Special Issues

Listed below is a list of selected special articles.

Articles by Year

4 records are found

Identifying Factors Related to Severe Flooding Vulnerability, Preparedness, and Resiliency in Long Island and New York City

November 23, 2021
(*Please email us at info@jyem.org for the information on membership to get access to the full article.)

 


Abstract: 

Current estimates reveal that approximately 1.2 billion people reside in areas susceptible to flooding. However, due to human-inflicted changes to the environment, it is predicted that within the next 30 years, this number will increase by at least 400 million. Despite the prevailing belief that the effects of flooding are diminutive, catastrophic destruction is possible, especially when victims belong to vulnerable populations. Aside from physical damage, severe flooding often prevents individuals from securing the bare necessities- water, food, shelter, and medical attention- leading to health crises and social segregation. Following Hurricane Sandy, these adverse effects devastated communities on the East Coast, namely those in New York City and Long Island. To mitigate complications during recuperation, researchers proposed updating strategies and policies to take into account factors such as social capital and economic vulnerability. Doing so may ensure that all communities have equal access to ample resources and services, regardless of demographic composition. Therefore, this study investigated the role of community support, as opposed to socioeconomic status, in the vulnerability and resiliency of New York residents to flooding from Hurricane Sandy. Those who are more engaged in politics tend to be more vigilant about the efforts of their local government. If local politicians are unjustly favoring a certain demographic and neglecting the needs of others, people who pay attention to politics are able to identify the problem and understand how it can be rectified. Furthermore, people who pay attention to the workings of their government are more inclined to address social issues. For vulnerable families, this is relevant because an unsupportive, inept government is frequently the root of problems including forced evacuation/homelessness, poverty, inaccessible resources, etc. If political attentiveness could be quantified, policymakers and community organizations would be able to ascertain which populations are less educated about flooding preparation/reconstruction and which populations can assist the former.

Keywords: Flooding, social segregation, flooding preparation/reconstruction, Hurricane Sandy, devastated communities, in New York City and Long Island.


