Add gradient-decoding paper and posters (#334)

* Add gradient-decoding paper

* Create dependabot.yml

* Add ACNN-BDN poster

* Update 2022-09-14-peraza-gradient-decoding-acnn-bdn.md

* Update 2022-09-14-peraza-gradient-decoding-acnn-bdn.png

* Add OHBM poster

.github/dependabot.yml

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+version: 2
+updates:
+  - package-ecosystem: "bundler"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+    open-pull-requests-limit: 5

papers/_posts/2024-02-02-peraza-gradient-decoding.md

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+---
+layout: paper
+title: "Methods for decoding cortical gradients of functional connectivity"
+nickname: 2022-08-12-sutherland-the-association-of
+authors: "Peraza JA, Salo T, Riedel MC, Bottenhorn KL, Poline J-B, Dockès J, Kent JD, Bartley JE, Flannery JS, Hill-Bowen LD, Lobo RP, Poudel R, Ray KL, Robinson JL, Laird RW, Sutherland MT, de la Vega A, Laird AR"
+year: "2024"
+journal: "Imaging Neuroscience"
+volume: 2
+issue:
+pages: 1-32
+is_published: true
+image: /assets/images/papers/imaging-neuroscience.png
+projects:
+tags: []
+
+# Text
+fulltext:
+pdf:
+pdflink:
+pmcid: "PMC10418206"
+preprint: "https://www.biorxiv.org/content/10.1101/2023.08.01.551505v2"
+supplement:
+
+# Links
+doi: "doi.org/10.1162/imag_a_00081"
+pmid: 37577598
+
+# Data and code
+github:
+  [
+    "https://github.com/NBCLab/gradient-decoding",
+    "https://github.com/JulioAPeraza/gradec",
+  ]
+neurovault:
+openneuro:
+figshare:
+  [
+    "https://figshare.com/projects/Meta-analytic_decoding_of_the_cortical_gradient_of_functional_connectivity/172347",
+  ]
+figshare_names:
+osf: "https://osf.io/xzfrt/"
+---
+
+{% include JB/setup %}
+
+# 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 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 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 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. 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.

posters/_posts/2022-09-14-peraza-gradient-decoding-acnn-bdn.md

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+---
+layout: poster
+title: "Meta-analytic decoding of macroscale gradients: Segmentation of connectivity gradients"
+nickname: 2022-09-14-peraza-gradient-decoding-acnn-bdn
+authors: "Peraza JA, Salo T, Riedel MC, Bottenhorn KL, Poline J-B, Dockès J, Kent JD, de la Vega A, Laird AR"
+year: "2022"
+conference: "ACNN-BDN"
+image: /assets/images/posters/2022-09-14-peraza-gradient-decoding-acnn-bdn.png
+projects: ["mmmm"]
+tags: []
+
+# Content
+fulltext:
+pdf: https://osf.io/download/4uzpk/
+
+# Links
+doi:
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+f1000:
+---
+
+{% include JB/setup %}
+
+# Abstract
+
+## Introduction
+
+Macroscale gradients have emerged as a central principle for understanding functional brain organization. Margulies et al. demonstrated that a principal gradient of connectivity 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 interpretation of macroscale gradients remains a central topic of discussion, with some studies demonstrating that gradients may be described using meta-analytic functional decoding. Using the Neurosynth database, meta-analytic decoding of a connectivity gradient has previously been performed by first segmenting the gradient spectrum (i.e., from high- to low-intensity voxels) into five-percentile increments, binarizing the 20 resultant maps, and decoding each map. This segmented approach has been used in prior studies and allows characterization of the full gradient spectrum; however, existing segmentation procedures are arbitrarily determined with equidistant intervals (i.e., five-percentile increments) and should be based on a more data-driven approach. We investigated the performance of various methods to establish a principled approach for gradient segmentation and functional decoding.
+
+## Methods
+
+To this end, we utilized the resting-state fMRI (rs-fMRI) group-average dense connectome from the Human Connectome Project (HCP) S1200 data release to identify the principal gradient of functional connectivity. Following Margulies et al., an inverse Fisher transform was applied to the z-transformed correlation from the dense connectome to scale the values between -1 and 1; connections that included vertex in the medial wall were zeroed. Diffusion embedding was applied to the cosine similarity affinity matrix using the mapalign repository. We utilized the gradient (i.e., eigenvector) with the highest variance explained (i.e., eigenvalue), also called the principal gradient, for further analysis. We evaluated three segmentation approaches: (i) percentile-based, equidistant segmentation, (ii) segmentation based on a 1D k-means clustering approach, and (iii) segmentation based on the Kernel Density Estimation (KDE) curve of the gradient axis (Figure 1). n=30 different segmentations of the principal gradient for each segmentation approach were generated, corresponding to numbers of segments ranging from k=3-32 . Mean silhouette scores were computed to determine relative performance across percentile-based, k-means-based, and KDE-based segmentations, with the highest mean silhouette score representing the “best” relative performance.
+
+## Results
+
+We observed that a data-driven segmentation approach (e.g., KMeans-based) is preferable to an equidistant segmentation (Figure 2). In particular, the k-means clustering approach showed the “best” relative performance across segment sizes.
+
+## Conclusions
+
+Continued work on this project will include assessment of six decoding strategies using two meta-analytic databases and three sets of meta-analytic maps. An optimization test will be performed to identify the segmentation that yielded a maximum association between meta-analytic maps and gradient maps for each of the six decoding strategies. Data are available on G-Node GIN and version controlled with DataLad; all code required to reproduce the analyses and figures in this paper is made available on GitHub. The decoding workflow is being made available for future re-use and be linked to NiMARE as a Python module for gradient meta-analytic decoding. Taken together, the current work aims to provide recommendations on best practices, along with open-source tools and flexible methods, for gradient-based functional decoding of fMRI data.

