From c32756a8237ca1f9e40048a4b694b0742ca650ce Mon Sep 17 00:00:00 2001
From: MLDataAnalytics <128093454+MLDataAnalytics@users.noreply.github.com>
Date: Wed, 21 Aug 2024 21:37:02 -0400
Subject: [PATCH] Update README.md

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 # pNet
 
-pNet is a Python package of an [algorithm](https://pubmed.ncbi.nlm.nih.gov/28483721/) for computing personalized, sparse, non-negative large-scale functional networks from functional magnetic resonance imaging (fMRI) data, faciliating effective characterization of individual variation in [functional topography](https://pubmed.ncbi.nlm.nih.gov/32078800/). The personalized functional networks are comparable across subjects while maintaining [subject specific variation](https://pubmed.ncbi.nlm.nih.gov/28483721/), reflected by their [improved functional coherence](https://pubmed.ncbi.nlm.nih.gov/28483721/) compared with their group-level counterparts. The computation of personalized functional networks is accompanied by [quality control](https://pubmed.ncbi.nlm.nih.gov/36706636/), with visualization and quantification of their spatial correspondence and functional coherence in reference to their group-level counterparts. 
+pNet is a Python package of an [algorithm](https://pubmed.ncbi.nlm.nih.gov/28483721/) for computing personalized, sparse, non-negative large-scale functional networks from functional magnetic resonance imaging (fMRI) data, facilitating effective characterization of individual variation in [functional topography](https://pubmed.ncbi.nlm.nih.gov/32078800/). The personalized functional networks are comparable across subjects while maintaining [subject specific variation](https://pubmed.ncbi.nlm.nih.gov/28483721/), reflected by their [improved functional coherence](https://pubmed.ncbi.nlm.nih.gov/28483721/) compared with their group-level counterparts. The computation of personalized functional networks is accompanied by [quality control](https://pubmed.ncbi.nlm.nih.gov/36706636/), with visualization and quantification of their spatial correspondence and functional coherence in reference to their group-level counterparts. 
 
 The [algorithm](https://pubmed.ncbi.nlm.nih.gov/28483721/) has been successfuly applied to stuides of [individual variation in functional topography of association networks in youth](https://pubmed.ncbi.nlm.nih.gov/32078800/), [sex differences in the functional topography of association networks in youth](https://pubmed.ncbi.nlm.nih.gov/35939696/), [dissociable multi-scale patterns of development in personalized brain networks](https://pubmed.ncbi.nlm.nih.gov/35551181/), [functional network topography of psychopathology in youth](https://pubmed.ncbi.nlm.nih.gov/35927072/), and [multiscale functional connectivity patterns of the aging brain](https://pubmed.ncbi.nlm.nih.gov/36731813/).