Lack of orientation specific adaptation to vertically oriented Glass patterns in human visual cortex: an fMRI adaptation investigation

The perception of coherent form configurations in natural scenes relies on the activity of early visual areas that respond to local orientation cues. Subsequently, high-level visual areas pool these local signals to construct a global representation of the initial visual input. However, it is still debated whether neurons in the early visual cortex respond also to global form features. Glass patterns (GPs) are visual stimuli employed to investigate local and global form processing and consist of randomly distributed dots pairs called dipoles arranged to form specific global configurations. In the current study, we used GPs and functional magnetic resonance imaging (fMRI) adaptation to reveal the visual areas that subserve the processing of oriented GPs. Specifically, we adapted participants to vertically oriented GP, then we presented test GPs having either the same or different orientations with respect to the adapting GP. We hypothesized that if local form features are processed exclusively by early visual areas and global form by higher-order visual areas, then the effect of visual adaptation should be more pronounced in higher tier visual areas as it requires global processing of the pattern. Contrary to this expectation, our results revealed that adaptation to GPs is robust in early visual areas (V1, V2, and V3), but not in higher tier visual areas (V3AB and V4v), suggesting that form cues in oriented GPs are primarily derived from local-processing mechanisms that originate in V1. Finally, adaptation to vertically oriented GPs causes a modification in the BOLD response within early visual areas, regardless of the relative orientations of the adapting and test stimuli, indicating a lack of orientation selectivity

Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks

Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection

In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented. The method is based on an U-Net like convolutional neural network (CNN) for automated 2D slice-based-segmentation of brain MRI volumes. The individual modalities are encoded in separate downsampling branches without weight sharing, to leverage the specific features. Skip connections input feature maps to multi-scale feature fusion (MSFF) blocks at every decoder stage of the network. Those are followed by multi-scale feature upsampling (MSFU) blocks which use the information about lesion shape and location. The CNN is evaluated on two publicly available datasets: The ISBI 2015 longitudinal MS lesion segmentation challenge dataset containing 19 subjects and the MICCAI 2016 MSSEG challenge dataset containing 15 subjects from various scanners. The proposed multi-input 2D architecture is among the top performing approaches in the ISBI challenge, to which open-access papers are available, is able to outperform state-of-the-art 3D approaches without additional post-processing, can be adapted to other scanners quickly, is robust against scanner variability and can be deployed for inference even on a standard laptop without a dedicated GPU

Lack of orientation specific adaptation to vertically oriented Glass patterns in human visual cortex: an fMRI adaptation investigation

The perception of coherent form configurations in natural scenes relies on the activity of early visual areas that respond to local orientation cues. Subsequently, high-level visual areas pool these local signals to construct a global representation of the initial visual input. However, it is still debated whether neurons in the early visual cortex respond also to global form features. Glass patterns (GPs) are visual stimuli employed to investigate local and global form processing and consist of randomly distributed dots pairs called dipoles arranged to form specific global configurations. In the current study, we used GPs and functional magnetic resonance imaging (fMRI) adaptation to reveal the visual areas that subserve the processing of oriented GPs. Specifically, we adapted participants to vertically oriented GP, then we presented test GPs having either the same or different orientations with respect to the adapting GP. We hypothesized that if local form features are processed exclusively by early visual areas and global form by higher-order visual areas, then the effect of visual adaptation should be more pronounced in higher tier visual areas as it requires global processing of the pattern. Contrary to this expectation, our results revealed that adaptation to GPs is robust in early visual areas (V1, V2, and V3), but not in higher tier visual areas (V3AB and V4v), suggesting that form cues in oriented GPs are primarily derived from local-processing mechanisms that originate in V1. Finally, adaptation to vertically oriented GPs causes a modification in the BOLD response within early visual areas, regardless of the relative orientations of the adapting and test stimuli, indicating a lack of orientation selectivity

AUREMOL-QTA, a program package for NMR based automated recognition and characterization of local and global conformational changes in proteins induced by ligand binding as external perturbation

