Neuroscience and Biobehavioral Reviews

Search string for articles for systematic review

Also available at: https://github.com/FernandoSAguiarNeto/SystematicReviewDepressionBiomarkers2018 Depression Biomarkers Using EEG: A Review Supplementary material for systematic mapping Disclaimer: Please cite the original article if any of the material is used. File: ReviewMethodology.pdf Information: Detailed description of the review methodology used. Table 1: Accepted Articles References Information: Reference keys matching the article, title and year of accepted and reported articles. File: AcceptedArticlesTable.pdf BibTex with accepted articles: BibTex(RevSys-DepressBiom2018).bib Table 2: Accepted Articles Summary Information: BibTex Keys, year, summarized information by article, pie chart with biomarker approaches. File: AcceptedArticlesSummary.csv Table 3: Data extraction form Information: Data extraction form used to organize the reading using Parsifal to aid the systematic review. This spreadsheet also has some charts. File: ExtractionForm.csv

The Heritability of Self-Control: a Meta-Analysis Work by Y. E. Willems, N. Boesen, J. Li, C. Finkenauer and M. Bartels. Script multi-level meta-analysis heritability self-control: 20190130_meta_SC_heritability_script Data for the multilevel meta-analysis heritability self-control: 20190130_meta_SC_heritability_data

Data NTT test

Dataset of effects included in meta-analysis from Grebe, Sarafin, Strenth, and Zilioli (2018). Questions can be directed to Nicholas Grebe at nicholas.grebe@gmail.com

R code used for structural equation modeling analyses

Co-ordinate based meta-analysis data.

Dataset for meta-analysis on mice and rats studies on the effects of early life stress on biochemical indicators of the dopaminergic system. The dataset includes 90 publications, for a total of 1009 comparisons. A wide variety of variables have been recorded, including elements of experimental design and study quality.

These data are supplementary files for the following journal article published in Neuroscience and Biobehavioural Reviews in January 2022. de Sousa, A.A. Todorov, O.S. and Proulx, M.J. (2022) 'A natural history of vision loss: insight from evolution for human visual function.' Neuroscience & Biobehavioral Reviews. e104550. DOI: 10.1016/j.neubiorev.2022.104550. The three files attached to this record are as follows: 1. markdown_vision_loss.Rmd R code used for creating figures 2. acuityblind.csv Visual acuity data for mammals (with PanTHERIA data http://esapubs.org/archive/ecol/E090/184/) MT = method: A= anatomical, B = behavioral, C = subcutaneous eyes Sources: Dooley JC, Nguyen HM, Seelke AMH, Krubitzer L. (2012). Visual acuity in the short-tailed opossum (Monodelphis domestica). Neuroscience. 223:124–30. Heffner RS, Heffner HE. (1992). Visual factors in sound localization in mammals. J Comp Neurol, 317(3):219-32. Heffner, R. S., Koay, G., & Heffner, H. E. (1999). Sound localization in an Old-World fruit bat (Rousettus aegyptiacus): acuity, use of binaural cues, and relationship to vision. J Comp Psychol, 113(3):297-306. Heffner, R. S., Koay, G., & Heffner, H. E. (2008). Sound localization acuity and its relation to vision in large and small fruit-eating bats: II. Non-echolocating species, Eidolon helvum and Cynopterus brachyotis. Hear Res, 241(1-2): 80-86. Kirk E, Kay R. (2004). The evolution of high visual acuity in the Anthropoidea. In: Ross C, Kay R, eds. Anthropoid origins: new visions. New York: Kluwer Academic/Plenum Publishers. Pettigrew JD, Manger PR, Fine SLB. (1998). The sensory world of the platypus. Philos Trans R Soc B Biol Sci, 353:1199–210. Veilleux CC, Kirk EC. (2014) Visual acuity in mammals: Effects of eye size and ecology. Brain Behav Evol, 83(1):43-53. 3. tree.nwk from TimeTree S. Kumar, G. Stecher, M. Suleski, and S.B. Hedges, 2017. TimeTree: a resource for timelines, timetrees, and divergence times. Molecular Biology and Evolution 34: 1812-1819, DOI: 10.1093/molbev/msx116.

During fear conditioning, thalamic and cortical af- ferents convey conditioned stimulus (CS) and unconditioned stimulus (US) sensory information into the baso-lateral amyg- dala (BLA), causing a marked potentiation of excitatory syn- apses by long-term synaptic plasticity (LTP). This phenomenon causes indeed an increase of firing activity of a specific class of neurons, called “fear neurons”. Figure legend A) Fear neurons stimulate central amygdala (CeA) (serial processing) causing a strong increase of the whole amygdala output triggering both motor and auto- nomic fear behaviour by involving periaqueductal gray matter (PAG) and hypothalamus. Connection between cortical pre- limbic (PrL) and BLA neurons is able to stimulate the activity of BLA fear neurons causing a further increase in CeA activity. BLA neurons activity is also potentiated by a backward excitatory pathway originating from CeA. CS-US association induces in CeA neurons similar learning-dependent modifications occur- ring in BLA (parallel processing). From BLA, CS-US information is also redirected to Basal Ganglia which support the instru- mental response. B) During the extinction phase, the input from cortical infralimbic (IL) area suppresses the BLA fear neurons activity by the interplay with intrinsic inhibitory interneurons. Moreover, IL area directly inhibits the CeA trough activation of intercalated cells (ITCs), a scattered group of GABAergic neu- rons mainly located around the BLA area. The local inhibitory circuit further reduces the neural activity of fear neurons of BLA neurons and the subsequent BLA-CeA pathway, thereby sup- pressing the fear response.