Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset.

MRI plays a crucial role in multiple sclerosis diagnostic and patient follow-up. In particular, the delineation of T2-FLAIR hyperintense lesions is crucial although mostly performed manually - a tedious task. Many methods have thus been proposed to automate this task. However, sufficiently large datasets with a thorough expert manual segmentation are still lacking to evaluate these methods. We present a unique dataset for MS lesions segmentation evaluation. It consists of 53 patients acquired on 4 different scanners with a harmonized protocol. Hyperintense lesions on FLAIR were manually delineated on each patient by 7 experts with control on T2 sequence, and gathered in a consensus segmentation for evaluation. We provide raw and preprocessed data and a split of the dataset into training and testing data, the latter including data from a scanner not present in the training dataset. We strongly believe that this dataset will become a reference in MS lesions segmentation evaluation, allowing to evaluate many aspects: evaluation of performance on unseen scanner, comparison to individual experts performance, comparison to other challengers who already used this dataset, etc.

Visual Dysfunction of the Superior Colliculus in De Novo Parkinsonian Patients.

OBJECTIVE: The dual hit hypothesis about the pathogenesis of Parkinson disease (PD) suggests that the brainstem is a convergent area for the propagation of pathological α-synuclein from the periphery to the brain. Although brainstem structures are likely to be affected early in the course of the disease, detailed information regarding specific brainstem regions is lacking. The aim of our study was to investigate the function of the superior colliculus, a sensorimotor brainstem structure, in de novo PD patients compared to controls using brain functional magnetic imaging and visual stimulation paradigms. METHODS: De novo PD patients and controls were recruited. PD subjects were imaged before and after starting PD medications. A recently developed functional magnetic resonance imaging protocol was used to stimulate and visualize the superior colliculus and 2 other visual structures: the lateral geniculate nucleus and the primary visual cortex. RESULTS: In the 22 PD patients, there was no modulation of the superior colliculus responses to the luminance contrasts compared to controls. This implies a hypersensitivity to low luminance contrast and abnormal rapid blood oxygenation level-dependent signal saturation to high luminance contrasts. The lateral geniculate nucleus was only modulated by 3 to 9% luminance contrasts compared to controls. No major differences were found in the primary visual cortex between both groups. INTERPRETATION: Our findings suggest that pathological superior colliculus visual responses in de novo PD patients are present early in the course of the disease. Changes in imaging the superior colliculus could play an important role as a preclinical biomarker of the disease. ANN NEUROL 2020;87:533-546.

Neural correlates of intra-saccadic motion perception.

Retinal motion of the visual scene is not consciously perceived during ocular saccades in normal everyday conditions. It has been suggested that extra-retinal signals actively suppress intra-saccadic motion perception to preserve stable perception of the visual world. However, using stimuli optimized to preferentially activate the M-pathway, Castet and Masson (2000) demonstrated that motion can be perceived during a saccade. Based on this psychophysical paradigm, we used electroencephalography and eye-tracking recordings to investigate the neural correlates related to the conscious perception of intra-saccadic motion. We demonstrated the effective involvement during saccades of the cortical areas V1-V2 and MT-V5, which convey motion information along the M-pathway. We also showed that individual motion perception was related to retinal temporal frequency.

Multivariate pattern analysis of fMRI data for imaginary and real colours in grapheme-colour synaesthesia.

