References of "Eickhoff, Simon"
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See detailEmpirical examination of the replicability of associations between brain structure and psychological variables
Kharabian Masouleh, Shahrzad; Eickhoff, Simon; Hoffstaedter, Felix et al

in eLife (in press)

Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the ... [more ▼]

Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the previously-reported “structural brain behavior” (SBB)-associations has been questioned, recently. Here, we conducted an empirical investigation, assessing replicability of SBB among heathy adults. For a wide range of psychological measures, the replicability of associations with gray matter volume was assessed. Our results revealed that among healthy individuals 1) finding an association between performance at standard psychological tests and brain morphology is relatively unlikely 2) significant associations, found using an exploratory approach, have overestimated effect sizes and 3) can hardly be replicated in an independent sample. After considering factors such as sample size and comparing our findings with more replicable SBB-associations in a clinical cohort and replicable associations between brain structure and non-psychological phenotype, we discuss the potential causes and consequences of these findings. [less ▲]

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See detailMultimodal parcellations and extensive behavioral profiling tackling the hippocampus gradient
Plachti, Anna; Eickhoff, Simon; Hoffstaedter, Felix et al

in Cerebral Cortex (in press)

The hippocampus displays a complex organization and function that is perturbed in many neuropathologies. Histological work revealed a complex arrangement of subfields along the medial-lateral and the ... [more ▼]

The hippocampus displays a complex organization and function that is perturbed in many neuropathologies. Histological work revealed a complex arrangement of subfields along the medial-lateral and the ventral-dorsal dimension, which contrasts with the anterior-posterior functional differentiation. The variety of maps has raised the need for an integrative multimodal view. We applied connectivity- based parcellation to 1) intrinsic connectivity 2) task-based connectivity and 3) structural covariance, as complementary windows into structural and functional differentiation of the hippocampus. Strikingly, while functional properties (i.e., intrinsic and task-based) revealed similar partitions dominated by an anterior-posterior organization, structural covariance exhibited a hybrid pattern reflecting both functional and cytoarchitectonic subdivision. Capitalizing on the consistency of functional parcellations, we defined robust functional maps at different levels of partitions, which are openly available for the scientific community. Our functional maps demonstrated a head-body and tail partition, subdivided along the anterior-posterior and medial-lateral axis. Behavioral profiling of these fine partitions based on activation data indicated an emotion-cognition gradient along the anterior-posterior axis and additionally suggested a self-world centric gradient supporting the role of the hippocampus in the construction of abstract representations for spatial navigation and episodic memory. [less ▲]

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See detailDifferent factorisation approaches of the Delis-Kaplan Executive System Battery converge towards a bi-factor model
Camilleri, Julia; Weis, Susanne; Chen, Ji et al

Poster (2019, June)

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See detailProbing hippocampus’ functional properties with activation databases
Genon, Sarah ULiege; Plachti, Anna; Pinho, Ana Luisa et al

Poster (2019, June)

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See detailEin Dienstplan des Gehirns
Genon, Sarah ULiege; Plachti, Anna; Eickhoff, Simon

Article for general public (2019)

Seit Jahrhunderten erforschen Neurowissenschaftler die Rolle einzelner Hirnareale. Dank großer Datenbanken erstellen sie heute erstaunlich detaillierte funktionelle Karten, etwa des Hippocampus.

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See detailThe effect of outliers and their exclusion on resting- state connectivity-based parcellation
Reuter, Niels; Genon, Sarah ULiege; Kharabian, Shahrzad et al

Poster (2018, July 12)

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See detailReplicability of structure-phenotype associations and its influencing factors
Kharabian Masouleh, Shahrzad; Falkiewicz, Marcel; Hoffstaedter, Felix et al

Poster (2018, June)

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See detailThe effect of outliers and their exclusion on resting-state connectivity-based parcellation
Reuter, Niels; Genon, Sarah ULiege; Kharabian, Shahrzad et al

Poster (2018, June)

Introduction Regional connectivity-based parcellation (CBP) aims to find biologically meaningful parcels or subregions. This is achieved by clustering the voxels in a region of interest (ROI) based on ... [more ▼]

