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Investigating the Influences of Biological Variation on

Neural Progenitor Cell Proliferation

Ellie Hadden-Ford

Senior Honors Thesis

Department of Psychology and Neuroscience

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Abstract

The human cerebral cortex is uniquely expanded in surface area, cortical thickness, and overall

volume, and this expansion is thought to underlie the unique cognitive abilities of humans (Rakic

2006). In fact, patients with neuropsychiatric illnesses such as depression, obsessive-compulsive

disorder, schizophrenia, or autism have been shown to have cortical surface area differences as

compared to neurotypical controls (Schmall, Hibar, S ä mann et al. 2017, Boedhoe, Schmaal, Abe

et al. 2017; Piven, Elison, & Zylka 2017; van Erp, Walton, Hibar et al. 2018). Therefore,

understanding what factors influence the surface area of the brain can lead to a better

understanding of neuropsychiatric illnesses. The radial unit hypothesis proposes that increased

proliferation of neural progenitor cells causes increased cortical surface area through an increase

in radial columns (Rakic 1995). This study aims to elucidate biological factors that influence the

proliferation of neural progenitor cells. Cell lines generated from 71 unique donated fetal cortical

tissue sections were grown, stained for the proliferative marker Ki67, and percentage of

proliferative cells was quantified with automated image analysis. After controlling for technical

factors, neither gestation week nor sex of the donor was significantly related to the proportion of

proliferative cells (p=0.473, p=0.237). Data from this study can be used in many future analyses,

including genome-wide association studies to find common genetic variation that is significantly

associated with proliferation rate, and whether these genetic variants are related to genetic

variation that has been found to influence cortical surface area or risk for neuropsychiatric

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Investigating the Influences of Biological Variation on Neural Progenitor Cell Proliferation The cerebral cortex is the outer layer of the brain and is greatly expanded in total volume,

thickness, and surface area along our human lineage (Rakic 2009). This cortical expansion may

underlie humans’ unique cognitive abilities and implies that the architecture of the human cortex

has functional implications. Indeed, the surface area of the visual cortex is related to visual

perception of illusions ( Schwarzkopf, Song, & Rees 2011) as well as acuity of visual perception

( Song, Schwarzkopf, Kanai, & Rees 2015). Cortical surface area differences are observed in

individuals with neuropsychiatric diseases relative to neurotypical controls. In adolescents with

major depressive disorder, for instance, cortical surface area was reduced relative to neurotypical

control groups (Schmall, Hibar, S ä mann et al. 2017). Other diseases, such as schizophrenia,

obsessive-compulsive disorder, and autism have also shown cortical surface area differences

between cases and controls (Boedhoe, Schmaal, Abe et al. 2017; Piven, Elison, & Zylka 2017;

van Erp, Walton, Hibar et al. 2018). These examples show that the surface area of the cerebral

cortex is related to functionality of the brain and altered brain structure is present in individuals

with neuropsychiatric diseases, some of which have profound effects on the daily life of people

affected by these diseases, as well as their caretakers. Understanding the origin of cortical

surface area inter-individual variability is an important step on the road toward more effective

treatment for neuropsychiatric illnesses.

The increased surface area of the human cortex may begin during development,

particularly when neurogenesis, the process of generating neurons from progenitor cells, takes

place in unique transient structures of the developing primate brain (Rakic 2009). Neurogenesis

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produce two identical daughter cells, and asymmetric divisions that produce one neuron and one

progenitor cell. Each progenitor cell that is produced in a division can subsequently undergo

symmetric or asymmetric divisions, but the neurons never proliferate. Once cells divide and

differentiate into immature neurons, they migrate up radial glia towards the surface of the cortex.

The proliferative pool of neural progenitor cells is expanded in primates and humans in particular

(Lui, Hansen, & Kriegstein 2011). The radial unit hypothesis states that as the number of

progenitor cells increases, the number of radial columns increases (Rakic 1995). This hypothesis

then states that the increased number of radial columns causes an increase in the cortical surface

area (Rakic 1995). In humans, a unique cell type called outer radial glia (oRG) undergoes many

rounds of division to proliferate within the outer subventricular zone (oSVZ) (Pollen et al.,

2015). These rounds of division produce an exponential expansion in the number of cells present

in the oSVZ, which is hypothesized to lead to the increased cortical surface area in humans.