Grapheme-colour synaesthesia is a subjective phenomenon related to perception and imagination, in which some people involuntarily but systematically associate specific, idiosyncratic colours to achromatic letters or digits. Its investigation is relevant to unravel the neural correlates of colour perception in isolation from low-level neural processing of spectral components, as well as the neural correlates of imagination by being able to reliably trigger imaginary colour experiences. However, functional MRI studies using univariate analyses failed to provide univocal evidence of the activation of the "colour network" by synaesthesia. Applying multivariate (multivoxel) pattern analysis (MVPA) on 20 synaesthetes and 20 control participants, we tested whether the neural processing of real colours (concentric rings) and synaesthetic colours (black graphemes) shared patterns of activations. Region of interest analyses in retinotopically and anatomically defined visual areas revealed neither evidence of shared circuits for real and synaesthetic colour processing, nor processing difference between synaesthetes and controls. We also found no correlation with individual experiences, characterised by measuring the strength of synaesthetic associations. The whole brain searchlight analysis led to similar results. We conclude that revealing the neural coding of the synaesthetic experience of colours is a hard task which requires the improvement of our current methodology: for example involving more individuals and achieving higher MR signal to noise ratio and spatial resolution. So far, we have not found any evidence of the involvement of the cortical colour network in the subjective experience of synaesthetic colours.

Neural circuits for long-range color filling-in.

Surface color appearance depends on both local surface chromaticity and global context. How are these inter-dependencies supported by cortical networks? Combining functional imaging and psychophysics, we examined if color from long-range filling-in engages distinct pathways from responses caused by a field of uniform chromaticity. We find that color from filling-in is best classified and best correlated with appearance by two dorsal areas, V3A and V3B/KO. In contrast, a field of uniform chromaticity is best classified by ventral areas hV4 and LO. Dynamic causal modeling revealed feedback modulation from area V3A to areas V1 and LO for filling-in, contrasting with feedback from LO modulating areas V1 and V3A for a matched uniform chromaticity. These results indicate a dorsal stream role in color filling-in via feedback modulation of area V1 coupled with a cross-stream modulation of ventral areas suggesting that local and contextual influences on color appearance engage distinct neural networks.

Effects of aging on low luminance contrast processing in humans.

Luminance contrast is a fundamental visual cue. Using a dedicated neuroimaging framework, we sought to characterize typical Blood Oxygen Level Dependent (BOLD) responses in two subcortical regions, the superior colliculus (SC) and the lateral geniculate nucleus (LGN), and V1, the primary visual cortex area, and how they change over the lifespan. For imaging subcortical activity related to luminance contrast modulation, specific measurements were introduced to rule out possible signal contamination by cardiovascular activity and vascular alterations with age that could hamper the BOLD signal interpretation. Clearly, BOLD responses increased in these three regions with luminance contrast, with a statistically significant diminution in LGN and V1 for older compared to younger participants, while basal perfusion remained unchanged. Additionally, perceptual responses, as assessed with psychophysical experiments, were highly correlated to BOLD measures in the three studied regions. Taken together, fMRI and psychophysics results indicate an alteration of luminance contrast processing with normal aging. Based on this knowledge we can better recognize when age-related brain changes vary from these expectations especially during neurodegenerative diseases progression where the functioning of subcortical structures is altered. The proposed fMRI-physchophysics methodology allows performing such investigation.

Feasibility and reliability of an automated controller of inspired oxygen concentration during mechanical ventilation.

INTRODUCTION: Hypoxemia and high fractions of inspired oxygen (FiO2) are concerns in critically ill patients. An automated FiO2 controller based on continuous oxygen saturation (SpO2) measurement was tested. Two different SpO2-FiO2 feedback open loops, designed to react differently based on the level of hypoxemia, were compared. The results of the FiO2 controller were also compared with a historical control group. METHODS: The system measures SpO2, compares with a target range (92% to 96%), and proposes in real time FiO2 settings to maintain SpO2 within target. In 20 patients under mechanical ventilation, two different FiO2-SpO2 open loops were applied by a dedicated research nurse during 3 hours, each in random order. The times spent in and outside the target SpO2 values were measured. The results of the automatic controller were then compared with a retrospective control group of 30 ICU patients. SpO2-FiO2 values of the control group were collected over three different periods of 6 hours. RESULTS: Time in the target range was higher than 95% with the controller. When the 20 patients were separated according to the median PaO2/FiO2 (160(133-176) mm Hg versus 239(201-285)), the loop with the highest slope was slightly better (P = 0.047) for the more-hypoxemic patients. Hyperoxemia and hypoxemia durations were significantly shorter with the controller compared with usual care: SpO2 target range was reached 90% versus 24%, 27% and 32% (P < .001) with the controller, compared with three historical control-group periods. CONCLUSION: A specific FiO2 controller is able to maintain SpO2 reliably within a predefined target range. Two different feedback loops can be used, depending on the initial PaO2/FiO2; with both, the automatic controller showed excellent performance when compared with usual care.

