Arnaud Cachia

Structural Asymmetries in the Infant Language and Sensori-Motor Networks

Both language capacity and strongly lateralized hand preference are among the most intriguing particularities of the human species. They are associated in the adult brain with functional and anatomical hemispheric asymmetries in the speech perception-production network and in the sensori-motor system. Only studies in early life can help us to understand how such asymmetries arise during brain development, and to which point structural left-right differences are the source or the consequence of functional lateralization. In this study, we aimed to provide new in vivo structural markers of hemispheric asymmetries in infants from 1 to 4 months of age, with diffusion tensor imaging. We used 3 complementary analysis methods based on local diffusion indices and spatial localizations of tracts. After a prospective approach over the whole brain, we demonstrated early leftward asymmetries in the arcuate fasciculus and in the cortico-spinal tract. These results suggest that the early macroscopic geometry, microscopic organization, and maturation of these white matter bundles are related to the development of later functional lateralization.

Neuroimaging Auditory Hallucinations in Schizophrenia: From Neuroanatomy to Neurochemistry and Beyond

Despite more than 2 decades of neuroimaging investigations, there is currently insufficient evidence to fully understand the neurobiological substrate of auditory hallucinations (AH). However, some progress has been made with imaging studies in patients with AH consistently reporting altered structure and function in speech and language, sensory, and nonsensory regions. This report provides an update of neuroimaging studies of AH with a particular emphasis on more recent anatomical, physiological, and neurochemical imaging studies. Specifically, we provide (1) a review of findings in schizophrenia and nonschizophrenia voice hearers, (2) a discussion regarding key issues that have interfered with progress, and (3) practical recommendations for future studies.

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

Recently introduced hybrid PET/MR scanners provide the opportunity to measure simultaneously, and in direct spatial correspondence, both metabolic demand and functional activity of the brain, hence capturing complementary information on the brains physiological state. Here we exploited PET/MR simultaneous imaging to explore the relationship between the metabolic information provided by resting-state fluorodeoxyglucose-PET (FDG-PET) and fMRI (rs-fMRI) in neurologically healthy subjects. Regional homogeneity (ReHo), fractional amplitude of low frequency fluctuations (fALFF), and degree of centrality (DC) maps were generated from the rs-fMRI data in 23 subjects, and voxel-wise comparison to glucose uptake distribution provided by simultaneously acquired FDG-PET was performed. The mutual relationships among each couple of these four metrics were explored in terms of similarity, both of spatial distribution across the brain and the whole group, and voxel-wise across subjects, taking into account partial volume effects by adjusting for grey matter (GM) volume. Although a significant correlation between the spatial distribution of glucose uptake and rs-fMRI derived metrics was present, only a limited percentage of GM voxels correlated with PET across subjects. Moreover, the correlation between the spatial distributions of PET and rs-fMRI-derived metrics is spatially heterogeneous across both anatomic regions and functional networks, with lowest correlation strength in the limbic network (Spearman rho around -0.11 for DC), and strongest correlation for the default-mode network (up to 0.89 for ReHo and 0.86 for fALFF). Overall, ReHo and fALFF provided significantly higher correlation coefficients with PET (p=10(-8) and 10(-7), respectively) as compared to DC, while no significant differences were present between ReHo and fALFF. Local GM volume variations introduced a limited overestimation of the rs-fMRI to FDG correlation between the modalities under investigation through partial volume effects. These novel results provide the basis for future studies of alterations of the coupling between brain metabolism and functional connectivity in pathologic conditions.

