Update citation

NBCLab · Feb 6, 2024 · 0b47507 · 0b47507
1 parent 4ae339a
commit 0b47507
Show file tree

Hide file tree

Showing 2 changed files with 125 additions and 119 deletions.
diff --git a/CITATION.cff b/CITATION.cff
@@ -1,33 +1,34 @@
 cff-version: 1.2.0
 message: If you use this code, please cite it as below.
 
-title: Meta-analytic decoding of the cortical gradient of functional connectivity
+title: Methods for decoding cortical gradients of functional connectivity
 
 abstract: |
   Macroscale gradients have emerged as a central principle for understanding functional brain 
   organization. Previous studies have demonstrated that a principal gradient of connectivity in 
-  the human brain exists, with unimodal primary sensorimotor regions situated at one end, and 
+  the human brain exists, with unimodal primary sensorimotor regions situated at one end and 
   transmodal regions associated with the default mode network and representative of abstract 
   functioning at the other. The functional significance and interpretation of macroscale gradients 
   remains a central topic of discussion in the neuroimaging community, with some studies 
   demonstrating that gradients may be described using meta-analytic functional decoding techniques. 
-  However, additional methodological development is necessary to more fully leverage available 
+  However, additional methodological development is necessary to fully leverage available 
   meta-analytic methods and resources and quantitatively evaluate their relative performance. 
   Here, we conducted a comprehensive series of analyses to investigate and improve the framework 
   of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient 
-  segmentation and functional decoding. We found that a small number of segments determined by a 
-  K-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery 
+  segmentation and functional decoding. We found that a two-segment solution determined by a 
+  k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery 
   database was the optimal combination of methods for decoding functional connectivity gradients. 
-  Taken together, the current work aims to provide recommendations on best practices, along with 
-  flexible methods, for gradient-based functional decoding of fMRI data.
+  Finally, we proposed a method for decoding additional components of the gradient decomposition. 
+  The current work aims to provide recommendations on best practices and flexible methods for 
+  gradient-based functional decoding of fMRI data.
 
 repository-code: https://github.com/NBCLab/gradient-decoding
 
 identifiers:
   - type: doi
-    value: https://doi.org/10.1101/2023.08.01.551505
-    description: Meta-analytic decoding of the cortical gradient of functional connectivity
-    
+    value: https://doi.org/10.1162/imag_a_00081
+    description: Methods for decoding cortical gradients of functional connectivity
+
 contact:
   - given-names: Julio A
     family-names: Peraza
@@ -64,7 +65,7 @@ authors:
   - given-names: James D
     family-names: Kent
     orcid: 0000-0002-4892-2659
-    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA 
+    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA
   - given-names: Jessica E
     family-names: Bartley
     orcid: 0000-0001-7269-9701
@@ -88,7 +89,7 @@ authors:
   - given-names: Kimberly L
     family-names: Ray
     orcid: 0000-0003-1302-2834
-    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA 
+    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA
   - given-names: Jennifer L
     family-names: Robinson
     orcid: 0000-0001-7389-3047
@@ -103,89 +104,89 @@ authors:
   - given-names: Alejandro
     family-names: de la Vega
     orcid: 0000-0001-9062-3778
-    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA 
+    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA
   - given-names: Angela R
     family-names: Laird
     orcid: 0000-0003-3379-8744
     affiliation: Department of Physics, Florida International University, Miami, FL, USA
 
