Jean‐Baptiste Poline

Abstract Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accomodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data. The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis. © 1995 Wiley‐Liss, Inc.

Abstract This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time‐series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley‐Liss, Inc.

In logic a conjunction is defined as an AND between truth statements. In neuroimaging, investigators may look for brain areas activated by task A AND by task B, or a conjunction of tasks (Price, C.J., Friston, K.J., 1997. Cognitive conjunction: a new approach to brain activation experiments. NeuroImage 5, 261-270). Friston et al. (Friston, K., Holmes, A., Price, C., Buchel, C., Worsley, K., 1999. Multisubject fMRI studies and conjunction analyses. NeuroImage 10, 85-396) introduced a minimum statistic test for conjunction. We refer to this method as the minimum statistic compared to the global null (MS/GN). The MS/GN is implemented in SPM2 and SPM99 software, and has been widely used as a test of conjunction. However, we assert that it does not have the correct null hypothesis for a test of logical AND, and further, this has led to confusion in the neuroimaging community. In this paper, we define a conjunction and explain the problem with the MS/GN test as a conjunction method. We present a survey of recent practice in neuroimaging which reveals that the MS/GN test is very often misinterpreted as evidence of a logical AND. We show that a correct test for a logical AND requires that all the comparisons in the conjunction are individually significant. This result holds even if the comparisons are not independent. We suggest that the revised test proposed here is the appropriate means for conjunction inference in neuroimaging.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

This paper is about detecting activations in statistical parametric maps and considers the relative sensitivity of a nested hierarchy of tests that we have framed in terms of the level of inference (voxel level, cluster level, and set level). These tests are based on the probability of obtainingc, or more, clusters withk, or more, voxels, above a thresholdu. This probability has a reasonably simple form and is derived using distributional approximations from the theory of Gaussian fields. The most important contribution of this work is the notion ofset-level inference.Set-level inference refers to the statistical inference that the number of clusters comprising an observed activation profile is highly unlikely to have occurred by chance. This inference pertains to the set of activations reaching criteria and represents a new way of assigningPvalues to distributed effects. Cluster-level inferences are a special case of set-level inferences, which obtain when the number of clustersc= 1. Similarly voxel-level inferences are special cases of cluster-level inferences that result when the cluster can be very small (i.e.,k = 0). Using a theoretical power analysis of distributed activations, we observed that set-level inferences are generally more powerful than cluster-level inferences and that cluster-level inferences are generally more powerful than voxel-level inferences. The price paid for this increased sensitivity is reduced localizing power: Voxel-level tests permit individual voxels to be identified as significant, whereas cluster- and set-level inferences only allow clusters or sets of clusters to be so identified. For all levels of inference the spatial size of the underlying signalf(relative to resolution) determines the most powerful thresholds to adopt. For set-level inferences iffis large (e.g., fMRI) then the optimum extent threshold should be greater than the expected number of voxels for each cluster. Iffis small (e.g., PET) the extent threshold should be small. We envisage that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.

Event-related fMRI was used to test the hypothesis that the visual word form area in the left fusiform gyrus holds a modality-specific and prelexical representation of visual words. Subjects were engaged in a repetition-detection task on pairs of words or pronounceable pseudo-words that could be written or spoken. The visual word form area responded only to written stimuli, not to spoken stimuli, independently of their semantic content. We propose that the occasional activation of the fusiform gyrus when listening to spoken words is due to the topdown recruitment of visual orthographic or object representations.

During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function.

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

The aim of group fMRI studies is to relate contrasts of tasks or stimuli to regional brain activity increases. These studies typically involve 10 to 16 subjects. The average regional activity statistical significance is assessed using the subject to subject variability of the effect (random effects analyses). Because of the relatively small number of subjects included, the sensitivity and reliability of these analyses is questionable and hard to investigate. In this work, we use a very large number of subject (more than 80) to investigate this issue. We take advantage of this large cohort to study the statistical properties of the inter-subject activity and focus on the notion of reproducibility by bootstrapping. We asked simple but important methodological questions: Is there, from the point of view of reliability, an optimal statistical threshold for activity maps? How many subjects should be included in group studies? What method should be preferred for inference? Our results suggest that i) optimal thresholds can indeed be found, and are rather lower than usual corrected for multiple comparison thresholds, ii) 20 subjects or more should be included in functional neuroimaging studies in order to have sufficient reliability, iii) non-parametric significance assessment should be preferred to parametric methods, iv) cluster-level thresholding is more reliable than voxel-based thresholding, and v) mixed effects tests are much more reliable than random effects tests. Moreover, our study shows that inter-subject variability plays a prominent role in the relatively low sensitivity and reliability of group studies.

Fluent readers recognize visual words across changes in case and retinal location, while maintaining a high sensitivity to the arrangement of letters. To evaluate the automaticity and functional anatomy of invariant word recognition, we measured brain activity during subliminal masked priming. By preceding target words with an unrelated prime, a repeated prime, or an anagram made of the same letters, we separated letter-level and whole-word codes. By changing the case and the retinal location of primes and targets, we evaluated the invariance of those codes. Our results indicate that an invariant binding of letters into words is achieved unconsciously through a series of increasingly invariant stages in the left occipito-temporal pathway.

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.

Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subject images, parcellation approaches use brain activity (e.g., found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and k-means clustering algorithms. We show that in general Ward's clustering performs better than alternative methods with regard to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.

Significance Most brain activity is not directly evoked by specific external events. This ongoing activity is correlated across distant brain regions within large-scale networks. This correlation or functional connectivity may reflect communication across brain regions. Strength and spatial organization of functional connectivity changes dynamically over seconds to minutes. Using functional MRI, we show that these ongoing changes correlate with behavior. The connectivity state before playback of a faint sound predicted whether the participant was going to perceive the sound on that trial. Connectivity states preceding missed sounds showed weakened modular structure, in which connectivity was more random and less organized across brain regions. These findings suggest that ongoing brain connectivity dynamics contribute to explaining behavioral variability.

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

Objective: Adolescents are particularly vulnerable to addiction, and in the case of smoking, this often leads to long-lasting nicotine dependence. The authors investigated a possible neural mechanism underlying this vulnerability. Method: Functional MRI was performed during reward anticipation in 43 adolescent smokers and 43 subjects matched on age, gender, and IQ. The authors also assessed group differences in novelty seeking, impulsivity, and reward delay discounting. Results: In relation to the comparison subjects, the adolescent smokers showed greater reward delay discounting and higher scores for novelty seeking. Neural responses in the ventral striatum during reward anticipation were significantly lower in the smokers than in the comparison subjects, and in the smokers this response was correlated with smoking frequency. Notably, the lower response to reward anticipation in the ventral striatum was also observed in smokers (N=14) who had smoked on fewer than 10 occasions. Conclusions: The present findings suggest that a lower response to reward anticipation in the ventral striatum may be a vulnerability factor for the development of early nicotine use.

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as ρ ≥ 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

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.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.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.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.

The dorsolateral prefrontal cortex (DLPFC) plays a key role in working memory (WM). Yet its precise contribution (the storage, manipulation and/or utilization of information for the forthcoming response) remains to be determined. To test the hypothesis that the DLPFC is more involved in the preparation of actions than in the maintenance of information in short-term memory (STM), we undertook a functional magnetic resonance imaging investigation in normal subjects performing two delayed response tasks (matching and reproduction tasks) in a visuospatial task sequence (presentation, delay, response). In the two tasks, the presentation and delay phases were similar, but the expected response was different: in the matching task, subjects had to indicate whether a visuospatial sequence matched the sequence presented before the delay period; in the reproduction task, subjects had to reproduce the sequence and, therefore, to mentally organize their response during the delay. Using a fMRI paradigm focusing on the delay period, we observed a significant DLPFC activation when subjects were required to mentally prepare a sequential action based on the information stored in STM. When subjects had only to maintain a visuospatial stimulus in STM, no DLPFC activation was found. These results suggest that a parietal-premotor network is sufficient to store visuospatial information in STM whereas the DLPFC is involved when it is necessary to mentally prepare a forthcoming sequential action based on the information stored in STM.