Among all existing techniques for protein structure determination, NMR spectroscopy has the advantage to provide the most complete characterization of molecular structures in solution. On the other hand, the low sensitivity of NMR limits the available signal-to-noise ratio. This often leads to the disappearance of cross peaks and to the misinterpretations of noise peaks as peaks originating from the protein under consideration. These properties hamper the determination of the consistency between the two investigated spectra. In this work, a completely new quality control (AUREMOL-QTA) package has been developed in order to automatically infer structural conformational changes from spectral modifications (in a set of investigated spectra). These differences may be induced by alterations of the external conditions (e.g. temperature, pressure, pH and ligand binding) as well as (partial) protein denaturation. The complex task of manually collecting and interpreting vicinity relationships changes (through space and bonds) in order to extract structural modifications of the molecule is time demanding. The developed AUREMOL-QTA package facilitates this investigation by means of multi-dimensional NMR spectra and it is applicable to three main possible sceneries: 1. Quality control of a protein sample and assessment of the intact three- dimensional structure of the protein. 2. Automated identification of ligand binding sites by means of NMR spectroscopy. 3. Identification of structural changes (in the atomic resolution) induced by external perturbations such as pressure, pH and temperature. The AURMOL-QTA begins with a pre-processing step where one (or more) reference spectra of the target protein and one or more test spectra are normalized (spectral width, offset, receiver gain and the number of scans) to be properly compared. A possible spectrum shift is evaluated and if it has occurred it is corrected. The simulated spectrum of a protein can be used as an adjunctive reference spectrum and to recognize peak multiplets in the experimental spectra. If there are more reference spectra only the common peaks are retained and used for the requested comparison. The routine continues with a low-level analysis involving a peak feature collection (volume, line width, chemical shift, cross-correlation in the time domain and shape) with a consequent association of cross peaks among the spectra. A mid-level analysis relies on the calculation of feature-related Bayesian probabilities that the associated peaks represent the same signal. It facilitates the detection of missing signals and the identification of feature differences between the compared spectra. The ratio of matching peaks is computed in order to quantitatively determine the similarity of the investigated spectra. A high-level analysis allows the identification of structurally altered parts of the molecule, mapping the feature variations and computing the fraction of residues involved in the modifications. In particular, if NOESY and TOCSY spectra are investigated the symmetrical properties are exploited in order to perform an adjunctive pattern analysis of the residues. The method has been successfully tested on HSCQ spectra (pressurized with xenon) and on HSQC-TROSY spectra (with high pressure and temperature variations) of the human prion protein (huPrPC). It has been also used to compare the NOESY spectrum of the wild HPr protein from Staphylococcus aureus with the mutant (H15A) of the same protein and with the artificially denatured (partially and totally) ones

Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection

Abstract In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented. The method is based on an U-Net like convolutional neural network (CNN) for automated 2D slice-based-segmentation of brain MRI volumes. The individual modalities are encoded in separate downsampling branches without weight sharing, to leverage the specific features. Skip connections input feature maps to multi-scale feature fusion (MSFF) blocks at every decoder stage of the network. Those are followed by multi-scale feature upsampling (MSFU) blocks which use the information about lesion shape and location. The CNN is evaluated on two publicly available datasets: The ISBI 2015 longitudinal MS lesion segmentation challenge dataset containing 19 subjects and the MICCAI 2016 MSSEG challenge dataset containing 15 subjects from various scanners. The proposed multi-input 2D architecture is among the top performing approaches in the ISBI challenge, to which open-access papers are available, is able to outperform state-of-the-art 3D approaches without additional post-processing, can be adapted to other scanners quickly, is robust against scanner variability and can be deployed for inference even on a standard laptop without a dedicated GPU

Lack of orientation specific adaptation to vertically oriented Glass patterns in human visual cortex: an fMRI adaptation investigation

The perception of coherent form configurations in natural scenes relies on the activity of early visual areas that respond to local orientation cues. Subsequently, high-level visual areas pool these local signals to construct a global representation of the initial visual input. However, it is still debated whether neurons in the early visual cortex respond also to global form features. Glass patterns (GPs) are visual stimuli employed to investigate local and global form processing and consist of randomly distributed dots pairs called dipoles arranged to form specific global configurations. In the current study, we used GPs and functional magnetic resonance imaging (fMRI) adaptation to reveal the visual areas that subserve the processing of oriented GPs. Specifically, we adapted participants to vertically oriented GP, then we presented test GPs having either the same or different orientations with respect to the adapting GP. We hypothesized that if local form features are processed exclusively by early visual areas and global form by higher-order visual areas, then the effect of visual adaptation should be more pronounced in higher tier visual areas as it requires global processing of the pattern. Contrary to this expectation, our results revealed that adaptation to GPs is robust in early visual areas (V1, V2, and V3), but not in higher tier visual areas (V3AB and V4v), suggesting that form cues in oriented GPs are primarily derived from local-processing mechanisms that originate in V1. Finally, adaptation to vertically oriented GPs causes a modification in the BOLD response within early visual areas, regardless of the relative orientations of the adapting and test stimuli, indicating a lack of orientation selectivity

Brain connectivity studies on structure-function relationships: a short survey with an emphasis on machine learning

This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate

Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning

This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate

Vestibular Stimulation Modulates Neural Correlates of Own-body Mental Imagery

There is growing evidence that vestibular information is not only involved in reflexive eye movements and the control of posture but it also plays an important role in higher order cognitive processes. Previous behavioral research has shown that concomitant vestibular stimuli influence performance in tasks that involve imagined self-rotations. These results suggest that imagined and perceived body rotations share common mechanisms. However, the nature and specificity of these effects remain largely unknown. Here, we investigated the neural mechanisms underlying this vestibulocognitive interaction. Participants (n = 20) solved an imagined self-rotation task during caloric vestibular stimulation. We found robust main effects of caloric vestibular stimulation in the core region of the vestibular network, including the rolandic operculum and insula bilaterally, and of the cognitive task in parietal and frontal regions. Interestingly, we found an interaction of stimulation and task in the left inferior parietal lobe, suggesting that this region represents the modulation of imagined body rotations by vestibular input. This result provides evidence that the inferior parietal lobe plays a crucial role in the neural integration of mental and physical body rotation