add citation

app.py

@@ -19,6 +19,7 @@ The dataset has the following characteristics:
 * Generative models were trained on 40,000 subjects from the iSTAGING consortium to synthesize 145 brain anatomical region-of-interest (ROI) volumes which are derived from structural T1-weighted magnetic resonance imaging (MRI).
 * The dataset includes participants’ demographic information, such as sex, age and race, which are beneficial for research focusing on mitigating algorithmic bias and promoting fairnes.
 * Besides 18,000 samples in the dataset, we also share the pre-trained generative models for users to customize their needs for data synthesis. 
+* Please check our paper for more details: https://arxiv.org/abs/2407.12897.
 
 ## Dataset Download
 
@@ -34,12 +35,10 @@ wget https://rongguangw-neuro-synth.hf.space/file=dataset/neurosynth_dataset.csv
 citation = """
 ## Citation
 ```
-@article{,
-  title={},
-  author={},
-  journal={},
-  volume={},
-  pages={},
+@article{chintapalli2024neurosynth,
+  title={NeuroSynth: MRI-Derived Neuroanatomical Generative Models and Associated Dataset of 18,000 Samples},
+  author={Chintapalli, Sai Spandana and Wang, Rongguang and Yang, Zhijian and Tassopoulou, Vasiliki and Yu, Fanyang and Bashyam, Vishnu and Erus, Guray and Chaudhari, Pratik and Shou, Haochang and Davatzikos, Christos},
+  journal={arXiv preprint arXiv:2407.12897},
   year={2024}
 }
 ```