References of "Genon, Sarah"
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See detailResting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps
Varikuti, Deepthi; Hoffstaedter, Felix; Genon, Sarah ULg et al

in Brain Structure & Function (in press)

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 ... [more ▼]

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. [less ▲]

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See detailSelf in Dementia
Antoine, Nicolas ULg; Genon, Sarah ULg; Bastin, Christine ULg et al

in Mishara; Corlett, P.; Fletcher, P. (Eds.) et al Phenomenological Neuropsychiatry, How Patient Experience Bridges Clinic with Clinical Neuroscience (in press)

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See detailThe right dorsal premotor mosaic: organization, functions, and connectivity
Genon, Sarah ULg; Li, Hai; Fan, Lingzhong et al

in Cerebral Cortex (in press)

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 ... [more ▼]

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 CBP results to 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 five distinct subregions that formed a cognitive-motor gradient along a rostro-caudal axis. In particular, we found (i) a rostral subregion functionally connected with prefrontal cortex, which likely supports high-level cognitive processes, such as working memory (ii) a central subregion showing a mixed behavioral profile and functional connectivity to parietal regions of the dorsal attention network, and (iii) a caudal subregion closely integrated with the motor system. Additionally, we found (iv) a dorsal subregion, preferentially related to hand movements and connected to both cognitive and motor regions, and (v) 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 five subregions. [less ▲]

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See detailL’anosognosie dans la maladie d’Alzheimer: Soi, mémoire et jugement
Genon, Sarah ULg

Speech/Talk (2016)

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See detailDisrupted interaction between self and memory in patients with Alzheimer’s disease
Genon, Sarah ULg; Salmon, Eric ULg; Collette, Fabienne ULg et al

Conference (2016, July)

In humans, self and memory processes interact as evidenced by the self reference (SRE) and self reference recollection effects (SRRE). However, in patients with Alzheimer’s disease (AD), this relationship ... [more ▼]

In humans, self and memory processes interact as evidenced by the self reference (SRE) and self reference recollection effects (SRRE). However, in patients with Alzheimer’s disease (AD), this relationship can be disrupted. This was evidenced by impaired SRE and SRRE in AD for recognition of adjectives previously judged for self-relevance, as well as recall of names of people previously linked to the self. For both materials, a qualitative impairment of the recollective experience for the self-related items was also observed in AD. A neuroimaging approach suggested that reduced SRE is related to decreased grey matter volume in the lateral prefrontal cortex (lPFC). Thus, retrieval of recent self-related memories is impaired in relation to altered high-order processes in lPFC in AD. [less ▲]

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See detailMapping cortical modules, their connectivity and functions
Genon, Sarah ULg; Eickhoff, Simon

Conference (2016, April)

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See detailANIMA: A data-sharing initiative for neuroimaging meta-analyses
Reid, Andrew T.; Bzdok, Danilo; Genon, Sarah ULg et al

in NeuroImage (2016), 124(B), 1245-1253

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 ... [more ▼]

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. [less ▲]

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See detailMultimodal evidence of a rostro-caudal and ventro-dorsal organization in the dorsal premotor cortex
Genon, Sarah ULg; Li, Hai; Fan, Lingzhong et al

Poster (2016)

Introduction Different methods for in-vivo characterization have resulted in different maps of the human dorsal premotor cortex (PMd): Task-based functional studies suggested a rostro-caudal gradient ... [more ▼]

