scholarly journals Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Dongya Wu ◽  
Xin Li ◽  
Jun Feng
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Face Area ◽  
The Face ◽  
The Individual ◽  
The Right ◽  
And Function ◽  
The Relationship ◽  
The Brain

Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.

scholarly journals Multi-hops functional connectivity improves individual prediction of fusiform face activation via a graph neural network

2020 ◽  
Author(s):  
Dongya Wu ◽  
Xin Li ◽  
Jun Feng
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Face Area ◽  
The Face ◽  
The Individual ◽  
The Right ◽  
And Function ◽  
The Relationship ◽  
The Brain

AbstractBrain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, based on a preliminary analysis, this proposal is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face response of the right fusiform face area (rFFA) via a multi-layers graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the 2-layers graph neural network is the best in characterizing rFFA’s face response and revealed a hierarchical network for the face processing of rFFA.

Abstract 2713: Functional Recovery Associated with Connectivity Changes in Contralesional Hemisphere after Severe Transient Stroke.

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
WOO HYUN SHIM ◽  
Bruce Rosen ◽  
Jaeseung Jeong ◽  
Young Kim
Keyword(s):  
Functional Connectivity ◽  
Functional Recovery ◽  
Brain Connectivity ◽  
Control Group ◽  
Sprague Dawley ◽  
Sensorimotor Deficit ◽  
Stroke Models ◽  
Time Courses ◽  
The Right ◽  
The Brain

Stroke impairs connections in the brain system, commonly resulting in significant sensorimotor deficits. Some degree of functional recovery typically occurs even after a severe stroke, yet changes in the brain connectivity that underlie such recovery are poorly understood. In this study, using rat stroke models, we monitored functional connectivity when the sensorimotor deficit recovered after a severe ischemic stroke (defined DWI by more than 15% of the entire brain volume). We used seven Sprague-Dawley rats (∼350g), which showed nearly full recovery of both motor and sensory functions approximately 180 days after 90 min occlusion of the right middle cerebral artery. Six healthy age controlled rats were used for the control group. BOLD MRI time courses during rest (10min, TR=1s, 9 slices) were collected. Both the seed-voxel analysis and the ROI-based analysis were performed, in which seed voxels were selected in the left S1FL, and multiple ROIs were placed over the somatosensory regions. Stroke rats showed the markedly decreased functional connectivity in the ipsilesional side (right) for both voxelwise and ROI-based methods. Interestingly, in contralesional (non-stroke) side (left), the voxelwise connectivity spatially expanded into the entire cortical area. The cross-correlation coefficient values between ROI’s slightly increased in the contralesional hemisphere compared to the control rats. In conclusion, we demonstrated that the restoration of sensorimotor function is associated more with the increase and spatial expansion of functional connectivity within the contralesional than the ipsilesional hemisphere.

scholarly journals Connectome spectral analysis to track EEG task dynamics on a subsecond scale

2020 ◽  
Author(s):  
Katharina Glomb ◽  
Joan Rue Queralt ◽  
David Pascucci ◽  
Michaël Defferrard ◽  
Sebastien Tourbier ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Mathematical Framework ◽  
Activation Patterns ◽  
Face Area ◽  
Eeg Data ◽  
And Function ◽  
White Matter Connectivity ◽  
The Brain

AbstractWe present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or “network harmonics”. These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.

scholarly journals Hierarchical Brain Network for Face and Voice Integration of Emotion Expression

2018 ◽  
Vol 29 (9) ◽  
pp. 3590-3605 ◽  
Author(s):  
Jodie Davies-Thompson ◽  
Giulia V Elli ◽  
Mohamed Rezk ◽  
Stefania Benetti ◽  
Markus van Ackeren ◽  
...  
Keyword(s):  
Emotional Expression ◽  
Brain Network ◽  
Causal Modeling ◽  
Emotional Expressions ◽  
Face Area ◽  
Sensory Modalities ◽  
The Face ◽  
The Right ◽  
The Brain ◽  
Posterior Superior Temporal Sulcus

Abstract The brain has separate specialized computational units to process faces and voices located in occipital and temporal cortices. However, humans seamlessly integrate signals from the faces and voices of others for optimal social interaction. How are emotional expressions, when delivered by different sensory modalities (faces and voices), integrated in the brain? In this study, we characterized the brains’ response to faces, voices, and combined face–voice information (congruent, incongruent), which varied in expression (neutral, fearful). Using a whole-brain approach, we found that only the right posterior superior temporal sulcus (rpSTS) responded more to bimodal stimuli than to face or voice alone but only when the stimuli contained emotional expression. Face- and voice-selective regions of interest, extracted from independent functional localizers, similarly revealed multisensory integration in the face-selective rpSTS only; further, this was the only face-selective region that also responded significantly to voices. Dynamic causal modeling revealed that the rpSTS receives unidirectional information from the face-selective fusiform face area, and voice-selective temporal voice area, with emotional expression affecting the connection strength. Our study promotes a hierarchical model of face and voice integration, with convergence in the rpSTS, and that such integration depends on the (emotional) salience of the stimuli.

scholarly journals Hierarchical Brain Network for Face and Voice Integration of Emotion Expression

