Add Julio's posters

posters/_posts/2024-04-01-peraza-gradient-decoding-gsaw.md

@@ -0,0 +1,63 @@
+---
+layout: poster
+title: "Methods for decoding cortical gradients of functional connectivity"
+nickname: 2024-04-01-peraza-gradient-decoding-gsaw
+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"
+conference: "GSAW"
+image: /assets/images/posters/2024-04-01-peraza-gradient-decoding-gsaw.png
+projects: ["mmmm"]
+tags: []
+
+# Content
+fulltext:
+pdf: https://osf.io/6grs3
+
+# Links
+doi:
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+f1000:
+---
+
+{% include JB/setup %}
+
+# Abstract
+
+## Background
+
+- Macroscale gradients of brain connectivity have emerged as a central principle for understanding functional brain organization.
+- The functional significance and interpretation of gradients remain a central topic of discussion in the neuroimaging community.
+- Previous studies have demonstrated that the 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.
+
+## Goals
+
+**Overall Objective:** investigate and improve the framework of data-driven methods for decoding the principal gradient of functional connectivity.
+
+- Examine and evaluate different methods for decoding brain maps on surface space.
+- Establish a principled approach for gradient segmentation and meta-analytic decoding.
+- Provide recommendations on best practices and develop flexible methods for gradient-based functional decoding.
+
+## Methods
+
+We used 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. We evaluated three segmentation approaches: (i) percentile-based, (ii) segmentation based on a 1D k-means clustering approach, and (iii) segmentation based on the Kernel Density Estimation curve of the gradient axis. We assessed six different decoding strategies that used two meta-analytic databases (i.e., Neurosynth and NeuroQuery) and three methods to produce meta-analytic maps (i.e., term-based, LDA-based, and GC-LDA-based decoding). In addition, we proposed a method for decoding lower-order gradient maps combined with the principal gradient in a high-dimensional space.
+
+## Results
+
+- For small numbers of segments, a k-means algorithm yields the most confident distribution of boundaries, as shown by the silhouette coefficients, variance ratio, and cluster separation.
+- LDA-based produced meta-analytic maps that yielded a relatively high correlation value and a collection of terms that naturally improved the information content, TFIDF, and SNR.
+- NS and NQ performed similarly regarding their correlation profile.
+- We reproduced the results from Margulies et al., showing the continuous transition from primary sensorimotor to transmodal regions.
+- We proposed methods for decoding lower-order gradient maps.
+
+## Conclusions
+
+- 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 the principal gradient of functional connectivity.
+- This combination of approaches and our recommended visualization method for reporting meta-analytic decoding findings will enhance the overall interpretability of macroscale gradients in the fMRI community.

posters/_posts/2024-04-04-peraza-gradec-pdrd.md

@@ -0,0 +1,33 @@
+---
+layout: poster
+title: "Gradec: Meta-analytic decoder of connectivity gradients"
+nickname: 2024-04-04-peraza-gradec-pdrd
+authors: "Peraza JA, Salo T, Kent JD, de la Vega A, Laird AR"
+year: "2024"
+conference: "PDRD"
+image: /assets/images/posters/2024-04-04-peraza-gradec-pdrd.png
+projects: ["mmmm"]
+tags: []
+
+# Content
+fulltext:
+pdf: https://osf.io/bkmca
+
+# Links
+doi:
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+f1000:
+---
+
+{% include JB/setup %}
+
+# Abstract
+
+Macroscale gradients of brain connectivity have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that the principal gradient of functional connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network at the other. The functional significance and interpretation of gradients remain a central topic of discussion in the neuroimaging community, with some studies demonstrating that they may be described using meta-analytic functional decoding techniques. However, additional methodological development and tools are necessary to fully leverage available meta-analytic methods to describe connectivity gradients. To this end, we developed Gradec, an open-source Python package for surface-based functional decoding of connectivity gradients. Gradec implements a range of methods for clustering highly connected regions within the principal gradients and meta-analytic decoding on surface space. In addition, it presents a set of visualization functions to facilitate the interpretation of the functional decoding results. Notably, Gradec supports clustering and decoding on a multidimensional space, which helps describe additional components of the gradient decomposition. The current tool aims to provide recommendations on best practices and flexible tools and methods for gradient-based functional decoding of connectivity data.