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Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., Depto. de Psicobiología y Metodología en Ciencias del Comportamiento, Fac. de Psicología, TRUE, pub

Contains fulltext : 231832.pdf (Publisher’s version ) (Open Access), A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative resul

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., Depto. de Psicobiología y Metodología en Ciencias del Comportamiento, Fac. de Psicología, TRUE, pub

Contains fulltext : 187792.pdf (Publisher’s version ) (Closed access), Despite the common conception of the dorsal premotor cortex (PMd) as a single brain region, its diverse connectivity profiles and behavioral heterogeneity argue for a differentiated organization of the PMd. A previous study revealed that the right PMd is characterized by a rostro-caudal and a ventro-dorsal distinction dividing it into five subregions: rostral, central, caudal, ventral and dorsal. The present study assessed whether a similar organization is present in the left hemisphere, by capitalizing on a multimodal data-driven approach combining connectivity-based parcellation (CBP) based on meta-analytic modeling, resting-state functional connectivity, and probabilistic diffusion tractography. The resulting PMd modules were then characterized based on multimodal functional connectivity and a quantitative analysis of associated behavioral functions. Analyzing the clusters consistent across all modalities revealed an organization of the left PMd that mirrored its right counterpart to a large degree. Again, caudal, central and rostral modules reflected a cognitive-motor gradient and a premotor eye-field was found in the ventral part of the left PMd. In addition, a distinct module linked to abstract cognitive functions was observed in the rostro-ventral left PMd across all CBP modalities, implying greater differentiation of higher cognitive functions for the left than the right PMd.

Contains fulltext : 167029.pdf (publisher's version ) (Closed access), Aging leads to global changes in brain structure and cognitive performance, with reorganization of functional brain networks. Importantly, these age-related changes show higher inter-individual variability in older subjects. To particularly address this variability is a challenge for studies on lifetime trajectories from early to late adulthood. The present study therefore had a dedicated focus on late adulthood to characterize the functional connectivity in resting-state networks (RSFC) in relation to age and cognitive performance in 711 older adults (55-85 years) from the 1000BRAINS project. The executive, left and right frontoparietal resting-state (RS) networks showed age-related increases in RSFC. However, older adults did not show changes in RSFC in the default mode network (DMN). Furthermore, lower performance in working memory was associated with higher RSFC in the left frontoparietal RS network. The results suggest age-related compensatory increases in RSFC which might help to maintain cognitive performance. Nevertheless, the negative correlation between RSFC and working memory performance hints at limited cognitive reserve capacity in lower performing older adults. Consequently, the current results provide evidence for a functional reorganization of the brain until late adulthood that might additionally explain parts of the variability of cognitive abilities in older adults.

Contains fulltext : 169032.pdf (publisher's version ) (Closed access), The right dorsal premotor cortex (PMd) of humans has been reported to be involved in a broad range of motor and cognitive functions. We explored the basis of this behavioral heterogeneity by performing a connectivity-based parcellation using meta-analytic approach applied to PMd coactivations. We compared our connectivity-based parcellation results with parcellations obtained through resting-state functional connectivity and probabilistic diffusion tractography. Functional connectivity profiles and behavioral decoding of the resulting PMd subregions allowed characterizing their respective behavior profile. These procedures divided the right PMd into 5 distinct subregions that formed a cognitive-motor gradient along a rostro-caudal axis. In particular, we found 1) a rostral subregion functionally connected with prefrontal cortex, which likely supports high-level cognitive processes, such as working memory, 2) a central subregion showing a mixed behavioral profile and functional connectivity to parietal regions of the dorsal attention network, and 3) a caudal subregion closely integrated with the motor system. Additionally, we found 4) a dorsal subregion, preferentially related to hand movements and connected to both cognitive and motor regions, and 5) a ventral subregion, whose functional profile fits the concept of an eye movement-related field. In conclusion, right PMd may be considered as a functional mosaic formed by 5 subregions.

Contains fulltext : 168978.pdf (publisher's version ) (Closed access), Human neuroimaging methods have provided a number of means by which the connectivity structure of the human brain can be inferred. For instance, correlations in blood-oxygen-level-dependent (BOLD) signal time series are commonly used to make inferences about "functional connectivity". Correlations across samples in structural morphometric measures, such as voxel-based morphometry (VBM) or cortical thickness (CT), have also been used to estimate connectivity, putatively through mutually trophic effects on connected brain areas. In this study, we have compared seed-based connectivity estimates obtained from four common correlational approaches: resting-state functional connectivity (RS-fMRI), meta-analytic connectivity modeling (MACM), VBM correlations, and CT correlations. We found that the two functional approaches (RS-fMRI and MACM) had the best agreement. While the two structural approaches (CT and VBM) had better-than-random convergence, they were no more similar to each other than to the functional approaches. The degree of correspondence between modalities varied considerably across seed regions, and also depended on the threshold applied to the connectivity distribution. These results demonstrate some degrees of similarity between connectivity inferred from structural and functional covariances, particularly for the most robust functionally connected regions (e.g., the default mode network). However, they also caution that these measures likely capture very different aspects of brain structure and function.