References

  1. Becker, J. S., Taylor, H. L., Doody, B. J., Wright, K. C., Gruntfest, E., & Webber, D. (2015). A Review of People's Behavior in and around Floodwater. Weather, Climate, and Society, 7(4), 321-332. https://doi.org/10.1175/WCAS-D-14-00030.1
  2. Bukvic, A., Zhu, H., Lavoie, R., & Becker, A. (2018). The role of proximity to waterfront in residents' relocation decision-making post-Hurricane Sandy. Ocean & Coastal Management, 154. https://doi.org/10.1016/j.ocecoaman.2018.01.002
  3. Campbell, K. A., Laurien, F., Czajkowski, J., Keating, A., Hochrainer-Stigler, S., & Montgomery, M. (2019). First insights from the Flood Resilience Measurement Tool: A large-scale community flood resilience analysis. International Journal of Disaster Risk Reduction, 40, 101257. https://doi.org/10.1016/j.ijdrr.2019.101257
  4. Chakraborty, L., Rus, H., Henstra, D., Thistlethwaite, J., & Scott, D. (2020). A place-based socioeconomic status index: Measuring social vulnerability to flood hazards in the context of environmental justice. International Journal of Disaster Risk Reduction, 43, 101394. https://doi.org/10.1016/j.ijdrr.2019.101394
  5. Clay, P. M., Colburn, L. L., & Seara, T. (2016). Social bonds and recovery: An analysis of Hurricane Sandy in the first year after landfall. Marine Policy, 74, 334-340. https://doi.org/10.1016/j.marpol.2016.04.049
  6. Deria, A., Ghannad, P., & Lee, Y.-C. (2020). Evaluating implications of flood vulnerability factors with respect to income levels for building long-term disaster resilience of low-income communities. International Journal of Disaster Risk Reduction, 48, 101608. https://doi.org/10.1016/j.ijdrr.2020.101608
  7. Flores, A. B., Collins, T. W., Grineski, S. E., & Chakraborty, J. (2020). Social vulnerability to Hurricane Harvey: Unmet needs and adverse event experiences in Greater Houston, Texas. International Journal of Disaster Risk Reduction, 46. https://doi.org/10.1016/j.ijdrr.2020.101521
  8. Fujimi, T., & Fujimura, K. (2020). Testing public interventions for flash flood evacuation through environmental and social cues: The merit of virtual reality experiments. International Journal of Disaster Risk Reduction, 50, 101690. https://doi.org/10.1016/j.ijdrr.2020.101690
  9. Gibbens, S. (2019, February). Hurricane Sandy, explained. In National Geographic. https://www.nationalgeographic.com/environment/natural-disasters/reference/hurricane-sandy/#close
  10. Graham, L., Debucquoy, W., & Anguelovski, I. (2016). The influence of urban development dynamics on community resilience practice in New York City after Superstorm Sandy: Experiences from the Lower East Side and the Rockaways. Global Environment Change, 40. https://doi.org/10.1016/j.gloenvcha.2016.07.001
  11. Hamilton, K., Demant, D., Peden, A. E., & Hagger, M. S. (2020). A systematic review of human behaviour in and around floodwater. International Journal of Disaster Risk Reduction, 47, 101561. https://doi.org/10.1016/j.ijdrr.2020.101561
  12. Maantay, J., & Maroko, A. (2009). Mapping urban risk: Flood hazards, race, & environmental justice in New York. Applied Geography, 29(1). https://doi.org/10.1016/j.apgeog.2008.08.002
  13. Martins, V. N., Nigg, J., Louis-Charles, H. M., & Kendra, J. M. (2019). Household preparedness in an imminent disaster threat scenario: The case of superstorm sandy in New York City. International Journal of Disaster Risk Reduction, 34, 316-325. https://doi.org/10.1016/j.ijdrr.2018.11.003
  14. McGuire, A. P., Gauthier, J. M., Anderson, L. M., Hollingsworth, D. W., Tracy, M., Galea, S., & Coffey, S. F. (2018). Social Support Moderates Effects of Natural Disaster Exposure on Depression and Posttraumatic Stress Disorder Symptoms: Effects for Displaced and Nondisplaced Residents. Journal of Traumatic Stress, 31(2), 223-233. https://doi.org/10.1002/jts.22270
  15. Morss, R. E., Mulder, K. J., Lazo, J. K., & Demuth, J. L. (2016). How do people perceive, understand, and anticipate responding to flash flood risks and warnings? Results from a public survey in Boulder, Colorado, USA. Journal of Hydrology, 541, 649-664. http://dx.doi.org/10.1016/j.jhydrol.2015.11.047
  16. Ntontis, E., Drury, J., Amlôt, R., Rubin, G. J., & Williams, R. (2020). Endurance or decline of emergent groups following a flood disaster: Implications for community resilience. International Journal of Disaster Risk Reduction, 45, 101493. https://doi.org/10.1016/j.ijdrr.2020.101493
  17. Pourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. (2019). Understanding communication dynamics on Twitter during natural disasters: A case study of Hurricane Sandy. International Journal of Disaster Risk Reduction, 37. https://doi.org/10.1016/j.ijdrr.2019.101176
  18. Rezende, O. M., Ribeiro da Cruz de Franco, A. B., Beleño de Oliveira, A. K., Miranda, F. M., Pitzer Jacob, A. C., Martins de Sousa, M., & Miguez, M. G. (2020). Mapping the flood risk to Socioeconomic Recovery Capacity through a multicriteria index. Journal of Cleaner Production, 255, 120251. https://doi.org/10.1016/j.jclepro.2020.120251
  19. Thistlethwaite, J., Henstra, D., Brown, C., & Scott, D. (2017). How Flood Experience and Risk Perception Influences Protective Actions and Behaviours among Canadian Homeowners. Environmental Management, 61(2), 197-208. https://doi.org/10.1007/s00267-017-0969-2
  20. Wang, Z., Lam, N. S.N., Obradovich, N., & Ye, X. (2019). Are vulnerable communities digitally left behind in social responses to natural disasters? An evidence from Hurricane Sandy with Twitter data. Applied Geography, 108, 1-8. https://doi.org/10.1016/j.apgeog.2019.05.

 

Evaluation of Brain Structure and Function in Currently Depressed Adults with a History of Early Life Stress

September 15, 2021
(*Please email us at info@jyem.org for the information on membership to get access to the full article.)