Introduction Regional connectivity-based parcellation (CBP) aims to find biologically meaningful parcels or subregions. This is achieved by clustering the voxels in a region of interest (ROI) based on their connectivity profiles. Using a large resting-state fMRI (rs-fMRI) sample, we show that deviant connectivity profiles substantially influence group-based clustering results. Such outliers can arise due to various reasons and we investigated one possible reason for high dimensional data: difference in intrinsic dimensionality. Methods The Right (R) insula ROI (Fig. 2C), subject to repeated CBP analyses [1], was defined using the Harvard Oxford Atlas [2]. rs-fMRI data from 408 healthy unrelated subjects (2mm isotropic, TR=0.72s, age 22-37, 205 males) from the Human Connectome Project [3] were included. FIX- denoised data was preprocessed with SPM8 [4] using unified segmentation [5], 5mm FWHM smoothed, WM-CSF signal regressed, and frequency-filtered (0.01-0.08 Hz). Correlations between time-series of each ROI voxel and all brain gray-matter voxels were computed and Fisher Z- transformed, yielding an ROI-to-whole-brain connectivity matrix per subject. k-means (with k from 2 to 5) was performed on each connectivity matrix. To identify outliers, for each subject a nearest- neighbor subject was identified using Euclidean distance between connectivity matrices. The resulting vector d was Z-scored (Fig. 1A). k-means (k=2) clustering of d revealed a separation around 0, providing a conservative threshold (Fig.1B). Two further thresholds were chosen: 1.69 (.95 left tail area on a standard normal distribution) and a liberal 2.5. Group parcellations for each k using hierarchical clustering with average linkage and Hamming distance were calculated after excluding outliers based on these thresholds. The adjusted rand index (ARI) between k-means cluster results of all subjects was computed, retaining the highest values per subject as a similarity vector a (Fig. 1C). Lastly, principal component analysis was performed on the connectivity matrices, noting the number of components retaining 95% of variance. Correlating the component numbers to d uncovers whether there is a relationship between intrinsic dimensionality of the connectivity matrices and their distances to one another. Results Applying the thresholds of 0, 1.69, and 2.5 removed 134, 32, and 14 subjects, respectively. When correlating distances d (Fig. 1A) to the similarity vector a (Fig. 1C), we found correlations of -.38, -.41, -.49, and -.53, for k=2, 3, 4, and 5, respectively. This result suggests outliers cluster differently, thus including them into a group-level consensus might be detrimental. Accordingly, differences were found between group-level parcellations (Fig. 2A). For instance, when comparing the liberal 2.5 threshold-removed group parcellation (Fig. 2D, column two) with a group parcellation without outlier removal (Fig. 2D, column one), there was only an 81% overlap, ARI=.55 for k=3 (ARI=.67 and .71 for k=4, 5, resp.). Further comparisons are illustrated in Figure 2D. The distances d were related to the number of principal components retaining 95% of variance with correlation of -.79 (Fig. 2B). That is, if intrinsic dimensionality was low for a subject, the associated connectivity matrix would be more distant to the rest of the sample (Fig. 2D). Conclusion The differences in clusterings highlights the influence of outliers. While assessment of the group- level parcellations reveals that clustering results were relatively stable across thresholds for k=2 (Fig. 2D), ample evidence suggests more than 2 clusters in the R-insula [6,7,8]. As linkage algorithms in hierarchical clustering as well as k-means clustering are sensitive to outliers [9], it is important to remove them by using a proper identification threshold. In the future we will focus on automatic identification of parameters that lead to biologically meaningful parcellations. [less ▲]

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See detailWhich denoising for resting-state based parcellation?: reliability & reproducibility in hippocampus
Plachti, Anna; Eickhoff, Simon; Hoffstaedter, Felix et al

Poster (2018, June)

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See detailLocalized compression of grey matter maps for age prediction in healthy and clinical populations
Genon, Sarah ULiege; Varikuti, Deepthi; Sotiras, Aristeidis et al

Poster (2018, June)

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See detailThe heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization
Genon, Sarah ULiege; Reid, Andrew; Li, Hai et al

in NeuroImage (2018), 170

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 ... [more ▼]

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. [less ▲]

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See detailConnectivity-based parcellation: In vivo mapping of the human brain
Eickhoff, Simon; Genon, Sarah ULiege

Poster (2018, April 09)

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See detailHow to Characterize the Function of a Brain Region
Genon, Sarah ULiege; Reid, Andrew; Langner, Robert et al

in Trends in Cognitive Sciences (2018), 22(04), 350-364

Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have ... [more ▼]

Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks. [less ▲]

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See detailImaging-based parcellations of the human brain
Eickhoff, Simon; Yeo, Thomas; Genon, Sarah ULiege

in Nature Reviews. Neuroscience (2018), 19

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation — defining distinct partitions in the brain, be they ... [more ▼]

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation — defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions — is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies. [less ▲]

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See detailEvaluation of non-negative matrix factorization of grey matter in age prediction
Varikuti, Deepthi; Genon, Sarah ULiege; Sotiras, Aristeidis et al

in NeuroImage (2018), 173

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals’ age based on structural imaging. Raw data, voxel ... [more ▼]