Little is currently known about the brain regions involved in central processing of dyspnea. We performed a functional imaging study with positron emission tomography (PET) to assess brain activation associated with an important component of dyspnea, respiratory discomfort during loaded breathing. We induced respiratory discomfort in eight healthy volunteers by adding external resistive loads during inspiration and expiration. Brain activation was characterized by a significant increase in regional cerebral blood flow (rCBF) (Z score of peak activation > 3.09). As compared with the unloaded control condition, high loaded breathing was associated with neural activation in three distinct brain regions, the right anterior insula, the cerebellar vermis, and the medial pons (respective Z scores = 4.75, 4.44, 4.41). For these brain regions, we further identified a positive correlation between rCBF and the perceived intensity of respiratory discomfort (respective Z scores = 4.45, 4.75, 4.74) as well as between rCBF and the mean amplitude of mouth pressure swings ( Δ Pm), the index of the main generating mechanism of the sensation (respective Z scores = 4.67, 4.36, 4.31), suggesting a common activation by these two parameters. Furthermore, we identified an area in the right posterior cingulate cortex where neural activation was specifically associated with perceived intensity of respiratory discomfort that is not related to Δ Pm (Z score = 4.25). Our results suggest that respiratory discomfort related to loaded breathing may be subserved by two distinct neural networks, the first being involved in the concomitant processing of the genesis and perception of respiratory discomfort and the second in the modulation of perceived intensity of the sensation by various factors other than its main generating mechanism, which may include emotional processing.

The assessment of significant activations in functional imaging using voxel-based methods often relies on results derived from the theory of Gaussian random fields. These results solve the multiple comparison problem and assume that the spatial correlation or smoothness of the data is known or can be estimated. End results (i.e., P values associated with local maxima, clusters, or sets of clusters) critically depend on this assessment, which should be as exact and as reliable as possible. In some earlier implementations of statistical parametric mapping (SPM) (SPM94, SPM95) the smoothness was assessed on Gaussianized t-fields (Gt-f) that are not generally free of physiological signal. This technique has two limitations. First, the estimation is not stable (the variance of the estimator being far from negligible) and, second, physiological signal in the Gt-f will bias the estimation. In this paper, we describe an estimation method that overcomes these drawbacks. The new approach involves estimating the smoothness of standardized residual fields which approximates the smoothness of the component fields of the associated t-field. Knowing the smoothness of these component fields is important because it allows one to compute corrected P values for statistical fields other than the t-field or the Gt-f (e.g., the F-map) and eschews bias due to deviation from the null hypothesis. We validate the method on simulated data and demonstrate it using data from a functional MRI study.

Abstract In this study, long (∼1,300 ms) and short duration (∼450 ms) estimation trials in an event‐related functional MRI (fMRI) study were contrasted in order to reveal the regions within a time estimation network yielding increased activation with the increase of the duration to be estimated. In accordance with numerous imaging studies, our results showed that the presupplementary motor area (preSMA), the anterior cingulate, the prefrontal and parietal cortices, and the basal ganglia were involved in the estimation trials whatever the duration to be estimated. Moreover, only a subset of the regions within this distributed cortical and subcortical network yielded increased activation with increasing time, namely, the preSMA, the anterior cingulate cortex, the right inferior frontal gyrus (homolog to Broca's area), the bilateral premotor cortex, and the right caudate nucleus. This suggests that these regions are directly involved in duration estimation. We propose that the caudate‐preSMA circuit, the anterior cingulate, and the premotor‐inferior frontal regions may support a clock mechanism, decision and response‐related processes, and active maintenance of temporal information, respectively. Hum. Brain Mapping, 2005. © 2005 Wiley‐Liss, Inc.

Bilateral temporal hypoperfusion at rest was recently described in autism. In normal adults, these regions are activated by listening to speech-like sounds. To investigate auditory cortical processing in autism, the authors performed a positron emission tomography activation study.Regional cerebral blood flow was measured in five autistic adults and eight comparison subjects during rest and while listening to speech-like sounds.Similar to the comparison subjects, autistic patients showed a bilateral activation of the superior temporal gyrus. However, an abnormal pattern of hemispheric activation was observed in the autistic group. The volume of activation was larger on the right side in the autistic patients, whereas the reverse pattern was found in the comparison group. The direct comparison between the two groups showed that the right middle frontal gyrus exhibited significantly greater activation in the autistic group. Conversely, the left temporal areas exhibited less activation in autistic patients.These findings suggest that abnormal auditory cortical processing is implicated in the language impairments and the inadequate response to sounds typically seen in autism.

Abstract The analysis of functional magnetic resonance imaging (fMRI) data recorded on several subjects resorts to the so‐called spatial normalization in a common reference space. This normalization is usually carried out on a voxel‐by‐voxel basis, assuming that after coregistration of the functional images with an anatomical template image in the Talairach reference system, a correct voxel‐based inference can be carried out across subjects. Shortcomings of such approaches are often dealt with by spatially smoothing the data to increase the overlap between subject‐specific activated regions. This procedure, however, cannot adapt to each anatomo‐functional subject configuration. We introduce a novel technique for intra‐subject parcellation based on spectral clustering that delineates homogeneous and connected regions. We also propose a hierarchical method to derive group parcels that are spatially coherent across subjects and functionally homogeneous. We show that we can obtain groups (or cliques) of parcels that well summarize inter‐subject activations. We also show that the spatial relaxation embedded in our procedure improves the sensitivity of random‐effect analysis. Hum Brain Mapp, 2005. © 2005 Wiley‐Liss, Inc.

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.

In this paper we present a general multivariate approach to the analysis of functional imaging studies. This analysis uses standard multivariate techniques to make statistical inferences about activation effects and to describe the important features of these effects. More specifically, the proposed analysis uses multivariate analysis of covariance (ManCova) with Wilk's lambda to test for specific effects of interest (e.g., differences among activation conditions), and canonical variates analysis (CVA) to characterize differential responses in terms of distributed brain systems. The data are subject to ManCova after transformation using their principal components or eigenimages. After significance of the activation effect has been assessed, underlying changes are described in terms of canonical images. Canonical images are like eigenimages but take explicit account of the effects of error or noise. The generality of this approach is assured by the general linear model used in the ManCova. The design and inferences sought are embodied in the design matrix and can, in principle, accommodate most parametric statistical analyses. This multivariate analysis may provide a statistical approach to PET activation studies that 1) complements univariate approaches like statistical parametric mapping, and 2) may facilitate the extension of existing multivariate techniques, like the scaled subprofile model and eigenimage analysis, to include hypothesis testing and statistical inference.

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.

Individual variation in reward sensitivity may have an important role in early substance use and subsequent development of substance abuse. This may be especially important during adolescence, a transition period marked by approach behavior and a propensity toward risk taking, novelty seeking and alteration of the social landscape. However, little is known about the relative contribution of personality, behavior, and brain responses for prediction of alcohol use in adolescents. In this study, we applied factor analyses and structural equation modeling to reward-related brain responses assessed by functional magnetic resonance imaging during a monetary incentive delay task. In addition, novelty seeking, sensation seeking, impulsivity, extraversion, and behavioral measures of risk taking were entered as predictors of early onset of drinking in a sample of 14-year-old healthy adolescents (N=324). Reward-associated behavior, personality, and brain responses all contributed to alcohol intake with personality explaining a higher proportion of the variance than behavior and brain responses. When only the ventral striatum was used, a small non-significant contribution to the prediction of early alcohol use was found. These data suggest that the role of reward-related brain activation may be more important in addiction than initiation of early drinking, where personality traits and reward-related behaviors were more significant. With up to 26% of explained variance, the interrelation of reward-related personality traits, behavior, and neural response patterns may convey risk for later alcohol abuse in adolescence, and thus may be identified as a vulnerability factor for the development of substance use disorders.

Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level.81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects.This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.

In this review, we first set out the general linear model (GLM) for the non technical reader, as a tool able to do both linear regression and ANOVA within the same flexible framework. We present a short history of its development in the fMRI community, and describe some interesting examples of its early use. We offer a few warnings, as the GLM relies on assumptions that may not hold in all situations. We conclude with a few wishes for the future of fMRI analyses, with or without the GLM. The appendix develops some aspects of use of contrasts for testing for the more technical reader.

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.

Computational techniques are central in many areas of neuroscience and are relatively easy to share. This paper describes why computer programs underlying scientific publications should be shared and lists simple steps for sharing. Together with ongoing efforts in data sharing, this should aid reproducibility of research.

Novelty-seeking tendencies in adolescents may promote innovation as well as problematic impulsive behaviour, including drug abuse. Previous research has not clarified whether neural hyper- or hypo-responsiveness to anticipated rewards promotes vulnerability in these individuals. Here we use a longitudinal design to track 144 novelty-seeking adolescents at age 14 and 16 to determine whether neural activity in response to anticipated rewards predicts problematic drug use. We find that diminished BOLD activity in mesolimbic (ventral striatal and midbrain) and prefrontal cortical (dorsolateral prefrontal cortex) regions during reward anticipation at age 14 predicts problematic drug use at age 16. Lower psychometric conscientiousness and steeper discounting of future rewards at age 14 also predicts problematic drug use at age 16, but the neural responses independently predict more variance than psychometric measures. Together, these findings suggest that diminished neural responses to anticipated rewards in novelty-seeking adolescents may increase vulnerability to future problematic drug use.

Here we report the first and most robust evidence about how sleep habits are associated with regional brain grey matter volumes and school grade average in early adolescence. Shorter time in bed during weekdays, and later weekend sleeping hours correlate with smaller brain grey matter volumes in frontal, anterior cingulate, and precuneus cortex regions. Poor school grade average associates with later weekend bedtime and smaller grey matter volumes in medial brain regions. The medial prefrontal - anterior cingulate cortex appears most tightly related to the adolescents' variations in sleep habits, as its volume correlates inversely with both weekend bedtime and wake up time, and also with poor school performance. These findings suggest that sleep habits, notably during the weekends, have an alarming link with both the structure of the adolescent brain and school performance, and thus highlight the need for informed interventions.

<h3>Importance;</h3> Copy number variants (CNVs) classified as pathogenic are identified in 10% to 15% of patients referred for neurodevelopmental disorders. However, their effect sizes on cognitive traits measured as a continuum remain mostly unknown because most of them are too rare to be studied individually using association studies. <h3>Objective</h3> To measure and estimate the effect sizes of recurrent and nonrecurrent CNVs on IQ. <h3>Design, Setting, and Participants</h3> This study identified all CNVs that were 50 kilobases (kb) or larger in 2 general population cohorts (the IMAGEN project and the Saguenay Youth Study) with measures of IQ. Linear regressions, including functional annotations of genes included in CNVs, were used to identify features to explain their association with IQ. Validation was performed using intraclass correlation that compared IQ estimated by the model with empirical data. <h3>Main Outcomes and Measures</h3> Performance IQ (PIQ), verbal IQ (VIQ), and frequency of de novo CNV events. <h3>Results</h3> The study included 2090 European adolescents from the IMAGEN study and 1983 children and parents from the Saguenay Youth Study. Of these, genotyping was performed on 1804 individuals from IMAGEN and 977 adolescents, 445 mothers, and 448 fathers (484 families) from the Saguenay Youth Study. We observed 4928 autosomal CNVs larger than 50 kb across both cohorts. For rare deletions, size, number of genes, and exons affect IQ, and each deleted gene is associated with a mean (SE) decrease in PIQ of 0.67 (0.19) points (<i>P</i> = 6 × 10⁻⁴); this is not so for rare duplications and frequent CNVs. Among 10 functional annotations, haploinsufficiency scores best explain the association of any deletions with PIQ with a mean (SE) decrease of 2.74 (0.68) points per unit of the probability of being loss-of-function intolerant (<i>P</i> = 8 × 10⁻⁵). Results are consistent across cohorts and unaffected by sensitivity analyses removing pathogenic CNVs. There is a 0.75 concordance (95% CI, 0.39-0.91) between the effect size on IQ estimated by our model and IQ loss calculated in previous studies of 15 recurrent CNVs. There is a close association between effect size on IQ and the frequency at which deletions occur de novo (odds ratio, 0.86; 95% CI, 0.84-0.87;<i>P</i> = 2.7 × 10⁻⁸⁸). There is a 0.76 concordance (95% CI, 0.41-0.91) between de novo frequency estimated by the model and calculated using data from the DECIPHER database. <h3>Conclusions and Relevance</h3> Models trained on nonpathogenic deletions in the general population reliably estimate the effect size of pathogenic deletions and suggest omnigenic associations of haploinsufficiency with IQ. This represents a new framework to study variants too rare to perform individual association studies and can help estimate the cognitive effect of undocumented deletions in the neurodevelopmental clinic.

Abstract Neuroimaging enables rich noninvasive measurements of human brain activity, but translating such data into neuroscientific insights and clinical applications requires complex analyses and collaboration among a diverse array of researchers. The open science movement is reshaping scientific culture and addressing the challenges of transparency and reproducibility of research. To advance open science in neuroimaging the Organization for Human Brain Mapping created the Committee on Best Practice in Data Analysis and Sharing (COBIDAS), charged with creating a report that collects best practice recommendations from experts and the entire brain imaging community. The purpose of this work is to elaborate the principles of open and reproducible research for neuroimaging using Magnetic Resonance Imaging (MRI), and then distill these principles to specific research practices. Many elements of a study are so varied that practice cannot be prescribed, but for these areas we detail the information that must be reported to fully understand and potentially replicate a study. For other elements of a study, like statistical modelling where specific poor practices can be identified, and the emerging areas of data sharing and reproducibility, we detail both good practice and reporting standards. For each of seven areas of a study we provide tabular listing of over 100 items to help plan, execute, report and share research in the most transparent fashion. Whether for individual scientists, or for editors and reviewers, we hope these guidelines serve as a benchmark, to raise the standards of practice and reporting in neuroimaging using MRI.

To move Alzheimer Disease (AD) research forward it is essential to collect data from large cohorts, but also make such data available to the global research community. We describe the creation of an open science dataset from the PREVENT-AD (PResymptomatic EValuation of Experimental or Novel Treatments for AD) cohort, composed of cognitively unimpaired older individuals with a parental or multiple-sibling history of AD. From 2011 to 2017, 386 participants were enrolled (mean age 63 years old ± 5) for sustained investigation among whom 349 have retrospectively agreed to share their data openly. Repositories are findable through the unified interface of the Canadian Open Neuroscience Platform and contain up to five years of longitudinal imaging data, cerebral fluid biochemistry, neurosensory capacities, cognitive, genetic, and medical information. Imaging data can be accessed openly at https://openpreventad.loris.ca while most of the other information, sensitive by nature, is accessible by qualified researchers at https://registeredpreventad.loris.ca. In addition to being a living resource for continued data acquisition, PREVENT-AD offers opportunities to facilitate understanding of AD pathogenesis.

Abstract Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g., because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts break machine-learning–extracted biomarkers, as well as detection and correction strategies.

Abstract Background The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. Methods Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction. Results Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. Conclusions This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.

Abstract In this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as the International Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data be Findable, Accessible, Interoperable, and Reusable (FAIR) . As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects.