The processes of proliferation and neurogenesis is complex and dynamic, and is

influenced by changes in cell micro-environment, cell-types, signaling, and programs of gene

expression in the areas of the developing brain (Rakic 2009). Substantial work has already been

done in a variety of model systems which has led to discovery of pathways that regulate the

proliferation of neural progenitor cells. Some of these pathways include Hedgehog (HH) (Wang

2016), and the canonical Wnt signaling pathway (Woodhead 2006, Zhou 2006). Elucidating the

cellular mechanisms controlling neural progenitor proliferation and differentiation can lead to

greater understanding of developmental processes and may identify new targets for new

therapeutics.

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Common genetic variation associated with proliferation can identify cellular pathways

that have an influence on brain structure, including cortical surface area (Grasby et. al, 2020). In

order to identify common genetic variation related to proliferation, a genome wide association

study (GWAS) can be performed. GWAS studies correlate a phenotype such as proliferation,

with common genotypes at single-nucleotide polymorphisms (SNPs) throughout the genome.

GWAS studies have successfully identified many loci associated with many phenotypes

including brain structure and disease risk, but have not yet been associated with cellular traits

like proliferation ( Visscher, Wray, Zhang et al. 2017 ). Loci that influence proliferation may help

explain part of the causal mechanism by which genetic variation leads to changes in brain

structure or risk for neuropsychiatric disorders. These loci can then be functionally validated

through experimental manipulation to provide further evidence for their role in neural progenitor

proliferation. Through further study, these causal pathways can be targeted for therapeutic

discovery and treatment of neuropsychiatric diseases. Before a GWAS can be performed, it is

essential to confirm that the phenotype to be used in the association can be measured reliably,

and that technical variables that influence the phenotype can be determined. If the phenotype

cannot be reliably measured and technical effects are not accounted for, the GWAS will not

produce sound results.

Currently, the effects of common genetic variation and other biological variation on

proliferation are not well understood. In order to study these effects on proliferation, we

generated human neural progenitors from donated fetal brain tissue of 71 unique and ancestrally

diverse donors, and cultured them for 3 weeks. Following fixation, we quantified total numbers

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and proliferative percentage of cells were automatically quantified through the deep learning

segmentation program CellPose in conjunction with a CellProfiler pipeline. Using this

information, we were able to determine the reproducibility of cell counts across donors and the

association between cell counts and technical variables which influence these measures. This is

the first stage toward conducting a GWAS of proliferation phenotypes in human neural

progenitors.

Methods Tissue Acquisition

Following IRB regulations, human fetal brain tissue was obtained from the UCLA Gene

and Cell Therapy Core. Thin pieces of tissue that had the morphology of the developing cortex

were selected in Daniel Geschwind’s lab, and the presumed cortical tissue was dissociated into a

single cell suspension. The cells were then cultured as neurospheres for a low number of

passages (average 2.5) on laminin/fibronectin and polyornithine coated plates and cryopreserved

as human neural progenitors (HNPs). The preserved HNPs were shipped to UNC-Chapel Hill

after a material transfer agreement signed by both institutions. All proliferation and analysis was

performed at UNC Chapel Hill.

Cell Culture and Fixation

HNPs were thawed in rounds of 8-10 randomly selected donors into 10 cm plates on a

Monday, and fed with half feeds using 2x proliferation media (see media formulations in table

below) on Wednesdays and Fridays. 10 cm and 96 well plates for the following week were

coated in a two-day process on Thursdays and Fridays. HNPs were passaged on Mondays and

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per well with proliferation media on a Monday, and fixed on Thursday, three days after the last

passage. In total, 98 donors were cultured with 64 of 98 donors replicated once, and 22 of 98

donors replicated two or more times. HNPs were fixed at room temperature in 4% PFA solution.

After 15 minutes, the PFA was removed and cells were washed with 1x PBS three times, then

stored at 4 °C in PBS and 0.02% sodium azide.

