Gender classification and manifold learning on functional brain - PowerPoint PPT Presentation

Jul 15, 2023 •137 likes •301 views

Gender classification and manifold learning on functional brain networks Sofia Ira Ktena , Salim Arslan, and Daniel Rueckert How to compare brain networks? Richiardi and Ng (2013) Brain networks as SPD matrices Brain networks derived

Gender classification and manifold learning on   functional brain networks Sofia Ira Ktena , Salim Arslan, and Daniel Rueckert
How to compare brain networks? Richiardi and Ng (2013)
Brain networks as SPD matrices • Brain networks derived from correlation analysis of fMRI data can be characterized by symmetric positive semi- definite matrices • Sparse estimators impose simple models and provide good fit to the data ( GLASSO algorithm)
Recovering connectivity structure
Riemannian manifolds • Covariances do not conform to Euclidean geometry but rather form a Riemannian manifold • In the manifold setting, a SPD matrix can be represented as an element in a vector space • Convenient computations with eigenvalue decomposition P = U diag ( σ 1 , . . . , σ n ) U T expm( P ) = U diag (exp( σ 1 ) , . . . , exp( σ n )) U T logm( P ) = U diag (log( σ 1 ) , . . . , log( σ n )) U T
Log-Riemannian manifold
Dimensionality reduction • Keep PCs that explain 98% of the variance in training set
Dataset • HCP data • 2 rfMRI sessions (30min each) • 100 subjects (46 male, 54 female) • Pre-processed fMRI data • Normalized timeseries to 0 mean and standard deviation 1 • How are the nodes defined? - Each node corresponds to a ROI from a parcellation scheme • What is the representative timeseries? - Region average timeseries • How are the edge weights defined? - Pearson’s correlation coefficient • Subject-level analysis
Anatomical Parcellations
Functional Parcellations
Framework evaluation • Two different sets of networks based on the two different fMRI sessions • Check whether networks from the subject lie closer to each other in Riemannian rather than Euclidean space
Gender classification (Riemannian space)
Conclusions • Riemannian framework picks up networks generated from the same subject more accurately than Euclidean setting • Functional parcellations (and especially the 3LAYER one) outperform the anatomical parcellations in the same task • Random parcellations perform equivalently well due to more evenly sized parcels • Differences between the two genders are not significant, but still better than Euclidean setting • More parcels do not guarantee higher discriminative power • Framework limited by correspondence between network nodes
Thank you

Recommend

Manifold Learning: Applications in Neuroimaging Robin Wolz 23/09/2011 Overview Manifold

Your own logo here Manifold Learning: Applications in Neuroimaging Robin Wolz 23/09/2011 Overview Manifold learning for Atlas Propagation Multi-atlas segmentation Challenges LEAP Manifold learning for classification

466 views • 29 slides

Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and

Microsoft Research technical report TR-2011-114 Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning A. Criminisi 1 , J. Shotton 2 and E. Konukoglu 3 1 Microsoft Research Ltd, 7 J J

1.88k views • 151 slides

A manifold structure on the set of functional observers Jochen Trumpf University of W urzburg

A manifold structure on the set of functional observers Jochen Trumpf University of W urzburg Math. Institute Germany A manifold structure on the set of functional observers p.1/12 Contents motivating problem A manifold structure on

756 views • 37 slides

Charting the Right Manifold: Manifold Mixup for Few-Shot Learning Puneet Mangla 1,2* , Mayank

Charting the Right Manifold: Manifold Mixup for Few-Shot Learning Puneet Mangla 1,2* , Mayank Singh 1* , Abhishek Sinha 1* , Nupur Kumari 1* , Balaji Krishnamurthy 1 , Vineeth N Balasubramanian 2 1 Media and Data Science Research, Adobe Inc. Noida,

1.22k views • 14 slides

Game Bot Identification Game Bot Identification based on Manifold Learning based on Manifold

Game Bot Identification Game Bot Identification based on Manifold Learning based on Manifold Learning Kuan Ta Chen Academia Sinica Hsing Kuo Pao NTUST Hong Chung Chang NTUST ACM NetGames 2008 Game Bots Game bots: automated AI programs

371 views • 36 slides

Manifold Regularization Lorenzo Rosasco MIT, 9.520 L. Rosasco Manifold Regularization About

Manifold Regularization Lorenzo Rosasco MIT, 9.520 L. Rosasco Manifold Regularization About this class Goal To analyze the limits of learning from examples in high dimensional spaces. To introduce the semi-supervised setting and the use of

437 views • 31 slides

Efficient Krylov Approximation for Manifold Learning Shinjae Yoo Computational Science

Efficient Krylov Approximation for Manifold Learning Shinjae Yoo Computational Science Initiative Outline Projects at BNL Big data and unsupervised learning Challenges of manifold learning in Big data Diverse Power Iteration

540 views • 23 slides

Image Masking Schemes for Local Manifold Learning Methods Marco F. Duarte Joint work with

Image Masking Schemes for Local Manifold Learning Methods Marco F. Duarte Joint work with Hamid Dadkhahi IEEE ICASSP - April 23 2015 Manifold Learning Given training points in , learn the mapping to the underlying K -dimensional

404 views • 22 slides

Optimizing Physical Assets with Machine Learning Rajendra Koppula WWW.MANIFOLD.AI About Us

