Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance.

Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real-life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large-step scaffold hopping problems. In particular, we assessed how difficult these problems are, particularly for classical 2D and 3D ligand-based methods. We also showed that a machine-learning chemogenomic algorithm outperforms classical methods and we provided some useful hints for future improvements.

Molecular International Prognostic Scoring System for Myelodysplastic Syndromes.

BACKGROUND: Risk stratification and therapeutic decision-making for myelodysplastic syndromes (MDS) are based on the International Prognostic Scoring System­Revised (IPSS-R), which considers hematologic parameters and cytogenetic abnormalities. Somatic gene mutations are not yet used in the risk stratification of patients with MDS. METHODS: To develop a clinical-molecular prognostic model (IPSS-Molecular [IPSS-M]), pretreatment diagnostic or peridiagnostic samples from 2957 patients with MDS were profiled for mutations in 152 genes. Clinical and molecular variables were evaluated for associations with leukemia-free survival, leukemic transformation, and overall survival. Feature selection was applied to determine the set of independent IPSS-M prognostic variables. The relative weights of the selected variables were estimated using a robust Cox multivariable model adjusted for confounders. The IPSS-M was validated in an external cohort of 754 Japanese patients with MDS. RESULTS: We mapped at least one oncogenic genomic alteration in 94% of patients with MDS. Multivariable analysis identified TP53multihit, FLT3 mutations, and MLLPTD as top genetic predictors of adverse outcomes. Conversely, SF3B1 mutations were associated with favorable outcomes, but this was modulated by patterns of comutation. Using hematologic parameters, cytogenetic abnormalities, and somatic mutations of 31 genes, the IPSS-M resulted in a unique risk score for individual patients. We further derived six IPSS-M risk categories with prognostic differences. Compared with the IPSS-R, the IPSS-M improved prognostic discrimination across all clinical end points and restratified 46% of patients. The IPSS-M was applicable in primary and secondary/therapy-related MDS. To simplify clinical use of the IPSS-M, we developed an open-access Web calculator that accounts for missing values. CONCLUSIONS: Combining genomic profiling with hematologic and cytogenetic parameters, the IPSS-M improves the risk stratification of patients with MDS and represents a valuable tool for clinical decision-making. (Funded by Celgene Corporation through the MDS Foundation, the Josie Robertson Investigators Program, the Edward P. Evans Foundation, the Projects of National Relevance of the Italian Ministry of University and Research, Associazione Italiana per la Ricerca sul Cancro, the Japan Agency for Medical Research and Development, Cancer Research UK, the Austrian Science Fund, the MEXT [Japanese Ministry of Education, Culture, Sports, Science and Technology] Program for Promoting Research on the Supercomputer Fugaku, the Japan Society for the Promotion of Science, the Taiwan Department of Health, and Celgene Corporation through the MDS Foundation.)

Subject-specific segregation of functional territories based on deep phenotyping.

Functional magnetic resonance imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. Contrariwise, recent data-collection efforts have started to target a systematic spatial representation of multiple mental functions. In this paper, we leverage the Individual Brain Charting (IBC) dataset-a high-resolution task-fMRI dataset acquired in a fixed environment-in order to study the feasibility of individual mapping. First, we verify that the IBC brain maps reproduce those obtained from previous, large-scale datasets using the same tasks. Second, we confirm that the elementary spatial components, inferred across all tasks, are consistently mapped within and, to a lesser extent, across participants. Third, we demonstrate the relevance of the topographic information of the individual contrast maps, showing that contrasts from one task can be predicted by contrasts from other tasks. At last, we showcase the benefit of contrast accumulation for the fine functional characterization of brain regions within a prespecified network. To this end, we analyze the cognitive profile of functional territories pertaining to the language network and prove that these profiles generalize across participants.

The functional database of the ARCHI project: Potential and perspectives.

More than two decades of functional magnetic resonance imaging (fMRI) of the human brain have succeeded to identify, with a growing level of precision, the neural basis of multiple cognitive skills within various domains (perception, sensorimotor processes, language, emotion and social cognition …). Progress has been made in the comprehension of the functional organization of localized brain areas. However, the long time required for fMRI acquisition limits the number of experimental conditions performed in a single individual. As a consequence, distinct brain localizations have mostly been studied in separate groups of participants, and their functional relationships at the individual level remain poorly understood. To address this issue, we report here preliminary results on a database of fMRI data acquired on 78 individuals who each performed a total of 29 experimental conditions, grouped in 4 cross-domains functional localizers. This protocol has been designed to efficiently isolate, in a single session, the brain activity associated with language, numerical representation, social perception and reasoning, premotor and visuomotor representations. Analyses are reported at the group and at the individual level, to establish the ability of our protocol to selectively capture distinct regions of interest in a very short time. Test-retest reliability was assessed in a subset of participants. The activity evoked by the different contrasts of the protocol is located in distinct brain networks that, individually, largely replicate previous findings and, taken together, cover a large proportion of the cortical surface. We provide detailed analyses of a subset of regions of relevance: the left frontal, left temporal and middle frontal cortices. These preliminary analyses highlight how combining such a large set of functional contrasts may contribute to establish a finer-grained brain atlas of cognitive functions, especially in regions of high functional overlap. Detailed structural images (structural connectivity, micro-structures, axonal diameter) acquired in the same individuals in the context of the ARCHI database provide a promising situation to explore functional/structural interdependence. Additionally, this protocol might also be used as a way to establish individual neurofunctional signatures in large cohorts.

Shared genetic aetiology between cognitive performance and brain activations in language and math tasks.

Cognitive performance is highly heritable. However, little is known about common genetic influences on cognitive ability and brain activation when engaged in a cognitive task. The Human Connectome Project (HCP) offers a unique opportunity to study this shared genetic etiology with an extended pedigree of 785 individuals. To investigate this common genetic origin, we took advantage of the HCP dataset, which includes both language and mathematics activation tasks. Using the HCP multimodal parcellation, we identified areals in which inter-individual functional MRI (fMRI) activation variance was significantly explained by genetics. Then, we performed bivariate genetic analyses between the neural activations and behavioral scores, corresponding to the fMRI task accuracies, fluid intelligence, working memory and language performance. We observed that several parts of the language network along the superior temporal sulcus, as well as the angular gyrus belonging to the math processing network, are significantly genetically correlated with these indicators of cognitive performance. This shared genetic etiology provides insights into the brain areas where the human-specific genetic repertoire is expressed. Studying the association of polygenic risk scores, using variants associated with human cognitive ability and brain activation, would provide an opportunity to better understand where these variants are influential.

Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping.

Functional Magnetic Resonance Imaging (fMRI) has furthered brain mapping on perceptual, motor, as well as higher-level cognitive functions. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a cohort of 12 participants performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The present article gives a detailed description of the first release of the IBC dataset. It comprises a dozen of tasks, addressing both low- and high- level cognitive functions. This openly available dataset is thus intended to become a reference for cognitive brain mapping.

The Brainomics/Localizer database.

The Brainomics/Localizer database exposes part of the data collected by the in-house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects. Since regulatory rules prevent us from making genetic data available for download, the database serves only anatomical MRI scans, functional MRI data, behavioral and demographic data. To publish this set of heterogeneous data, we use dedicated software based on the open-source CubicWeb semantic web framework. Through genericity in the data model and flexibility in the display of data (web pages, CSV, JSON, XML), CubicWeb helps us expose these complex datasets in original and efficient ways.

Genetic and Environmental Influences on the Visual Word Form and Fusiform Face Areas.

Two areas of the occipitotemporal cortex show a remarkable hemispheric lateralization: written words activate the visual word form area (VWFA) in the left fusiform gyrus and faces activate a symmetrical site in the right hemisphere, the fusiform face area (FFA). While the lateralization of the VWFA fits with the leftward asymmetry of the speech processing network, origin of the rightward asymmetry for faces is still unclear. Using fMRI data from 64 subjects (including 16 monozygotic (MZ) and 13 dizygotic (DZ) twin pairs), we investigated how activations evoked by written words, faces, and spoken language are co-lateralized in the temporal lobe, and whether this organization reflects genetic factors or individual reading expertise. We found that the lateralization of the left superior temporal activation for spoken language correlates with the lateralization of occipitotemporal activations for both written words and faces. Behavioral reading scores also modulate the responses to words and faces. Estimation of genetic and environmental contributions shows that activations of the VWFA, the occipital face area, and the temporal speech areas are partially under genetic control whereas activation of the FFA is primarily influenced by individual experience. Our results stress the importance of both genetic factors and acquired expertise in the occipitotemporal organization.

Principal component regression predicts functional responses across individuals.

Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes.

Anatomical connections of the visual word form area.

The visual word form area (VWFA), a region systematically involved in the identification of written words, occupies a reproducible location in the left occipitotemporal sulcus in expert readers of all cultures. Such a reproducible localization is paradoxical, given that reading is a recent invention that could not have influenced the genetic evolution of the cortex. Here, we test the hypothesis that the VWFA recycles a region of the ventral visual cortex that shows a high degree of anatomical connectivity to perisylvian language areas, thus providing an efficient circuit for both grapheme-phoneme conversion and lexical access. In two distinct experiments, using high-resolution diffusion-weighted data from 75 human subjects, we show that (1) the VWFA, compared with the fusiform face area, shows higher connectivity to left-hemispheric perisylvian superior temporal, anterior temporal and inferior frontal areas; (2) on a posterior-to-anterior axis, its localization within the left occipitotemporal sulcus maps onto a peak of connectivity with language areas, with slightly distinct subregions showing preferential projections to areas respectively involved in grapheme-phoneme conversion and lexical access. In agreement with functional data on the VWFA in blind subjects, the results suggest that connectivity to language areas, over and above visual factors, may be the primary determinant of VWFA localization.

Genetic and environmental contributions to brain activation during calculation.

Twin studies have long suggested a genetic influence on inter-individual variations in mathematical abilities, and candidate genes have been identified by genome-wide association studies. However, the localization of the brain regions under genetic influence during number manipulation is still unexplored. Here we investigated fMRI data from a group of 19 MZ (monozygotic) and 13 DZ (dizygotic) adult twin pairs, scanned during a mental calculation task. We examined both the activation and the degree of functional lateralization in regions of interest (ROIs) centered on the main activated peaks. Heritability was first investigated by comparing the respective MZ and DZ correlations. Then, genetic and environmental contributions were jointly estimated by fitting a ACE model classically used in twin studies. We found that a subset of the activated network was under genetic influence, encompassing the bilateral posterior superior parietal lobules (PSPL), the right intraparietal sulcus (IPS) and a left superior frontal region. An additional region of the left inferior parietal cortex (IPC), whose deactivation correlated with a behavioral calculation score, also presented higher similarity between MZ than between DZ twins, thus offering a plausible physiological basis for the observable inheritance of math scores. Finally, the main impact of the shared environment was found in the lateralization of activation within the intraparietal sulcus. These maps of genetic and environmental contributions provide precise candidate phenotypes for further genetic association analyses, and illuminate how genetics and education shape the development of number processing networks.

Cohort-level brain mapping: learning cognitive atoms to single out specialized regions.

Functional Magnetic Resonance Imaging (fMRI) studies map the human brain by testing the response of groups of individuals to carefully-crafted and contrasted tasks in order to delineate specialized brain regions and networks. The number of functional networks extracted is limited by the number of subject-level contrasts and does not grow with the cohort. Here, we introduce a new group-level brain mapping strategy to differentiate many regions reflecting the variety of brain network configurations observed in the population. Based on the principle of functional segregation, our approach singles out functionally-specialized brain regions by learning group-level functional profiles on which the response of brain regions can be represented sparsely. We use a dictionary-learning formulation that can be solved efficiently with on-line algorithms, scaling to arbitrary large datasets. Importantly, we model inter-subject correspondence as structure imposed in the estimated functional profiles, integrating a structure-inducing regularization with no additional computational cost. On a large multi-subject study, our approach extracts a large number of brain networks with meaningful functional profiles.