 preferred-citation:
   authors:
-  - given-names: Julio A
-    family-names: Peraza
-    orcid: 0000-0003-3816-5903
-    affiliation: Department of Physics, Florida International University, Miami, FL, USA
-  - given-names: Taylor
-    family-names: Salo
-    orcid: 0000-0001-9813-3167
-    affiliation: Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
-  - given-names: Michael C
-    family-names: Michael
-    orcid: 0000-0002-1860-4449
-    affiliation: LTI Engineering and Software, Quebec City, QC, Canada
-  - given-names: Katherine L
-    family-names: Bottenhorn
-    orcid: 0000-0002-7796-8795
-    affiliation: Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
-  - given-names: Jean-Baptiste
-    family-names: Poline
-    orcid: 0000-0002-9794-749X
-    affiliation: Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
-  - given-names: Jérôme
-    family-names: Dockès
-    orcid: 0000-0002-5304-2496
-    affiliation: Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
-  - given-names: James D
-    family-names: Kent
-    orcid: 0000-0002-4892-2659
-    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA 
-  - given-names: Jessica E
-    family-names: Bartley
-    orcid: 0000-0001-7269-9701
-    affiliation: Department of Physics, Florida International University, Miami, FL, USA
-  - given-names: Jessica S
-    family-names: Flannery
-    orcid: 0000-0003-3274-1578
-    affiliation: Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
-  - given-names: Lauren D
-    family-names: Hill-Bowen
-    orcid: 0000-0002-9817-7757
-    affiliation: Department of Psychology, Florida International University, Miami, FL, USA
-  - given-names: Rosario
-    family-names: Pintos Lobo
-    orcid: 0000-0002-7679-1385
-    affiliation: Department of Psychology, Florida International University, Miami, FL, USA
-  - given-names: Ranjita
-    family-names: Poudel
-    orcid: 0000-0003-4343-1153
-    affiliation: Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, USA
-  - given-names: Kimberly L
-    family-names: Ray
-    orcid: 0000-0003-1302-2834
-    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA 
-  - given-names: Jennifer L
-    family-names: Robinson
-    orcid: 0000-0001-7389-3047
-    affiliation: Department of Psychology, Auburn University, Auburn, AL, USA
-  - given-names: Robert W
-    family-names: Laird
-    affiliation: Department of Physics, Florida International University, Miami, FL, USA
-  - given-names: Matthew T
-    family-names: Sutherland
-    orcid: 0000-0002-6091-4037
-    affiliation: Department of Psychology, Florida International University, Miami, FL, USA
-  - given-names: Alejandro
-    family-names: de la Vega
-    orcid: 0000-0001-9062-3778
-    affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA 
-  - given-names: Angela R
-    family-names: Laird
-    orcid: 0000-0003-3379-8744
-    affiliation: Department of Physics, Florida International University, Miami, FL, USA
-  
-  title: "Meta-analytic decoding of the cortical gradient of functional connectivity"
-  doi: 10.1101/2023.08.01.551505
-  date-released: 2023-08-03
-  url: "https://www.biorxiv.org/content/10.1101/2023.08.01.551505v1"
-  journal: bioRxiv
-  type: article
+    - given-names: Julio A
+      family-names: Peraza
+      orcid: 0000-0003-3816-5903
+      affiliation: Department of Physics, Florida International University, Miami, FL, USA
+    - given-names: Taylor
+      family-names: Salo
+      orcid: 0000-0001-9813-3167
+      affiliation: Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
+    - given-names: Michael C
+      family-names: Michael
+      orcid: 0000-0002-1860-4449
+      affiliation: LTI Engineering and Software, Quebec City, QC, Canada
+    - given-names: Katherine L
+      family-names: Bottenhorn
+      orcid: 0000-0002-7796-8795
+      affiliation: Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
+    - given-names: Jean-Baptiste
+      family-names: Poline
+      orcid: 0000-0002-9794-749X
+      affiliation: Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
+    - given-names: Jérôme
+      family-names: Dockès
+      orcid: 0000-0002-5304-2496
+      affiliation: Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
+    - given-names: James D
+      family-names: Kent
+      orcid: 0000-0002-4892-2659
+      affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA
+    - given-names: Jessica E
+      family-names: Bartley
+      orcid: 0000-0001-7269-9701
+      affiliation: Department of Physics, Florida International University, Miami, FL, USA
+    - given-names: Jessica S
+      family-names: Flannery
+      orcid: 0000-0003-3274-1578
+      affiliation: Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
+    - given-names: Lauren D
+      family-names: Hill-Bowen
+      orcid: 0000-0002-9817-7757
+      affiliation: Department of Psychology, Florida International University, Miami, FL, USA
+    - given-names: Rosario
+      family-names: Pintos Lobo
+      orcid: 0000-0002-7679-1385
+      affiliation: Department of Psychology, Florida International University, Miami, FL, USA
+    - given-names: Ranjita
+      family-names: Poudel
+      orcid: 0000-0003-4343-1153
+      affiliation: Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, USA
+    - given-names: Kimberly L
+      family-names: Ray
+      orcid: 0000-0003-1302-2834
+      affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA
+    - given-names: Jennifer L
+      family-names: Robinson
+      orcid: 0000-0001-7389-3047
+      affiliation: Department of Psychology, Auburn University, Auburn, AL, USA
+    - given-names: Robert W
+      family-names: Laird
+      affiliation: Department of Physics, Florida International University, Miami, FL, USA
+    - given-names: Matthew T
+      family-names: Sutherland
+      orcid: 0000-0002-6091-4037
+      affiliation: Department of Psychology, Florida International University, Miami, FL, USA
+    - given-names: Alejandro
+      family-names: de la Vega
+      orcid: 0000-0001-9062-3778
+      affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA
+    - given-names: Angela R
+      family-names: Laird
+      orcid: 0000-0003-3379-8744
+      affiliation: Department of Physics, Florida International University, Miami, FL, USA
+
+  title: "Methods for decoding cortical gradients of functional connectivity"
+  doi: 10.1162/imag_a_00081
+  date-released: 2024-02-02
+  url: "https://doi.org/10.1162/imag_a_00081"
+  journal: Imaging Neuroscience
+  type: article
diff --git a/README.md b/README.md
@@ -1,47 +1,49 @@
 # gradient-decoding
-Meta-analytic decoding of the cortical gradient of functional connectivity
+
+Methods for decoding cortical gradients of functional connectivity
 