Spatial selectivity of the watercolor effect.

The spatial selectivity of the watercolor effect (WCE) was assessed by measuring its strength as a function of the luminance contrast of its inducing contours for different spatial configurations, using a maximum likelihood scaling procedure. The approach has previously been demonstrated to provide an efficient method for investigating the WCE as well as other perceptual dimensions. We show that the strength is narrowly tuned to the width of the contour, that it is optimal when its pair of inducing contours are of equal width, and that the strength can be increased by varying the overall size of the stimulus when the width of the inducing contour is not optimal. The results support a neural substrate that has characteristics not unlike double-opponent, color-luminance cells observed in cortical area V1.

Contributions of contour frequency, amplitude, and luminance to the watercolor effect estimated by conjoint measurement.

The watercolor effect is a long-range, assimilative, filling-in phenomenon induced by a pair of distant, wavy contours of different chromaticities. Here, we measured joint influences of the contour frequency and amplitude and the luminance of the interior contour on the strength of the effect. Contour pairs, each enclosing a circular region, were presented with two of the dimensions varying independently across trials (luminance/frequency, luminance/amplitude, frequency/amplitude) in a conjoint measurement paradigm (Luce & Tukey, 1964). In each trial, observers judged which of the stimuli evoked the strongest fill-in color. Control stimuli were identical except that the contours were intertwined and generated little filling-in. Perceptual scales were estimated by a maximum likelihood method (Ho, Landy, & Maloney, 2008). An additive model accounted for the joint contributions of any pair of dimensions. As shown previously using difference scaling (Devinck & Knoblauch, 2012), the strength increases with luminance of the interior contour. The strength of the phenomenon was nearly independent of the amplitude of modulation of the contour but increased with its frequency up to an asymptotic level. On average, the strength of the effect was similar along a given dimension regardless of the other dimension with which it was paired, demonstrating consistency of the underlying estimated perceptual scales.

Graph-based methods coupled with specific distributional distances for adversarial attack detection.

Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These adversarial attacks have been the focus of extensive research. Likewise, there has been an abundance of research in ways to detect and defend against them. We introduce a novel approach of detection and interpretation of adversarial attacks from a graph perspective. For an input image, we compute an associated sparse graph using the layer-wise relevance propagation algorithm (Bach et al., 2015). Specifically, we only keep edges of the neural network with the highest relevance values. Three quantities are then computed from the graph which are then compared against those computed from the training set. The result of the comparison is a classification of the image as benign or adversarial. To make the comparison, two classification methods are introduced: (1) an explicit formula based on Wasserstein distance applied to the degree of node and (2) a logistic regression. Both classification methods produce strong results which lead us to believe that a graph-based interpretation of adversarial attacks is valuable.

Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis.

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. End users are particularly reluctant to rely on the opaque predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential solution, to reduce the black-box effect of DL models and increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated with DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their variable quality, as well as constraints associated with real-world clinical routine. Moreover, we discuss the concept of structural uncertainty, a corpus of methods to facilitate the alignment of segmentation uncertainty estimates with clinical attention. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges for uncertainty quantification in the medical field.

Retinotopic and lateralized processing of spatial frequencies in human visual cortex during scene categorization.

