SPM: a history.

Karl Friston began the SPM project around 1991. The rest is history

GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction

In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exists another type of features introduced during the preprocessing steps and we call them "\textbf{Procedural Bias}". Besides, such bias can be leveraged to improve classification accuracy. Nevertheless, most existing models suffer from either under-fit without considering procedural bias or poor interpretability without differentiating such bias from lesion ones. In this paper, a novel dual-task algorithm namely \emph{GSplit LBI} is proposed to resolve this problem. By introducing an augmented variable enforced to be structural sparsity with a variable splitting term, the estimators for prediction and selecting lesion features can be optimized separately and mutually monitored by each other following an iterative scheme. Empirical experiments have been evaluated on the Alzheimer's Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.Comment: Conditional Accepted by Miccai,201

Symmetric diffeomorphic modeling of longtudinal structural MRI

This technology report describes the longitudinal registration approach that we intend to incorporate into SPM12. It essentially describes a group-wise intra-subject modeling framework, which combines diffeomorphic and rigid-body registration, incorporating a correction for the intensity inhomogeneity artifact usually seen in MRI data. Emphasis is placed on achieving internal consistency and accounting for many of the mathematical subtleties that most implementations overlook. The implementation was evaluated using examples from the OASIS Longitudinal MRI Data in Non-demented and Demented Older Adults

The Issue of Crops Establishment in Burkina Faso Western Area

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is an Invited Paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 5 (2003): G. Son, E.H. Bourarach, and J. Ashburner. The Issue of Crops Establishment in Burkina Faso Western Area. Vol. V. September 2003

Variational inference for medical image segmentation

Variational inference techniques are powerful methods for learning probabilistic models and provide significant advantages over maximum likelihood (ML) or maximum a posteriori (MAP) approaches. Nevertheless they have not yet been fully exploited for image processing applications. In this paper we present a variational Bayes (VB) approach for image segmentation. We aim to show that VB provides a framework for generalising existing segmentation algorithms that rely on an expectation–maximisation formulation, while increasing their robustness and computational stability. We also show how optimal model complexity can be automatically determined in a variational setting, as opposed to ML frameworks which are intrinsically prone to overfitting. Finally, we demonstrate how suitable intensity priors, that can be used in combination with the presented algorithm, can be learned from large imaging data sets by adopting an empirical Bayes approach

Groupwise Multimodal Image Registration Using Joint Total Variation

In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algorithm on rigidly aligning both simulated and real 3D brain scans. This validation shows robustness to strong intensity non-uniformities and low registration errors for CT/PET to MRI alignment. Our implementation is publicly available at https://github.com/brudfors/coregistration-njtv

Tempo and intensity of pre-task music modulate neural activity during reactive task performance

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 The Authors.Research has shown that not only do young athletes purposively use music to manage their emotional state (Bishop, Karageorghis, & Loizou, 2007), but also that brief periods of music listening may facilitate their subsequent reactive performance (Bishop, Karageorghis, & Kinrade, 2009). We report an fMRI study in which young athletes lay in an MRI scanner and listened to a popular music track immediately prior to performance of a three-choice reaction time task; intensity and tempo were modified such that six excerpts (2 intensities × 3 tempi) were created. Neural activity was measured throughout. Faster tempi and higher intensity collectively yielded activation in structures integral to visual perception (inferior temporal gyrus), allocation of attention (cuneus, inferior parietal lobule, supramarginal gyrus), and motor control (putamen), during reactive performance. The implications for music listening as a pre-competition strategy in sport are discussed

Age- and sex-related variations in the brain white matter fractal dimension throughout adulthood: an MRI study.

To observe age- and sex-related differences in the complexity of the global and hemispheric white matter (WM) throughout adulthood by means of fractal dimension (FD)