fix reference text

04-discussion.Rmd

@@ -18,6 +18,9 @@ We presented evidence that interneurons indeed constitute most of the functional
 
 To our surprise, IN-L1 cells were inhibited by the activation of PV^+^ fibres, a connection not reported in previous studies investigating MS PV^+^ projections. Due to the position of IN-L1 cells in cortical circuits, this novel connection could have significant new implications for the network. It has been shown in the somato-sensory and visual cortex of rats and mice that layer I interneurons exhibit recurrent connections to pyramidal cells of layer II and III, broadly inhibit a vast majority of layer II and III interneurons and are highly interconnected [@jiang_principles_2015; @olah_output_2007; @wozny_specificity_2011]. This places them in a central position where they can inhibit a whole interneuron network while also modulating pyramidal cell activity. Whether or not we have similar connectivity schemes outside of known sensory cortices in the PaS or MEC remains unknown. This open question could provide important information about the workings of septal-parahippocampal interactions. Combining these results, I hypothesise that septal PV^+^ cells contribute to an accurately timed inhibition of interneurons, which leads to an increase in DS firing while background activity -- not synchronised inputs -- might be reduced through IN-L1 cells regulating the overall inhibition (Figure \@ref(fig:ProjectionScheme)A). Overall, the increase in DS activity through disinhibition from septal PV^+^ cells will increase excitatory output to the MEC [@canto_all_2012] and relay information to entorhinal pyramidal cell clusters known for their spatial coding [@tang_functional_2016]. Consequently, excitation from the PaS could drive grid-cells in the MEC and synchronise other behaviourally relevant cells across the parahippocampal network. It is not known whether or not the PaS receives distinct inputs from the MS which are integrated in the parahippocampal network independently of other regions. Studies investigating projection patterns using juxta-cellular recordings provided evidence that MS PV^+^ orchid cells can simultaneously target MEC and PrS cells, although, none of the cells projected to the PaS [@viney_shared_2018]. A failure to detect common projections could arise due to the limited number of cells which can be labelled using the juxta technique or suggest, given the high density of projections to the PaS, that the PaS is targeted by a different cell group which exclusively and with high divergence inhibits the PaS or other structures. Answering this question could lead to a better understanding of how MS inhibitory signals are integrated in the parahippocampal network and could elicit new questions for the future.
 
+(ref:ProjectionShortScheme) Schematic of PaS network for PV^+^ and ChAT^+^ inputs 
+(ref:ProjectionScheme) **Schematic of PaS network for PV^+^ and ChAT^+^ inputs.** The connectivity scheme for inputs from the MS. Arrows indicate the effect of MS inputs onto cells. Red cells represent FS cells, pink IN cells, turquoise IN-L1 cells, and blue DS cells. Synapses are represented as circles for excitatory or cholinergic synapses and lines for inhibitory GABAergic synapses. Local IN-L1 connectivity is not yet confirmed in the PaS and is informed from studies in other cortical areas [@jiang_principles_2015; @olah_output_2007; @wozny_specificity_2011]. Septal fibres are presented in green and arrows indicate the MS input effect on the target cells (up +/down -/both ±). DS cells project to the MEC and have local recurrent connections. The black line at the bottom indicates the pial surface. Cells connecting to themselves do not necessarily represent autaptic connections to a single cell but cell type connections to the same cell type. (A) shows the scheme for PV^+^ MS fibres and (B) the corresponding scheme for ChAT^+^.