Using large natural scenes filtered in spatial frequencies, we aimed to demonstrate that spatial frequency processing could not only be retinotopically mapped but could also be lateralized in both hemispheres. For this purpose, participants performed a categorization task using large black and white photographs of natural scenes (indoors vs. outdoors, with a visual angle of 24° × 18°) filtered in low spatial frequencies (LSF), high spatial frequencies (HSF), and nonfiltered scenes, in block-designed fMRI recording sessions. At the group level, the comparison between the spatial frequency content of scenes revealed first that, compared with HSF, LSF scene categorization elicited activation in the anterior half of the calcarine fissures linked to the peripheral visual field, whereas, compared with LSF, HSF scene categorization elicited activation in the posterior part of the occipital lobes, which are linked to the fovea, according to the retinotopic property of visual areas. At the individual level, functional activations projected on retinotopic maps revealed that LSF processing was mapped in the anterior part of V1, whereas HSF processing was mapped in the posterior and ventral part of V2, V3, and V4. Moreover, at the group level, direct interhemispheric comparisons performed on the same fMRI data highlighted a right-sided occipito-temporal predominance for LSF processing and a left-sided temporal cortex predominance for HSF processing, in accordance with hemispheric specialization theories. By using suitable method of analysis on the same data, our results enabled us to demonstrate for the first time that spatial frequencies processing is mapped retinotopically and lateralized in human occipital cortex.

The neural bases of grapheme-color synesthesia are not localized in real color-sensitive areas.

The subjective experience of color by synesthetes when viewing achromatic letters and numbers supposedly relates to real color experience, as exemplified by the recruitment of the V4 color center observed in some brain imaging studies. Phenomenological reports and psychophysics tests indicate, however, that both experiences are different. Using functional magnetic resonance imaging, we tried to precise the degree of coactivation by real and synesthetic colors, by evaluating each color center individually, and applying adaptation protocols across real and synesthetic colors. We also looked for structural differences between synesthetes and nonsynesthetes. In 10 synesthetes, we found that color areas and retinotopic areas were not activated by synesthetic colors, whatever the strength of synesthetic associations measured objectively for each subject. Voxel-based morphometry revealed no white matter (WM) or gray matter difference in those regions when compared with 25 control subjects. But synesthetes had more WM in the retrosplenial cortex bilaterally. The joint coding of real and synesthetic colors, if it exists, must therefore be distributed rather than localized in the visual cortex. Alternatively, the key to synesthetic color experience might not lie in the color system.

Nodal-statistics-based equivalence relation for graph collections.

Node role explainability in complex networks is very difficult yet is crucial in different application domains such as social science, neurosciences, or computer science. Many efforts have been made on the quantification of hubs revealing particular nodes in a network using a given structural property. Yet, in several applications, when multiple instances of networks are available and several structural properties appear to be relevant, the identification of node roles remains largely unexplored. Inspired by the node automorphically equivalence relation, we define an equivalence relation on graph nodes associated with any collection of nodal statistics (i.e., any functions on the node set). This allows us to define new graph global measures: the power coefficient and the orthogonality score to evaluate the parsimony and heterogeneity of a given nodal statistics collection. In addition, we introduce a new method based on structural patterns to compare graphs that have the same vertices set. This method assigns a value to a node to determine its role distinctiveness in a graph family. Extensive numerical results of our method are conducted on both generative graph models and real data concerning human brain functional connectivity. The differences in nodal statistics are shown to be dependent on the underlying graph structure. Comparisons between generative models and real networks combining two different nodal statistics reveal the complexity of human brain functional connectivity with differences at both global and nodal levels. Using a group of 200 healthy controls connectivity networks, our method computes high correspondence scores among the whole population to detect homotopy and finally quantify differences between comatose patients and healthy controls.

ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization.