posters/_posts/2023-07-24-peraza-gradient-decoding-ohbm.md

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+---
+layout: poster
+title: "Meta-analytic decoding of the cortical gradient of functional connectivity"
+nickname: 2023-07-24-peraza-gradient-decoding-ohbm
+authors: "Peraza JA, Salo T, Riedel MC, Bottenhorn KL, Poline J-B, Dockès J, Kent JD, Bartley JE, Flannery JS, Hill-Bowen LD, Lobo RP, Poudel R, Ray KL, Robinson JL, Laird RW, Sutherland MT, de la Vega A, Laird AR"
+year: "2023"
+conference: "OHBM"
+image: /assets/images/posters/2023-07-24-peraza-gradient-decoding-ohbm.png
+projects: ["mmmm"]
+tags: []
+
+# Content
+fulltext:
+pdf: https://osf.io/download/hy32g/
+
+# Links
+doi:
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+f1000:
+---
+
+{% include JB/setup %}
+
+# Abstract
+
+## Introduction
+
+Macroscale gradients have emerged as a central principle for understanding functional brain organization. Margulies et al. demonstrated that a principal gradient of connectivity 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 interpretation of macroscale gradients remains a central topic of discussion, with some studies demonstrating that gradients may be described using meta-analytic functional decoding. Using the Neurosynth database, meta-analytic decoding of a connectivity gradient has previously been performed by first segmenting the gradient spectrum (i.e., from high- to low-intensity voxels) into five-percentile increments, binarizing the 20 resultant maps, and decoding each map. This segmented approach has been used in prior studies and allows characterization of the full gradient spectrum. However, existing segmentation procedures are arbitrarily determined with equidistant intervals (i.e., five-percentile increments) and could be based on a more data-driven approach. We investigated the performance of various methods to establish a principled approach for gradient segmentation and functional decoding.
+
+## Methods
+
+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. 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 “activation” brain maps for decoding. The three segmentation approaches were evaluated using the silhouette score. 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 and NQ: NeuroQuery). The resultant 18 different decoding strategies were evaluated using four performance metrics, assessed by comparing correlation profiles, semantic similarity metrics (i.e., information content and TFIDF), and signal-to-noise ratio (SNR). Finally, we select the strategy with better performance across metrics for visualization, where the non-functional terms were removed from the model.
+
+## Results
+
+The KMeans algorithm shows the most consistent result across segment solutions, with a distribution having the highest median value and the lowest number of vertices with negative silhouette coefficients. The KMeans approach consistently resulted in better segment assignment across the cortical surface.Data-driven segmentation determined by a KMeans algorithm produced the most balanced distributions of boundaries with the highest vertex-wise and mean silhouette coefficient across segment solutions. A small number of segments is preferred if we want to produce gradient maps with high silhouette scores, and to find the highest association between the subsegment maps and meta-analytic maps. KMeans provided the best and most consistent segment assignments for the 30 segmentation solutions, with the highest values observed between 3-8 segments. With regard to segmentation approaches, PCT showed the best performance, while KMeans performed better than KDE. LDA-based decoder, in addition to showing a high average of top correlation scores, yielded the highest information content, TFIDF, and SNR, We observed little differences in correlation profile between NS and NQ combinations; in some cases, NS performed slightly better (e.g., Term-PCT), and in others, NQ showed higher correlation values to some degree (e.g., LDA-PCT). The NeuroQuery database showed a higher information content, provided by the richness of its vocabulary as compared to Neurosynth. We propose a figure that delineates the results in a way that most effectively facilitates the interpretation of the functional gradients. The figure captures the continuous transition between different functional systems, from unimodal primary sensorimotor regions associated with “motor” and “movement”, to transmodal regions associated with the default mode network and representative of abstract functioning.
+
+## Discussion and Conclusions
+
+For small numbers of segments, a KMeans algorithm yields the most confident distribution of boundaries as shown by the vertex-wise and mean silhouette coefficients. We determined that a large number of segments was detrimental to the performance of correlation decoders, as the average of the top correlation values decreases exponentially with the increase of the segment solution. LDA-based produced meta-analytic maps that yielded both a relatively high correlation value and a collection of terms that naturally improved the information content, TFIDF, and SNR. NS and NQ performed similarly in terms of their correlation profile, given the similarity between their corpora. The size of the vocabulary may help improve the information content of a decoder, as well as the production of more functional terms. As such we recommend using a large database with a large and rich vocabulary like NeuroQuery. In conclusion, we provide recommendations on best practices for gradient-based functional decoding of fMRI data. We found that 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.