+
 ```{r ProjectionScheme, fig.scap='(ref:ProjectionShortScheme)', fig.cap = '(ref:ProjectionScheme)',fig.show='hold', fig.align='center',echo=FALSE, fig.width=10, fig.height=4, dpi=600}
 #PVScheme  <- readPNG(source = "/alzheimer/Daniel_Data/R/Thesis/Figures/Schemes/PVScheme.png", native = T)
 #ChATScheme  <- readPNG(source = "/alzheimer/Daniel_Data/R/Thesis/Figures/Schemes/ChATScheme.png", native = T)
@@ -46,9 +49,6 @@ In particular, FS cells were similarly likely to be connected responded in most
 
 A different effect of an increase in acetylcholine in the PaS and MEC, is a reduction in amplitude of excitatory inputs combined with an increased facilitation allowing for enhanced temporal summation of inputs, which is especially efficient at $\gamma$-frequency [@glasgow_cholinergic_2012; @sparks_contribution_2014]. Such a mechanism could potentially prime PaS pyramidal cells for synaptic modification through increased acetylcholine concentration during MS mediated $\theta$ and, therefore, strengthen relevant synchronously occurring inputs to the MEC and PaS, as was previously shown in the hippocampus [@huerta_bidirectional_1995]. Evidence to support the hypothesis that the MS could contribute to such an acetylcholine increase was presented by @hamam_cholinergic_2007, who have shown that facilitation of inputs mediated by muscarinic receptors in the MEC is larger during movement as opposed to an immobile state. Additionally, @dudar_release_1979 recorded an increase of hippocampal acetylcholine concentration during running phases and were able to abolish the increase by lesioning the septal area. Furthermore, a block of muscarinic receptors will lead to a reduced speed-$\theta$ relationship in the MEC and, more importantly, change the coupling of $\theta$ nested $\gamma$ in the MEC and hippocampus [@hentschke_muscarinic_2007; @newman_cholinergic_2013]. Given the strong excitatory output of the PaS to the MEC, improved temporal summation could occur during $\theta$ nested $\gamma$ cycles and play an important role in memory formation [@huerta_bidirectional_1995].
 
-(ref:ProjectionShortScheme) Schematic of PaS network for PV^+^ and ChAT^+^ inputs 
-(ref:ProjectionScheme) **Schematic of PaS network for PV^+^ and ChAT^+^ inputs.** The connectivity scheme for inputs from the MS. Arrows indicate the effect of MS inputs onto cells. Red cells represent FS cells, pink IN cells, turquoise IN-L1 cells, and blue DS cells. Synapses are represented as circles for excitatory or cholinergic synapses and lines for inhibitory GABAergic synapses. Local IN-L1 connectivity is not yet confirmed in the PaS and is informed from studies in other cortical areas [@jiang_principles_2015; @olah_output_2007; @wozny_specificity_2011]. Septal fibres are presented in green and arrows indicate the MS input effect on the target cells (up +/down -/both ±). DS cells project to the MEC and have local recurrent connections. The black line at the bottom indicates the pial surface. Cells connecting to themselves do not necessarily represent autaptic connections to a single cell but cell type connections to the same cell type. (A) shows the scheme for PV^+^ MS fibres and (B) the corresponding scheme for ChAT^+^.
-
 Taken together, this suggests the importance of MS cholinergic projections, especially during behavioural states via the activation of layer I interneurons and disinhibition of the PaS through FS cells, but also a potential impact on memory formation and strengthening of connections through synaptic facilitation.
