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Results in Journal Brain Research: 65,475

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Yunxia Li, Renren Li, Meng Liu, , Eric R. Muir,
Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147224

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, Elena Tonolli, Marta Bortoletto,
Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147227

The publisher has not yet granted permission to display this abstract.
Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147257

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Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147225

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Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147229

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Chuan-Yi Fu, Chun-Rong Zhong, Yuan-Tao Yang, Mao Zhang, Wen-An Li, Qing Zhou, Fan Zhang
Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147236

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Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/s0006-8993(21)00012-3

Xiao Cai, Mingkun Ouyang, Yulong Yin,
Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147231

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, Jin-Hee Kim, Mateusz Zurowski, Nancy Lobaugh, Sofia Chavez, ,
Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147235

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Duk-Shin Lee,
Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147262

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Lauren V. Owens, Alexandre Benedetto, Neil Dawson, Christopher J. Gaffney,
Published: 15 February 2021
Brain Research, Volume 1753; doi:10.1016/j.brainres.2020.147264

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Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147223

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, Maosheng Xia, , Vladimir Parpura,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147234

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Rebecca J. Curry,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147258

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Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147232

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Zhenxiu Qin, Ziming Ye, Jingqun Tang, Baozi Huang, Xiangren Chen, Yi Liu, Xiang Qu, Jinggui Gao, Shenghua Li, Hongming Liang, et al.
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2021.147278

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Wei Chen, Lingfei Jiang, YueQiang Hu, , Ni Liang, Xing-Feng Li, Ye-Wen Chen, Hongling Qin,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147216

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Matthew J. Lavoie
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2021.147294

Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147333

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, Hans Adriaensen, Arsène Ella, Pierre-Marie Chevillard, Martine Batailler, Jean-Philippe Dubois, Matthieu Keller, Martine Migaud
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147390

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Bradley M. Keegan, Annie L. Dreitzler, Tammy Sexton, Thomas J.R. Beveridge, Hilary R. Smith, Mack D. Miller, Bruce E. Blough, Linda J. Porrino, Steven R. Childers,
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147387

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Julie Sesen, Jessica Driscoll, Nishali Shah, Alexander Moses-Gardner, Gabrielle Luiselli, , David Zurakowski, Patricia A. Baxter, Jack M. Su, Katie Pricola Fehnel, et al.
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147348

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Erik de Water, Madeline N. Rockhold, DONOVAN J. Roediger, Alyssa M. Krueger, Bryon A. Mueller, Christopher J. Boys, Mariah J. Schumacher, Sarah N. Mattson, Kenneth L. Jones, Kelvin O. Lim, et al.
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147388

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Yunzepeng Li, Yumeng Shen, ,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147265

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Yu Tang, Maohua Wang, Ting Zheng, Yan Xiao, Song Wang, Fugang Han,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147219

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Luis Fernando Rubio-Atonal, Norma Serrano-García, Jorge Humberto Limón-Pacheco, José Pedraza-Chaverri,
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147337

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Sai-Ying Wan, Gui-Su Li, Chen Tu, Wen-Lin Chen, Xue-Wen Wang, Yun-Nan Wang, Lie-Biao Peng,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147228

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, , Glen Forester, Gregor Domes
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147238

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, Michael B. Maggiora, Kathryn J. Vaughn, Christina J. Maggiora, Amir-Vala Tavakoli, William Liang, David Zava, , Agatha Lenartowicz
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147203

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Masahito Takiguchi, Sonoko Morinobu,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147252

Abstract:
Perineuronal nets are extracellular matrix structures that surround neuronal cell bodies and their proximal dendrites in the central nervous system. Chondroitin sulfate proteoglycans, which contain chondroitin sulfates (CSs) are major components of perineuronal nets. CSs are considered to have inhibitory roles in neural plasticity, although the effects differ according to their sulfation pattern. In the present study, we investigated the expression of the CS subtypes CS-A and CS-C surrounding spinal motoneurons in different postnatal periods to explore the potential influence of altered CS sulfation patterns on spinal development. CS-A-positive structures were observed around motoneurons in the cervical, thoracic, and lumbar segments as early as postnatal day (P) 5. Most motoneurons were covered with CS-A-positive structures during the first 2 postnatal weeks. The percentage of motoneurons covered with CS-A-positive structures decreased after P20, becoming lower than 70% in the cervical, and lumber segments after P35. CS-C-positive structures were occasionally observed around motoneurons during the first 2 postnatal weeks. The percentage of motoneurons covered with CS-C-positive structures increased after P20, becoming significantly higher after P25 than before P20. The expression pattern of Wisteria Floribunda agglutinin-positive structures around motoneurons was similar to that of the CS-C–positive structures. The present findings revealed that CS-A and CS-C are differentially expressed in the extracellular matrix surrounding motoneurons. The altered sulfation pattern with increased CS-C expression is associated with the maturation of perineuronal nets and might lead to changes in the motoneuron plasticity.
Jiliang Wen, Zhenghao Chen, Si Wang, Mengmeng Zhao, Shaoyong Wang, Shengtian Zhao,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147251