Introduction Different methods for in-vivo characterization have resulted in different maps of the human dorsal premotor cortex (PMd): Task-based functional studies suggested a rostro-caudal gradient corresponding to a cognitive-motor gradient1,2 and mapping based on resting-state functional connectivity (RSFC) likewise suggested a subdivision along the rostro-caudal axis3. In contrast, mapping based on structural connectivity as assessed by probabilistic diffusion tractography (PDT) provided evidence that the dorsal part of the precentral gyrus (PG) is organized along a ventro-dorsal axis4. However, there is currently no multimodal mapping of a broadly defined PMd. The present study used a multimodal approach to (1) identify a robust topographical organization of the right PMd by using connectivity-based parcellation (CBP) applied to a meta-analytic approach of task-related coactivation data (i.e. meta-analytic connectivity modeling, MACM5,6), and (2) examine whether the thus obtained parcellation pattern would be reproduced by CBP based on two other connectivity modalities: unconstrained functional (as reflected by RSFC) and structural as measured by PDT based on diffusion-weighted imaging (DWI). Methods A volume of interest (VOI) was defined by merging PMd activation sites from several meta-analyses serving as robust functional localizers of the PMd while excluding primary sensorimotor areas. MACM-CBP6 was performed using Brainmap database. RSFC-CBP and PDT-CBP were performed on distinct datasets to ensure independent parcellation7. RSFC-CBP was computed on RS data of 124 healthy subjects (age: 39.5 ± 11.5 yrs., 66 males) from the 1000BRAINS project8. The voxels’ time series (TS) were first cleaned from confounding effects using PCA denoising and global signal regression. RSFC was then computed by Pearson correlations between the TS of the VOI voxels and those of the rest of the brain. PDT-CBP9 was performed on DWI data of 20 healthy subjects (age: 18.5 ± .76, 10 males) using FSL. Several cluster solutions (k solution) were examined with k-means for MACM- and RSFC-CBP and spectral clustering for PDT-CBP. The choice of the k solution was driven by task-based functional data (MACM) based on three criteria: variation of information, percentage of deviants, and silhouette value6. Results The selected criteria jointly identified the 5-cluster (5k) solution as optimal across the range of functional studies from Brainmap database (Figure 1). Examination of stability across subjects following PDT-CBP further suggested that k=5 may be considered a local optimum within 2 ≤ k ≤ 6. This solution revealed a similar pattern of topographical organization across modalities (Figure 2) with a subdivision along both rostro-caudal and ventro-dorsal axes, including a rostral cluster lying mainly anteriorly to the PG, a central one at the intersection of the precentral sulcus and the superior frontal gyrus, a caudal one in the posterior part of the PG, a ventral one adjacent to ventral PM, and a dorsal one adjacent to the inter-hemispheric premotor areas. Discussion For the first time our study revealed that the PMd could be divided along two axes: rostro-caudal and ventro-dorsal. This is consistent with previous functional2 and microstructure studies10 in humans and non-human primates suggesting a rostro-caudal distinction and with a previous PDT-CBP of the (dorsal) precentral gyrus4 showing that it could be subdivided in the ventro-dorsal direction. Importantly, this topographical organization was found in the independent analysis of three different connectivity aspects: task functional, unconstrained functional and structural, each based on different datasets. In sum, different modalities consistently show that the PMd can be subdivided into 5 subregions organized along both rostro-caudal and ventro-dorsal axes, comprehensively integrating patterns previously revealed by different methods. [less ▲]

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See detailResting-state test-retest reliability over different preprocessing steps
Varikuti, Deepthi; Hoffstaedter, Felix; Genon, Sarah ULg et al

Poster (2016)

Introduction: Resting-state (RS) functional connectivity (FC) analysis has become a widely used method for the investigation of human brain connectivity and pathology. While most of the current ... [more ▼]

Introduction: Resting-state (RS) functional connectivity (FC) analysis has become a widely used method for the investigation of human brain connectivity and pathology. While most of the current applications are based on data-driven analyses, the use of functionally specific, a priori defined networks provided by neuroimaging meta-analyses represent an important alternative to these, as they allow the standardized assessment of connectivity patterns. Neuronal activity as measured by functional MRI is influenced by various nuisance signals including system noise, thermal noise, and noise induced by physiological processes of the participant. The presence of these confounds in turn have an impact on the estimation of functional connectivity. Several methods exist to deal with 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 investigated the test-retest reliability of FC analyses in meta-analytically defined networks after removing confounding noise regressors. Methods: RS-fMRI data of 42 healthy subjects with an average age of 42 ± 20 years were obtained in two sessions with an average time interval of 175 ± 75 days. A seed-based FC analysis was conducted after spatial preprocessing, approach specific confound-regression, and band-pass filtering [0.01-0.08 Hz]. We focused on the effects of various commonly used confound removals in the resting state studies such as PCA de-noising, global mean signal regression, and removal of tissue-class specific mean signals (in particular, white matter (WM) + cerebrospinal fluid (CSF) and WM + CSF + grey matter (GM)) [2,3,4,5,7]. Additionally, we examined GM specific time-series extraction from seed regions. In order to compute the seed based FC, a priori defined networks were analyzed (extended socio-affective default mode [1] and working-memory [6]). Both networks show robust within network resting state connectivity as well as anti-correlation between each other. The reliability of FC was measured using two different measures Spearman correlations and the absolute differences of functional connectivity scores. The different approaches defined by the combination of different masking / confound removal approaches were compared using a non-parametric Friedman ANOVA. Results: The summary ranking across both indices of reliability (Spearman correlations and absolute differences) reflects the major patterns noted in the individual analyses (Fig.1). GM masking, in particular using the group-mean segmentation, improves reliability. PCA denoising in turn reduces it. Within-network connections are most reliably estimated when not using any global or tissue-class specific signal regression, with removing the global WM and CSF signals representing the next-best approach. In contrast, between-network connections are most reliably measured by linear and second order removal of global signals of all three-tissue classes. Conclusion: Our results show that GM masking of the seed regions based on the group-average GM probabilities is advisable when investigating meta-analytically defined networks. In turn, PCA de-noising reduces the reliability of connectivity estimates. Finally, with respect to global signal regression, we observe that refraining from this approach enhances reliability, but comes at the expense of potentially poorer biological validity, indicated by missing anti-correlations between what has been previously described as antagonistic networks. Here, removal of global WM and CSF signals seems to provide a good compromise, as this approach yielded reliable and meaningful estimates of within and between-network connections (Fig.2). We noted that reliability is proportional to the retained variance, presumably including structured noise. Consequently, we would argue that compromises are needed between maximizing reliability and removing variance that may be attributable to non-neuronal sources. [less ▲]