 


Abstract: 

Even though Major Depressive Disorder (MDD) is the leading cause of disability worldwide impacting over 300 million individuals, early detection and intervention is hindered by the limited knowledge of its underlying mechanisms. One association found to be significant within MDD is the presence of early life stress (ELS), such as sexual abuse, emotional abuse and family conflict. However, the biological mechanism linking ELS and MDD are unknown.

To properly assess the function consequences of ELS within MDD and address these open questions, we propose an analysis of the metabolism of AMY, ACC, HIP, and DLPFC through FDG PET in addition to a structural MRI in MDD patients with and without ELS. We hypothesize that in MDD patients with prior history of ELS, compared to those without ELS, will have a smaller volume/cortical thickness as measured by MRI and decreased metabolism as measured by PET scans in the bilateral DLPFC, ACC, HIP, and AMY. This study would for the first time, assess both structure and function of critical regions of the HPA axis in MDD, while accounting for the common confounder of ELS.

Keywords: Major Depressive Disorder (MDD), early life stress (ELS), emotional abuse, family conflict. bilateral DLPFC, ACC, HIP


References

  1. Fitzgerald, P.B., et al., A meta-analytic study of changes in brain activation in depression. Hum Brain Mapp, 2008. 29(6): p. 683-95.
  2. Kaplow, J.B. and C.S. Widom, Age of onset of child maltreatment predicts long-term mental health outcomes. J Abnorm Psychol, 2007. 116(1): p. 176-87.
  3. Martins, C.M., et al., Emotional abuse in childhood is a differential factor for the development of depression in adults. J Nerv Ment Dis, 2014. 202(11): p. 774-82.
  4. Kessler, R.C. and W.J. Magee, Childhood family violence and adult recurrent depression. J Health Soc Behav, 1994. 35(1): p. 13-27.
  5. Teicher, M.H., et al., The effects of childhood maltreatment on brain structure, function and connectivity. Nat Rev Neurosci, 2016. 17(10): p. 652-66.
  6. Parr, L.A., et al., Early life stress affects cerebral glucose metabolism in adult rhesus monkeys (Macaca mulatta). Dev Cogn Neurosci, 2012. 2(1): p. 181-93.
  7. Harkness, K.L., A.E. Bruce, and M.N. Lumley, The role of childhood abuse and neglect in the sensitization to stressful life events in adolescent depression. J Abnorm Psychol, 2006. 115(4): p. 730-41.
  8. Adolphs, R., Cognitive neuroscience of human social behaviour. Nat Rev Neurosci, 2003. 4(3): p. 165-78.
  9. Hari, R. and M.V. Kujala, Brain basis of human social interaction: from concepts to brain imaging. Physiol Rev, 2009. 89(2): p. 453-79.
  10. Quaedflieg, C.W., et al., Temporal dynamics of stress-induced alternations of intrinsic amygdala connectivity and neuroendocrine levels. PLoS One, 2015. 10(5): p. e0124141.
  11. van Marle, H.J., et al., From specificity to sensitivity: how acute stress affects amygdala processing of biologically salient stimuli. Biol Psychiatry, 2009. 66(7): p. 649-55.
  12. Critchley, H.D., et al., Human cingulate cortex and autonomic control: converging neuroimaging and clinical evidence. Brain, 2003. 126(Pt 10): p. 2139-52.
  13. Devinsky, O., M.J. Morrell, and B.A. Vogt, Contributions of anterior cingulate cortex to behaviour. Brain, 1995. 118 ( Pt 1): p. 279-306.
  14. Lisman, J., et al., Viewpoints: how the hippocampus contributes to memory, navigation and cognition. Nat Neurosci, 2017. 20(11): p. 1434-1447.
  15. van Bodegom, M., J.R. Homberg, and M. Henckens, Modulation of the Hypothalamic-Pituitary-Adrenal Axis by Early Life Stress Exposure. Front Cell Neurosci, 2017. 11: p. 87.
  16. McEwen, B.S., C. Nasca, and J.D. Gray, Stress Effects on Neuronal Structure: Hippocampus, Amygdala, and Prefrontal Cortex. Neuropsychopharmacology, 2016. 41(1): p. 3-23.
