Processing of invisible social cues.

AbstractSuccessful interactions between people are dependent on rapid recognition of social cues. We investigated whether head direction – a powerful social signal – is processed in the absence of conscious awareness. We used continuous flash interocular suppression to render stimuli invisible and compared the reaction time for face detection when faces were turned towards the viewer and turned slightly away. We found that faces turned towards the viewer break through suppression faster than faces that are turned away, regardless of eye direction. Our results suggest that detection of a face with attention directed at the viewer occurs even in the absence of awareness of that face. While previous work has demonstrated that stimuli that signal threat are processed without awareness, our data suggest that the social relevance of a face, defined more broadly, is evaluated in the absence of awareness

The Neural Representation of Personally Familiar and Unfamiliar Faces in the Distributed System for Face Perception

Personally familiar faces are processed more robustly and efficiently than unfamiliar faces. The human face processing system comprises a core system that analyzes the visual appearance of faces and an extended system for the retrieval of person-knowledge and other nonvisual information. We applied multivariate pattern analysis to fMRI data to investigate aspects of familiarity that are shared by all familiar identities and information that distinguishes specific face identities from each other. Both identity-independent familiarity information and face identity could be decoded in an overlapping set of areas in the core and extended systems. Representational similarity analysis revealed a clear distinction between the two systems and a subdivision of the core system into ventral, dorsal and anterior components. This study provides evidence that activity in the extended system carries information about both individual identities and personal familiarity, while clarifying and extending the organization of the core system for face perception

Prioritized Detection of Personally Familiar Faces

We investigated whether personally familiar faces are preferentially processed in conditions of reduced attentional resources and in the absence of conscious awareness. In the first experiment, we used Rapid Serial Visual Presentation (RSVP) to test the susceptibility of familiar faces and faces of strangers to the attentional blink. In the second experiment, we used continuous flash interocular suppression to render stimuli invisible and measured face detection time for personally familiar faces as compared to faces of strangers. In both experiments we found an advantage for detection of personally familiar faces as compared to faces of strangers. Our data suggest that the identity of faces is processed with reduced attentional resources and even in the absence of awareness. Our results show that this facilitated processing of familiar faces cannot be attributed to detection of low-level visual features and that a learned unique configuration of facial features can influence preconscious perceptual processing

Spatially dense 3D facial heritability and modules of co-heritability in a father-offspring design

Introduction: The human face is a complex trait displaying a strong genetic component as illustrated by various studies on facial heritability. Most of these start from sparse descriptions of facial shape using a limited set of landmarks. Subsequently, facial features are preselected as univariate measurements or principal components and the heritability is estimated for each of these features separately. However, none of these studies investigated multivariate facial features, nor the co-heritability between different facial features. Here we report a spatially dense multivariate analysis of facial heritability and co-heritability starting from data from fathers and their children available within ALSPAC. Additionally, we provide an elaborate overview of related craniofacial heritability studies. Methods: In total, 3D facial images of 762 father-offspring pairs were retained after quality control. An anthropometric mask was applied to these images to establish spatially dense quasi-landmark configurations. Partial least squares regression was performed and the (co-)heritability for all quasi-landmarks (∼7160) was computed as twice the regression coefficient. Subsequently, these were used as input to a hierarchical facial segmentation, resulting in the definition of facial modules that are internally integrated through the biological mechanisms of inheritance. Finally, multivariate heritability estimates were obtained for each of the resulting modules. Results: Nearly all modular estimates reached statistical significance under 1,000,000 permutations and after multiple testing correction (p ≤ 1.3889 × 10-3), displaying low to high heritability scores. Particular facial areas showing the greatest heritability were similar for both sons and daughters. However, higher estimates were obtained in the former. These areas included the global face, upper facial part (encompassing the nasion, zygomas and forehead) and nose, with values reaching 82% in boys and 72% in girls. The lower parts of the face only showed low to moderate levels of heritability. Conclusion: In this work, we refrain from reducing facial variation to a series of individual measurements and analyze the heritability and co-heritability from spatially dense landmark configurations at multiple levels of organization. Finally, a multivariate estimation of heritability for global-to-local facial segments is reported. Knowledge of the genetic determination of facial shape is useful in the identification of genetic variants that underlie normal-range facial variation

Spatially Dense 3D Facial Heritability and Modules of Co-heritability in a Father-Offspring Design