 
 ## *In-vivo* MS PV^+^ activation

05-references_and_appendix.Rmd

@@ -11,7 +11,9 @@
 <div id="refs"></div>
 
 \endgroup
-\clearpage
+
+<!-- \clearpage -->
+
 \backmatter
 # Appendix {- #appendix}
 ## Additional Tables {-}
@@ -177,5 +179,5 @@ Diese Arbeit hat in gleicher oder ähnlicher Form noch keiner anderen Prüfungsb
 \vspace{24pt}
 
 \noindent
-Berlin,  `r format(x = as.Date(Sys.Date()), '%d.%m.%Y')`
+Berlin, 28.09.2021
 \vspace*{\fill}

_output.yml

@@ -1,11 +1,14 @@
 bookdown::pdf_book:
+  includes:
+    in_header: "preamble.tex"
   toc: no
   # toc-depth: 3
 #  latex_engine: lualatex
-  includes:
-    in_header: preamble.tex
 #  citation_package: natbib
   keep_tex: yes
+  # extra_dependencies: ["flafter"]
+  # citation_package: biblatex
+  # biblatexoptions: [backend=biber, maxbibnames=999]
 #bookdown::gitbook:
 #  css: style.css
 #  config:

index.Rmd

@@ -32,19 +32,21 @@ subtitle: |
   | submitted to the Department of Biology, Chemistry and Pharmacy
   | of Freie Universität Berlin
   | by
-biblio-style: apalike
-# toc-depth: 3
+# biblio-style: apalike
 output:
-    pdf_document2:
-      extra_dependencies: ["flafter"]
-      toc: no
-    citation_package: biblatex
-    keep_tex: yes
-    biblatexoptions: [backend=biber, maxbibnames=999]
+  bookdown::pdf_book:
+    includes:
+      in_header: "preamble.tex"
+    extra_dependencies: ["flafter"]
+    toc: false
+  citation_package: biblatex
+  keep_tex: yes
+  biblatexoptions: [backend=biber, maxbibnames=999]
 indent: true
 header-includes:
-- \AtBeginDocument{\frontmatter}
+  - \AtBeginDocument{\frontmatter}
 ---
+
 \pagenumbering{gobble} 
 \newpage
 

packages.bib

@@ -23,14 +23,70 @@ @Manual{R-data.table
   url = {https://CRAN.R-project.org/package=data.table},
 }
 
+@Manual{R-ggh4x,
+  title = {ggh4x: Hacks for ggplot2},
+  author = {Teun {van den Brand}},
+  year = {2021},
+  note = {R package version 0.2.1},
+  url = {https://github.com/teunbrand/ggh4x},
+}
+
+@Manual{R-ggplot2,
+  title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
+  author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani and Dewey Dunnington},
+  year = {2021},
+  note = {R package version 3.3.5},
+  url = {https://CRAN.R-project.org/package=ggplot2},
+}
+
+@Manual{R-kableExtra,
+  title = {kableExtra: Construct Complex Table with kable and Pipe Syntax},
+  author = {Hao Zhu},
+  year = {2021},
+  note = {R package version 1.3.4},
+  url = {https://CRAN.R-project.org/package=kableExtra},
+}
+
 @Manual{R-knitr,
   title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
   author = {Yihui Xie},
   year = {2021},
-  note = {R package version 1.36},
+  note = {R package version 1.37},
   url = {https://yihui.org/knitr/},
 }
 
+@Manual{R-magrittr,
+  title = {magrittr: A Forward-Pipe Operator for R},
+  author = {Stefan Milton Bache and Hadley Wickham},
+  year = {2020},
+  note = {R package version 2.0.1},
+  url = {https://CRAN.R-project.org/package=magrittr},
+}
+
+@Manual{R-patchwork,
+  title = {patchwork: The Composer of Plots},
+  author = {Thomas Lin Pedersen},
+  year = {2020},
+  note = {R package version 1.1.1},
+  url = {https://CRAN.R-project.org/package=patchwork},
+}
+
+@Manual{R-png,
+  title = {png: Read and write PNG images},
+  author = {Simon Urbanek},
+  year = {2013},
+  note = {R package version 0.1-7},
+  url = {http://www.rforge.net/png/},
+}
+
+@Manual{R-readxl,
+  title = {readxl: Read Excel Files},
+  author = {Hadley Wickham and Jennifer Bryan},
+  year = {2019},
+  note = {R package version 1.3.1},
+  url = {https://CRAN.R-project.org/package=readxl},
+}
+
 @Manual{R-rmarkdown,
   title = {rmarkdown: Dynamic Documents for R},
   author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
@@ -49,6 +105,15 @@ @Book{bookdown2016
   url = {https://bookdown.org/yihui/bookdown},
 }
 
+@Book{ggplot22016,
+  author = {Hadley Wickham},
+  title = {ggplot2: Elegant Graphics for Data Analysis},
+  publisher = {Springer-Verlag New York},
+  year = {2016},
+  isbn = {978-3-319-24277-4},
+  url = {https://ggplot2.tidyverse.org},
+}
+
 @Book{knitr2015,
   title = {Dynamic Documents with {R} and knitr},
   author = {Yihui Xie},