Edouard Duchesnay

Asynchrony of the Early Maturation of White Matter Bundles in Healthy Infants: Quantitative Landmarks Revealed Noninvasively by Diffusion Tensor Imaging

Normal cognitive development in infants follows a well‐known temporal sequence, which is assumed to be correlated with the structural maturation of underlying functional networks. Postmortem studies and, more recently, structural MR imaging studies have described qualitatively the heterogeneous spatiotemporal progression of white matter myelination. However, in vivo quantification of the maturation phases of fiber bundles is still lacking. We used noninvasive diffusion tensor MR imaging and tractography in twenty‐three 1–4‐month‐old healthy infants to quantify the early maturation of the main cerebral fascicles. A specific maturation model, based on the respective roles of different maturational processes on the diffusion phenomena, was designed to highlight asynchronous maturation across bundles by evaluating the time‐course of mean diffusivity and anisotropy changes over the considered developmental period. Using an original approach, a progression of maturation in four relative stages was determined in each tract by estimating the maturation state and speed, from the diffusion indices over the infants group compared with an adults group on one hand, and in each tract compared with the average over bundles on the other hand. Results were coherent with, and extended previous findings in 8 of 11 bundles, showing the anterior limb of the internal capsule and cingulum as the most immature, followed by the optic radiations, arcuate and inferior longitudinal fascicles, then the spinothalamic tract and fornix, and finally the corticospinal tract as the most mature bundle. Thus, this approach provides new quantitative landmarks for further noninvasive research on brain‐behavior relationships during normal and abnormal development. Hum Brain Mapp, 2008.

A framework to study the cortical folding patterns.

This paper describes a decade-long research program focused on the variability of the cortical folding patterns. The program has developed a framework of using artificial neuroanatomists that are trained to identify sulci from a database. The framework relies on a renormalization of the brain warping problem, which consists in matching the cortices at the scale of the folds. Another component of the program is the search for the alphabet of the folding patterns, namely, a list of indivisible elementary sulci. The search relies on the study of the cortical folding process using antenatal imaging and on backward simulations of morphogenesis aimed at revealing traces of the embryologic dimples in the mature cortical surface. The importance of sulcal-based morphometry is illustrated by a simple study of the correlates of handedness on asymmetry indices. The study shows for instance that the central sulcus is larger in the dominant hemisphere.

Inverse retinotopy: Inferring the visual content of images from brain activation patterns

Traditional inference in neuroimaging consists in describing brain activations elicited and modulated by different kinds of stimuli. Recently, however, paradigms have been studied in which the converse operation is performed, thus inferring behavioral or mental states associated with activation images. Here, we use the well-known retinotopy of the visual cortex to infer the visual content of real or imaginary scenes from the brain activation patterns that they elicit. We present two decoding algorithms: an explicit technique, based on the current knowledge of the retinotopic structure of the visual areas, and an implicit technique, based on supervised classifiers. Both algorithms predicted the stimulus identity with significant accuracy. Furthermore, we extend this principle to mental imagery data: in five data sets, our algorithms could reconstruct and predict with significant accuracy a pattern imagined by the subjects.

Object-based morphometry of the cerebral cortex

Most of the approaches dedicated to automatic morphometry rely on a point-by-point strategy based on warping each brain toward a reference coordinate system. In this paper, we describe an alternative object-based strategy dedicated to the cortex. This strategy relies on an artificial neuroanatomist performing automatic recognition of the main cortical sulci and parcellation of the cortical surface into gyral patches. A set of shape descriptors, which can be compared across subjects, is then attached to the sulcus and gyrus related objects segmented by this process. The framework is used to perform a study of 142 brains of the International Consortium for Brain Mapping (ICBM) database. This study reveals some correlates of handedness on the size of the sulci located in motor areas, which was not detected previously using standard voxel based morphometry.

A Multicenter Tractography Study of Deep White Matter Tracts in Bipolar I Disorder: Psychotic Features and Interhemispheric Disconnectivity

IMPORTANCE Tractography studies investigating white matter (WM) abnormalities in patients with bipolar disorder have yielded heterogeneous results owing to small sample sizes. The small size limits their generalizability, a critical issue for neuroimaging studies of biomarkers of bipolar I disorder (BPI). OBJECTIVES To study WM abnormalities using whole-brain tractography in a large international multicenter sample of BPI patients and to compare these alterations between patients with or without a history of psychotic features during mood episodes. DESIGN, SETTING, AND PARTICIPANTS A cross-sectional, multicenter, international, Q-ball imaging tractography study comparing 118 BPI patients and 86 healthy control individuals. In addition, among the patient group, we compared those with and without a history of psychotic features. University hospitals in France, Germany, and the United States contributed participants. INTERVENTIONS Participants underwent assessment using the Diagnostic Interview for Genetic Studies at the French sites or the Structured Clinical Interview for DSM-IV at the German and US sites. Diffusion-weighted magnetic resonance images were acquired using the same acquisition parameters and scanning hardware at each site. We reconstructed 22 known deep WM tracts using Q-ball imaging tractography and an automatized segmentation technique. MAIN OUTCOMES AND MEASURES Generalized fractional anisotropy values along each reconstructed WM tract. RESULTS Compared with controls, BPI patients had significant reductions in mean generalized fractional anisotropy values along the body and the splenium of the corpus callosum, the left cingulum, and the anterior part of the left arcuate fasciculus when controlling for age, sex, and acquisition site (corrected for multiple testing). Patients with a history of psychotic features had a lower mean generalized fractional anisotropy value than those without along the body of the corpus callosum (corrected for multiple testing). CONCLUSIONS AND RELEVANCE In this multicenter sample, BPI patients had reduced WM integrity in interhemispheric, limbic, and arcuate WM tracts. Interhemispheric pathways are more disrupted in patients with than in those without psychotic symptoms. Together these results highlight the existence of an anatomic disconnectivity in BPI and further underscore a role for interhemispheric disconnectivity in the pathophysiological features of psychosis in BPI.