Abstract The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

Although it is largely accepted that visual-mental imagery and perception draw on many of the same neural structures, the existence and nature of neural processing in the primary visual cortex (or area V1) during visual imagery remains controversial. We tested two general hypotheses: The first was that V1 is activated only when images with many details are formed and used, and the second was that V1 is activated whenever images are formed, even if they are not necessarily used to perform a task. We used event-related functional magnetic resonance imaging (ER-fMRI) to detect and characterize the activity in the calcarine sulcus (which contains the primary visual cortex) during single instances of mental imagery. The results revealed reproducible transient activity in this area whenever participants generated or evaluated a mental image. This transient activity was strongly enhanced when participants evaluated characteristics of objects, whether or not details actually needed to be extracted from the image to perform the task. These results show that visual imagery processing commonly involves the earliest stages of the visual system.

Parametric study designs can reveal information about the relationship between a study parameter (e.g., word presentation rate) and regional cerebral blood flow (rCBF) in functional imaging. The brain's responses in relation to study parameters might be nonlinear, therefore the (linear) correlation coefficient as often used in the analysis of parametric studies might not be a proper characterization. We present a noninteractive method, which fits nonlinear functions of stimulus or task parameters to rCBF responses, using second-order polynomial expansions. This technique is implemented in the context of the general linear model and statistical parametric mapping. We also consider the usefulness of statistical inferences, based onFfields, about similarities and differences of these nonlinear responses in different groups. This approach is illustrated with a 12-run H215O PET activation study using an auditory paradigm of increasing word presentation rates. A patient who had recovered from severe aphasia and a normal control were studied. We demonstrate the ability of this new technique to identify brain regions where rCBF is closely related to increasing word presentation rate in both subjects without constraining the nature of this relationship and where these nonlinear responses differ.

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 γ2 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.

In this study, we used event-related functional magnetic resonance imaging to investigate whether visual mental images retinotopically activate early visual cortex. Six participants were instructed to visualize or view horizontally or vertically oriented flashing bow-tie shaped stimuli. When compared to baseline, imagery globally activated Area V1. When the activation evoked by the stimuli at the different orientations was directly compared, distinct spatial activation patterns were obtained for each orientation in most participants. Not only was the topography of the activation patterns from imagery similar to the topography obtained with a corresponding visual perception task, but it closely matched the individual cortical representation of either the horizontal or the vertical visual field meridians. These findings strongly support that visual imagery and perception share low-level anatomical substrate and functional processes. Binding of spatial features is suggested as one possible mechanism.

In this paper, we propose a new representation of the cortical surface that may be used to study the cortex folding process and to recover some putative stable anatomical landmarks called sulcal roots usually buried in the depth of adult brains. This representation is a primal sketch derived from a scale space computed for the mean curvature of the cortical surface. This scale-space stems from a diffusion equation geodesic to the cortical surface. The primal sketch is made up of objects defined from mean curvature minima and saddle points. The resulting sketch aims first at highlighting significant elementary cortical folds, second at representing the fold merging process during brain growth. The relevance of the framework is illustrated by the study of central sulcus sulcal roots from antenatal to adult age. Some results are proposed for ten different brains. Some preliminary results are also provided for superior temporal sulcus.

Some models of word comprehension postulate that the processing of words presented in different modalities and languages ultimately converges toward common cerebral systems associated with semantic-level processing and that the localization of these systems may vary with the category of semantic knowledge being accessed. We used functional magnetic resonance imaging to investigate this hypothesis with two categories of words, numerals, and body parts, for which the existence of distinct category-specific areas is debated in neuropsychology. Across two experiments, one with a blocked design and the other with an event-related design, a reproducible set of left-hemispheric parietal and prefrontal areas showed greater activation during the manipulation of topographical knowledge about body parts and a right-hemispheric parietal network during the manipulation of numerical quantities. These results complement the existing neuropsychological and brain-imaging literature by suggesting that within the extensive network of bilateral parietal regions active during both number and body-part processing, a subset shows category-specific responses independent of the language and modality of presentation.

A methodology for fMRI data analysis confined to the cortex, Cortical Surface Mapping (CSM), is presented.CSM retains the flexibility of the General Linear Model based estimation, but the procedures involved are adapted to operate on the cortical surface, while avoiding to resort to explicit flattening.The methodology is tested by means of simulations and application to a real fMRI protocol.The results are compared with those obtained with a standard, volume-oriented approach (SPM), and it is shown that CSM leads to local differences in sensitivity, with generally higher sensitivity for CSM in two of the three subjects studied.The discussion provided is focused on the benefits of the introduction of anatomical information in fMRI data analysis, and the relevance of CSM as a step toward this goal.Hum.

We propose a fast, efficient, general, simple, valid, and robust method of estimating and making inference about the delay of the fMRI response modeled as a temporal shift of the hemodynamic response function (HRF). We estimate the shift unbiasedly using two optimally chosen basis functions for a spectrum of time shifted HRFs. This is done at every voxel, to create an image of estimated delays and their standard deviations. This can be used to compare delays for the same stimulus at different voxels, or for different stimuli at the same voxel. Our method is compared to other alternatives and validated on an fMRI data set from an experiment in pain perception.

Functional magnetic resonance imaging was used to study the cortical bases of 3-D structure perception from visual motion in human. Nine subjects underwent three experiments designed to locate the areas involved in (i) motion processing (random motion versus static dots), (ii) coherent motion processing (expansion/ contraction versus random motion) and (iii) 3-D shape from motion reconstruction (3-D surface oscillating in depth versus random motion). Two control experiments tested the specific influence of speed distribution and surface curvature on the activation results. All stimuli consisted of random dots so that motion parallax was the only cue available for 3-D shape perception. As expected, random motion compared with static dots induced strong activity in areas V1/V2, V5+ and the superior occipital gyrus (SOG; presumptive V3/V3A). V1/V2 and V5+ showed no activity increase when comparing coherent motion (expansion or 3-D surface) with random motion. Conversely, V3/V3A and the dorsal parieto-occipital junction were highlighted in both comparisons and showed gradually increased activity for random motion, coherent motion and a curved surface rotating in depth, which suggests their involvement in the coding of 3-D shape from motion. Also, the ventral aspect of the left occipito-temporal junction was found to be equally responsive to random and coherent motion stimuli, but showed a specific sensitivity to curved 3-D surfaces compared with plane surfaces. As this region is already known to be involved in the coding of static object shape, our results suggest that it might integrate various cues for the perception of 3-D shape.

In human vision, two features of the same object can be identified concurrently without loss of accuracy. Performance declines, however, when the features belong to different objects in opposite visual fields. We hypothesized that different positron emission tomography activation patterns would reflect these behavioral results. We first delineated an attention network for single discriminations in left or right visual field and then compared this with the activation pattern when subjects divided attention over two features of a single object or over two objects in opposite hemifields. When subjects attended to a single feature, parietal, premotor, and anterior cingulate cortex were activated. These effects were strongest in the right hemisphere and were, remarkably, unaffected by the direction of attention. In contrast, direction of attention affected occipital and frontal activity: right occipital and left lateral frontal activity were higher with attention to the left, whereas right lateral frontal activity was higher with attention to the right. When subjects identified two features of the same object, parietal, premotor, and anterior cingulate activity was enhanced further, predominantly this time in the left hemisphere. Again, there was no direction sensitivity. Direction-sensitive activation of lateral frontal cortex also was increased. Finally, when subjects divided their attention over opposite hemifields, activity in the direction-sensitive occipital and frontal regions fell to a level midway between those seen during exclusively leftward or rightward attention. Thus, the behavioral efficiency with which we attend to multiple features of a single peripheral object is paralleled by enhanced activity in structures generally active during peripheral selective attention as well as in structures that depend on the specific direction of attention, most notably lateral frontal cortex. In addition, in the direction-sensitive regions, dividing attention over hemifields causes a compromise pattern between the extreme levels obtained during unilateral attention.