Table 1 . Cell Culture Media Reagents

Item Vendor Catalog Number Volume

Proliferation base

Neurobasal A Life Technologies 10888022 500 mL

Primocin Invivogen ant-pm-2 100 μg/mL

BIT 9500 HPSCC NA 60 mL

GlutaMAX (100x) Life Technologies 35050-061 6 mL

Heparin Sigma Aldrich H3393-10KU 1 μg/mL

1x Proliferation

Proliferation base NA NA 100 mL

EGF/FGF Life Technologies PHG0313/PHG0023 20 ng/mL

LIF Life Technologies PHC9481 2 ng/mL

PDGF Life Technologies PHG1034 20 ng/mL

2x Proliferation

Proliferation base NA NA 100 mL

EGF/FGF Life Technologies PHG0313/PHG0023 40 ng/mL

LIF Life Technologies PHC9481 40 ng/mL

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Table 2 . Plate Coating Reagents

Plate Day 1 (Thursday)

Vendor Catalog

Number

Day 2 (Friday)

Vendor Catalog Number

10 cm plate

1x PBS Life

Technologies

10010072 1x PBS Life

Technologies

10010072

polyornithine (15 μg/mL)

Sigma Aldrich

P3655-500MG fibronectin (5 μg/mL)

Sigma Aldrich

F1141-5mg

96 well plate

Molecular grade water

Fisher Scientific

16750-128 1x PBS Life

Technologies

10010072

polyornithine (15 μg/mL)

Sigma Aldrich

P3655-500MG laminin (10 μg/mL)

Life

Technologies

23017015

Immunocytochemistry

Plates were removed from cold storage and allowed to warm to room temperature. First, a

PBST solution was created with 0.02% Tween in 1xPBS. Cells were permeabilized with PBST

and 0.4% Triton solution for 10 minutes. Next, cells incubated at room temperature for at least 1

hour in a 10% goat serum solution diluted in PBST. After blocking, a goat serum/PBST primary

antibody solution was added to the cells, which sat overnight in 4 °C storage. The next day, the 3

washes were performed with PBST, and the cells were incubated in fluorophore-conjugated

secondary antibody solution for 50 minutes. After 50 minutes, DAPI was added at a final

concentration of 2.5ug/mL and cells were incubated for an additional 10 minutes. The cells were

washed 3 times with PBST, stored in PBS and sodium azide solution at 4 °C in a dark, enclosed

box. Two sets of antibodies were used to examine different cell types of the neural progenitors,

designated experiment 1 and experiment 2. The antibodies for experiment 1 were as follows:

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Table 3 . Primary Antibodies

Antibody Species Dilution Vendor Catalog #

Sox2 Rabbit 1:500 EMD AB5603

Hopx Mouse 1:200 Santa Cruz sc-398703

Ki67 Rat 1:1000 eBio 14-5698-82

Pax6 Rabbit 1:300 BioLegend 901302

Nkx2.1 Mouse 1:500 EMD MAB5460

TTF-1 (Nkx2.1) Mouse 1:500 Santa Cruz SC53136X

Sox2 Mouse 1:500 Abcam ab79351

Hopx Rabbit 1:500 Sigma HPA030180

Table 4. Secondary Antibodies

Fluorophore Species Dilution Vendor Catalog #

488 Goat anti-mouse 1:1000 Life Technologies a28175 568 Goat anti-rabbit 1:1000 Life Technologies a11036 647 Goat anti-rat 1:1000 Life Technologies a21247 488 Goat anti-rabbit 1:1000 Life Technologies a27034 568 Goat anti-mouse 1:1000 Life Technologies a11031

Following fixation and before staining, 11 out of 27 total plates were exposed to air and

light for less than 18 hours. 2 of 27 plates were exposed after staining, but before imaging. By

eye, there was no discernable difference in image quality between plates that were exposed to air

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culture, a linear model found no significant relationship between exposure status and percentage

of cells positive for Ki67 (p=1).

Imaging

Plates were imaged on a Nikon Eclipse Ti2 system using the NIS Elements automated

microscopy software and a pco.edge 4.2Q High QE sCMOS camera at the UNC Neuroscience

Center Microscopy Core. Images of each well were acquired at 10x magnification using 4 filters

to capture fluorescence in the DAPI, GFP, TxRed, and Cy5 channels.

