Vincent Frouin

Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles.

Magnetic resonance diffusion tensor imaging (DTI) provides information about fiber local directions in brain white matter. This paper addresses inference of the connectivity induced by fascicles made up of numerous fibers from such diffusion data. The usual fascicle tracking idea, which consists of following locally the direction of highest diffusion, is prone to erroneous forks because of problems induced by fiber crossing. In this paper, this difficulty is partly overcomed by the use of a priori knowledge of the low curvature of most of the fascicles. This knowledge is embedded in a model of the bending energy of a spaghetti plate representation of the white matter used to compute a regularized fascicle direction map. A new tracking algorithm is then proposed to highlight putative fascicle trajectories from this direction map. This algorithm takes into account potential fan shaped junctions between fascicles. A study of the tracking behavior according to the influence given to the a priori knowledge is proposed and concrete tracking results obtained with in vivo human brain data are illustrated. These results include putative trajectories of some pyramidal, commissural, and various association fibers.

Automatic recognition of cortical sulci of the human brain using a congregation of neural networks

This paper describes a complete system allowing automatic recognition of the main sulci of the human cortex. This system relies on a preprocessing of magnetic resonance images leading to abstract structural representations of the cortical folding patterns. The representation nodes are cortical folds, which are given a sulcus name by a contextual pattern recognition method. This method can be interpreted as a graph matching approach, which is driven by the minimization of a global function made up of local potentials. Each potential is a measure of the likelihood of the labelling of a restricted area. This potential is given by a multi-layer perceptron trained on a learning database. A base of 26 brains manually labelled by a neuroanatomist is used to validate our approach. The whole system developed for the right hemisphere is made up of 265 neural networks. The mean recognition rate is 86% for the learning base and 76% for a generalization base, which is very satisfying considering the current weak understanding of the variability of the cortical folding patterns.

Correlated gene expression supports synchronous activity in brain networks

Cooperating brain regions express similar genes When the brain is at rest, a number of distinct areas are functionally connected. They tend to be organized in networks. Richiardi et al. compared brain imaging and gene expression data to build computational models of these networks. These functional networks are underpinned by the correlated expression of a core set of 161 genes. In this set, genes coding for ion channels and other synaptic functions such as neurotransmitter release dominate. Science, this issue p. 1241 Gene expression is more similar than expected by chance in brain regions that are functionally connected. During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function.

Neuropsychosocial profiles of current and future adolescent alcohol misusers

A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention.

Towards inference of human brain connectivity from MR diffusion tensor data.

This paper describes a method to infer the connectivity induced by white matter fibers in the living human brain. This method stems from magnetic resonance tensor imaging (DTI), a technique which gives access to fiber orientations. Given typical DTI spatial resolution, connectivity is addressed at the level of fascicles made up by a bunch of parallel fibers. We propose first an algorithm dedicated to fascicle tracking in a direction map inferred from diffusion data. This algorithm takes into account fan-shaped fascicle forks usual in actual white matter organization. Then, we propose a method of inferring a regularized direction map from diffusion data in order to improve the robustness of the tracking. The regularization stems from an analogy between white matter organization and spaghetti plates. Finally, we propose a study of the tracking behavior according to the weight given to the regularization and some examples of the tracking results with in vivo human brain data.

Fast Nonsupervised 3D Registration of PET and MR Images of the Brain

We propose a fully nonsupervised methodology dedicated to the fast registration of positron emission tomography (PET) and magnetic resonance images of the brain. First, discrete representations of the surfaces of interest (head or brain surface) are automatically extracted from both images. Then, a shape-independent surface-matching algorithm gives a rigid body transformation, which allows the transfer of information between both modalities. A three-dimensional (3D) extension of the chamfer-matching principle makes up the core of this surface-matching algorithm. The optimal transformation is inferred from the minimization of a quadratic generalized distance between discrete surfaces, taking into account between-modality differences in the localization of the segmented surfaces. The minimization process is efficiently performed via the precomputation of a 3D distance map. Validation studies using a dedicated brain-shaped phantom have shown that the maximum registration error was of the order of the PET pixel size (2 mm) for the wide variety of tested configurations. The software is routinely used today in a clinical context by the physicians of the Service Hospitalier Frédéric Joliot (>150 registrations performed). The entire registration process requires ∼5 min on a conventional workstation.

Low-Dose Exposure to γ Rays Induces Specific Gene Regulations in Normal Human Keratinocytes

Abstract Franco, N., Lamartine, J., Frouin, V., Le Minter, P., Petat, C., Leplat, J.-J., Libert, F., Gidrol, X. and Martin, M. T. Low-Dose Exposure to γ Rays Induces Specific Gene Regulations in Normal Human Keratinocytes. Radiat. Res. 163, 623–635 (2005). Skin is the organ most exposed to various environmental aggressors, including ionizing radiation. Low-dose and low-dose-rate exposures to γ rays account for most occupational, medical or environmental irradiations. To examine whether this type of exposure triggers specific molecular responses, cultured primary keratinocytes isolated from adult normal skin were irradiated with single acute doses of 1 cGy or 2 Gy. DNA microarrays containing 10,500 probes were used to assess transcriptional changes over a time course between 3 and 72 h postirradiation. Keratinocytes were studied at a differentiated stage to mimic the response of cells from the suprabasal layers of the epidermis. A major finding of this study was the identification of an important number of low-dose-specific genes (140), most of which were modulated at 48 h. Clustering analysis also revealed low-dose-specific profiles. One of these clusters (17 known genes) was further analyzed using Gibbs sampling algorithm, which led to the identification of 7 putative promoter sequences. These results show for the first time that low-dose ionizing radiation is able to induce specific transcriptional responses in human keratinocytes. Our findings support the potential usefulness of microarrays in biological dosimetry studies after low-dose exposures.