Contains fulltext : 168981.pdf (publisher's version ) (Closed access), Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that gray matter masking improved the reliability of connectivity estimates, whereas denoising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test–retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources.

Item does not contain fulltext, Recently, we showed that the functional heterogeneity of the right dorsal premotor (PMd) cortex could be better understood by dividing it into five subregions that showed different behavioral associations according to task-based activations studies. The present study investigated whether the revealed behavioral profile could be corroborated and complemented by a structural brain behavior correlation approach in two healthy adults cohorts. Grey matter volume within the five volumes of interest (VOI-GM) was computed using voxel-based morphometry. Associations between the inter-individual differences in VOI-GM and performance across a range of neuropsychological tests were assessed in the two cohorts with and without correction for demographical variables. Additional analyses were performed in random smaller subsamples drawn from each of the two cohorts. In both cohorts, correlation coefficients were low; only few were significant and a considerable number of correlations were counterintuitive in their directions (i.e., higher performance related to lower GMV). Furthermore, correlation patterns were inconsistent between the two cohorts. Subsampling revealed that correlation patterns could vary widely across small samples and that negative correlations were as likely as positive correlations. Thus, the structural brain/behavior approach did not corroborate the functional profiles of the PMd subregions inferred from activation studies, suggesting that local recruitment by fMRI studies does not necessarily imply covariance of local structure with behavioral performance in healthy adults. We discuss the limitations of such studies and related recommendations for future studies.

Item does not contain fulltext, Key nodes of neural networks for ocular motor control and visual motion processing have been localized using saccades, smooth pursuit, and optokinetic nystagmus (OKN). Within the context of an independent fMRI study using OKN, 9 bilateral network nodes were localized comprising cortical eye fields in frontal (FEF), supplementary motor (SEF), cingulate (CEF) and parietal cortex (PEF), visual motion centers MT+ and V6, the superior colliculus (SC), the lateral geniculate nucleus (LGN) and the globus pallidus (GP). Here, we examined the network's functional hierarchy as present in the structural co-variation (SCoV) and resting-state (RS) fMRI, and the effect of RS condition (eyes open/closed) on its' functional connectivity (FC). Two publicly available samples were analyzed consisting of the enhanced NKI sample with RS (TR 1.4s) and structural MR data (n = 124; age 46.7 ± 17.6; 40 male) and the "Beijing: eyes open eyes closed sample" measuring RS (TR 2s; n = 48; age 22.5 ± 2.2; 24 male). For the FC analysis, ICA-based denoising (FSL) was applied before spatial preprocessing (SPM) and band-pass filtering. Each bilateral ROI was represented by the first eigenvariate of the respective voxels' time-series and partial correlation were computed using FSLNets. One group t-tests were computed over Fisher's z transformed correlation coefficients. Each ROIs volume was approximated with voxel-based morphometry (VBM8) using non-linearly modulated gray matter density and partial correlations were computed for SCoV. Hierarchical cluster analysis was applied to determine sub-clustering within the OKN network. Edge-wise comparisons between RS conditions were performed using permutation testing and Bonferroni correction. Both FC and SCoV revealed two major subcluster. MT+ and V6 were similar to LGN and SC. The cortical eye fields clustered together with the GP. As effect of RS condition, with eyes closed the CEF switched to the visual subcluster. The edge-wise comparison revealed gener

Item does not contain fulltext, Meta-analytic techniques allow cognitive neuroscientists to pool large amounts of data across many individual task-based functional neuroimaging experiments. These methods have been aided by the introduction of online databases such as Brainmap.org or Neurosynth.org, which collate peak activation coordinates obtained from thousands of published studies. Findings from meta-analytic studies typically include brain regions which are consistently activated across studies for specific contrasts, investigating cognitive or clinical hypotheses. These regions can be subsequently used as the basis for seed-based connectivity analysis, or formally compared to neuroimaging data in order to help interpret new findings. To facilitate such approaches, we have developed a new online repository of meta-analytic neuroimaging results, named the Archive of Neuroimaging Meta-analyses (ANIMA). The ANIMA platform consists of an intuitive online interface for querying, downloading, and contributing data from published meta-analytic studies. Additionally, to aid the process of organizing, visualizing, and working with these data, we present an open-source desktop application called Volume Viewer. Volume Viewer allows users to easily arrange imaging data into composite stacks, and save these sessions as individual files, which can also be uploaded to the ANIMA database. The application also allows users to perform basic functions, such as computing conjunctions between images, or extracting regions-of-interest or peak coordinates for further analysis. The introduction of this new resource will enhance the ability of researchers to both share their findings and incorporate existing meta-analytic results into their own research.