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals’ age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer’s disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50 to 690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging. [less ▲]

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See detailMRI-based mapping of the dorsal premotor cortex (PMd)
Genon, Sarah ULiege; Eickhoff, Simon

Conference (2017, October 18)

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See detailEvaluation of Non-negative matrix Factorization of grey matter in age prediction
Varikuti, Deepthi; Genon, Sarah ULiege; Sotiras, Aristeidis et al

Poster (2017, June)

Introduction: It has been shown that machine-learning methods applied to voxel-based morphometry (VBM) data allows the prediction of brain age [1]. Dimensionality reduction is a critical aspect of such ... [more ▼]

Introduction: It has been shown that machine-learning methods applied to voxel-based morphometry (VBM) data allows the prediction of brain age [1]. Dimensionality reduction is a critical aspect of such brain-based prediction of phenotypical characteristics to counter the curse of dimensionality associated with voxel-wise analysis. While previous age-predictions have employed PCA based compression, non-negative matrix factorization (NNMF) has recently been suggested as a plausible factorization of high-dimensional VBM data [4]. Non-negativity and sparsity of the components obtained from NNMF facilitate relatively more optimal solution than the PCA based compression [4]. Here, we evaluate, i) whether NNMF compression allows predictions of biological age that reproduce those from previously reported analyses [2], ii) the impact of the NNMF’s granularity on the prediction accuracy, iii) the possible effect of the factorizations derived from different datasets on the prediction, and iv) whether explicit adjustment can address the model bias inherent to many brain-based predictions. Methods: VBM8 preprocessing (using only non-linear modulation and 8 mm FWHM smoothing [3]) was used to compute voxel-wise GM volumes for two datasets, 1) 693 healthy older adults (age: 55-75 years) scanned at a single site (“1000BRAINS) [1], 2) 1084 healthy adults (age: 18-81 years), scanned at multiple sites (“Mixed”) (Fig 1A). NNMF solutions for both groups were derived at different levels of granularity. Age prediction was performed by fitting LASSO regression models either on the coefficient matrix from the respective NNMF or by those that were derived from projecting a group’s data on the respective other groups components. Model generalization was evaluated by 10-fold cross-validation replicated 25 times. To address the known bias towards the mean, i.e., overestimation of young and underestimation of older subjects, we additionally tested models that explicitly fitted the regression-slope between the real and predicted training set and used this to adjust the expected slope of the test set to 45 degrees. Results: In both datasets, NNMF components resembled neurobiologically reasonable patterning of the brain (Fig 1B). Prediction accuracy based on the projection of data on the components from either group was virtually identical (Fig 2A). For both datasets, mean absolute errors (MAE) declined with higher granularity of the components and reached values well comparable to previous approaches even when using components derived from an independent sample (MAE: 3.6 years for 1000BRAINS; 6.4 years for Mixed). Plotting the prediction error relative to the biological age of the subjects revealed the bias towards the mean across both datasets (Fig 2B). Adjusting for the slope estimated in the training set allows removing this bias, though it needs to be noted that this comes at the cost of reduced precision, i.e., unbiased estimates yield a slightly higher MAE. Conclusion: NNMF allows the definition of co-variation patterns in VBM data. Due to the non- negativity and sparseness, NNMF enable substantially easier and higher biological interpretation than other methods for data compression such as PCA [4]. We showed that NNMF compression of VBM data over the lifespan allows predicting previously unseen subjects’ age with a precision that is comparable to earlier reports using PCA for data compression [2], while offering the potential for neurobiological interpretation. Importantly, accuracy seems to be independent of whether the components were derived from the same dataset or from a dataset that is not only independent but also different in age distribution. We note that accuracies tend to continuously decrease with higher granularity, although performance tends to plateau at about 300 components. Finally, adjusting the inherent bias of sparse regression models yields unbiased out-of-sample predictions but comes at the expense of slightly higher mean errors. [less ▲]

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See detailProfiling inferior left dorsal premotor cortex: when Area 55b meets Premotor Eye-Field
Genon, Sarah ULiege; Reid, Andrew; Langner, Robert et al

Poster (2017, June)

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See detailCo-activation mapping and Parcellation
Genon, Sarah ULiege; Müller, Veronika; Eickhoff, Simon

Learning material (2017)

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See detailNeurobiologische Korrelate von exekutiver Kontrolle im alternden Gehirn
Overhage, Sina; Eickhoff, Simon; Jockwitz, Christiane et al

Poster (2017)

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