ImUnity is an original 2.5D deep-learning model designed for efficient and flexible MR image harmonization. A VAE-GAN network, coupled with a confusion module and an optional biological preservation module, uses multiple 2D slices taken from different anatomical locations in each subject of the training database, as well as image contrast transformations for its training. It eventually generates 'corrected' MR images that can be used for various multi-center population studies. Using 3 open source databases (ABIDE, OASIS and SRPBS), which contain MR images from multiple acquisition scanner types or vendors and a large range of subjects ages, we show that ImUnity: (1) outperforms state-of-the-art methods in terms of quality of images generated using traveling subjects; (2) removes sites or scanner biases while improving patients classification; (3) harmonizes data coming from new sites or scanners without the need for an additional fine-tuning and (4) allows the selection of multiple MR reconstructed images according to the desired applications. Tested here on T1-weighted images, ImUnity could be used to harmonize other types of medical images.

A BOLD signature of eyeblinks in the visual cortex.

We are usually unaware of the brief but large illumination changes caused by blinks, presumably because of blink suppression mechanisms. In fMRI however, increase of the BOLD signal was reported in the visual cortex, e.g. during blocks of voluntary blinks (Bristow, Frith and Rees, 2005) or after spontaneous blinks recorded during the prolonged fixation of a static stimulus (Tse, Baumgartner and Greenlee, 2010). We tested whether such activation, possibly related to illumination changes, was also present during standard fMRI retinotopic and visual experiments and was large enough to contaminate the BOLD signal we are interested in. We monitored in a 3T scanner the eyeblinks of 14 subjects who observed three different types of visual stimuli, including periodic rotating wedges and contracting/expanding rings, event-related Mondrians and graphemes, while fixating. We performed event-related analyses on the set of detected spontaneous blinks. We observed large and widespread BOLD responses related to blinks in the visual cortex of every subject and whatever the visual stimulus. The magnitude of the modulation was comparable to visual stimulation. However, blink-related activations lay mostly in the anterior parts of retinotopic visual areas, coding the periphery of the visual field well beyond the extent of our stimuli. Blinks therefore represent an important source of BOLD variations in the visual cortex and a troublesome source of noise since any correlation, even weak, between the distribution of blinks and a tested protocol could trigger artifactual activities. However, the typical signature of blinks along the anterior calcarine and the parieto-occipital sulcus allows identifying, even in the absence of eyetracking, fMRI protocols possibly contaminated by a heterogeneous distribution of blinks.

Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients.

With the advent of recent deep learning techniques, computerized methods for automatic lesion segmentation have reached performances comparable to those of medical practitioners. However, little attention has been paid to the detection of subtle physiological changes caused by evolutive pathologies, such as neurodegenerative diseases. In this work, we leverage deep learning models to detect anomalies in brain diffusion tensor imaging (DTI) parameter maps of recently diagnosed and untreated (de novo) patients with Parkinson's disease (PD). For this purpose, we trained auto-encoders on parameter maps of healthy controls (n = 56) and tested them on those of de novo PD patients (n = 129). We considered large reconstruction errors between the original and reconstructed images to be anomalies that, when quantified, allow discerning between de novo PD patients and healthy controls. The most discriminating brain macro-region was found to be the white matter with a ROC-AUC 68.3 (IQR 5.4) and the best subcortical structure, the GPi (ROC-AUC 62.6 IQR 5.4). Our results indicate that our deep learning-based model can detect potentially pathological regions in de novo PD patients, without requiring any expert delineation. This may enable extracting neuroimaging biomarkers of PD in the future, but further testing on larger cohorts is needed. Such models can be seamlessly extended with additional parameter maps and applied to study the physio-pathology of other neurological diseases.