The neuroscience of hallucinations

Part I: The Basics of Hallucinations.- 1. An epistemological approach: history of concepts and ideas about hallucinations.- 2. Hallucinatory experiences in non-clinical populations.- 3. Hallucinations and other sensory deceptions in psychiatric disorders.- 4. Hallucinations associated with neurological disorders and sensory loss.- 5. Standardized assessment of hallucinations.- Part II: Cognitive Models of Hallucinations.- 6. The bottom-up and top-down components of the hallucinatory phenomenon.- 7. Speech processing and auditory verbal hallucinations.- 8. The role of memory retrieval and emotional salience in the emergence of hallucinations.- 9. Misattribution models (I): meta-cognitive believes and hallucinations.- 10. Misattributions models (II): source monitoring in hallucinating schizophrenia subjects.- 11. Time perception and discrimination in individuals suffering from hallucinations.- Part III: Neurobiological and Computational Models of Hallucinations.- 12. A neurodevelopmental perspective on hallucinations.- 13. Candidate genes involved in the expression of psychotic symptoms: a focus on hallucinations.- 14. Animal models and hallucinogenic drugs.- 15. Cannabis and hallucinations: studies in human subjects.- 16. Computational models of hallucinations.- Part IV: Brain-Imaging Insight into Hallucinations.- 17. Electrophysiological exploration of hallucinations (EEG, MEG).- 18. Structural imaging of the hallucinating brain in schizophrenia.- 19. Functional brain imaging of auditory hallucinations: from self-monitoring deficits to co-opted neural resources.- 20. Functional brain imaging of hallucinations: symptom capture studies.- 21. Brain functioning when the voices are silent: aberrant default-mode in auditory verbal hallucinations.- 22. Connectivity issues of the hallucinating brain.- Part V: Innovative Therapeutic Approaches of Hallucinations.- 23. Beyond monotherapy: the HIT-story.- 24. The psychopharmacology of hallucinations: ironic insights into mechanisms of action.- 25. Neuromodulation techniques to treat hallucinations.- 26. The future of brain stimulation to treat hallucinations.- 27. Perspectives in brain-imaging and computer-assisted technologies for the treatment of hallucinations.- Conclusion: Key-issues for future research in the neuroscience of hallucinations.

Are Hallucinations Due to an Imbalance Between Excitatory and Inhibitory Influences on the Brain

This review from the International Consortium on Hallucinations Research intends to question the pertinence of the excitatory-to-inhibitory (E/I) imbalance hypothesis as a model for hallucinations. A large number of studies suggest that subtle impairments of the E/I balance are involved in neurological and psychiatric conditions, such as schizophrenia. Emerging evidence also points to a role of the E/I balance in maintaining stable perceptual representations, suggesting it may be a plausible model for hallucinations. In support, hallucinations have been linked to inhibitory deficits as shown with impairment of gamma-aminobutyric acid transmission, N-methyl-d-aspartate receptor plasticity, reductions in gamma-frequency oscillations, hyperactivity in sensory cortices, and cognitive inhibition deficits. However, the mechanisms by which E/I dysfunctions at the cellular level might relate to clinical symptoms and cognitive deficits remain unclear. Given recent data advances in the field of clinical neuroscience, it is now possible to conduct a synthesis of available data specifically related to hallucinations. These findings are integrated with the latest computational frameworks of hallucinations, and recommendations for future research are provided.

Feature selection and classification of imbalanced datasets: application to PET images of children with autistic spectrum disorders.

Learning with discriminative methods is generally based on minimizing the misclassification of training samples, which may be unsuitable for imbalanced datasets where the recognition might be biased in favor of the most numerous class. This problem can be addressed with a generative approach, which typically requires more parameters to be determined leading to reduced performances in high dimension. In such situations, dimension reduction becomes a crucial issue. We propose a feature selection/classification algorithm based on generative methods in order to predict the clinical status of a highly imbalanced dataset made of PET scans of forty-five low-functioning children with autism spectrum disorders (ASD) and thirteen non-ASD low functioning children. ASDs are typically characterized by impaired social interaction, narrow interests, and repetitive behaviors, with a high variability in expression and severity. The numerous findings revealed by brain imaging studies suggest that ASD is associated with a complex and distributed pattern of abnormalities that makes the identification of a shared and common neuroimaging profile a difficult task. In this context, our goal is to identify the rest functional brain imaging abnormalities pattern associated with ASD and to validate its efficiency in individual classification. The proposed feature selection algorithm detected a characteristic pattern in the ASD group that included a hypoperfusion in the right Superior Temporal Sulcus (STS) and a hyperperfusion in the contralateral postcentral area. Our algorithm allowed for a significantly accurate (88%), sensitive (91%) and specific (77%) prediction of clinical category. For this imbalanced dataset, with only 13 control scans, the proposed generative algorithm outperformed other state-of-the-art discriminant methods. The high predictive power of the characteristic pattern, which has been automatically identified on whole brains without any priors, confirms previous findings concerning the role of STS in ASD. This work offers exciting possibilities for early autism detection and/or the evaluation of treatment response in individual patients.