  17. Kim, E.J., B. Pellman, and J.J. Kim, Stress effects on the hippocampus: a critical review. Learn Mem, 2015. 22(9): p. 411-6.
  18. Carballedo, A., et al., Brain-derived neurotrophic factor Val66Met polymorphism and early life adversity affect hippocampal volume. Am J Med Genet B Neuropsychiatr Genet, 2013. 162B(2): p. 183-90.
  19. Vyas, A., et al., Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons. J Neurosci, 2002. 22(15): p. 6810-8.
  20. Hanson, J.L., et al., Behavioral problems after early life stress: contributions of the hippocampus and amygdala. Biol Psychiatry, 2015. 77(4): p. 314-23.
  21. Grigoryan, G. and M. Segal, Lasting Differential Effects on Plasticity Induced by Prenatal Stress in Dorsal and Ventral Hippocampus. Neural Plast, 2016. 2016: p. 2540462.
  22. Demir-Lira, O.E., et al., Early-life stress exposure associated with altered prefrontal resting-state fMRI connectivity in young children. Dev Cogn Neurosci, 2016. 19: p. 107-14.
  23. Zhang, K., et al., Molecular, Functional, and Structural Imaging of Major Depressive Disorder. Neurosci Bull, 2016. 32(3): p. 273-85.
  24. Jaworska, N., et al., Subgenual anterior cingulate cortex and hippocampal volumes in depressed youth: The role of comorbidity and age. J Affect Disord, 2016. 190: p. 726-732.
  25. van Tol, M.J., et al., Regional brain volume in depression and anxiety disorders. Arch Gen Psychiatry, 2010. 67(10): p. 1002-11.
  26. Grieve, S.M., et al., Widespread reductions in gray matter volume in depression. Neuroimage Clin, 2013. 3: p. 332-9.
  27. Fu, C., et al., Functional assessment of prefrontal lobes in patients with major depression disorder using a dual-mode technique of 3D-arterial spin labeling and (18)F-fluorodeoxyglucose positron emission tomography/computed tomography. Exp Ther Med, 2017. 14(2): p. 1058-1064.
  28. Cohen, R.A., et al., Early life stress and morphometry of the adult anterior cingulate cortex and caudate nuclei. Biol Psychiatry, 2006. 59(10): p. 975-82.
  29. Zhai, Z.W., et al., Childhood trauma moderates inhibitory control and anterior cingulate cortex activation during stress. Neuroimage, 2019. 185: p. 111-118.
  30. Chocyk, A., et al., Impact of early-life stress on the medial prefrontal cortex functions - a search for the pathomechanisms of anxiety and mood disorders. Pharmacol Rep, 2013. 65(6): p. 1462-70.
  31. Myers, B., J.M. McKlveen, and J.P. Herman, Glucocorticoid actions on synapses, circuits, and behavior: implications for the energetics of stress. Front Neuroendocrinol, 2014. 35(2): p. 180-196.
  32. Daskalakis, N.P., et al., Early Life Stress Effects on Glucocorticoid-BDNF Interplay in the Hippocampus. Front Mol Neurosci, 2015. 8: p. 68.
  33. Gupta, A., et al., Interactions of early adversity with stress-related gene polymorphisms impact regional brain structure in females. Brain Struct Funct, 2016. 221(3): p. 1667-79.
  34. Arnett, M.G., et al., The role of glucocorticoid receptor-dependent activity in the amygdala central nucleus and reversibility of early-life stress programmed behavior. Transl Psychiatry, 2015. 5: p. e542.
  35. van der Doelen, R.H., et al., Early life stress and serotonin transporter gene variation interact to affect the transcription of the glucocorticoid and mineralocorticoid receptors, and the co-chaperone FKBP5, in the adult rat brain. Front Behav Neurosci, 2014. 8: p. 355.
  36. Staffaroni, A.M., et al., The functional neuroanatomy of verbal memory in Alzheimer's disease: [(18)F]-Fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) correlates of recency and recognition memory. J Clin Exp Neuropsychol, 2017. 39(7): p. 682-693.
