From 2415c62200a990e497f18bba148b5199e8eabd10 Mon Sep 17 00:00:00 2001
From: smruthig <75429779+smruthig@users.noreply.github.com>
Date: Mon, 12 Feb 2024 23:12:27 -0800
Subject: [PATCH] Update README.md

---
 README.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/README.md b/README.md
index 17645a3..e407fc5 100644
--- a/README.md
+++ b/README.md
@@ -27,6 +27,9 @@
 ##  Quick Links
 
 > - [ Overview](#overview)
+>   - [ Background](#background)
+>   - [ Results](#results)
+>   - [ Conclusions](#conclusions)
 > - [ Repository Structure](#repository-structure)
 > - [ Instructions](#instructions)
 >   - [ Installation Locally](#installation-locally)
@@ -46,6 +49,8 @@ The performance of our graph neural network models is on par with that of our ma
 ### Conclusions
 This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.
 
+---
+
 ##  Repository Structure
 
 ```sh
@@ -73,6 +78,7 @@ This study provides insights into the role of advanced graph theoretical methods
     ├── README.md
     └── requirements.txt
 ```
+---
 
 ## Instructions
 
@@ -116,6 +122,8 @@ Use the following command to run Schizophrenia_Detection using Jupyter:
 jupyter notebook
 ```
 
+---
+
 ##  Contributing
 
 Contributions are welcome! Here are several ways you can contribute: