Genomic architecture of human neuroanatomical diversity.

Human brain anatomy is strikingly diverse and highly inheritable: genetic factors may explain up to 80% of its variability. Prior studies have tried to detect genetic variants with a large effect on neuroanatomical diversity, but those currently identified account for <5% of the variance. Here, based on our analyses of neuroimaging and whole-genome genotyping data from 1765 subjects, we show that up to 54% of this heritability is captured by large numbers of single-nucleotide polymorphisms of small-effect spread throughout the genome, especially within genes and close regulatory regions. The genetic bases of neuroanatomical diversity appear to be relatively independent of those of body size (height), but shared with those of verbal intelligence scores. The study of this genomic architecture should help us better understand brain evolution and disease.

Single nucleotide polymorphism in the neuroplastin locus associates with cortical thickness and intellectual ability in adolescents.

Despite the recognition that cortical thickness is heritable and correlates with intellectual ability in children and adolescents, the genes contributing to individual differences in these traits remain unknown. We conducted a large-scale association study in 1583 adolescents to identify genes affecting cortical thickness. Single-nucleotide polymorphisms (SNPs; n=54,837) within genes whose expression changed between stages of growth and differentiation of a human neural stem cell line were selected for association analyses with average cortical thickness. We identified a variant, rs7171755, associating with thinner cortex in the left hemisphere (P=1.12 × 10(-)(7)), particularly in the frontal and temporal lobes. Localized effects of this SNP on cortical thickness differently affected verbal and nonverbal intellectual abilities. The rs7171755 polymorphism acted in cis to affect expression in the human brain of the synaptic cell adhesion glycoprotein-encoding gene NPTN. We also found that cortical thickness and NPTN expression were on average higher in the right hemisphere, suggesting that asymmetric NPTN expression may render the left hemisphere more sensitive to the effects of NPTN mutations, accounting for the lateralized effect of rs7171755 found in our study. Altogether, our findings support a potential role for regional synaptic dysfunctions in forms of intellectual deficits.

Resilience and corpus callosum microstructure in adolescence.

BACKGROUND: Resilience is the capacity of individuals to resist mental disorders despite exposure to stress. Little is known about its neural underpinnings. The putative variation of white-matter microstructure with resilience in adolescence, a critical period for brain maturation and onset of high-prevalence mental disorders, has not been assessed by diffusion tensor imaging (DTI). Lower fractional anisotropy (FA) though, has been reported in the corpus callosum (CC), the brain's largest white-matter structure, in psychiatric and stress-related conditions. We hypothesized that higher FA in the CC would characterize stress-resilient adolescents. METHOD: Three groups of adolescents recruited from the community were compared: resilient with low risk of mental disorder despite high exposure to lifetime stress (n = 55), at-risk of mental disorder exposed to the same level of stress (n = 68), and controls (n = 123). Personality was assessed by the NEO-Five Factor Inventory (NEO-FFI). Voxelwise statistics of DTI values in CC were obtained using tract-based spatial statistics. Regional projections were identified by probabilistic tractography. RESULTS: Higher FA values were detected in the anterior CC of resilient compared to both non-resilient and control adolescents. FA values varied according to resilience capacity. Seed regional changes in anterior CC projected onto anterior cingulate and frontal cortex. Neuroticism and three other NEO-FFI factor scores differentiated non-resilient participants from the other two groups. CONCLUSION: High FA was detected in resilient adolescents in an anterior CC region projecting to frontal areas subserving cognitive resources. Psychiatric risk was associated with personality characteristics. Resilience in adolescence may be related to white-matter microstructure.

Interoperable atlases of the human brain.

The last two decades have seen an unprecedented development of human brain mapping approaches at various spatial and temporal scales. Together, these have provided a large fundus of information on many different aspects of the human brain including micro- and macrostructural segregation, regional specialization of function, connectivity, and temporal dynamics. Atlases are central in order to integrate such diverse information in a topographically meaningful way. It is noteworthy, that the brain mapping field has been developed along several major lines such as structure vs. function, postmortem vs. in vivo, individual features of the brain vs. population-based aspects, or slow vs. fast dynamics. In order to understand human brain organization, however, it seems inevitable that these different lines are integrated and combined into a multimodal human brain model. To this aim, we held a workshop to determine the constraints of a multi-modal human brain model that are needed to enable (i) an integration of different spatial and temporal scales and data modalities into a common reference system, and (ii) efficient data exchange and analysis. As detailed in this report, to arrive at fully interoperable atlases of the human brain will still require much work at the frontiers of data acquisition, analysis, and representation. Among them, the latter may provide the most challenging task, in particular when it comes to representing features of vastly different scales of space, time and abstraction. The potential benefits of such endeavor, however, clearly outweigh the problems, as only such kind of multi-modal human brain atlas may provide a starting point from which the complex relationships between structure, function, and connectivity may be explored.

Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts.

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models ("conv5-FC3", ResNet and "SECNN") as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM and QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the "conv5-FC3" network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization (acceptable performances on all tested cohorts including some that are not included in training). The trained models will be made publicly available should the manuscript be accepted.

Why shared data should not be acknowledged on the author byline.

We argue that the emerging practice of using the author byline to acknowledge shared data is incompatible with current established standards for academic authorship. Non-author contributors, whether groups or individuals, should not be added to the author list of published papers. Deviation from these principles devalues authorship and raises issues, such as equal treatment of groups and individuals, credit for shared data vs. other shared resources, and ultimately guest authorship. Such dilution of authorship standards is problematic because it can compromise fair evaluations in the scientific community. We briefly discuss viable alternatives for crediting contributors, such as citations of papers describing shared data, reference to dataset publications, inclusion in the Acknowledgments section, or credit of individuals for sharing data in an Appendix, a solution that has been used in academic evaluation.

The Neuroimaging Data Model Linear Regression Tool (nidm_linreg): PyNIDM Project.

The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries within a Resource Description Framework (RDF) document to describe data and metadata for integration and query. Data from different studies, using locally defined variable names, can be retrieved by linking them to higher-order concepts from established ontologies and terminologies. Through these capabilities, NIDM documents are expected to improve reproducibility and facilitate data discovery and reuse. PyNIDM is a Python toolbox supporting the creation, manipulation, and querying of NIDM documents. Using the query tools available in PyNIDM, users are able interrogate datasets to find studies that have collected variables measuring similar phenotypic properties. This, in turn, facilitates the transformation and combination of data across multiple studies. The focus of this manuscript is the linear regression tool which is a part of the PyNIDM toolbox and works directly on NIDM documents. It provides a high-level statistical analysis that aids researchers in gaining more insight into the data that they are considering combining across studies. This saves researchers valuable time and effort while showing potential relationships between variables. The linear regression tool operates through a command-line interface integrated with the other tools (pynidm linear-regression) and provides the user with the opportunity to specify variables of interest using the rich query techniques available for NIDM documents and then conduct a linear regression with optional contrast and regularization.

A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility.

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

Boys do it the right way: sex-dependent amygdala lateralization during face processing in adolescents.

Previous studies have observed a sex-dependent lateralization of amygdala activation related to emotional memory. Specifically, it was shown that the activity of the right amygdala correlates significantly stronger with memory for images judged as arousing in men than in women, and that there is a significantly stronger relationship in women than in men between activity of the left amygdala and memory for arousing images. Using a large sample of 235 male adolescents and 235 females matched for age and handedness, we investigated the sex-specific lateralization of amygdala activation during an emotional face perception fMRI task. Performing a formal sex by hemisphere analysis, we observed in males a significantly stronger right amygdala activation as compared to females. Our results indicate that adolescents display a sex-dependent lateralization of amygdala activation that is also present in basic processes of emotional perception. This finding suggests a sex-dependent development of human emotion processing and may further implicate possible etiological pathways for mental disorders most frequent in adolescent males (i.e., conduct disorder).

The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology.

A fundamental function of the brain is to evaluate the emotional and motivational significance of stimuli and to adapt behaviour accordingly. The IMAGEN study is the first multicentre genetic-neuroimaging study aimed at identifying the genetic and neurobiological basis of individual variability in impulsivity, reinforcer sensitivity and emotional reactivity, and determining their predictive value for the development of frequent psychiatric disorders. Comprehensive behavioural and neuropsychological characterization, functional and structural neuroimaging and genome-wide association analyses of 2000 14-year-old adolescents are combined with functional genetics in animal and human models. Results will be validated in 1000 adolescents from the Canadian Saguenay Youth Study. The sample will be followed up longitudinally at the age of 16 years to investigate the predictive value of genetics and intermediate phenotypes for the development of frequent psychiatric disorders. This review describes the strategies the IMAGEN consortium used to meet the challenges posed by large-scale multicentre imaging-genomics investigations. We provide detailed methods and Standard Operating Procedures that we hope will be helpful for the design of future studies. These include standardization of the clinical, psychometric and neuroimaging-acquisition protocols, development of a central database for efficient analyses of large multimodal data sets and new analytic approaches to large-scale genetic neuroimaging analyses.

A group model for stable multi-subject ICA on fMRI datasets.

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study.

Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease.

Although the patterns of structural and metabolic brain alterations in Alzheimer's disease are being refined and discrepancies between them are being underlined, the exact relationships between atrophy and hypometabolism are still unclear. In this study, we aimed to provide a direct comparison between grey matter atrophy and hypometabolism in a sample of patients with clinically probable Alzheimer's disease, using a voxel-based method specially designed to statistically compare the two imaging modalities. Eighteen patients with probable Alzheimer's disease of mild severity and 15 healthy aged controls underwent both high-resolution T1 MRI and resting-state (18)FDG-PET. The MRI data sets were handled using optimized VBM. The PET data were coregistered to their corresponding MRI, corrected voxel-wise for partial volume averaging and spatially normalized using the same parameters as those of their corresponding MRI volume. A differential smoothing was applied on the MRI and PET data sets to equalize their effective smoothness and resolution. For each patient, Z-score maps of atrophy and hypometabolism were created by comparing to the controls data set, respectively averaged to provide the profile of hypometabolism and atrophy, and entered in a voxel-by-voxel SPM analysis to assess the statistical differences between hypometabolism and atrophy. The observed patterns of hypometabolism and atrophy were consistent with previous studies. However, the direct comparison revealed marked regional variability in the relationship between hypometabolism and atrophy. Thus, the hypometabolism significantly exceeded atrophy in most altered structures, particularly in the posterior cingulate-precuneus, orbitofrontal, inferior temporo-parietal, parahippocampal, angular and fusiform areas. In contrast, a few hypometabolic structures among which the hippocampus exhibited similar degrees of atrophy and hypometabolism, a profile that significantly differed from the posterior cingulate. Excessive hypometabolism relative to atrophy suggests the intervention of additional hypometabolism-inducing factors, such as disconnection and amyloid deposition, resulting in genuine functional perturbation ahead of actual atrophy and perhaps of pathology as well. Conversely, in the hippocampus, where disconnection processes are also likely to occur, relative synaptic compensatory mechanisms may be taking place, maintaining neuronal activity in the face of structural alterations.

Processing 3D form and 3D motion: respective contributions of attention-based and stimulus-driven activity.

This study aims at segregating the neural substrate for the 3D-form and 3D-motion attributes in structure-from-motion perception, and at disentangling the stimulus-driven and endogenous-attention-driven processing of these attributes. Attention and stimulus were manipulated independently: participants had to detect the transitions of one attribute--form, 3D motion or colour--while the visual stimulus underwent successive transitions of all attributes. We compared the BOLD activity related to form and 3D motion in three conditions: stimulus-driven processing (unattended transitions), endogenous attentional selection (task) or both stimulus-driven processing and attentional selection (attended transitions). In all conditions, the form versus 3D-motion contrasts revealed a clear dorsal/ventral segregation. However, while the form-related activity is consistent with previously described shape-selective areas, the activity related to 3D motion does not encompass the usual "visual motion" areas, but rather corresponds to a high-level motion system, including IPL and STS areas. Second, we found a dissociation between the neural processing of unattended attributes and that involved in endogenous attentional selection. Areas selective for 3D-motion and form showed either increased activity at transitions of these respective attributes or decreased activity when subjects' attention was directed to a competing attribute. We propose that both facilitatory and suppressive mechanisms of attribute selection are involved depending on the conditions driving this selection. Therefore, attentional selection is not limited to an increased activity in areas processing stimulus properties, and may unveil different functional localization from stimulus modulation.

Cerebral mechanisms of word masking and unconscious repetition priming.

We used functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) to visualize the cerebral processing of unseen masked words. Within the areas associated with conscious reading, masked words activated left extrastriate, fusiform and precentral areas. Furthermore, masked words reduced the amount of activation evoked by a subsequent conscious presentation of the same word. In the left fusiform gyrus, this repetition suppression phenomenon was independent of whether the prime and target shared the same case, indicating that case-independent information about letter strings was extracted unconsciously. In comparison to an unmasked situation, however, the activation evoked by masked words was drastically reduced and was undetectable in prefrontal and parietal areas, correlating with participants' inability to report the masked words.

Enhanced detection in brain activation maps using a multifiltering approach.

Current methods for detecting activation foci in positron emission tomography difference images include a low pass filtering step aimed at improving the signal-to-noise ratio. However, we show that detection sensitivity depends both on the activation signal and the filter sizes. Therefore, we propose to improve current detection methods by using a multifiltering strategy that is shown to be more sensitive when various kinds of signals are present in the brain activation images.

Analysis of individual positron emission tomography activation maps by detection of high signal-to-noise-ratio pixel clusters.

We present a new method for the analysis of individual brain positron emission tomography (PET) activation maps that looks for activated areas of a certain size rather than pixels with maximum values. High signal-to-noise-ratio pixel clusters (HSC) are identified and their sizes are statistically tested with respect to a Monte-Carlo-derived distribution of cluster sizes in pure noise images. From multiple HSC size tests, a strategy is proposed for control of the overall type I error. The sensitivity and specificity of this method have been assessed using realistic Monte Carlo simulations of brain activation maps. When compared with the gamma 2 statistic of the local maxima distribution, the proposed method showed enhanced sensitivity, particularly for signals of low magnitude and/or large size. Its potential for the individual analysis of PET activation studies is presented in two sets of subjects who underwent two cognitive protocols. Although it can be viewed as an alternative to the classical stereotactic averaging approach, this new method is intended to be a first step toward the analysis of single-subject PET activation studies.

PET study of changes in local brain hemodynamics and oxygen metabolism after unilateral middle cerebral artery occlusion in baboons.

Local cerebral hemodynamics and oxygen metabolism were measured by positron emission tomography (PET) with the oxygen-15 (15O) steady-state method in baboons, immediately before (T0), 1 (T1), and 3-4 (T2) h after permanent middle cerebral artery occlusion (MCAO). At T1, there was a marked fall in both cerebral blood flow (CBF) and the CBF/cerebral blood volume (CBV) ratio in the occluded territory; these changes were sustained at T2, indicating stable reduction in cerebral perfusion pressure and lack of spontaneous reperfusion within this time range. Compared with preocclusion conditions, the oxygen extraction fraction (OEF) in the occluded territory was elevated at both T1 and T2, indicative of a persistent oligemia/ischemia for up to 3 h after MCAO. At T2, however, this OEF increase had lessened, concomitantly with a decline in cerebral metabolic rate of oxygen (CMRO2). This impairment of oxidative metabolism occurred earlier in the deep, compared with the cortical, MCA territories; in the latter, the CMRO2 was essentially preserved at T1 and only moderately reduced at T2, possibly suggesting prolonged viability. Finally, no significant changes in CBF or CMRO2 were observed in the contralateral MCA territory in this time range after MCAO. Despite methodological limitations (mainly partial volume effects related to PET imaging, which may have resulted in an underestimation of true changes and an overlooking of heterogeneous changes) our study demonstrates the feasibility of the combined PET-MCAO paradigm in baboons; this experimental approach should be valuable in investigating the pathophysiology and therapy of acute stroke.

Temporal lobe dysfunction in childhood autism: a PET study. Positron emission tomography.

OBJECTIVE: The nature of the underlying brain dysfunction of childhood autism, a life-long severe developmental disorder, is not well understood. Although researchers using functional brain imaging have attempted to contribute to this debate, previous studies have failed to report consistent localized neocortical brain dysfunction. The authors reasoned that early methods may have been insensitive to such dysfunction, which may now be detectable with improved technology. METHOD: To test this hypothesis, regional cerebral blood flow was measured with positron emission tomography (PET) in 21 children with primary autism and in 10 nonautistic children with idiopathic mental retardation. Autistic and comparison groups were similar in average age and developmental quotients. The authors first searched for focal brain dysfunction in the autistic group by using a voxel-based whole brain analysis and then assessed the sensitivity of the method to detect the abnormality in individual children. An extension study was then performed in an additional group of 12 autistic children. RESULTS: The first autistic group had a highly significant hypoperfusion in both temporal lobes centered in associative auditory and adjacent multimodal cortex, which was detected in 76% of autistic children. Virtually identical results were found in the second autistic group in the extension study. CONCLUSIONS: PET and voxel-based image analysis revealed a localized dysfunction of the temporal lobes in school-aged children with idiopathic autism. Further studies will clarify the relationships between these temporal abnormalities and the perceptive, cognitive, and emotional developmental abnormalities characteristic of this disorder.