  37. Verger, A., et al., Changes of metabolism and functional connectivity in late-onset deafness: Evidence from cerebral (18)F-FDG-PET. Hear Res, 2017. 353: p. 8-16.
  38. Taylor, S.E., et al., Neural responses to emotional stimuli are associated with childhood family stress. Biol Psychiatry, 2006. 60(3): p. 296-301.
  39. Wang, L., et al., Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect. Hum Brain Mapp, 2014. 35(4): p. 1154-66.
  40. Yamamoto, T., et al., Increased amygdala reactivity following early life stress: a potential resilience enhancer role. BMC Psychiatry, 2017. 17(1): p. 27.
  41. Davis, M. and P.J. Whalen, The amygdala: vigilance and emotion. Mol Psychiatry, 2001. 6(1): p. 13-34.
  42. Merz, E.C., et al., Anxiety, depression, impulsivity, and brain structure in children and adolescents. Neuroimage Clin, 2018. 20: p. 243-251.
  43. Belleau, E.L., M.T. Treadway, and D.A. Pizzagalli, The Impact of Stress and Major Depressive Disorder on Hippocampal and Medial Prefrontal Cortex Morphology. Biol Psychiatry, 2019. 85(6): p. 443-453.
  44. Phillips, J.L., et al., A Prospective, Longitudinal Study of the Effect of Remission on Cortical Thickness and Hippocampal Volume in Patients with Treatment-Resistant Depression. Int J Neuropsychopharmacol, 2015. 18(8).
  45. Zuo, Z., et al., Altered Structural Covariance Among the Dorsolateral Prefrontal Cortex and Amygdala in Treatment-Naive Patients With Major Depressive Disorder. Front Psychiatry, 2018. 9: p. 323.
  46. Malykhin, N.V., et al., Fronto-limbic volumetric changes in major depressive disorder. J Affect Disord, 2012. 136(3): p. 1104-13.
  47. Colloby, S.J., et al., Cortical thickness and VBM-DARTEL in late-life depression. J Affect Disord, 2011. 133(1-2): p. 158-64.
  48. Perlman, G., et al., Cortical thickness is not associated with current depression in a clinical treatment study. Hum Brain Mapp, 2017. 38(9): p. 4370-4385.
  49. Yang, J., et al., Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp, 2018. 39(11): p. 4420-4439.
  50. Baeken, C., G.R. Wu, and R. De Raedt, Dorsomedial frontal cortical metabolic differences of comorbid generalized anxiety disorder in refractory major depression: A [(18)F] FDG PET brain imaging study. J Affect Disord, 2018. 227: p. 550-553.
  51. Su, L., et al., Cerebral metabolism in major depressive disorder: a voxel-based meta-analysis of positron emission tomography studies. BMC Psychiatry, 2014. 14: p. 321.
  52. Kennedy, S.H., et al., Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. Am J Psychiatry, 2001. 158(6): p. 899-905.
  53. Chaney, A., et al., Effect of childhood maltreatment on brain structure in adult patients with major depressive disorder and healthy participants. J Psychiatry Neurosci, 2014. 39(1): p. 50-9.
  54. Saleh, A., et al., Effects of early life stress on depression, cognitive performance and brain morphology. Psychol Med, 2017. 47(1): p. 171-181.
  55. Frodl, T., et al., Childhood adversity impacts on brain subcortical structures relevant to depression. J Psychiatr Res, 2017. 86: p. 58-65.
  56. Hill, K.R., et al., Fully quantitative pretreatment brain metabolism does not predict depression response to Escitalopram or Placebo, a Randomized Trial. 2020.
  57. Montgomery, S.A. and M. Asberg, A new depression scale designed to be sensitive to change. Br J Psychiatry, 1979. 134: p. 382-9.
  58. First, M.B., et al., Structured clinical interview for DSM-IV axis I disorders. New York: New York State Psychiatric Institute, 1995.
  59. Bernstein, D.P., et al., Initial reliability and validity of a new retrospective measure of child abuse and neglect. Am J Psychiatry, 1994. 151(8): p. 1132-6.
  60. Negele, A., et al., Childhood Trauma and Its Relation to Chronic Depression in Adulthood. Depress Res Treat, 2015. 2015: p. 650804.