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.

Genetic variants of FOXP2 and KIAA0319/TTRAP/THEM2 locus are associated with altered brain activation in distinct language-related regions.

Recent advances have been made in the genetics of two human communication skills: speaking and reading. Mutations of the FOXP2 gene cause a severe form of language impairment and orofacial dyspraxia, while single-nucleotide polymorphisms (SNPs) located within a KIAA0319/TTRAP/THEM2 gene cluster and affecting the KIAA0319 gene expression are associated with reading disability. Neuroimaging studies of clinical populations point to partially distinct cerebral bases for language and reading impairments. However, alteration of FOXP2 and KIAA0319/TTRAP/THEM2 polymorphisms on typically developed language networks has never been explored. Here, we genotyped and scanned 94 healthy subjects using fMRI during a reading task. We studied the correlation of genetic polymorphisms with interindividual variability in brain activation and functional asymmetry in frontal and temporal cortices. In FOXP2, SNPs rs6980093 and rs7799109 were associated with variations of activation in the left frontal cortex. In the KIAA0319/TTRAP/THEM2 locus, rs17243157 was associated with asymmetry in functional activation of the superior temporal sulcus (STS). Interestingly, healthy subjects bearing the KIAA0319/TTRAP/THEM2 variants previously identified as enhancing the risk of dyslexia showed a reduced left-hemispheric asymmetry of the STS. Our results confirm that both FOXP2 and KIAA0319/TTRAP/THEM2 genes play an important role in human language development, but probably through different cerebral pathways. The observed cortical effects mirror previous fMRI results in developmental language and reading disorders, and suggest that a continuum may exist between these pathologies and normal interindividual variability.

Improving accuracy and power with transfer learning using a meta-analytic database.

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Cortical representations of symbols, objects, and faces are pruned back during early childhood.

Regions of human ventral extrastriate visual cortex develop specializations for natural categories (e.g., faces) and cultural artifacts (e.g., words). In adults, category-based specializations manifest as greater neural responses in visual regions of the brain (e.g., fusiform gyrus) to some categories over others. However, few studies have examined how these specializations originate in the brains of children. Moreover, it is as yet unknown whether the development of visual specializations hinges on "increases" in the response to the preferred categories, "decreases" in the responses to nonpreferred categories, or "both." This question is relevant to a long-standing debate concerning whether neural development is driven by building up or pruning back representations. To explore these questions, we measured patterns of visual activity in 4-year-old children for 4 categories (faces, letters, numbers, and shoes) using functional magnetic resonance imaging. We report 2 key findings regarding the development of visual categories in the brain: 1) the categories "faces" and "symbols" doubly dissociate in the fusiform gyrus before children can read and 2) the development of category-specific responses in young children depends on cortical responses to nonpreferred categories that decrease as preferred category knowledge is acquired.

The enigma of Gerstmann's syndrome revisited: a telling tale of the vicissitudes of neuropsychology.

Eighty years ago, the Austrian neurologist Josef Gerstmann observed in a few patients a concomitant impairment in discriminating their own fingers, writing by hand, distinguishing left from right and performing calculations. He claimed that this tetrad of symptoms constituted a syndromal entity, assigned it to a lesion of the dominant parietal lobe and suggested that it was due to damage of a common functional denominator. Ever since, these claims have been debated and an astute synopsis and sceptical discussion was presented 40 years ago by MacDonald Critchley in this journal. Nonetheless, Gerstmann's syndrome has continued to intrigue both clinical neurologists and researchers in neuropsychology, and more frequently than not is described in textbooks as an example of parietal lobe damage. In this review, we revisit the chequered history of this syndrome, which can be seen as a case study of the dialectic evolution of concepts in neuropsychology. In light of several modern era findings of pure cases we conclude that it is legitimate to label the conjunction of symptoms first described by Gerstmann as a 'syndrome', but that it is very unlikely that damage to the same population of cortical neurons should account for all of the four symptoms. Instead, we propose that a pure form of Gerstmann's syndrome might arise from disconnection, via a lesion, to separate but co-localized fibre tracts in the subcortical parietal white matter, a hypothesis for which we have recently provided evidence using combined imaging of functional and structural organization in the healthy brain.