Automated Image Analysis Cellpose Segmentation

Nuclei stained with DAPI were identified in images using the deep learning algorithm Cellpose

(Stringer, Michaelos, & Pachitariu, 2020; github: https://github.com/MouseLand/cellpose ).

Full-size images were cropped into quarters to speed up processing time. Final nuclei masks

within each quarter image were created by subtracting the ‘outlines’ output from the ‘mask’

output to assist in separation of clumped cells in the CellProfiler analysis.

CellProfiler Analysis

First, nuclei were identified based on the output mask of Cellpose, with allowed sizes of

nuclei of 5-35 pixels, corresponding to 3.5-24.5 micrometers. Next, objects in the DAPI channel

were identified with size boundaries of 1 to 100 pixels to create a set of masking objects used to

ensure bright debris does not influence calculations of thresholds. Each channel was masked, and

the background intensity was measured. Based on this background intensity value, a threshold

was calculated to determine the number of nuclei positive for each immunolabel. The intensity at

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with the determined value to determine the number of cells positive or negative for each

immunolabel in each channel. For the Cy5 channel (Ki67), the threshold is

of the image background intensity. median 3 tandard deviation))  

2 * ( + ( * s

Technical Reproducibility

To evaluate CellPose and CellProfiler identification of cells, the number of cells in the

DAPI, TxRed, and Cy5 channels were manually counted in 4 cropped images. These cell counts

were used to calculate precision and recall of the number of cells automatically identified (Figure

2) with the following formulas:

Recall =  T rue positives + F alse negativesT rue positives Precision =  T rue positives + F alse positivesT rue positives

Pre-Analysis Processing in R

After the number of cells positive for Ki67 in each cropped image was calculated through image

processing described above, this data was read into R, where cell counts from the 4 cropped

images from one well were summed so that each well had 1 data point. Because each donor had

multiple wells, all the wells for one donor were averaged so that each donor had 1 data point.

Before further analysis, these data points were rank normalized.

Technical Variation

Linear models were used to examine the effects of technical variables on the percentage

of cells expressing Ki67 across donors. ANOVA tests were used to iteratively test if adding a

technical variable improves the linear model, and these results were used to build the final linear

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Biological Variation

Linear models were used to evaluate the relationship between round-corrected proportion

of proliferative cells and biological variation, including sex and gestation week of the donor.

Results

Following three weeks of proliferation, cell lines from 71 donors were fixed, stained with

Ki67 antibody and DAPI, and imaged (Figure 1, Figure 2). Each multi-channel image was

cropped into 4 images to speed up processing time, and each DAPI channel was run through the

deep-learning segmentation program Cellpose on the Longleaf Computing Cluster and the UNC

Neuroscience Center Microscopy Core Server in order to parallelize computing. To quantify the

accuracy of Cellpose segmentation, 6 images were manually counted and compared to

automatically generated results. Cellpose had high precision and recall (average 0.9977 and

0.9923 respectively), indicating a low rate of false positives and negatives (Figure 3c). While

Cellpose had split and merge errors, where detected cells are incorrectly identified (Figure 3A,

3B), they affected relatively few cells in each image. On average, 0.786% of cells identified had

split errors, and 2.51% of cells identified had merge errors. Following automated identification

of cells, the results from each of the 4 cropped images were added together to represent results

from 1 well. These results were subsetted to include only Ki67+ percentages, averaged by donor

so that the final dataset included 1 data point per donor, and rank normalized prior to analysis

(Figure 4). On average, each well contained 15336.84 (standard deviation 5914.104) nuclei and

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Technical Variable Correction

Using linear models, the data was examined for effects of technical variables. Round of

cell culture had a significant relationship with Ki67 expression (Figure 5, multiple R 2 =0.809,

p<2.2x10 -16 ). To correct for this effect, the residuals from a linear model including only round of

cell culture were used to examine effects of other technical variables, including cell culture

conditions, exposure to light and air for an extended period of time, time between fixing and

staining, and time between staining and imaging. None of these technical variables displayed a

significant relationship with Ki67 expression (Figure 6).