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Rachel Feldman-Goriachnik,
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147384

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E.F. Sanches, A.S. Carvalho, Y. van de Looij, A. Toulotte, A.T. Wyse, C.A. Netto,
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147389

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, Alexander T. Sack, Elisabeth Bruggen, Peiran Jiao, Teresa Schuhmann
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147365

Abstract:
Most of our decisions involve a certain degree of risk regarding the outcomes of our choices. People vary in the way they make decisions, resulting in different levels of risk-taking behavior. These differences have been linked to prefrontal theta band activity. However, a direct functional relationship between prefrontal theta band activity and risk-taking has not yet been demonstrated. We used noninvasive brain stimulation to test the functional relevance of prefrontal oscillatory theta activity for the regulatory control of risk-taking behavior. In a within-subject experiment, 31 healthy participants received theta (6.5 Hertz [Hz]), gamma (40 Hz), and sham transcranial alternating current stimulation (tACS) over the left prefrontal cortex (lPFC). During stimulation, participants completed a task assessing their risk-taking behavior as well as response times and sensitivity to value and outcome probabilities. Electroencephalography (EEG) was recorded before and immediately after stimulation to investigate possible long-lasting stimulation effects. Theta band, but not gamma band or sham, tACS led to a significant reduction in risk-taking behavior, indicating a frequency-specific effect of prefrontal brain stimulation on the modulation of risk-taking behavior. Moreover, theta band stimulation led to increased response times and decreased sensitivity to reward values. EEG data analyses did not show an offline increase in power in the stimulated frequencies after the stimulation protocol. These findings provide direct empirical evidence for the effects of prefrontal theta band stimulation on behavioral risk-taking regulation.
, Erkka Heinilä, Joona Muotka, Ilona Ruotsalainen, Hanna-Maija Lapinkero, Heidi Syväoja, Tuija H. Tammelin, Tiina Parviainen
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147392

Abstract:
Current knowledge about the underlying brain processes of exercise-related benefits on executive functions and the specific contributions of physical activity and aerobic fitness during adolescence is inconclusive. We explored whether and how physical activity and aerobic fitness are associated with the oscillatory dynamics underlying anticipatory spatial attention. We studied whether the link between physical exercise level and cognitive control in adolescents is mediated by task-related oscillatory activity. Magnetoencephalographic alpha oscillations during a modified modified Posner’s cueing paradigm were measured in 59 adolescents (37 females and 22 males, 12 to 17 years). Accelerometer-measured physical activity and aerobic fitness (20-m shuttle run test) were used to divide the sample into higher- and lower-performing groups. The interhemispheric alpha asymmetry during selective attention was larger in the high than in the low physical activity group, but there was no difference between the high and low aerobic fitness groups. Exploratory mediation analysis suggested that anticipatory interhemispheric asymmetry mediates the association between physical activity status and drift rate in the selective attention task. Higher physical activity was related to increased cue-induced asymmetry, which in turn was associated with less efficient processing of information. Behaviorally, more physically active males showed stronger dependence on the cue, while more fit females showed more efficient processing of information. Our findings suggest that physical activity may be associated with a neural marker of anticipatory attention in adolescents. These findings might help to explain the varying results regarding the association of physical activity and aerobic fitness with attention and inhibition in adolescents.
, Thomas D. Ferguson, , Olave E. Krigolson
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147393

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Madelyn R. Baker, Rose K. Sciortino, Veronica M. So,
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147371

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, Olga L. Lopatina, Iana V. Gorina, Anton N. Shuvaev, Anatoly Chernykh, Ilia V. Potapenko, Alla B. Salmina
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147220

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, Shaojun Pi, Yun-Bo Zhao, Huijiao Wang,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147221

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Frederick Bonsack,
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147222

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, Érica Leandro Marciano Vieira, Bárbara Boni Rocha Dias, Marcelo Vidigal Caliari, Ana Paula Gonçalves, Alexandre Varela Giannetti, José Maurício Siqueira, Claudia Kimie Suemoto, Renata Elaine Paraizo Leite, Ricardo Nitrini, et al.
Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/j.brainres.2020.147230

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Published: 1 February 2021
Brain Research, Volume 1752; doi:10.1016/s0006-8993(20)30628-4

Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147335

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Thomas Avino,
Published: 1 February 2021
Brain Research; doi:10.1016/j.brainres.2021.147350

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