Beyond hemispheric dominance: brain regions underlying the joint lateralization of language and arithmetic to the left hemisphere.

Language and arithmetic are both lateralized to the left hemisphere in the majority of right-handed adults. Yet, does this similar lateralization reflect a single overall constraint of brain organization, such an overall "dominance" of the left hemisphere for all linguistic and symbolic operations? Is it related to the lateralization of specific cerebral subregions? Or is it merely coincidental? To shed light on this issue, we performed a "colateralization analysis" over 209 healthy subjects: We investigated whether normal variations in the degree of left hemispheric asymmetry in areas involved in sentence listening and reading are mirrored in the asymmetry of areas involved in mental arithmetic. Within the language network, a region-of-interest analysis disclosed partially dissociated patterns of lateralization, inconsistent with an overall "dominance" model. Only two of these areas presented a lateralization during sentence listening and reading which correlated strongly with the lateralization of two regions active during calculation. Specifically, the profile of asymmetry in the posterior superior temporal sulcus during sentence processing covaried with the asymmetry of calculation-induced activation in the intraparietal sulcus, and a similar colateralization linked the middle frontal gyrus with the superior posterior parietal lobule. Given recent neuroimaging results suggesting a late emergence of hemispheric asymmetries for symbolic arithmetic during childhood, we speculate that these colateralizations might constitute developmental traces of how the acquisition of linguistic symbols affects the cerebral organization of the arithmetic network.

A disconnection account of Gerstmann syndrome: functional neuroanatomy evidence.

OBJECTIVE: To examine the functional neuroanatomy that could account for pure Gerstmann syndrome, which is the selective association of acalculia, finger agnosia, left-right disorientation, and agraphia. METHODS: We used structural and functional neuroimaging at high spatial resolution in healthy subjects to seek a shared cortical substrate of the Grundstörung posited by Gerstmann, ie, a common functional denominator accounting for this clinical tetrad. We construed a functional activation paradigm that mirrors each of the four clinical deficits in Gerstmann syndrome and determined cortical activation patterns. We then applied fiber tracking to diffusion tensor images and used cortical activation foci in the four functional domains as seed regions. RESULTS: None of the subjects showed parietal overlap of cortical activation patterns from the four cognitive domains. In every subject, however, the parietal activation patterns across all four domains consistently connected to a small region of subcortical parietal white matter at a location that is congruent with the lesion in a well-documented case of pure Gerstmann syndrome. INTERPRETATION: Our functional neuroimaging findings are not in agreement with Gerstmann's postulate of damage to a common cognitive function underpinning clinical semiology. Our evidence from intact functional neuroanatomy suggests that pure forms of Gerstmann's tetrad do not arise from lesion to a shared cortical substrate but from intraparietal disconnection after damage to a focal region of subcortical white matter.

The neural development of an abstract concept of number.

As literate adults, we appreciate numerical values as abstract entities that can be represented by a numeral, a word, a number of lines on a scorecard, or a sequence of chimes from a clock. This abstract, notation-independent appreciation of numbers develops gradually over the first several years of life. Here, using functional magnetic resonance imaging, we examine the brain mechanisms that 6- and 7-year-old children and adults recruit to solve numerical comparisons across different notation systems. The data reveal that when young children compare numerical values in symbolic and nonsymbolic notations, they invoke the same network of brain regions as adults including occipito-temporal and parietal cortex. However, children also recruit inferior frontal cortex during these numerical tasks to a much greater degree than adults. Our data lend additional support to an emerging consensus from adult neuroimaging, nonhuman primate neurophysiology, and computational modeling studies that a core neural system integrates notation-independent numerical representations throughout development but, early in development, higher-order brain mechanisms mediate this process.