Introduction: The human face is a complex trait displaying a strong genetic component as illustrated by various studies on facial heritability. Most of these start from sparse descriptions of facial shape using a limited set of landmarks. Subsequently, facial features are preselected as univariate measurements or principal components and the heritability is estimated for each of these features separately. However, none of these studies investigated multivariate facial features, nor the co-heritability between different facial features. Here we report a spatially dense multivariate analysis of facial heritability and co-heritability starting from data from fathers and their children available within ALSPAC. Additionally, we provide an elaborate overview of related craniofacial heritability studies.Methods: In total, 3D facial images of 762 father-offspring pairs were retained after quality control. An anthropometric mask was applied to these images to establish spatially dense quasi-landmark configurations. Partial least squares regression was performed and the (co-)heritability for all quasi-landmarks (∼7160) was computed as twice the regression coefficient. Subsequently, these were used as input to a hierarchical facial segmentation, resulting in the definition of facial modules that are internally integrated through the biological mechanisms of inheritance. Finally, multivariate heritability estimates were obtained for each of the resulting modules.Results: Nearly all modular estimates reached statistical significance under 1,000,000 permutations and after multiple testing correction (p ≤ 1.3889 × 10-3), displaying low to high heritability scores. Particular facial areas showing the greatest heritability were similar for both sons and daughters. However, higher estimates were obtained in the former. These areas included the global face, upper facial part (encompassing the nasion, zygomas and forehead) and nose, with values reaching 82% in boys and 72% in girls. The lower parts of the face only showed low to moderate levels of heritability.Conclusion: In this work, we refrain from reducing facial variation to a series of individual measurements and analyze the heritability and co-heritability from spatially dense landmark configurations at multiple levels of organization. Finally, a multivariate estimation of heritability for global-to-local facial segments is reported. Knowledge of the genetic determination of facial shape is useful in the identification of genetic variants that underlie normal-range facial variation

Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of <i>C9orf72</i>

Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer's disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration.© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain

Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72

Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer\u27s disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration

Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72

Supplementary data: fcac182_Supplementary_Data - https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/braincomms/4/4/10.1093_braincomms_fcac182/1/fcac182_supplementary_data.pdf?Expires=1665138780&Signature=oJFozMlNZiAmxd4~XZaq7YKd7waxislas45NEOp9AiZv-fUYr7X~LhZxFgvYXpCVINyQUQrXe0pgrm9L5kv7xdb0LltVuoEOjwb5uVveMyHMfuqTdCBsEzTVZidx9GuuOB79JsHNYHkUZPsXLiU8-lrosrTb3tasr8Mpv31u7ZVZT~4uGdUf06UsIRu7AEn4bfKf64iwudmFr1QyrLJkXMZm0uJ4e5kh8f7k6Xm~rZGqkaiphsQ~Oat4JHssfuCe5Wibgc4m~rMjQeOmutR3R7KicfH4j3xuab1mzCbf-H~~Ed5Yt8mtlMTsyDB3t-8z3dNVaS2aBrwCABvfa3G2yg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA (pdf file).GENFI consortium authors Sónia Afonso, Maria Rosario Almeida, Sarah Anderl-Straub, Christin Andersson, Anna Antonell, Silvana Archetti, Andrea Arighi, Mircea Balasa, Myriam Barandiaran, Nuria Bargalló, Robart Bartha, Benjamin Bender, Alberto Benussi, Sandra Black, Martina Bocchetta, Sergi Borrego-Ecija, Jose Bras, Marta Canada, Valentina Cantoni, Paola Caroppo, David Cash, Miguel Castelo-Branco, Rhian Convery, Thomas Cope, Giuseppe Di Fede, Alina Díez, Diana Duro, Chiara Fenoglio, Catarina B. Ferreira, Nick Fox, Morris Freedman, Giorgio Fumagalli, Alazne Gabilondo, Roberto Gasparotti, Serge Gauthier, Stefano Gazzina, Giorgio Giaccone, Ana Gorostidi, Caroline Greaves, Rita Guerreiro, Carolin Heller, Tobias Hoegen, Begoña Indakoetxea, Vesna Jelic, Lize Jiskoot, Hans-Otto Karnath, Ron Keren, Tobias Langheinrich, Maria João Leitão, Albert Lladó, Sandra Loosli, Carolina Maruta, Simon Mead, Lieke Meeter, Gabriel Miltenberger, Rick van Minkelen, Sara Mitchell, Katrina Moore, Jennifer Nicholas, Linn Öijerstedt, Jaume Olives, Sebastien Ourselin, Alessandro Padovani, Jessica Panman, Janne M. Papma, Georgia Peakman, Yolande Pijnenburg, Enrico Premi, Sara Prioni, Catharina Prix, Rosa Rademakers, Veronica Redaelli, Tim Rittman, Ekaterina Rogaeva, Pedro Rosa-Neto, Giacomina Rossi, Mar tin Rossor, Beatriz Santiago, Elio Scarpini, Sonja Schönecker, Elisa Semler, Rachelle Shafei, Christen Shoesmith, Miguel Tábuas-Pereira, Mikel Tainta, Ricardo Taipa, David Tang-Wai, David L Thomas, Paul Thompson, Hakan Thonberg, Carolyn Timberlake, Pietro Tiraboschi, Emily Todd, Michele Veldsman, Ana Verdelho, Jorge Villanua, Jason Warren, Carlo Wilke, Ione Woollacott, Elisabeth Wlasich, Henrik Zetterberg, Miren ZulaicaCopyright © The Author(s) 2022. Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer’s disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration.KU Leuven’s ‘Mady Browaeys Fonds voor Onderzoek naar Frontotemporale Degeneratie’