Early Cannabis Use, Polygenic Risk Score for Schizophrenia and Brain Maturation in Adolescence

IMPORTANCE Cannabis use during adolescence is known to increase the risk for schizophrenia in men. Sex differences in the dynamics of brain maturation during adolescence may be of particular importance with regard to vulnerability of the male brain to cannabis exposure. OBJECTIVE To evaluate whether the association between cannabis use and cortical maturation in adolescents is moderated by a polygenic risk score for schizophrenia. DESIGN, SETTING, AND PARTICIPANTS Observation of 3 population-based samples included initial analysis in 1024 adolescents of both sexes from the Canadian Saguenay Youth Study (SYS) and follow-up in 426 adolescents of both sexes from the IMAGEN Study from 8 European cities and 504 male youth from the Avon Longitudinal Study of Parents and Children (ALSPAC) based in England. A total of 1577 participants (aged 12-21 years; 899 [57.0%] male) had (1) information about cannabis use; (2) imaging studies of the brain; and (3) a polygenic risk score for schizophrenia across 108 genetic loci identified by the Psychiatric Genomics Consortium. Data analysis was performed from March 1 through December 31, 2014. MAIN OUTCOMES AND MEASURES Cortical thickness derived from T1-weighted magnetic resonance images. Linear regression tests were used to assess the relationships between cannabis use, cortical thickness, and risk score. RESULTS Across the 3 samples of 1574 participants, a negative association was observed between cannabis use in early adolescence and cortical thickness in male participants with a high polygenic risk score. This observation was not the case for low-risk male participants or for the low- or high-risk female participants. Thus, in SYS male participants, cannabis use interacted with risk score vis-à-vis cortical thickness (P = .009); higher scores were associated with lower thickness only in males who used cannabis. Similarly, in the IMAGEN male participants, cannabis use interacted with increased risk score vis-à-vis a change in decreasing cortical thickness from 14.5 to 18.5 years of age (t137 = -2.36; P = .02). Finally, in the ALSPAC high-risk group of male participants, those who used cannabis most frequently (≥61 occasions) had lower cortical thickness than those who never used cannabis (difference in cortical thickness, 0.07 [95% CI, 0.01-0.12]; P = .02) and those with light use (<5 occasions) (difference in cortical thickness, 0.11 [95% CI, 0.03-0.18]; P = .004). CONCLUSIONS AND RELEVANCE Cannabis use in early adolescence moderates the association between the genetic risk for schizophrenia and cortical maturation among male individuals. This finding implicates processes underlying cortical maturation in mediating the link between cannabis use and liability to schizophrenia.

Toward global tractography

Diffusion-based tractography is an ill-posed problem, because the step-by-step reconstruction of a fibre bundle trajectory cannot afford any serious mistake in the evaluation of the local fibre orientations. Such evaluation is difficult, however, because the myriad fibres passing through a single voxel follow different directions. Modelling tractography as a global inverse problem is a simple framework which addresses the ill-posed nature of the problem. The key idea is that the results of tractography in the neighbourhood of an ambiguous local diffusion profile can help to infer the local fibre directions. This paper provides an overview of past achievements of global tractography and proposes guidelines for a future research programme in the hope that the potential of the technique will increase the interest of the community.

The Brain's Response to Reward Anticipation and Depression in Adolescence: Dimensionality, Specificity, and Longitudinal Predictions in a Community-Based Sample.

OBJECTIVE The authors examined whether alterations in the brains reward network operate as a mechanism across the spectrum of risk for depression. They then tested whether these alterations are specific to anhedonia as compared with low mood and whether they are predictive of depressive outcomes. METHOD Functional MRI was used to collect blood-oxygen-level-dependent (BOLD) responses to anticipation of reward in the monetary incentive task in 1,576 adolescents in a community-based sample. Adolescents with current subthreshold depression and clinical depression were compared with matched healthy subjects. In addition, BOLD responses were compared across adolescents with anhedonia, low mood, or both symptoms, cross-sectionally and longitudinally. RESULTS Activity in the ventral striatum was reduced in participants with subthreshold and clinical depression relative to healthy comparison subjects. Low ventral striatum activation predicted transition to subthreshold or clinical depression in previously healthy adolescents at 2-year follow-up. Brain responses during reward anticipation decreased in a graded manner between healthy adolescents, adolescents with current or future subthreshold depression, and adolescents with current or future clinical depression. Low ventral striatum activity was associated with anhedonia but not low mood; however, the combined presence of both symptoms showed the strongest reductions in the ventral striatum in all analyses. CONCLUSIONS The findings suggest that reduced striatal activation operates as a mechanism across the risk spectrum for depression. It is associated with anhedonia in healthy adolescents and is a behavioral indicator of positive valence systems, consistent with predictions based on the Research Domain Criteria.