Optimizing Physical Assets with Machine Learning Rajendra Koppula WWW.MANIFOLD.AI About Us Manifold is a full-service AI development services firm that accelerates AI development for leading companies. Our team has a proven ability to design,

413 views • 38 slides

Medical Applica+ons for Manifold Learning: I)

Medical Applica+ons for Manifold Learning: I) Mul+-Modal Registra+on II) Breathing Ga+ng Chris+an Wachinger (wachinge@in.tum.de)

506 views • 39 slides

Gender Classification with Support vector machines (SVMs) Support Vector Machines The 3

Readings Supervised object recognition, Brief overview of classifiers in context of gender recognition: unsupervised object recognition http://www.merl.com/reports/docs/TR2000-01.pdf, Gender Classification with Support Vector Machines

548 views • 20 slides

Manifold Regularization Lorenzo Rosasco 9.520 Class 10 March 6, 2011 L. Rosasco Manifold

Manifold Regularization Lorenzo Rosasco 9.520 Class 10 March 6, 2011 L. Rosasco Manifold Regularization About this class Goal To analyze the limits of learning from examples in high dimensional spaces. To introduce the semi-supervised

1.16k views • 31 slides

Co-manifold learning with missing data Gal Mishne, Eric C. Chi and Ronald R. Coifman Department

Co-manifold learning with missing data Gal Mishne, Eric C. Chi and Ronald R. Coifman Department of Mathematics, Yale University Department of Statistics, North Carolina State University June 12, 2019 Gal Mishne (Yale) Co-Manifold Learning

556 views • 17 slides

Advanced Introduction to Machine Learning, CMU-10715 Manifold Learning Barnabs Pczos

Advanced Introduction to Machine Learning, CMU-10715 Manifold Learning Barnabs Pczos Motivation Find meaningful low-dimensional structures hidden in high-dimensional observations. The human brain confronts the same problem in

1.06k views • 85 slides

Collectively Sharing and Learning: Promoting Gender-Intentional and PYD Integration Across Youth

Collectively Sharing and Learning: Promoting Gender-Intentional and PYD Integration Across Youth Development Programming Chisina Kapungu and Chelsea Ricker, Co-Champions Gender and PYD Community of Practice (CoP) Promotes gender and

754 views • 16 slides

Urban Transportation Planning Dr. Randa Oqab Mujalli Why Functional classification is important?

Urban Transportation Planning Dr. Randa Oqab Mujalli Why Functional classification is important? It is a method of communication among Engineers, Administrators, and the General public. Dr. Randa Oqab Mujalli Functional Classification

1.02k views • 74 slides

Large-Scale Face Manifold Learning Sanjiv Kumar Google Research New York, NY * Joint work with

Large-Scale Face Manifold Learning Sanjiv Kumar Google Research New York, NY * Joint work with A. Talwalkar, H. Rowley and M. Mohri 1 Face Manifold Learning 2500 50 x 50 pixel faces 50 x 50 pixel random images Space of face images

441 views • 43 slides

Weakly Supervised Classification Weakly Supervised Classification and Robust Learning and Robust

IIT-H and RIKEN-AIP Joint Workshop on March 15, 2019 Machine Learning and Applications, Hyderabad, India Weakly Supervised Classification Weakly Supervised Classification and Robust Learning and Robust Learning --- Overview of Our Recent

1.06k views • 25 slides

AT FIRST SITE-AN INVITING AND FUNCTIONAL CLASSROOM K AT I E G E R E T Y LEARNING TARGETS

AT FIRST SITE-AN INVITING AND FUNCTIONAL CLASSROOM K AT I E G E R E T Y LEARNING TARGETS I can create a fun and functional learning space for my students that promotes social and emotional development. I can integrate centers into my

812 views • 24 slides

Introduction, Difficulties and Perspectives Tutorial on Manifold Learning with Medical Images

Introduction, Difficulties and Perspectives Tutorial on Manifold Learning with Medical Images Diana Mateus CAMP (Computed Aided Medical Procedures) TUM (Technische Universitt Mnchen) & Helmholtz Zentrum September 22, 2011 1 Outline

732 views • 49 slides

Introduction to Machine Learning Classification: Tasks Sonar Learning goals 0.20 Understand

Introduction to Machine Learning Classification: Tasks Sonar Learning goals 0.20 Understand the main difference between regression and 0.15 classification Response V2 M R 0.10 Know that classification can be binary or multiclass 0.05

510 views • 8 slides

Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability By Keith

Outline Introduction Method Experiments Conclusions Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability By Keith Bush and Joelle Pineau at NIPS 2009 Presented by Chenghui Cai Duke University, ECE

402 views • 18 slides

Manifold Learning Algorithms for Localization in Wireless Sensor Networks Neal Patwari and

Manifold Learning Algorithms for Localization in Wireless Sensor Networks Neal Patwari and Alfred O. Hero III University of Michigan Dept. of Electrical Engineering & Computer Science http://www.engin.umich.edu/~npatwari ICASSP04

660 views • 26 slides

Linear Manifold Clustering Robert Haralick and Rave Harpaz Outline Background The linear

Linear Manifold Clustering Robert Haralick and Rave Harpaz Outline Background The linear manifold cluster model The Linear manifold clustering algorithm Linear manifold modeling Linear manifold subspace correlation clustering Conclusion

677 views • 67 slides