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Statistical Parametric Mapping The Analysis Of Functional Brain Images

Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.

/pdf/deep-neural-networks-reveal-a-gradient-in-the-complexity-of-53iez5y5se.pdf

Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream

Building a Science of Individual Differences from fMRI.

Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study

This issue marks a transition and a changing of the guard for Computational and Mathematical Methods in Medicine (CMMM). It is with some nostalgia that we look back on our long and illustrious association with Taylor and Francis; however, at the same time we look to the future with optimism and hope as Hindawi takes the helm and converts CMMM to the community-based, open access model that they have so successfully championed. The Hindawi Publishing Corporation is one of the fastest growing academic publishers worldwide with over 200 academic journals in their portfolio and a commitment to the highest levels of peer review and excellence. 

Reflecting on the genesis and evolution of CMMM, it is clear that Brian Sleeman, the founding Editor-in-Chief, showed great foresight in creating a journal that brought together the disparate disciplines of mathematics and medicine and that continues to play a major role in the development of mathematical medicine. He worked passionately to develop and promote the journal through some difficult times, with the insight and courage to bring together both biomedical/clinical scientists and mathematical scientists onto a single editorial board (a practice that has become more commonplace in subsequent journals in the field). The success that the journal has enjoyed thus far is a clear testament to his hard work, dedication, and vision. 

The journal has continued to provide a unique forum for the dissemination of interdisciplinary research resulting from collaborations between clinicians/experimentalists and theoreticians. CMMM has also continued to evolve rapidly, reflecting the increased focus on systems and interdisciplinary collaborative efforts across the breadth of biomedical, clinical, and translational research areas. The past year also saw the result of much hard work, with the inclusion of the journal in PubMed/Medline and the Science Citation Index Expanded. This was a great development for the journal since it not only has had an enormous impact on the general awareness and profile of the journal but has also resulted in increased submissions and downloads from the journal website over the past year. It has been exciting and rewarding to see the journal develop and evolve in this manner, and we look forward to increased success following this higher profile.

The future looks extremely bright for the field of mathematical medicine as it emerges from its period of infancy and takes its place as a legitimate and central field of research and enquiry. Our sincere hope and wish is that CMMM continues from strength to strength and fulfills its role and promise as envisioned originally by its founding editor.


Pamela Jones

Sivabal Sivaloganathan

/pdf/computational-and-mathematical-methods-in-medicine-4yt57ciuah.pdf

Computational and Mathematical Methods in Medicine

Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF by means of a rank constraint, forcing the estimated HRF to be equal across events or experimental conditions, yet permitting it to differ across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method, exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding scores on two different datasets. Our results show that the R1-GLM model outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.

New Results - Data-driven HRF estimation for encoding and decoding models

Inter-subject fMRI analyses have specific issues regarding the reliability of the results concerning both the detection of brain activation patterns and the estimation of the underlying dynamics. Among these issues lies the variability of the hemodynamic response function (HRF), that is usually accounted for using functional basis sets in the general linear model context. Here, we use the joint detection-estimation approach (JDE) (Makni et al., 2008; Vincent et al., 2010) which combines regional nonparametric HRF inference with spatially adaptive regularization of activation clusters to avoid global smoothing of fMRI images. We show that the JDE-based inference brings a significant improvement in statistical sensitivity for detecting evoked activity in parietal regions. In contrast, the canonical HRF associated with spatially adaptive regularization is more sensitive in other regions, such as motor cortex. This different regional behavior is shown to reflect a larger discrepancy of HRF with the canonical model. By varying parallel imaging acceleration factor, SNR-specific region-based hemodynamic parameters (activation delay and duration) were extracted from the JDE inference. Complementary analyses highlighted their significant departure from the canonical parameters and the strongest between-subject variability that occurs in the parietal region, irrespective of the SNR value. Finally, statistical evidence that the fluctuation of the HRF shape is responsible for the significant change in activation detection performance is demonstrated using paired t-tests between hemodynamic parameters inferred by GLM and JDE.