Relationship of Ki67 Expression to Biological Variables

The residuals from the linear model controlling Ki67 percentages for round to round

variability were used to examine the relationship between gestation week of the donor tissue and

Ki67 percentages. A linear model revealed no statistically significant relationship between

gestation week and Ki67 percentages in cell culture (Figure 7a, p=0.473). Sex of the donor was

examined in relation to Ki67 expression, also revealing no statistically signficiant relationship

(Figure 7b, p=0.237).

Although my original hypothesis was that donors with earlier gestation weeks would

display higher rates of proliferation as measured by Ki67 expression, this result could be

explained by the culturing conditions the cells were grown in. Because each cell line was

exposed to the same growth factors, their stage of development could have been “reset” to the

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Future Directions

The data set generated from this project can be the basis for many more analyses,

including genetic association analyses. With significant technical variables detected, the rate of

proliferation for each donor can be used to run a genome-wide association study to search for

genetic variation that is significantly associated with neural progenitor cell proliferation rate.

Percentage of cells positive for neural progenitor cell type markers Pax6 and Sox2 was also

measured, and although not analyzed in this study, these markers can also be used to run

genome-wide association studies. Once significant locations in the genome are found, they can

be compared to significant genetic factors found by mRNA or chromatin accessibility studies. If

a particular genetic locus is found to be significantly associated with both proliferation and

cortical surface area, this would be evidence to support the radial unit hypothesis and a role for

proliferation in determination of cortical surface area. Functional experiments using CRISPR or

other genetic tools in animal models such as mice to test different alleles for a particular genetic

locus could allow measurement of both surface area and behavioral effects of a genetic variant,

providing an experimental link to phenotypes that could model neuropsychiatric illness and

contribute to our understanding of the origins of these diseases.

Acknowledgements: I would like to thank the Stein Lab for being an exciting and challenging

place to work; in particular I would like to thank Dr. Stein, Oleh Krupa, and Nil Aygun for their

help with my thesis and Jessica Mory for her collaboration and moral support. This study was

supported by the National Institute of Mental Health (R00MH102357 to Dr. Stein). Microscopy

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funding from the NIH-NINDS Neuroscience Center Support Grant P30 NS045892 and the

NIH-NICHD Intellectual and Developmental Disabilities Research Center Support Grant U54

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Figure 1 . Schematic Cartoon of Experimental Design

Figure 2. Representative Images of Ki67 and DAPI staining

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Figure 3. Evaluation of Image Analysis Accuracy

Figure 3 . A). Quantification of merge and split errors in DAPI segmentation. On average, 2.5%

of identified cells had merge errors and 0.78% of identified cells had split errors. B)

Representative examples of split and merge errors. C). Precision and recall for DAPI

segmentation. D). Precision and recall for identification of Ki67+ cells in the Cy5 channel.

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Figure 5. Relationship of Round of Cell Culture and Ki67 Expression

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Figure 6. Technical Variables and Ki67 Expression

Figure 6. A) Time between staining and imaging is not significantly related to Ki67 expression

(p=0.478). B) Time between fixation and staining is not significantly related to Ki67 expression

(p=0.999). C) Light exposure status is not significantly related to Ki67 expression (p=1). D) Cell

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Figure 7. Biological Variation and Ki67 Expression

Figure 7. There was not a significant relationship between gestation week of the donor (A,

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References

Boedhoe, P. S. W., Schmaal, L., Abe, Y., Alonso, P., Ameis, S. H., Anticevic, A., … van den

Heuvel, O. A. (2017). Cortical abnormalities associated with pediatric and adult

obsessive-compulsive disorder: Findings from the enigma obsessive-compulsive disorder

working group. American Journal of Psychiatry, 175(5), 453–462.

https://doi.org/10.1176/appi.ajp.2017.17050485

Durak, O., Gao, F., Kaeser-Woo, Y. J., Rueda, R., Martorell, A. J., Nott, A., … Tsai, L.-H.

(2016). Chd8 mediates cortical neurogenesis via transcriptional regulation of cell cycle and Wnt

signaling. Nature Neuroscience, 19(11), 1477–1488. https://doi.org/10.1038/nn.4400

Erp, T. G. M. van, Walton, E., Hibar, D. P., Schmaal, L., Jiang, W., Glahn, D. C., … Turner, J. A.

(2018). Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control

subjects via the enhancing neuro imaging genetics through meta analysis (Enigma) consortium.