Spatial normalization of brain images and beyond

The deformable atlas paradigm has been at the core of computational anatomy during the last two decades. Spatial normalization is the variant endowing the atlas with a coordinate system used for voxel-based aggregation of images across subjects and studies. This framework has largely contributed to the success of brain mapping. Brain spatial normalization, however, is still ill-posed because of the complexity of the human brain architecture and the lack of architectural landmarks in standard morphological MRI. Multi-atlas strategies have been developed during the last decade to overcome some difficulties in the context of segmentation. A new generation of registration algorithms embedding architectural features inferred for instance from diffusion or functional MRI is on the verge to improve the architectural value of spatial normalization. A better understanding of the architectural meaning of the cortical folding pattern will lead to use some sulci as complementary constraints. Improving the architectural compliance of spatial normalization may impose to relax the diffeomorphic constraint usually underlying atlas warping. A two-level strategy could be designed: in each region, a dictionary of templates of incompatible folding patterns would be collected and matched in a way or another using rare architectural information, while individual subjects would be aligned using diffeomorphisms to the closest template. Manifold learning could help to aggregate subjects according to their morphology. Connectivity-based strategies could emerge as an alternative to deformation-based alignment leading to match the connectomes of the subjects rather than images.

Influences of Brain Size, Sex, and Sex Chromosome Complement on the Architecture of Human Cortical Folding.

Abstract Gyrification is a fundamental property of the human cortex that is increasingly studied by basic and clinical neuroscience. However, it remains unclear if and how the global architecture of cortical folding varies with 3 interwoven sources of anatomical variation: brain size, sex, and sex chromosome dosage (SCD). Here, for 375 individuals spanning 7 karyotype groups (XX, XY, XXX, XYY, XXY, XXYY, XXXXY), we use structural neuroimaging to measure a global sulcation index (SI, total sulcal/cortical hull area) and both determinants of sulcal area: total sulcal length and mean sulcal depth. We detail large and patterned effects of sex and SCD across all folding metrics, but show that these effects are in fact largely consistent with the normative scaling of cortical folding in health: larger human brains have disproportionately high SI due to a relative expansion of sulcal area versus hull area, which arises because disproportionate sulcal lengthening overcomes a lack of proportionate sulcal deepening. Accounting for these normative allometries reveals 1) brain size‐independent sulcal lengthening in males versus females, and 2) insensitivity of overall folding architecture to SCD. Our methodology and findings provide a novel context for future studies of human cortical folding in health and disease.

Sulcus-Based MR Analysis of Focal Cortical Dysplasia Located in the Central Region

Objective Focal cortical dysplasias (FCDs) are mainly located in the frontal region, with a particular tropism for the central sulcus. Up to 30% of lesions are undetected (magnetic resonance [MR]-negative FCD patients) or belatedly diagnosed by visual analysis of MR images. We propose an automated sulcus-based method to analyze abnormal sulcal patterns associated with central FCD, taking into account the normal interindividual sulcal variability. Methods We retrospectively studied 29 right-handed patients with FCD in the central region (including 12 MR negative histologically-confirmed cases) and 29 right-handed controls. The analysis of sulcal abnormalities from T1-weighted MR imaging (MRI) was performed using a graph-based representation of the cortical folds and an automated sulci recognition system, providing a new quantitative criterion to describe sulcal patterns, termed sulcus energy. Results Group analysis showed that the central sulcus in the hemisphere ipsilateral to the FCD exhibited an abnormal sulcal pattern compared with controls (p = 0.032). FCDs were associated with abnormal patterns of the central sulci compared with controls (p = 0.006), a result that remained significant when MR-negative and MR-positive patients were considered separately, while the effects of sex, age and MR-field were not significant. At the individual level, sulcus energy alone failed to detect the FCD lesion. We found, however, a significant association between maximum z-scores and the site of FCD (p = 0.0046) which remained significant in MR-negative (p = 0.024) but not in MR-positive patients (p = 0.058). The maximum z-score pointed to an FCD sulcus in four MR-negative and five MR-positive patients. Conclusions We identified abnormal sulcal patterns in patients with FCD of the central region compared with healthy controls. The abnormal sulcal patterns ipsilateral to the FCD and the link between sulcus energy and the FCD location strengthen the interest of sulcal abnormalities in FCD patients.

Sulcus Identification and Labeling

The complexity and the variability of the cortical folding pattern are overwhelming for human experts. Computational anatomy helps the field to harness the folding variability considered as a proxy for architectural variability. First, bottom-up processing pipelines convert the implicit encoding of the cortical folding pattern embedded in the geometry of the cortical surface into a synthetic graphic representation. Then, learning-based pattern recognition methods assemble the building blocks of the folding making up this representation in order to reconstruct the sulci of the standard nomenclature. Some attempts at improving current folding models using the same bottom-up strategy could have some impact in the near future.