New Results - Group-level impacts of within- and between-subject hemodynamic variability in fMRI

https://hal-cea.archives-ouvertes.fr/cea-00333746/document

A half-quadratic block-coordinate descent method for spectral estimation

Cet article presente une application en neuroimagerie fonctionnelle de la communication compagne, visant a simuler au sein d'un algorithme MCMC des echantillons moins correles par l'inclusion d'une etape de reechantillonnage d'un parametre d'echelle lorsque le modele d'observation est bilineaire vis-a-vis des parametres inconnus. L'application visee concerne la detection-estimation conjointe de l'activite cerebrale en Imagerie par Resonance Magnetique fonctionnelle (IRMf). Il s'agit de recouvrer a la fois un filtre hemodynamique regional et des cartes d'activations specifiques a chaque type de stimulus implique dans le paradigme experimental. L'apport de la methode du reechantillonnage de l'echelle est illustre sur des simulations realistes.

http://documents.irevues.inist.fr/bitstream/handle/2042/17520/GRETSI_2007_373.pdf;sequence=1

Application du rééchantillonnage stochastique de l'échelle en détection-estimation de l'activité cérébrale par IRMf

In this paper, we review classical and advanced methodologies for analysing within-subject functional Magnetic Resonance Imaging (fMRI) data. Such data are usually acquired during sensory or cognitive experiments that aims at stimulating the subject in the scanner and eliciting evoked brain activity. From such four-dimensional datasets (three in space, one in time), the goal is twofold: first, detecting brain regions involved in the sensory or cognitives processes that the experimental design manipulates; second, estimating the underlying activation dynamics. The first issue is usually addressed in the context of the General Linear Model (GLM), which a priori assumes a functional form for the impulse response of the hemodynamic filter. The second question aims at estimating this shape which makes sense in activating regions only. In the last five years, a novel Joint Detection-Estimation (JDE) framework addressing these two questions simultaneously has been proposed in (59, 60, 102). We show to which extent this methodology outperforms the GLM approach in terms of statistical sensitivity and specificity, which additional questions it allows us to investigate theoretically and how it provides us with a well-adapted framework to treat spatially unsmoothed real fMRI data both in the 3D acquisition volume and on the cortical surface. Resume : Dans cet article, nous synthetisons la methodologie usuelle et des variantes plus elaborees pour analyser des donnees individuelles d'Imagerie par Resonance Magnetique fonctionnelle (IRMf). De telles donnees sont acquises au cours d'experiences sensorielles ou cognitives dont le but est de stimuler le sujet dans le scanner afin d'evoquer une activite cerebrale typique. A partir de donnees quadri-dimensionnelles (trois dans l'espace et le temps), le but est double : d'abord, detecter les regions impliquees dans les processus sensoriels ou cognitifs que le protocole experimental manipule ; ensuite, estimer la dynamique cerebrale sous-jacente. La premiere question est generalement abordee dans le contexte du Modele Lineaire General (MLG) qui suppose a priori connue la reponse impulsionnelle du filtre hemodynamique. La seconde question traite de l'estimation de la forme de cette reponse qui fait sens uniquement dans les regions activees. Durant ces cinq dernieres annees, un nouveau cadre de detection-estimation conjointe traitant ces deux questions simultanement a ete propose successivement dans (59, 60, 102). Nous montrons ici jusqu'a quel point cette methodologie etend les approches a base de MLG, les ameliore en termes de sensibilite et specificite statistique, quelles questions supplementaires ce cadre permet d'investiguer theoriquement et comment il fournit un moyen aux utilisateurs de traiter de facon adaptee leur donnees non-lissees spatialement, aussi bien dans le repere 3D de l'acquisition que sur la surface corticale.

Philippe Ciuciu

Papers

New Results - Data-driven HRF estimation for encoding and decoding models

New Results - Group-level impacts of within- and between-subject hemodynamic variability in fMRI

A half-quadratic block-coordinate descent method for spectral estimation

Application du rééchantillonnage stochastique de l'échelle en détection-estimation de l'activité cérébrale par IRMf

A joint detection-estimation framework for analysing within-subject fMRI data