 ## Summary
-This repository contains all code required to reproduce the analyses and figures of the 
-"Meta-analytic decoding of the cortical gradient of functional connectivity" paper.
-See the preprint version for more details: https://doi.org/10.1101/2023.08.01.551505
+
+This repository contains all code required to reproduce the analyses and figures of the
+"Methods for decoding cortical gradients of functional connectivity" paper.
+Please refer to the paper for further details: https://doi.org/10.1162/imag_a_00081
 
 ## Workflow
 
 The workflow consists of the following steps:
 
 1. Functional Connectivity Gradients:
-    * HCP S1200 resting-state fMRI data were used to generate functional connectivity and compute 
-    the affinity matrix.
-    * Diffusion map embedding was applied to identify the principal gradient of functional 
-    connectivity.
+   - HCP S1200 resting-state fMRI data were used to generate functional connectivity and compute
+     the affinity matrix.
+   - Diffusion map embedding was applied to identify the principal gradient of functional
+     connectivity.
 2. Segmentation and Gradient Maps:
-    * Whole-brain gradient maps were segmented to divide the gradient spectrum into a finite number 
-    of brain maps.
-    * Three different segmentation approaches were evaluated: percentile-based (PCT), k-means 
-    (KMeans), and KDE. 
-    * Individual segments were transformed into pseudo-activation brain maps for decoding. 
-    * The three segmentation approaches were evaluated using the silhouette, variance ratio,
-    and cluster separation scores. 
+   - Whole-brain gradient maps were segmented to divide the gradient spectrum into a finite number
+     of brain maps.
+   - Three different segmentation approaches were evaluated: percentile-based (PCT), k-means
+     (KMeans), and KDE.
+   - Individual segments were transformed into pseudo-activation brain maps for decoding.
+   - The three segmentation approaches were evaluated using the silhouette, variance ratio,
+     and cluster separation scores.
 3. Meta-analytic Functional Decoding:
-    * Six different meta-analytic decoding strategies were implemented on surface space, derived 
-    from three sets of meta-analytic maps (i.e., term-based (Term), LDA, and GCLDA) and two 
-    databases (i.e., NS: [Neurosynth](https://github.com/neurosynth/neurosynth-data) 
-    and NQ: [NeuroQuery](https://github.com/neuroquery/neuroquery_data)). 
+   - Six different meta-analytic decoding strategies were implemented on surface space, derived
+     from three sets of meta-analytic maps (i.e., term-based (Term), LDA, and GCLDA) and two
+     databases (i.e., NS: [Neurosynth](https://github.com/neurosynth/neurosynth-data)
+     and NQ: [NeuroQuery](https://github.com/neuroquery/neuroquery_data)).
 4. Performance of Decoding Strategies:
-    * The resultant 18 different decoding strategies were evaluated using four performance metrics,
-    assessed by comparing correlation profiles, semantic similarity metrics (i.e., information 
-    content (IC) and TFIDF), and signal-to-noise ratio (SNR). 
+   - The resultant 18 different decoding strategies were evaluated using four performance metrics,
+     assessed by comparing correlation profiles, semantic similarity metrics (i.e., information
+     content (IC) and TFIDF), and signal-to-noise ratio (SNR).
 5. Multidimensional Decoding:
-    * Finally, we performed a multidimensional decoding using the first four components together.
+   - Finally, we performed a multidimensional decoding using the first four components together.
 
 ![Fig-01](https://github.com/NBCLab/gradient-decoding/assets/52050407/fcdc4ae3-9219-4c58-9d17-8336c56c6bb8)
 
 ## How to use
 
 ### 1. Install dependencies
 
-In order to execute the workflow (`workflow.py`), you will need to install all of the Python libraries 
+In order to execute the workflow (`workflow.py`), you will need to install all of the Python libraries
 that are required. The required library and associated versions are available in `requirements.txt`.
 
 The easiest way to install the requirements is with Conda.
@@ -51,10 +53,12 @@ conda create -p /path/to/gradientdec_env pip python=3.9
 conda activate /path/to/gradientdec_env
 pip install -r requirements.txt
 ```
+
 ### 2. Download data files
 
-The analysis workflow is computationally intensive. If a user would like to skip any step, they will need to 
-download the necessary files in `data` and `results` from our OSF page: https://osf.io/xzfrt/. 
+The analysis workflow is computationally intensive. If a user would like to skip any step, they
+will need to download the necessary files in `data` and `results` from our OSF
+page: https://osf.io/xzfrt/.
 
 ### 3. Run the workflow
 
@@ -72,10 +76,11 @@ sbatch ./jobs/run_workflow.sh
 
 ## Citation
 
-If you use this code in your research, please acknowledge this work by citing the paper: https://doi.org/10.1101/2023.08.01.551505.
+If you use this code in your research, please acknowledge this work by citing the
+paper: https://doi.org/10.1162/imag_a_00081.
 
 ## Note
 
 The script `workflow.py` should be used only for reproducibility purposes of the linked paper.
-In order to perform the proposed analysis in your data, please refer to the Python package 
+In order to perform the proposed analysis in your data, please refer to the Python package
 [Gradec](https://github.com/JulioAPeraza/gradec).