Striatal and extrastriatal dopamine transporter in cannabis and tobacco addiction: a high‐resolution PET study

The dopamine (DA) system is known to be involved in the reward and dependence mechanisms of addiction. However, modifications in dopaminergic neurotransmission associated with long‐term tobacco and cannabis use have been poorly documented in vivo. In order to assess striatal and extrastriatal dopamine transporter (DAT) availability in tobacco and cannabis addiction, three groups of male age‐matched subjects were compared: 11 healthy non‐smoker subjects, 14 tobacco‐dependent smokers (17.6 ± 5.3 cigarettes/day for 12.1 ± 8.5 years) and 13 cannabis and tobacco smokers (CTS) (4.8 ± 5.3 cannabis joints/day for 8.7 ± 3.9 years). DAT availability was examined in positron emission tomography (HRRT) with a high resolution research tomograph after injection of [11C]PE2I, a selective DAT radioligand. Region of interest and voxel‐by‐voxel approaches using a simplified reference tissue model were performed for the between‐group comparison of DAT availability. Measurements in the dorsal striatum from both analyses were concordant and showed a mean 20% lower DAT availability in drug users compared with controls. Whole‐brain analysis also revealed lower DAT availability in the ventral striatum, the midbrain, the middle cingulate and the thalamus (ranging from −15 to −30%). The DAT availability was slightly lower in all regions in CTS than in subjects who smoke tobacco only, but the difference does not reach a significant level. These results support the existence of a decrease in DAT availability associated with tobacco and cannabis addictions involving all dopaminergic brain circuits. These findings are consistent with the idea of a global decrease in cerebral DA activity in dependent subjects.

Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach.

Classification Based on Cortical Folding Patterns

We describe here a classification system based on automatically identified cortical sulci. Multivariate recognition methods are required for the detection of complex brain patterns with a spatial distribution. However, such methods may face the well-known issue of the curse of dimensionality-the risk of overfitting the training dataset in high-dimensional space. We overcame this problem, using a classifier pipeline with one- or two-stage of descriptor selection based on machine-learning methods, followed by a support vector machine classifier or linear discriminant analysis. We compared alternative designs of the pipeline on two different datasets built from the same database corresponding to 151 brains. The first dataset dealt with cortex asymmetry and the second dealt with the effect of the subjects sex. Our system successfully (98%) distinguished between the left and right hemispheres on the basis of sulcal shape (size, depth, etc.). The sex of the subject could be determined with a success rate of 85%. These results highlight the attractiveness of multivariate recognition models combined with appropriate descriptor selection. The sulci selected by the pipeline are consistent with previous whole-brain studies on sex effects and hemispheric asymmetries

Strategic white matter tracts for processing speed deficits in age-related small vessel disease

Objective: Cerebral small vessel disease is the most common cause of vascular cognitive impairment and typically manifests with slowed processing speed. We investigated the impact of lesion location on processing speed in age-related small vessel disease. Methods: A total of 584 community-dwelling elderly underwent brain MRI followed by segmentation of white matter hyperintensities. Processing speed was determined by the timed measure of the Trail Making Test part B. The impact of the location of white matter hyperintensities was assessed by voxel-based lesion-symptom mapping and graph-based statistical models on regional lesion volumes in major white matter tracts. Results: Voxel-based lesion-symptom mapping identified multiple voxel clusters where the presence of white matter hyperintensities was associated with slower performance on the Trail Making Test part B. Clusters were located bilaterally in the forceps minor and anterior thalamic radiation. Region of interest–based Bayesian network analyses on lesion volumes within major white matter tracts depicted the same tracts as direct predictors for an impaired Trail Making Test part B performance. Conclusions: Our findings highlight damage to frontal interhemispheric and thalamic projection fiber tracts harboring frontal-subcortical neuronal circuits as a predictor for processing speed performance in age-related small vessel disease.

Cortical folding difference between patients with early-onset and patients with intermediate-onset bipolar disorder

OBJECTIVES Cerebral abnormalities have been detected in patients with bipolar disorder (BD). In comparison to BD with a later onset, early-onset BD has been found to have a poorer outcome. However, it is yet unknown whether neuroanatomical abnormalities differ between age-at-onset subgroups of the illness. We searched for cortical folding differences between early-onset (before 25 years) and intermediate-onset (between 25 and 45 years) BD patients. METHODS Magnetic resonance images of 22 early-onset BD patients, 14 intermediate-onset BD patients, and 50 healthy participants were analyzed using a fully automated method to extract, label, and measure the sulcal area in the whole cortex. Cortical folding was assessed by computing global sulcal indices (the ratio between total sulcal area and total outer cortex area) for each hemisphere, and local sulcal indices for 12 predefined regions in both hemispheres. RESULTS Intermediate-onset BD patients had a significantly reduced local sulcal index in the right dorsolateral prefrontal cortex in comparison to both early-onset BD patients and healthy subjects, and lower global sulcal indices in both hemispheres in comparison to healthy subjects (p < 0.05, Bonferroni corrected). Brain tissue volumes did not differ between groups. CONCLUSIONS This study provided the first evidence of a neuroanatomic difference between intermediate-onset and early-onset BD, which lends further support to the existence of different age-at-onset subgroups of BD.