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.

Protocol to conduct functional magnetic resonance spectroscopy in different age groups of human participants.

We present a protocol to conduct functional magnetic resonance spectroscopy (fMRS) in human participants before, during, and after training on a visual task. We describe steps for participant setup, volume-of-interest placement, fMRS measurement, and post-scan tests. We discuss the design, analysis, and interpretation of fMRS experiments. This protocol can be adapted to investigate the dynamics of chief excitatory and inhibitory neurotransmitters (glutamate and γ-aminobutyric acid, GABA, respectively) while participants perform or learn perceptual, motor, or cognitive tasks. For complete details on the use and execution of this protocol, please refer to Frank et al. (2022).1.

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.

Efficient learning in children with rapid GABA boosting during and after training.

It is generally thought that children learn more efficiently than adults. One way to accomplish this is to have learning rapidly stabilized such that it is not interfered with by subsequent learning. Although γ-aminobutyric acid (GABA) plays an important role in stabilization, it has been reported that GABAergic inhibitory processing is not fully matured yet in children compared with adults. Does this finding indicate that more efficient learning in children is not due to more rapid stabilization? Here, we measured the concentration of GABA in early visual cortical areas in a time-resolved fashion before, during, and after visual perceptual learning (VPL) within subjects using functional MRS (fMRS) and then compared the concentrations between children (8 to 11 years old) and adults (18 to 35 years old). We found that children exhibited a rapid boost of GABA during visual training that persisted after training ended, whereas the concentration of GABA in adults remained unchanged. Moreover, behavioral experiments showed that children exhibited rapid development of resilience to retrograde interference, which indicates that children stabilize VPL much faster than adults. These results together suggest that inhibitory processing in children's brains is more dynamic and adapts more quickly to stabilize learning than in adults, making learning more efficient in children.

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.

Visual Attention Modulates Glutamate-Glutamine Levels in Vestibular Cortex: Evidence from Magnetic Resonance Spectroscopy.

Attending to a stimulus enhances the neuronal responses to it, while responses to nonattended stimuli are not enhanced and may even be suppressed. Although the neural mechanisms of response enhancement for attended stimuli have been intensely studied, the neural mechanisms underlying attentional suppression remain largely unknown. It is uncertain whether attention acts to suppress the processing in sensory cortical areas that would otherwise process the nonattended stimulus or the subcortical input to these cortical areas. Moreover, the neurochemical mechanisms inducing a reduction or suppression of neuronal responses to nonattended stimuli are as yet unknown. Here, we investigated how attention directed toward visual processing cross-modally acts to suppress vestibular responses in the human brain. By using functional magnetic resonance spectroscopy in a group of female and male subjects, we find that attention to visual motion downregulates in a load-dependent manner the concentration of excitatory neurotransmitter (glutamate and its precursor glutamine, referred to together as Glx) within the parietoinsular vestibular cortex (PIVC), a core cortical area of the vestibular system, while leaving the concentration of inhibitory neurotransmitter (GABA) in PIVC unchanged. This makes PIVC less responsive to excitatory thalamic vestibular input, as corroborated by functional magnetic resonance imaging. Together, our results suggest that attention acts to suppress the processing of nonattended sensory cues cortically by neurochemically rendering the core cortical area of the nonattended sensory modality less responsive to excitatory thalamic input.SIGNIFICANCE STATEMENT Here, we address a fundamental problem that has eluded attention research for decades, namely, how the brain ignores irrelevant stimuli. To date, three classes of solutions to this problem have been proposed: (1) enhancement of GABAergic interneuron activity in cortex, (2) downregulation of glutamatergic cell activity in cortex; and (3) downregulation of neural activity in thalamic projection areas, which would then provide the cortex with less input. Here, we use magnetic resonance spectroscopy in humans and find support for the second hypothesis, implying that attention to one sensory modality involves the suppression of irrelevant stimuli of another sensory modality by downregulating glutamate in the cortex.

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.

PSCD Domains of Pleuralin-1 from the Diatom Cylindrotheca fusiformis: NMR Structures and Interactions with Other Biosilica-Associated Proteins.

Diatoms are eukaryotic unicellular algae characterized by silica cell walls and associated with three unique protein families, the pleuralins, frustulins, and silaffins. The NMR structure of the PSCD4 domain of pleuralin-1 from Cylindrotheca fusiformis contains only three short helical elements and is stabilized by five unique disulfide bridges. PSCD4 contains two binding sites for Ca(2+) ions with millimolar affinity. NMR-based interaction studies show an interaction of the domain with native silaffin-1A as well as with α-frustulins. The interaction sites of the two proteins mapped on the PSCD4 structure are contiguous and show only a small overlap. A plausible functional role of pleuralin could be to bind simultaneously silaffin-1A located inside the cell wall and α-frustulin coating the cell wall, thus connecting the interfaces between hypotheca and epitheca at the girdle bands. Restrained molecular dynamics calculations suggest a bead-chain-like structure of the central part of pleuralin-1.

Singular spectrum analysis for an automated solvent artifact removal and baseline correction of 1D NMR spectra.

NMR spectroscopy in biology and medicine is generally performed in aqueous solutions, thus in (1)H NMR spectroscopy, the dominant signal often stems from the partly suppressed solvent and can be many orders of magnitude larger than the resonances of interest. Strong solvent signals lead to a disappearance of weak resonances of interest close to the solvent artifact and to base plane variations all over the spectrum. The AUREMOL-SSA/ALS approach for automated solvent artifact removal and baseline correction has been originally developed for multi-dimensional NMR spectroscopy. Here, we describe the necessary adaptations for an automated application to one-dimensional NMR spectra. Its core algorithm is still based on singular spectrum analysis (SSA) applied on time domain signals (FIDs) and it is still combined with an automated baseline correction (ALS) in the frequency domain. However, both steps (SSA and ALS) have been modified in order to achieve optimal results when dealing with one-dimensional spectra. The performance of the method has been tested on one-dimensional synthetic and experimental spectra including the back-calculated spectrum of HPr protein and an experimental spectrum of a human urine sample. The latter has been recorded with the typically used NOESY-type 1D pulse sequence including water pre-saturation. Furthermore, the fully automated AUREMOL-SSA/ALS procedure includes the managing of oversampled, digitally filtered and zero-filled data and the correction of the frequency domain phase shift caused by the group delay time shift from the digital finite response filtering.

Automated solvent artifact removal and base plane correction of multidimensional NMR protein spectra by AUREMOL-SSA.

Strong solvent signals lead to a disappearance of weak protein signals close to the solvent resonance frequency and to base plane variations all over the spectrum. AUREMOL-SSA provides an automated approach for solvent artifact removal from multidimensional NMR protein spectra. Its core algorithm is based on singular spectrum analysis (SSA) in the time domain and is combined with an automated base plane correction in the frequency domain. The performance of the method has been tested on synthetic and experimental spectra including two-dimensional NOESY and TOCSY spectra and a three-dimensional (1)H,(13)C-HCCH-TOCSY spectrum. It can also be applied to frequency domain spectra since an optional inverse Fourier transformation is included in the algorithm.