Tensor-Based Global-to-Local Morphometric Analyses in Neurodegenerative Diseases

Tensor-based morphometry (TBM) is a technique to identify morphological differences among different groups of interest based on structural MRI images. Specifically in this work, morphological analyses can provide valuable information about the form altering effects of neurodegenerative diseases, such as Alzheimer and frontotemporal dementia, in the human brain. This can result in a better understanding of the disease impact and development of image-based diagnostic tools that are essential towards disease-modifying therapies. However, standard tensor-based methods have difficulties identifying the subtle volumetric changes occurring at early stages of disease development. Those methods analyse group differences within the sizes of pre-defined and non-overlapping regions and interpret the statistical results in an independent region-wise manner. In contrast, this work proposes a fundamental extension to TBM based on a global-to-local tensor-based image segmentation and a comprehensive inference testing framework that allows to expose the complexity of volumetric interactions affected by neurodegenerative diseases at different levels of detail. The first contribution of this work is providing a TBM-based data-driven image segmentation that partition the brain into regions with correlated volumetric variations, based on the volumetric variability observed within a given population. Additionally, the segmentation is defined hierarchically with various global-to-local levels of spatial coverage. In its result, the complete brain partitioning constitutes of different global-to-local levels with overlapping regions across different levels and with non-overlapping regions in each level that are well-integrated internally and less integrated with other regions. The second contribution is a complete assessment of morphological variations within each of the regions and integrated across different levels in the hierarchical chain. The completeness is obtained by (1) analysing inter-group inferences on different aspects of volumetric patterns in the regions such as integrated univariate volumetric changes (size) and multivariate patterns of volumetric covariation (shape), (2) analysing inferences at different levels of detail and (3) the propagation of results across those levels in the global-to-local hierarchy. Alzheimer's disease and frontotemporal dementia are common neurodegenerative diseases and are specifically investigated in this work. The proposed segmentation technique and hypothesis testing framework were applied to study early development of both diseases. We localised size and shape effects at different locations in the brain and with different global-to-local penetrations of the effects. The completeness of the proposed inference framework adds new information to the TBM analyses for both disease groups. Our method showed a strong advantage over conventional TBM analyses in the way that it adapted an exhaustive, but complete, set of inference analyses into a hierarchically form integrated structure that is easy to understand. The proposed extension to TBM did not compromise on statistical power and allowed us to derive a single compact test statistic, through a local-to-global propagation of the inference results.status: publishe

The distributed neural code for facial identity

Multiples studies have investigated the role of familiarity, emotion and novelty in the detection and recognition of human faces (see Natu & O'Toole, 2011 for a review). Gobbini & Haxby (2007) have suggested a model of familiar face recognition that describes how prior knowledge of a person's identity modulates the visual systems involved in processing personally familiar faces. Building on this proposed model, the present study sought to investigate the neural representation of personally familiar (friends) and unfamiliar (matched for age and gender strangers) faces. Thirty-three participants underwent functional MRI while performing an oddball detection task in which they were presented with faces of friends and strangers. Univariate GLM and MVPA classification searchlight results agreed with previous findings of greater activity for the processing of familiar faces within some core visual modules and emotion-related regions (e.g. amygdala and insular cortex) suggested by the model. In addition, MVPA exclusively detected differential involvement of regions of the prefrontal cortex and the anterior temporal lobe providing further evidence of distributed representations of person knowledge. Furthermore, MVPA results for identity classification identified a cluster in the right middle frontal gyrus which was sensitive to identity (across friends and strangers combined). This suggests that familiarity differentially modulates brain regions implicated previously in carrying biographic knowledge. The same analysis also identified right inferior frontal gyrus which was also not found to be modulated by familiarity. Future directions are the investigation of functional connectivity between identified regions and subject-specific traits associated with individual stimuli