Biological Psychiatry, 84(9), 644–654. https://doi.org/10.1016/j.biopsych.2018.04.023

Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., …

Medland, S. E. (2020). The genetic architecture of the human cerebral cortex. Science ,

367 (6484). https://doi.org/10.1126/science.aay6690

Hibar, D. P., Westlye, L. T., Doan, N. T., Jahanshad, N., Cheung, J. W., Ching, C. R. K., …

Andreassen, O. A. (2018). Cortical abnormalities in bipolar disorder: An MRI analysis of 6503

individuals from the ENIGMA Bipolar Disorder Working Group. Molecular Psychiatry, 23(4),

932–942. https://doi.org/10.1038/mp.2017.73

Hirschhorn, J. N. (2009). Genomewide association studies—Illuminating biologic pathways.

(22)

Le, B. D., & Stein, J. L. (2019). Mapping causal pathways from genetics to neuropsychiatric

disorders using genome-wide imaging genetics: Current status and future directions. Psychiatry

and Clinical Neurosciences, 73(7), 357–369. https://doi.org/10.1111/pcn.12839

Llinares-Benadero, C., & Borrell, V. (2019). Deconstructing cortical folding: Genetic, cellular

and mechanical determinants. Nature Reviews Neuroscience, 20(3), 161–176.

https://doi.org/10.1038/s41583-018-0112-2

Lui, J. H., Hansen, D. V., & Kriegstein, A. R. (2011). Development and Evolution of the Human

Neocortex. Cell , 146 (1), 18–36. https://doi.org/10.1016/J.CELL.2011.06.030

Piven, J., Elison, J. T., & Zylka, M. J. (2017). Toward a conceptual framework for early brain

and behavior development in autism. Molecular Psychiatry , 22 (10), 1385–1394.

https://doi.org/10.1038/mp.2017.131

Pollen, A. A., Nowakowski, T. J., Chen, J., Retallack, H., Sandoval-Espinosa, C., Nicholas, C.

R., … Kriegstein, A. R. (2015). Molecular Identity of Human Outer Radial Glia during Cortical

Development. Cell , 163 (1), 55–67. https://doi.org/10.1016/j.cell.2015.09.004

Rakic, P. (2009). Evolution of the neocortex: A perspective from developmental biology. Nature

Reviews Neuroscience, 10(10), 724–735. https://doi.org/10.1038/nrn2719

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical

image segmentation. Retrieved from https://arxiv.org/abs/1505.04597v1

Schmaal, L., Hibar, D. P., Sämann, P. G., Hall, G. B., Baune, B. T., Jahanshad, N., … Veltman,

D. J. (2017). Cortical abnormalities in adults and adolescents with major depression based on

brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working

(23)

Schwarzkopf, D. S., Song, C., & Rees, G. (2011). The surface area of human V1 predicts the

subjective experience of object size. Nature Neuroscience, 14(1), 28–30.

https://doi.org/10.1038/nn.2706

Song, C., Schwarzkopf, D. S., Kanai, R., & Rees, G. (2015). Neural population tuning links

visual cortical anatomy to human visual perception. Neuron , 85 (3), 641–656.

https://doi.org/10.1016/j.neuron.2014.12.041

Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J.

(2017). 10 years of gwas discovery: Biology, function, and translation. The American Journal of

Human Genetics, 101(1), 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005

Wang, L., Hou, S., & Han, Y.-G. (2016). Hedgehog signaling promotes basal progenitor

expansion and the growth and folding of the neocortex. Nature Neuroscience, 19(7), 888–896.

https://doi.org/10.1038/nn.4307

Woodhead, G. J., Mutch, C. A., Olson, E. C., & Chenn, A. (2006). Cell-autonomous beta-catenin

signaling regulates cortical precursor proliferation. Journal of Neuroscience, 26(48),

12620–12630. https://doi.org/10.1523/JNEUROSCI.3180-06.2006

Zhou, C.-J., Borello, U., Rubenstein, J. L. R., & Pleasure, S. J. (2006). Neuronal production and

precursor proliferation defects in the neocortex of mice with loss of function in the canonical

Wnt signaling pathway. Neuroscience, 142(4), 1119–1131.

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