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.

In BOLD fMRI data analysis, robust and accurate estimation of the Hemodynamic Response Function (HRF) is still under investigation. Parametric methods assume the shape of the HRF to be known and constant throughout the brain, whereas non-parametric methods mostly rely on artificially increasing the signal-to-noise ratio. We extend and develop a previously proposed method that makes use of basic yet relevant temporal information about the underlying physiological process of the brain BOLD response in order to infer the HRF in a Bayesian framework. A general hypothesis test is also proposed, allowing to take advantage of the knowledge gained regarding the HRF to perform activation detection. The performances of the method are then evaluated by simulation. Great improvement is shown compared to the Maximum-Likelihood estimate in terms of estimation error, variance, and bias. Robustness of the estimators with regard to the actual noise structure or level, as well as the stimulus sequence, is also proven. Lastly, fMRI data with an event-related paradigm are analyzed. As suspected, the regions selected from highly discriminating activation maps resulting from the method exhibit a certain inter-regional homogeneity in term of HRF shape, as well as noticeable inter-regional differences.

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference.

This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms, account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.

Human functional MRI studies frequently reveal the joint activation of parietal and of lateral and mesial frontal areas during various cognitive tasks. To analyze the geometrical organization of those networks, we used an automatized clustering algorithm that parcels out sets of areas based on their similar profile of task-related activations or deactivations. This algorithm allowed us to reanalyze published fMRI data (Simon, O., Mangin, J.F., Cohen, L., Le Bihan, D., Dehaene, S., 2002. Topographical layout of hand, eye, calculation, and language-related areas in the human parietal lobe. Neuron 33, 475-487) and to reproduce the previously observed geometrical organization of activations for saccades, attention, grasping, pointing, calculation, and language processing in the parietal lobe. Further, we show that this organization extends to lateral and mesial prefrontal regions. Relative to the parietal lobe, the prefrontal functional geometry is characterized by a partially symmetrical anteroposterior ordering of activations, a decreased representation of effector-specific tasks, and a greater emphasis on higher cognitive functions of attention, higher-order spatial representation, calculation, and language. Anatomically, our results in humans are closely homologous to the known connectivity of parietal and frontal regions in the macaque monkey.

Coregistration of functional PET and T1-weighted MR images is a necessary step for combining functional information from PET images with anatomical information in MR images. Several coregistration algorithms have been published and are used in functional brain imaging studies. In this paper, we present a comparison and cross validation of the two most widely used coregistration routines (Fristonet al.,1995,Hum. Brain Map.2: 165–189; Woodset al.,1993,J. Comput. Assisted Tomogr.17: 536–546). Several transformations were applied to high-resolution anatomical MR images to generate simulated PET images so that the exact (rigid body) transformations between each MR image and its associated simulated PET images were known. The estimation error of a coregistration in relation to the known transformation allows a comparison of the performance of different coregistration routines. Under the assumption that the simulated PET images embody the salient features of real PET images with respect to coregistration, this study shows that the routines examined reliably solve the MRI to PET coregistration problem.

Objective The smoothness parameter that characterises the spatial dependence of pixel values in functional brain images is usually estimated empirically from the data. Since this parameter is essential for the assessment of significant changes in brain activity, it is important to know (a) the variance of its estimator and (b) how this variability affects the results of the ensuing statistical analysis. Materials and Methods In this article, we derive an approximate expression for the variance of the smoothness estimator and investigate the effects of this variability on assessing the significance of cerebral activation in statistical parametric maps using a verbal fluency PET activation experiment. Results Our results suggest that, for p values around 0.05, the variability in the p value (due to smoothness estimation) is ∼20%. Conclusion The effect of the assessment of the spatial dependency of the data is far from being negligible, and this suggests a more comprehensive methodology for functional imaging than the one used so far. This work provides a simple tool for taking into account this effect. Index Terms Emission computed tomography, physics and instrumentation—Maps and mapping—Emission computed tomography.

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.

Being able to detect reliably functional activity in a population of subjects is crucial in human brain mapping, both for the understanding of cognitive functions in normal subjects and for the analysis of patient data. The usual approach proceeds by normalizing brain volumes to a common three-dimensional template. However, a large part of the data acquired in fMRI aims at localizing cortical activity, and methods working on the cortical surface may provide better inter-subject registration than the standard procedures that process the data in the volume. Nevertheless, few assessments of the performance of surface-based (2D) versus volume-based (3D) procedures have been shown so far, mostly because inter-subject cortical surface maps are not easily obtained. In this paper we present a systematic comparison of 2D versus 3D group-level inference procedures, by using cluster-level and voxel-level statistics assessed by permutation, in random effects (RFX) and mixed-effects analyses (MFX). We consider different schemes to perform meaningful comparisons between thresholded statistical maps in the volume and on the cortical surface. We find that surface-based multi-subject statistical analyses are generally more sensitive than their volume-based counterpart, in the sense that they detect slightly denser networks of regions when performing peak-level detection; this effect is less clear for cluster-level inference and is reduced by smoothing. Surface-based inference also increases the reliability of the activation maps.

To map the neural substrate of mental function, cognitive neuroimaging relies on controlled psychological manipulations that engage brain systems associated with specific cognitive processes. In order to build comprehensive atlases of cognitive function in the brain, it must assemble maps for many different cognitive processes, which often evoke overlapping patterns of activation. Such data aggregation faces contrasting goals: on the one hand finding correspondences across vastly different cognitive experiments, while on the other hand precisely describing the function of any given brain region. Here we introduce a new analysis framework that tackles these difficulties and thereby enables the generation of brain atlases for cognitive function. The approach leverages ontologies of cognitive concepts and multi-label brain decoding to map the neural substrate of these concepts. We demonstrate the approach by building an atlas of functional brain organization based on 30 diverse functional neuroimaging studies, totaling 196 different experimental conditions. Unlike conventional brain mapping, this functional atlas supports robust reverse inference: predicting the mental processes from brain activity in the regions delineated by the atlas. To establish that this reverse inference is indeed governed by the corresponding concepts, and not idiosyncrasies of experimental designs, we show that it can accurately decode the cognitive concepts recruited in new tasks. These results demonstrate that aggregating independent task-fMRI studies can provide a more precise global atlas of selective associations between brain and cognition.

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the ‘last mile’ implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Significance Genetic factors play a major role in the development of human addiction. Identifying these genes and understanding their molecular mechanisms are necessary first steps in the development of targeted therapeutic intervention. Here, we have isolated the gene encoding Ras suppressor 1 (Rsu1) in an unbiased genetic screen for altered ethanol responses in the vinegar fly, Drosophila melanogaster . Our behavioral, genetic, and biochemical experiments show that Rsu1 links signaling from the integrin cell adhesion molecule to the small GTPase Rac1 in adult neurons to regulate actin dynamics and alcohol consumption preference. We also show that variants in human RSU1 associate with altered drinking and brain activation during a reward prediction task, thereby validating the predictive power of our approach.

Reproducibility has been shown to be limited in many scientific fields.This question is a fundamental tenet of scientific activity, but the related issues of reusability of scientific data are poorly documented.Here, we present a case study of our difficulties in reproducing a published bioinformatics method even though code and data were available.First, we tried to re-run the analysis with the code and data provided by the authors.Second, we reimplemented the whole method in a Python package to avoid dependency on a MATLAB license and ease the execution of the code on a high-performance computing cluster.Third, we assessed reusability of our reimplementation and the quality of our documentation, testing how easy it would be to start from our implementation to reproduce the results.In a second section, we propose solutions from this case study and other observations to improve reproducibility and research efficiency at the individual and collective levels.While finalizing our code, we created case-specific documentation and tutorials for the associated Python package StratiPy.Readers are invited to experiment with our reproducibility case study by generating the two confusion matrices (see more in section "Robustness: from MATLAB to Python, language and organization").Here, we propose two options: a step-by-step process to follow in a Jupyter/IPython notebook or a Docker container ready to be built and run.