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See detailPostretrieval overconfidence and anosognosia in patients with Alzheimer’s disease (AD)
Genon, Sarah ULg; Mélon, Marlène; Salmon, Eric et al

Conference (2016)

General self awareness (anosognosia) and metacognitive monitoring in memory tasks are both impaired in AD, but how they relate to each other is still an open question. We examined awareness with the ... [more ▼]

General self awareness (anosognosia) and metacognitive monitoring in memory tasks are both impaired in AD, but how they relate to each other is still an open question. We examined awareness with the Anosognosia Questionnaire Dementia (AQD) and monitoring within a memory task, during retrieval with feeling-of-knowing (FOK) and post-retrieval, with judgment-of-confidence (JOC). FOKs/JOCs were performed for names of people either previously linked to self or other. AD showed both impaired FOK and JOC. They also showed lower self metamemory effect in their JOCs and lower awareness of their behavioral functioning in the AQD, which was specifically related to overconfidence in their JOCs for self-related items. Thus, anosognosia and altered postretrieval monitoring for self-related information may be related in AD. [less ▲]

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See detailIn-vivo parcellation and structure-function relationships
Genon, Sarah ULg; Eickhoff, Simon

Scientific conference (2015, December 14)

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See detailSharing in neuroimaging: collecting with Brainmap, quantitatively analysing and sharing with ANIMA
Genon, Sarah ULg; Reid, Andrew; Eickhoff, Simon

Conference (2015, September 14)

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See detailOn the relationship between gray matter and behavioral data: lessons learned
Genon, Sarah ULg; Wensing, Tobias; Hoffstaedter, Felix et al

Poster (2015, June)

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See detailThe neuroimaging meta-analysis database: A data-sharing initiative for neuroimaging meta-analyses
Reid, Andrew; Bzdok, Danilo; Genon, Sarah ULg et al

Poster (2015, June)

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See detailCross-modal identification of six subregions within the left PMd and their functional characterization
Genon, Sarah ULg; Li, Hai; Fan, Lingzhong et al

Poster (2015, June)

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See detailArchives of Neuroimaging Meta Analyses (ANIMA): a data sharing initiative
Reid, Andrew; Bzdok, Danilo; Genon, Sarah ULg et al

in Proceedings of the Annual Meeting of the Organization of Human Brain Mapping (2015)

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See detailA connectivity ­based parcellation of the left dorsal premotor cortex
Genon, Sarah ULg; Müller, Veronika; Cieslik, Edna-Clarisse et al

in Proceedings of the Annual Meeting of the Organization of Human Brain Mapping (2015)

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See detailOn the relationship between gray matter and behavioral data: lessons learned
Genon, Sarah ULg; Wensing, Tobias; Hoffstaedter, Felix et al

in Proceedings of the Annual Meeting of the Organization of Human Brain Mapping (2015)

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See detailExamining the right dorsal premotor mosaic: a connectivity-based parcellation approach
Genon, Sarah ULg; Müller, Veronika I.; Cieslik, Edna et al

Poster (2014, June)

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