 

American Blacks: The Power of Representation

July 20, 2021

 


Abstract: African Americans are often viewed as a monolithic group in the United States because Black people generally have been subjected to the same racism and prejudice throughout American society. While African Americans have had many similar experiences in the United States, their opinions on the current political, social, and economic worldview may differ based on ethnic groups. The author chose to closely examine the extent to which family history and decade of one's arrival (or one's family's arrival) to the United States, and the region from which one (or one's family) originated, might influence the current political, social and economic worldview of adolescent and adult Americans who self-identify as Black. In order to study the effects of these variables, I administered surveys to 146 African American adults in suburban New York City. The online survey consisted of four parts. These parts included views on economic success, law enforcement, current events, specifically the Black Lives Matter Movement, and Black representation in American society. Ultimately the study found statistically significant differences between region/decade of arrival and societal world views. There were also gender gaps.

KeywordsAfrican-American, representation, BLM, Afro-Caribbean, African, economic success


Works Cited

  1. Bunyasi, T. L. (2019, February 6). Do All Black Lives Matter Equally to Black People? Respectability Politics and the Limitations of Linked Fate | Journal of Race, Ethnicity, and Politics. Cambridge Core. https://www.cambridge.org/core/journals/journal-of-race-ethnicity-and-politics/article/do-all-black-lives-matter-equally-to-black-people-respectability-politics-and-the-limitations-of-linked-fate/CBC842CABC6F8FAA6C892B08327B09DA
  2. Chetty, R., Hendren, N., Jones, M. R., & Porter, S. R. (2019, December 26). Race and Economic Opportunity in the United States: an Intergenerational Perspective*. OUP Academic. https://academic.oup.com/qje/article/135/2/711/5687353?login=true
  3. Davis, R., & Hendricks, N. (2007, January 1). Immigrants and Law Enforcement: A Comparison of Native-Born and Foreign-Born Americans’ Opinions of the Police. International Review of Victimology. https://journals.sagepub.com/doi/abs/10.1177/026975800701400105
  4. Fan, Y. (2019, February 13). Gender and cultural bias in student evaluations: Why representation matters. Plos One.

 

Sharp-Wave Ripples in Mammalian Behaviors

May 13, 2021
Keneil H. Soni, Herricks High School

Abstract: 

Though sharp-wave ripples have been recorded in the EEG data of the hippocampus of mammals for years, it remains unclear how ripples can contribute to memory for different behaviors.. Sharp wave ripples are one of the most synchronous patterns in the mammalian brain. These waves are most common during non-REM sleep, although they can also be associated with consummatory behaviors. In EEG recordings, these occurrences can be seen as large amplitude negative polarity deflections (40–100 ms) in CA1 stratum radiatum that are associated with a short-lived fast oscillatory pattern of the LFP in the CA1 pyramidal layer, known as “ripples.” The purpose of this study was to investigate the distinction between sleep and awake ripples along with the connection between sharp-wave ripples and specific mammalian behaviors during memory tasks. The hypothesis tested was that SPW-Rs occur when the animal has an experience that will help guide subsequent successful task completion that results in obtaining a desired reward. To conduct the experiment electrophysiological signals were collected from a rat’s hippocampus during various tasks. The data were then analyzed using Neuroscope and compared to a visual recording of the rat’s actions. The data suggest that sharp wave ripples are more likely to occur close to a reward, most often before the reward, and do not have a higher tendency to occur early or late in learning. Future research can further clarify these results and investigate the process by which these ripples occur.

