may be related to resting-state network complexity. We investigated these relationships in adults with TBI-only, TBI + ES, or TBI + PNES using Sample Entropy (SampEn), a measure of physiologic signals complexity.MethodsAdults with TBI-only (n = 60), TBI + ES (n = 21), or TBI + PNES (n = 56) completed the Beck Depression Inventory-II (BDI-II; depression symptom severity) and QOL in Epilepsy (QOLIE-31) assessments and underwent resting-state functional magnetic resonance imaging (rs-fMRI). SampEn values derived from six resting state functional networks were calculated per participant. Effects of group, network, and group-by-network-interactions for SampEn were investigated with a mixed-effects model. We examined relationships between BDI-II, QOL, and SampEn of each of the networks.ResultsGroups did not differ in age, but there was a higher proportion of women with TBI + PNES (p = 0.040). TBI + ES and TBI-only groups did not differ in BDI-II or QOLIE-31 scores, while the TBI + PNES group scored worse on both measures. The fixed effects of the model revealed significant differences in SampEn values across networks (lower SampEn for the frontoparietal network compared to other networks). The likelihood ratio test for group-by-network-interactions was significant (p = 0.033). BDI-II was significantly negatively associated with Overall QOL scale scores in all groups, and significantly negatively associated with network SampEn values only in the TBI + PNES group.SignificanceOnly TBI + PNES had significant relationships between depression symptom severity and network SampEn values indicating that the resting state network complexity is related to depression severity in this group but not in TBI + ES or TBI-only.Plain Language SummaryThe brain has a complex network of internal connections. How well these connections work may be affected by TBI and seizures and may underlie mental health symptoms including depression; the worse the depression, the worse the quality of life. Our study compared brain organization in people with TBI, people with epilepsy after TBI, and people with nonepileptic seizures after TBI. Only people with nonepileptic seizures after TBI showed a relationship between how organized their brain connections were and how bad was their depression. We need to better understand these relationships to develop more impactful, effective treatments.
引用
收藏
页码:969 / 980
页数:12
相关论文
共 43 条
[1]  
APP, 2013, DIAGNOSTIC STAT MANU
[2]   Psychopathology and trauma in epileptic and psychogenic seizure patients [J].
Arnold, LM ;
Privitera, MD .
PSYCHOSOMATICS, 1996, 37 (05) :438-443
[3]   Epidemiology of psychogenic nonepileptic seizures [J].
Asadi-Pooya, Ali A. ;
Sperling, Michael R. .
EPILEPSY & BEHAVIOR, 2015, 46 :60-65
[4]   A component based noise correction method (CompCor) for BOLD and perfusion based fMRI [J].
Behzadi, Yashar ;
Restom, Khaled ;
Liau, Joy ;
Liu, Thomas T. .
NEUROIMAGE, 2007, 37 (01) :90-101
[5]   Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy [J].
Bernhardt, Boris C. ;
Bonilha, Leonardo ;
Gross, Donald W. .
EPILEPSY & BEHAVIOR, 2015, 50 :162-170
[6]   Optimizing the order of operations for movement scrubbing: Comment on Power et al. [J].
Carp, Joshua .
NEUROIMAGE, 2013, 76 (01) :436-438
[7]   Initial reliability and validity of the Ohio State University TBI identification method [J].
Corrigan, John D. ;
Bogner, Jennifer .
JOURNAL OF HEAD TRAUMA REHABILITATION, 2007, 22 (06) :318-329
[8]   Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression [J].
Cukic, Milena ;
Stokic, Miodrag ;
Radenkovic, Slavoljub ;
Ljubisavljevic, Milos ;
Simic, Slobodan ;
Savic, Danka .
INTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH, 2020, 29 (02)
[9]   Differentiating between nonepileptic and epileptic seizures [J].
Devinsky, Orrin ;
Gazzola, Deana ;
LaFrance, W. Curt, Jr. .
NATURE REVIEWS NEUROLOGY, 2011, 7 (04) :210-220
[10]   Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects [J].
Eimon, Peter M. ;
Ghannad-Rezaie, Mostafa ;
De Rienzo, Gianluca ;
Allalou, Amin ;
Wu, Yuelong ;
Gao, Mu ;
Roy, Ambrish ;
Skolnick, Jeffrey ;
Yanik, Mehmet Fatih .
NATURE COMMUNICATIONS, 2018, 9