Dysfunctional reward processing is implicated in various mental disorders, including attention deficit hyperactivity disorder (ADHD) and addictions. Such impairments might involve different components of the reward process, including brain activity during reward anticipation. We examined brain nodes engaged by reward anticipation in 1,544 adolescents and identified a network containing a core striatal node and cortical nodes facilitating outcome prediction and response preparation. Distinct nodes and functional connections were preferentially associated with either adolescent hyperactivity or alcohol consumption, thus conveying specificity of reward processing to clinically relevant behavior. We observed associations between the striatal node, hyperactivity, and the vacuolar protein sorting-associated protein 4A (VPS4A) gene in humans, and the causal role of Vps4 for hyperactivity was validated in Drosophila Our data provide a neurobehavioral model explaining the heterogeneity of reward-related behaviors and generate a hypothesis accounting for their enduring nature.

Imaging genetics offers the possibility of detecting associations between genotype and brain structure as well as function, with effect sizes potentially exceeding correlations between genotype and behavior. However, study results are often limited due to small sample sizes and methodological differences, thus reducing the reliability of findings. The IMAGEN cohort with 2000 young adolescents assessed from the age of 14 onwards tries to eliminate some of these limitations by offering a longitudinal approach and sufficient sample size for analyzing gene-environment interactions on brain structure and function. Here, we give a systematic review of IMAGEN publications since the start of the consortium. We then focus on the specific phenotype 'drug use' to illustrate the potential of the IMAGEN approach. We describe findings with respect to frontocortical, limbic and striatal brain volume, functional activation elicited by reward anticipation, behavioral inhibition, and affective faces, and their respective associations with drug intake. In addition to describing its strengths, we also discuss limitations of the IMAGEN study. Because of the longitudinal design and related attrition, analyses are underpowered for (epi-) genome-wide approaches due to the limited sample size. Estimating the generalizability of results requires replications in independent samples. However, such densely phenotyped longitudinal studies are still rare and alternative internal cross-validation methods (e.g., leave-one out, split-half) are also warranted. In conclusion, the IMAGEN cohort is a unique, very well characterized longitudinal sample, which helped to elucidate neurobiological mechanisms involved in complex behavior and offers the possibility to further disentangle genotype × phenotype interactions.

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.

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

The Canadian Open Neuroscience Platform (CONP) takes a multifaceted approach to enabling open neuroscience, aiming to make research, data, and tools accessible to everyone, with the ultimate objective of accelerating discovery. Its core infrastructure is the CONP Portal, a repository with a decentralized design, where datasets and analysis tools across disparate platforms can be browsed, searched, accessed, and shared in accordance with FAIR principles. Another key piece of CONP infrastructure is NeuroLibre, a preprint server capable of creating and hosting executable and fully reproducible scientific publications that embed text, figures, and code. As part of its holistic approach, the CONP has also constructed frameworks and guidance for ethics and data governance, provided support and developed resources to help train the next generation of neuroscientists, and has fostered and grown an engaged community through outreach and communications. In this manuscript, we provide a high-level overview of this multipronged platform and its vision of lowering the barriers to the practice of open neuroscience and yielding the associated benefits for both individual researchers and the wider community.

In this note we draw attention to a source of potential ambiguity in functional neuroimaging results when data analysis is based on the resolution of a linear model. This ambiguity arises whenever there exists correlation between the model covariates. A single-subject PET activation experiment helps to illustrate to what extent correlation can affect statistical results interpretation, possibly leading to misinterpretation of part of the activation pattern. This note is intended to clarify this point and to suggest the use of a simple and well-known procedure to deal with these situations. In the Appendix, we suggest a convenient mathematical formulation for statistical tests particularly useful in such cases.

Abstract The functional organization of the perisylvian language network was examined using a functional MRI (fMRI) adaptation paradigm with spoken sentences. In Experiment 1 , a given sentence was presented every 14.4 s and repeated two, three, or four times in a row. The study of the temporal properties of the BOLD response revealed a temporal gradient along the dorsal–ventral and rostral–caudal directions: From Heschl's gyrus, where the fastest responses were recorded, responses became increasingly slower toward the posterior part of the superior temporal gyrus and toward the temporal poles and the left inferior frontal gyrus, where the slowest responses were observed. Repetition induced a decrease in amplitude and a speeding up of the BOLD response in the superior temporal sulcus (STS), while the most superior temporal regions were not affected. In Experiment 2 , small blocks of six sentences were presented in which either the speaker voice or the linguistic content of the sentence, or both, were repeated. Data analyses revealed a clear asymmetry: While two clusters in the left superior temporal sulcus showed identical repetition suppression whether the sentences were produced by the same speaker or different speakers, the homologous right regions were sensitive to sentence repetition only when the speaker voice remained constant. Thus, hemispheric left regions encode linguistic content while homologous right regions encode more details about extralinguistic features like speaker voice. The results demonstrate the feasibility of using sentence‐level adaptation to probe the functional organization of cortical language areas. Hum Brain Mapp, 2006. © 2006 Wiley‐Liss, Inc.

Visual exploration is organized in sequences of saccadic eye movements that depend on both perceptual and cognitive context. Using functional magnetic resonance imaging, we studied the neural basis of sequential oculomotor behavior and its dependence on different types of memory by analyzing cerebral activity during performance of newly learned and familiar sequences of eye movements. Compared to a resting condition, both types of sequences activated a common fronto-parietal network, including frontal and supplementary eye fields, and several parietal areas. Within this network, newly learned sequences induced stronger activation than familiar sequences, probably reflecting higher attentional demands. In addition, specific regions were recruited for the performance of new sequences, including pre-supplementary eye fields, the precuneus and the caudate nucleus. This indicates that in addition to attentional modulation, novelty of saccadic sequences requires specific cortical resources, probably related to effortful sequence preparation and coordination as well as to spatial working memory. For familiar sequences, recalled from long-term memory, we observed specific right medial temporo-occipital activation in the vicinity of the boundary between the parahippocampal and lingual gyri, as well as an activation site in the parieto-occipital fissure. We conclude that neuronal resources recruited by the gaze system can change with the familiarity of the scanpath to be executed. This study is important to better understand how the brain implements memorized scanpaths for visual exploration and orienting.

Correlation analysis has been widely used in the study of functional connectivity based on fMRI data. It assumes that the relevant information about the interactions of brain regions is reflected by a linear relationship between the values of two signals at the same time. However, this hypothesis has not been thoroughly investigated yet. In this work, we study in depth the information shared by BOLD signals of pairs of brain regions. In particular, we assess the amount of nonlinear and/or nonsynchronous interactions present in data. This is achieved by testing models reflecting linear, synchronous interactions against more general models, encompassing nonlinear, nonsynchronous interactions. Many factors influencing measured BOLD signals are critical for the study of connectivity, such as paradigm-induced BOLD responses, preprocessing, motion artifacts, and geometrical distortions. Interactions are also influenced by the proximity of brain regions. The influence of all these factors is taken into account and the nature of the interactions is studied using various experimental conditions such that the conclusions reached are robust with respect to variation of these factors. After defining nonlinear and/or nonsynchronous interaction models in the framework of general linear models, statistical tests are performed on different fMRI data sets to infer the nature of the interactions. Finally, a new connectivity metric is proposed which takes these inferences into account. We find that BOLD signal interactions are statistically more significant when taking into account the history of the distant signal, i.e., the signal from the interacting region, than when using a model of linear instantaneous interaction. Moreover, about 75% of the interactions are symmetric, as assessed with the proposed connectivity metric. The history-dependent part of the coupling between brain regions can explain a high percentage of the variance in the data sets studied. As these results are robust with respect to various confounding factors, this work suggests that models used to study the functional connectivity between brain areas should in general take the BOLD signal history into account.