Keywords: EEG data, non-REM sleep, harp-Wave Ripples, Mammalian Behaviors


References

  1. Bartsch, T., & Wulff, P. (2015, November 19). The hippocampus in aging and disease: From plasticity to vulnerability. Neuroscience309, 1-16. ScienceDirect. https://doi.org/10.1016/j.neuroscience.2015.07.084
  2. Bragin, A., Engel Jr, J., Wilson, C. L., Fried, I., & Buzsáki, G. (1999, April 15). High‐frequency oscillations in human brain. Hippocampus9(2), 137-142. Wiley Online Library. https://doi.org/10.1002/(SICI)1098-1063(1999)9:2%3C137::AID-HIPO5%3E3.0.CO;2-0
  3. Buzsáki, G. (2015, September 26). Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning. Hippocampus25(10), 1073-1188. Wiley Online Library. https://doi.org/10.1002/hipo.22488
  4. Buzsáki, G., Leung, L. W., & Vanderwolf, C. H. (1983, October). Cellular bases of hippocampal EEG in the behaving rat. Brain Research287(2), 139-171. PubMed Central. https://doi.org/10.1016/0165-0173(83)90037-1
  5. Buzsáki, G., Royer, S., Belluscio, M., Berényi, A., Diba, K., Fujisawa, S., Grosmark, A., Mao, D., Mizuseki, K., Patel, J., Stark, E., Sullivan, D., Watson, B., & Vandecasteele, M. (2012, March 04). Large-scale Recording of Neurons by Movable Silicon Probes in Behaving Rodents. Journal of Visualized Experiments61. PubMeb Central. https://dx.doi.org/10.3791%2F3568
  6. G Buzsáki 1 , Z Horváth, R Urioste, J Hetke, K Wise, G., Urioste, R., Hetke, J., & Wise, K. (1992, May 15). High-frequency network oscillation in the hippocampus. Science256(5059), 1025-1027. American Association for the Advancement of Science. https://doi.org/10.1126/science.1589772
  7. Jouvet, M., Michel, F., & Courjon, J. (1959). L'activite electrique du rhinencephale au cours du sommeil chez le chat. Comptes rendus des seances de la Societe de biologie et de ses filiales153(1), 98-101.
  8. Kanamori, N. (1985, May). A spindle-like wave in the cat hippocampus: a novel vigilance level-dependent electrical activity. Brain Research334(1), 180-182. ScienceDirect. https://doi.org/10.1016/0006-8993(85)90584-0
  9. Keefe, J. O. (1976). Place units in the hippocampus of the freely moving rat. Experimental Neurology51(1), 78-109. ScienceDirect. https://doi.org/10.1016/0014-4886(76)90055-8
  10. Keefe, J. O., & Nadel, L. (1978). The Hippocampus as a Cognitive Map. Oxford University Press.
  11. Knierim, J. J. (2015, December 07). The hippocampus. Primer25(23), PR1116-R1121. Current Biology. https://doi.org/10.1016/j.cub.2015.10.049
  12. Kropotov, J. D. (2009). Quantitative EEG, Event-Related Potentials and Neurotherapy. Elsevier. https://doi.org/10.1016/B978-0-12-374512-5.X0001-1
  13. Le Van Quyen, M., Staba, R., Bragin, A., Dickson, C., Valderrama, M., Fried, I., & Engel, J. (2010, June 9). Large-Scale Microelectrode Recordings of High-Frequency Gamma Oscillations in Human Cortex during Sleep. The Journal of Neuroscience30(23), 7770-7782. PubMed Central. https://dx.doi.org/10.1523%2FJNEUROSCI.5049-09.2010
  14. Lopes da Silva, F. (2013, December 4). EEG and MEG: Relevance to neuroscience. Neuron80, 1112-1128. PubMed Central. https://doi.org/10.1016/j.neuron.2013.10.017
  15. Moser, M.-B., & Moser, E. I. (January, 1999). Functional differentiation in the hippocampus. Hippocampus8(6), 608-619. Wiley Online Library. https://doi.org/10.1002/(SICI)1098-1063(1998)8:6%3C608::AID-HIPO3%3E3.0.CO;2-7
  16. Singh, K. A., & Dhikav, V. (2012, Oct-Dec). Hippocampus in health and disease: An overview. Indian Academy of Neurology15(4), 239-246. PubMed Central. https://dx.doi.org/10.4103%2F0972-2327.104323
  17. Steriade, M., Gloor, P., Llinas, R. R., Lopes de Silva, F. H., & Mesulam, M. M. (1990). Report of IFCN Committee on Basic Mechanisms. Basic mechanisms of cerebral rhythmic activities. Electroencephalography and Clinical Neurophysiology76, 481-508. PubMed Central. https://doi.org/10.1016/0013-4694(90)90001-z
  18. Suzuki, S. S., & Smith, G. K. (1987, October). Spontaneous EEG spikes in the normal hippocampus. Electroencephalography and Clinical Neurophysiology67(4), 348-359. PubMed Central. https://doi.org/10.1016/0013-4694(87)90123-4
  19. Vanderwolf, C. H. (1969, April). Hippocampal electrical activity and voluntary movement in the rat. Electroencephalography and Clinical Neurophysiology26(4), 407-418. PubMed Central. https://doi.org/10.1016/0013-4694(69)90092-3
  20. Zhang, H., & Jacobs, J. (2015, September 09). Traveling Theta Waves in the Human Hippocampus. The Journal of Neuroscience35(36), 12477-12487. PubMed Central. https://dx.doi.org/10.1523%2FJNEUROSCI.5102-14.2015