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IEEE J Transl Eng Health Med. 2019 Aug 20;7:2200211. doi: 10.1109/JTEHM.2019.2936348. eCollection 2019.

Locally Linear Embedding and fMRI Feature Selection in Psychiatric Classification.

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Department of Computing Science1-337 Athabasca HallUniversity of AlbertaEdmontonABT6G 2E8Canada.



Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear Embedding) to extract informative measures of the underlying neuronal activity from BOLD time-series. The method is validated using the Leave-One-Out-Cross-Validation (LOOCV) accuracy of classifying psychiatric diagnoses using resting-state and task-related fMRI.


Locally Linear Embedding of BOLD time-series (into each voxel's respective tensor) was used to optimise feature selection. This uses Gauß' Principle of Least Constraint to conserve quantities over both space and time. This conservation was assessed using LOOCV to greedily select time points in an incremental fashion on training data that was categorised in terms of psychiatric diagnoses.


The embedded fMRI gave highly diagnostic performances (> 80%) on eleven publicly-available datasets containing healthy controls and patients with either Schizophrenia, Attention-Deficit Hyperactivity Disorder (ADHD), or Autism Spectrum Disorder (ASD). Furthermore, unlike the original fMRI data before or after using Principal Component Analysis (PCA) for artefact reduction, the embedded fMRI furnished significantly better than chance classification (defined as the majority class proportion) on ten of eleven datasets.


Locally Linear Embedding appears to be a useful feature extraction procedure that retains important information about patterns of brain activity distinguishing among psychiatric cohorts.


(computer-assisted) diagnosis; Nonlinear; dimensionality reduction; evidence-based medicine; fMRI; image processing; integration; kernel methods; least squares; machine learning; method of image charges; neurophysiology; optimization; oscillations; theorema egregium

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