2017 ◽  
Author(s):  
Jodie Davies-Thompson ◽  
Giulia V. Elli ◽  
Mohamed Rezk ◽  
Stefania Benetti ◽  
Markus van Ackeren ◽  
...  
Keyword(s):  
Emotional Expression ◽  
Brain Network ◽  
Causal Modeling ◽  
Emotional Expressions ◽  
Face Area ◽  
Sensory Modalities ◽  
The Face ◽  
The Right ◽  
The Brain ◽  
Posterior Superior Temporal Sulcus

ABSTRACTThe brain has separate specialized computational units to process faces and voices located in occipital and temporal cortices. However, humans seamlessly integrate signals from the faces and voices of others for optimal social interaction. How are emotional expressions, when delivered by different sensory modalities (faces and voices), integrated in the brain? In this study, we characterized the brains’ response to faces, voices, and combined face-voice information (congruent, incongruent), which varied in expression (neutral, fearful). Using a whole-brain approach, we found that only the right posterior superior temporal sulcus (rpSTS) responded more to bimodal stimuli than to face or voice alone but only when the stimuli contained emotional expression. Face-and voice-selective regions of interest extracted from independent functional localizers, similarly revealed multisensory integration in the face-selective rpSTS only; further, this was the only face-selective region that also responded significantly to voices. Dynamic Causal Modeling revealed that the rpSTS receives unidirectional information from the face-selective fusiform face area (FFA), and voice-selective temporal voice area (TVA), with emotional expression affecting the connection strength. Our study promotes a hierarchical model of face and voice integration, with convergence in the rpSTS, and that such integration depends on the (emotional) salience of the stimuli.

Neurodevelopment in the first three years: implications for child development, professional practice and policy

2014 ◽  
Vol 9 (2) ◽  
pp. 154-164 ◽  
Author(s):  
Danya Glaser
Keyword(s):  
Brain Development ◽  
Brain Activity ◽  
Brain Maturation ◽  
Policy And Practice ◽  
Content Type ◽  
Key Issues ◽  
And Function ◽  
The Relationship ◽  
The Brain ◽  
Brain Connections

Purpose – The purpose of this paper is to outline brain structure and development, the relationship between environment and brain development and implications for practice. Design/methodology/approach – The paper is based on a selected review of the literature and clinical experience. Findings – While genetics determine the sequence of brain maturation, the nature of brain development and functioning is determined by the young child's caregiving environment, to which the developing brain constantly adapts. The absence of input during sensitive periods may lead to later reduced functioning. There is an undoubted immediate equivalence between every mind function – emotion, cognition, behaviour and brain activity, although the precise location of this in the brain is only very partially determinable, since brain connections and function are extremely complex. Originality/value – This paper provides an overview of key issues in neurodevelopment relating to the development of young children, and implications for policy and practice.

A Veiled Threat: Belcacemi and Oussar v Belgium

2018 ◽  
Vol 20 (2) ◽  
pp. 190-200
Author(s):  
Jasper Doomen
Keyword(s):  
Criminal Law ◽  
Freedom Of Conscience ◽  
Unequal Treatment ◽  
The Face ◽  
The Individual ◽  
Full Face ◽  
The Right

The freedom of the individual can easily come into conflict with his or her obligation to integrate in society. The case of Belcacemi and Oussar v Belgium provides a good example. It is evident that some restrictions of citizens’ freedoms must be accepted for a state to function and, more basically, persist; as a consequence, it is acceptable that certain demands, incorporated in criminal law, are made of citizens. The issue of the extent to which such restrictions are justified has increasingly become a topic of discussion. The present case raises a number of important questions with respect to the right to wear a full-face veil in public if the societal norm is that the face should be visible, the most salient of which are whether women should be ‘protected’ from unequal treatment against their will and to what extent society may impose values on the individual. I will argue that Belgian law places unwarranted restrictions on citizens and that the values behind it testify to an outlook that is difficult to reconcile with the freedom of conscience and religion.

scholarly journals Neural Tuning for Face Wholes and Parts in Human Fusiform Gyrus Revealed by fMRI Adaptation

2010 ◽  
Vol 104 (1) ◽  
pp. 336-345 ◽  
Author(s):  
Alison Harris ◽  
Geoffrey Karl Aguirre
Keyword(s):  
Functional Mri ◽  
Holistic Processing ◽  
Fusiform Face Area ◽  
Single Population ◽  
Face Area ◽  
Neural Populations ◽  
The Face ◽  
Adaptation Response ◽  
The Right ◽  
Fmri Adaptation

Although the right fusiform face area (FFA) is often linked to holistic processing, new data suggest this region also encodes part-based face representations. We examined this question by assessing the metric of neural similarity for faces using a continuous carryover functional MRI (fMRI) design. Using faces varying along dimensions of eye and mouth identity, we tested whether these axes are coded independently by separate part-tuned neural populations or conjointly by a single population of holistically tuned neurons. Consistent with prior results, we found a subadditive adaptation response in the right FFA, as predicted for holistic processing. However, when holistic processing was disrupted by misaligning the halves of the face, the right FFA continued to show significant adaptation, but in an additive pattern indicative of part-based neural tuning. Thus this region seems to contain neural populations capable of representing both individual parts and their integration into a face gestalt. A third experiment, which varied the asymmetry of changes in the eye and mouth identity dimensions, also showed part-based tuning from the right FFA. In contrast to the right FFA, the left FFA consistently showed a part-based pattern of neural tuning across all experiments. Together, these data support the existence of both part-based and holistic neural tuning within the right FFA, further suggesting that such tuning is surprisingly flexible and dynamic.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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