In this paper, we propose a generic framework for the analysis of steady-state fMRI datasets, applied here to resting-state datasets. Our approach avoids the introduction of user-defined seed regions for the study of spontaneous activity. Unlike existing techniques, it yields a sparse representation of resting-state activity networks which can be characterized and investigated fairly easily in a semi-interactive fashion. We proceed in several steps, based on the idea that spectral coherence of the fMRI time courses in the low frequency band carries the information of interest. In particular, we address the question of building adapted representations of the data from the spectral coherence matrix. We analyze nine datasets taken from three subjects and show resting-state networks validated by EEG-fMRI simultaneous acquisition literature, with low intra-subject variability; we also discuss the merits of different (rapid/slow) fMRI acquisition schemes.

Abstract Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html .

Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets.Historically, researchers have dealt with this complexity idiosyncratically, with every lab or individual implementing their own preprocessing and analysis procedures.The resulting lack of field-wide standards has severely limited reproducibility and data sharing and reuse.

Background Genetic, biologic and clinical data suggest that Parkinson's disease (PD) is an umbrella for multiple disorders with clinical and pathological overlap, yet with different underlying mechanisms. To better understand these and to move towards neuroprotective treatment, we have established the Quebec Parkinson Network (QPN), an open-access patient registry, and data and bio-samples repository. Objective To present the QPN and to perform preliminary analysis of the QPN data. Methods A total of 1,070 consecutively recruited PD patients were included in the analysis. Demographic and clinical data were analyzed, including comparisons between males and females, PD patients with and without RBD, and stratified analyses comparing early and late-onset PD and different age groups. Results QPN patients exhibit a male:female ratio of 1.8:1, an average age-at-onset of 58.6 years, an age-at-diagnosis of 60.4 years, and average disease duration of 8.9 years. REM-sleep behavior disorder (RBD) was more common among men, and RBD was associated with other motor and non-motor symptoms including dyskinesia, fluctuations, postural hypotension and hallucinations. Older patients had significantly higher rates of constipation and cognitive impairment, and longer disease duration was associated with higher rates of dyskinesia, fluctuations, freezing of gait, falls, hallucinations and cognitive impairment. Since QPN's creation, over 60 studies and 30 publications have included patients and data from the QPN. Conclusions The QPN cohort displays typical PD demographics and clinical features. These data are open-access upon application (http://rpq-qpn.ca/en/), and will soon include genetic, imaging and bio-samples. We encourage clinicians and researchers to perform studies using these resources.

<h3>Objective</h3> It is unclear whether deviations in brain and behavioral development, which may underpin elevated substance use during adolescence, are predispositions for or consequences of substance use initiation. Here, we examine behavioral and neuroimaging indices at early and mid-adolescence in drug-naive youths to identify possible predisposing factors for substance use initiation and its possible consequences. <h3>Method</h3> Among 304 drug-naive adolescents at baseline (age 14 years) from the IMAGEN dataset, 83 stayed drug-naive, 133 used alcohol on 1 to 9 occasions, 42 on 10 to 19 occasions, 27 on 20 to 39 occasions, and 19 on >40 occasions at follow-up (age 16 years). Baseline measures included brain activation during the Monetary Incentive Delay task. Data at both baseline and follow-up included measures of trait impulsivity and delay discounting. <h3>Results</h3> From baseline to follow-up, impulsivity decreased in the 0 and 1- to 9-occasions groups (<i>p</i> < .004), did not change in the 10- to 19-occasions and 20- to 29-occasions groups (<i>p</i> > .294), and uncharacteristically increased in the >40-occasions group (<i>p</i> = .046). Furthermore, blunted medial orbitofrontal cortex activation during reward outcome at baseline significantly predicted higher alcohol use frequency at follow-up, above and beyond behavioral and clinical variables (<i>p</i> = .008). <h3>Conclusion</h3> These results suggest that the transition from no use to frequent drinking in early to mid-adolescence may disrupt normative developmental changes in behavioral control. In addition, blunted activity of the medial orbitofrontal cortex during reward outcome may underscore a predisposition toward the development of more severe alcohol use in adolescents. This distinction is clinically important, as it informs early intervention efforts in preventing the onset of substance use disorder in adolescents.

NeuroLibre is a preprint server for neuroscience Jupyter Books, blending code, visualization and narrative text into one document. NeuroLibre archives the environment, code and data and also implements a technical review to ensure readers can reproduce the work. NeuroLibre offers an online platform where readers can reproduce or modify each preprint from a web browser, without any installation required. We hope that NeuroLibre will contribute to usher the research community in a new area of open and reproducible neuroscience. The preprint server is built with open source components, and can be freely adapted to meet the needs of other communities in the future as well.

We present the Canadian Open Neuroscience Platform (CONP) portal to answer the research community's need for flexible data sharing resources and provide advanced tools for search and processing infrastructure capacity. This portal differs from previous data sharing projects as it integrates datasets originating from a number of already existing platforms or databases through DataLad, a file level data integrity and access layer. The portal is also an entry point for searching and accessing a large number of standardized and containerized software and links to a computing infrastructure. It leverages community standards to help document and facilitate reuse of both datasets and tools, and already shows a growing community adoption giving access to more than 60 neuroscience datasets and over 70 tools. The CONP portal demonstrates the feasibility and offers a model of a distributed data and tool management system across 17 institutions throughout Canada.

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.

This paper introduces a method to study the variation of brain functional connectivity networks with respect to experimental conditions in fMRI data. It is related to the psychophysiological interaction technique introduced by Friston et al. and extends to networks of correlation modulation (CM networks). Extended networks containing several dozens of nodes are determined in which the links correspond to consistent correlation modulation across subjects. In addition, we assess inter-subject variability and determine networks in which the condition-dependent functional interactions can be explained by a subject-dependent variable. We applied the technique to data from a study on syntactical production in bilinguals and analysed functional interactions differentially across tasks (word reading or sentence production) and across languages. We find an extended network of consistent functional interaction modulation across tasks, whereas the network comparing languages shows fewer links. Interestingly, there is evidence for a specific network in which the differences in functional interaction across subjects can be explained by differences in the subjects' syntactical proficiency. Specifically, we find that regions, including ones that have previously been shown to be involved in syntax and in language production, such as the left inferior frontal gyrus, putamen, insula, precentral gyrus, as well as the supplementary motor area, are more functionally linked during sentence production in the second, compared with the first, language in syntactically more proficient bilinguals than in syntactically less proficient ones. Our approach extends conventional activation analyses to the notion of networks, emphasizing functional interactions between regions independently of whether or not they are activated. On the one hand, it gives rise to testable hypotheses and allows an interpretation of the results in terms of the previous literature, and on the other hand, it provides a basis for studying the structure of functional interactions as a whole, and hence represents a further step towards the notion of large-scale networks in functional imaging.

Abstract Background: Trait impulsiveness is a potential factor that predicts both substance use and certain psychiatric disorders. This study investigates whether there are common structural cerebral correlates of trait impulsiveness and cognitive functioning in a large sample of healthy adolescents from the IMAGEN project. Methods: Clusters of gray matter (GM) volume associated with trait impulsiveness, Cloningers' revised temperament, and character inventory impulsiveness (TCI‐R‐I) were identified in a whole brain analysis using optimized voxel‐based morphometry in 115 healthy 14‐year‐olds. The clusters were tested for correlations with performance on the nonverbal tests (Block Design, BD; Matrix Reasoning, MT) of the Wechsler Scale of Intelligence for Children IV reflecting perceptual reasoning. Results: Cloningers' impulsiveness (TCI‐R‐I) score was significantly inversely associated with GM volume in left orbitofrontal cortex (OFC). Frontal clusters found were positively correlated with performance in perceptual reasoning tasks (Bonferroni corrected). No significant correlations between TCI‐R‐I and perceptual reasoning were observed. Conclusions: The neural correlate of trait impulsiveness in the OFC matches an area where brain function has previously been related to inhibitory control. Additionally, orbitofrontal GM volume was associated with scores for perceptual reasoning. The data show for the first time structural correlates of both cognitive functioning and impulsiveness in healthy adolescent subjects. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.