Item does not contain fulltext, Meta-analytic techniques allow cognitive neuroscientists to pool large amounts of data across many individual task-based functional neuroimaging experiments. These methods have been aided by the introduction of online databases such as Brainmap.org or Neurosynth.org, which collate peak activation coordinates obtained from thousands of published studies. Findings from meta-analytic studies typically include brain regions which are consistently activated across studies for specific contrasts, investigating cognitive or clinical hypotheses. These regions can be subsequently used as the basis for seed-based connectivity analysis, or formally compared to neuroimaging data in order to help interpret new findings. To facilitate such approaches, we have developed a new online repository of meta-analytic neuroimaging results, named the Archive of Neuroimaging Meta-analyses (ANIMA). The ANIMA platform consists of an intuitive online interface for querying, downloading, and contributing data from published meta-analytic studies. Additionally, to aid the process of organizing, visualizing, and working with these data, we present an open-source desktop application called Volume Viewer. Volume Viewer allows users to easily arrange imaging data into composite stacks, and save these sessions as individual files, which can also be uploaded to the ANIMA database. The application also allows users to perform basic functions, such as computing conjunctions between images, or extracting regions-of-interest or peak coordinates for further analysis. The introduction of this new resource will enhance the ability of researchers to both share their findings and incorporate existing meta-analytic results into their own research.

Item does not contain fulltext, Recently, we functionally characterized five right dorsal premotor (PMdr) subregions (identified by coactivation based parcellation) by quantitative forward and reverse inference based on a wide range of task-based activation studies (Genon et al, in prep.). We found that the rostral subregion was associated to high-level cognitive tasks, the caudal one to motor functions, the dorsal one to hand-movements, the ventral one to visual functions while the central one showed a heterogeneous profile. Here, we aimed to reinforce and complement this characterization by using correlations between brain morphology and standard neuropsychological tests in two independent large datasets, which were matched for age, gender, education, depression and handedness (Forschungszentrum Jülich [FZJ, n=87, age range: 21-71] and Nathan Kline Institute [NKI; 135: 20-75]). T1 images were processed with the VBM8 toolbox (Friston using linear and non-linear modulation). The five PMdr subregions were used as regions of interest (ROIs), from which regional gray matter volume (GMV) was extracted. We performed partial correlations to examine correlation between each ROI GMV and neuropsychological test performance when controlling for confounding effects of age, education and handedness. To further examine the reliability of correlation, we also performed the analyses in 1000 subsamples of 30 randomly selected subjects within each sample individually. Our analyses yielded generally low correlation coefficients of which only few were statistically significant even at a lenient threshold of p < .05, uncorrected for multiple testing. Notably, a considerable number of correlations showed an inverse relationship between test performance and brain structure, i.e., higher performance was associated with lower GMV. In addition, the patterns of correlations were inconsistent across samples in magnitude, significance and direction. Finally, subsampling revealed that the pattern of correlations that could be

Item does not contain fulltext, Little is known about how several functions are organized or how putative subregions are coupled with wider brain networks in the human right PMd. To address these issues, we performed connectivity-based parcellation (CBP) based on PMd voxels' coactivations yielded by meta-analytic connectivity modeling (MACM). We additionally search for a similar cluster solution with CBP based on resting-state functional connectivity (RSFC) and probabilistic diffusion tractography. MACM and RSFC assessed FC of the MACM-CBP-derived subregions while functional characterization revealed their behavioral associations. This procedure showed that the right PMd could be divided into five distinct clusters highlighting a cognitive-motor gradient along a rostro-caudal axis including a rostral cluster functionally interacting with prefrontal cortex in supporting high-level cognitive processes and a caudal cluster closely integrated with the motor system. MACM-CBP also revealed a ventral cluster, whose functional profile fits the concept of an eye field, and a dorsal cluster, preferentially hand-related and connected to both cognitive and motor regions. Finally, MACM-CBP yielded a central cluster showing a mixed behavioral profile and FC to parietal regions of the dorsal attention network. Thus, our results showed that the right PMd may be considered as a functional mosaic formed by five subregions.

Item does not contain fulltext, OBJECTIVE: The heterogeneous human frontal pole has been identified as a node in the dysfunctional network of major depressive disorder. The contribution of the medial (socio-affective) versus lateral (cognitive) frontal pole to major depression pathogenesis is currently unclear. The authors performed morphometric comparison of the microstructurally informed subdivisions of human frontal pole between depressed patients and comparison subjects using both uni- and multivariate statistics. METHOD: Multisite voxel- and region-based morphometric MRI analysis was conducted in 73 depressed patients and 73 matched comparison subjects without psychiatric history. Frontal pole volume was first compared between depressed patients and comparison subjects by subdivision-wise classical morphometric analysis. In a second approach, frontal pole volume was compared by subdivision-naive multivariate searchlight analysis based on support vector machines. RESULTS: Subdivision-wise morphometric analysis found a significantly smaller medial frontal pole in depressed patients, with a negative correlation of disease severity and duration. Histologically uninformed multivariate voxel-wise statistics provided converging evidence for structural aberrations specific to the microstructurally defined medial area of the frontal pole in depressed patients. CONCLUSIONS: Across disparate methods, subregion specificity in the left medial frontal pole volume in depressed patients was demonstrated. Indeed, the frontal pole was shown to structurally and functionally connect to other key regions in major depression pathology, such as the anterior cingulate cortex and the amygdala via the uncinate fasciculus. Present and previous findings consolidate the left medial portion of the frontal pole as particularly altered in major depression.