BackgroundThe ability to replicate an entire experiment is crucial to the scientific method. With the development of more and more complex paradigms, and the variety of analysis techniques available, fMRI studies are becoming harder to reproduce.

Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.

In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.

We present NiMARE (Neuroimaging Meta‑Analysis Research Environment; RRID:SCR_0173981), a Python library for neuroimaging meta‑analyses and metaanalysis‑related analyses. NiMARE is an open source, collaboratively‑developed package that implements a range of meta‑ analytic algorithms, including coordinate‑ and image‑based meta‑analyses, automated annotation, functional decoding, and meta‑analytic coactivation modeling. By consolidating meta‑analytic methods under a common library and syntax, NiMARE makes it straightforward for users to employ the appropriate approach for a given analysis. In this paper, we describe NiMARE’s architecture and the methods implemented in the library. Additionally, we provide example code and results for each of the available tools in the library.

In this paper we investigate the use of classical fMRI Random Effect (RFX) group statistics when analyzing a very large cohort and the possible improvement brought from anatomical information. Using 1326 subjects from the IMAGEN study, we first give a global picture of the evolution of the group effect t-value from a simple face-watching contrast with increasing cohort size. We obtain a wide activated pattern, far from being limited to the reasonably expected brain areas, illustrating the difference between statistical significance and practical significance. This motivates us to inject tissue-probability information into the group estimation, we model the BOLD contrast using a matter-weighted mixture of Gaussians and compare it to the common, single-Gaussian model. In both cases, the model parameters are estimated per-voxel for one subgroup, and the likelihood of both models is computed on a second, separate subgroup to reflect model generalization capacity. Various group sizes are tested, and significance is asserted using a 10-fold cross-validation scheme. We conclude that adding matter information consistently improves the quantitative analysis of BOLD responses in some areas of the brain, particularly those where accurate inter-subject registration remains challenging.

Naturalistic stimuli show significant potential to inform behavioral, cognitive, and clinical neuroscience. To date, this impact is still limited by the relative inaccessibility of both generated neuroimaging data as well as the supporting naturalistic stimuli. In this perspective, we highlight currently available naturalistic datasets and technical solutions such as DataLad that continue to advance our ability to share this data. We also review scientific and sociological challenges in selecting naturalistic stimuli for reproducible research. Overall, we encourage researchers to share their naturalistic datasets to the full extent possible under local copyright law.

<h3>Objective</h3> Irritable mood, a common and impairing symptom in psychopathology, has been proposed to underlie the developmental link between oppositional problems in youth and depression in adulthood. We examined the neural correlates of adolescent irritability in IMAGEN, a sample of 2,024 14-year-old adolescents from 5 European countries. <h3>Method</h3> The Development and Well-Being Assessment (DAWBA) was used to assess attention-deficit/hyperactivity disorder, major depressive disorder, oppositional defiant disorder, and generalized anxiety disorder. Three items from the DAWBA, selected as close matches to the Affective Reactivity Index, were used to assess irritability. Structural magnetic resonance imaging was examined using whole-brain voxel-based morphometry analysis, and functional magnetic resonance imaging was examined during a stop signal task of inhibitory control. Imaging data were included in structural equation models to examine the direct and indirect associations between irritable mood and comorbid <i>DSM</i> diagnoses. <h3>Results</h3> Whole-brain voxelwise analysis showed that adolescent irritable mood was associated with less gray matter volume and less neural activation underlying inhibitory control in frontal and temporal cortical areas (cluster-correction at <i>p</i> < .05). Structural equation models suggested that part of the observed smaller gray matter volume was exclusively driven by irritability separate from direct relationships between generalized anxiety disorder (or attention-deficit/hyperactivity disorder, major depressive disorder, or oppositional defiant disorder) and gray matter volume. <h3>Conclusion</h3> This study identifies adolescent irritability as an independent construct and points to a neurobiological correlate to irritability that is an important contributing feature to many psychopathological disorders.

A bstract Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain structure, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and different spatial scales. Here, we present micapipe , an open processing pipeline for BIDS-conform multimodal MRI datasets. micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. These matrices are routinely generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe , documented at https://micapipe.readthedocs.io/ , and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/ . We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, and connectivity.

As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values. These large databases are well suited to train machine learning models, e.g., for forecasting or to extract biomarkers in biomedical settings. Such predictive approaches can use discriminative-rather than generative-modeling and thus open the door to new missing-values strategies. Yet existing empirical evaluations of strategies to handle missing values have focused on inferential statistics.Here we conduct a systematic benchmark of missing-values strategies in predictive models with a focus on large health databases: 4 electronic health record datasets, 1 population brain imaging database, 1 health survey, and 2 intensive care surveys. Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning. We investigate prediction accuracy and computational time. For prediction after imputation, we find that adding an indicator to express which values have been imputed is important, suggesting that the data are missing not at random. Elaborate missing-values imputation can improve prediction compared to simple strategies but requires longer computational time on large data. Learning trees that model missing values-with missing incorporated attribute-leads to robust, fast, and well-performing predictive modeling.Native support for missing values in supervised machine learning predicts better than state-of-the-art imputation with much less computational cost. When using imputation, it is important to add indicator columns expressing which values have been imputed.

Analyzing functional magnetic resonance imaging (fMRI) data restricted to the cortical surface is of particular interest for two reasons: (1) to increase detection sensitivity using anatomical constraints and (2) to compare or use fMRI results in the context of source localization from magneto/electro-encephalography (MEEG) data, which requires data to be projected on the same spatial support. Designing an optimal scheme to interpolate fMRI raw data or resulting activation maps on the cortical surface relies on a trade-off between choosing large enough interpolation kernels, because of the distributed nature of the hemodynamic response, and avoiding mixing data issued from different anatomical structures. We propose an original method that automatically adjusts the level of such a trade-off, by defining interpolation kernels around each vertex of the cortical surface using a geodesic Voronoï diagram. This Voronoï-based interpolation method was evaluated using simulated fMRI activation maps, manually generated on an anatomical MRI, and compared with a more standard approach where interpolation kernels were defined as local spheres of radius r = 3 or 5 mm. Several validation parameters were considered: the spatial resolution of the simulated activation map, the spatial resolution of the cortical mesh, the level of anatomical/functional data misregistration and the location of the vertices within the gray matter ribbon. Using an activation map at the spatial resolution of standard fMRI data, robustness to misregistration errors was observed for both methods, whereas only the Voronoï-based approach was insensitive to the position of the vertices within the gray matter ribbon.

Abstract In group average analyses, we generalize the classical one‐sample t test to account for heterogeneous within‐subject uncertainties associated with the estimated effects. Our test statistic is defined as the maximum likelihood ratio corresponding to a Gaussian mixed‐effect model. The test's significance level is calibrated using the same sign permutation framework as in Holmes et al., allowing for exact specificity control under a mild symmetry assumption about the subjects' distribution. Because our likelihood ratio test does not rely on homoscedasticity, it is potentially more sensitive than both the standard t test and its permutation‐based version. We present results from the Functional Imaging Analysis Contest 2005 dataset to support this claim. Hum Brain Mapp 27:402–410, 2006. © 2006 Wiley‐Liss, Inc.