Effective connectivity in subcortical visual structures in de novo Patients with Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) manifests with the appearance of non-motor symptoms before motor symptoms onset. Among these, dysfunctioning visual structures have recently been reported to occur at early disease stages. OBJECTIVE: This study addresses effective connectivity in the visual network of PD patients. METHODS: Using functional MRI and dynamic causal modeling analysis, we evaluated the connectivity between the superior colliculus, the lateral geniculate nucleus and the primary visual area V1 in de novo untreated PD patients (n = 22). A subset of the PD patients (n = 8) was longitudinally assessed two times at two months and at six months after starting dopaminergic treatment. Results were compared to those of age-matched healthy controls (n = 22). RESULTS: Our results indicate that the superior colliculus drives cerebral activity for luminance contrast processing both in healthy controls and untreated PD patients. The same effective connectivity was observed with neuromodulatory differences in terms of neuronal dynamic interactions. Our main findings were that the modulation induced by luminance contrast changes of the superior colliculus connectivity (self-connectivity and connectivity to the lateral geniculate nucleus) was inhibited in PD patients (effect of contrast: p = 0.79 and p = 0.77 respectively). The introduction of dopaminergic medication in a subset (n = 8) of the PD patients failed to restore the effective connectivity modulation observed in the healthy controls. INTERPRETATION: The deficits in luminance contrast processing in PD was associated with a deficiency in connectivity adjustment from the superior colliculus to the lateral geniculate nucleus and to V1. No differences in cerebral blood flow were observed between controls and PD patients suggesting that the deficiency was at the neuronal level. Administration of a dopaminergic treatment over six months was not able to normalize the observed alterations in inter-regional coupling. These findings highlight the presence of early dysfunctions in primary visual areas, which might be used as early markers of the disease.

Use of maximum end-tidal CO(2) values to improve end-tidal CO(2) monitoring accuracy.

BACKGROUND: The arterial partial pressure of CO(2) (P(aCO(2))) can be grossly estimated by the end-tidal partial pressure of CO(2) (P(ETCO(2))). This principle is used in SmartCare (Dräger, Lübeck, Germany), which is an automated closed-loop system that uses P(ETCO(2)) to estimate alveolar ventilation during mechanical ventilation. OBJECTIVE: To assess whether the maximum P(ETCO(2)) value (instead of the averaged P(ETCO(2)) value) over 2-min or 5-min periods improves P(aCO(2)) estimation, and determine the consequences for the SmartCare system. METHODS: We continuously monitored breath-by-breath P(ETCO(2)) during ventilation with SmartCare in 36 patients mechanically ventilated for various disorders, including 14 patients with COPD. Data were collected simultaneously from SmartCare recordings, every 2 min or 5 min, and through a dedicated software that recorded ventilation data every 10 s. We compared the maximum and averaged P(ETCO(2)) values over 2-min and 5-min periods to the P(aCO(2)) measured from 80 arterial blood samples clinically indicated in 26 patients. We also compared SmartCare's classifications of patient ventilatory status based on averaged P(ETCO(2)) values to what the classifications would have been with the maximum P(ETCO(2)) values. RESULTS: Mean P(aCO(2)) was 44 ± 11 mm Hg. P(aCO(2)) was higher than averaged P(ETCO(2)) by 10 ± 6 mm Hg, and this difference was reduced to 6 ± 6 mm Hg with maximum P(ETCO(2)). The results were similar whether patients had COPD or not. Very few aberrant values (< 0.01%) needed to be discarded. Among the 3,137 classifications made by the SmartCare system, 1.6% were changed by using the maximum P(ETCO(2)) value instead of the averaged P(ETCO(2)) value. CONCLUSIONS: Use of maximum P(ETCO(2)) reduces the difference between P(aCO(2)) and P(ETCO(2)) and improves SmartCare's classification of patient ventilatory status.

Automated Quantification of Brain Lesion Volume From Post-trauma MR Diffusion-Weighted Images.

Objectives: Determining the volume of brain lesions after trauma is challenging. Manual delineation is observer-dependent and time-consuming and cannot therefore be used in routine practice. The study aimed to evaluate the feasibility of an automated atlas-based quantification procedure (AQP) based on the detection of abnormal mean diffusivity (MD) values computed from diffusion-weighted MR images. Methods: The performance of AQP was measured against manual delineation consensus by independent raters in two series of experiments based on: (i) realistic trauma phantoms (n = 5) where low and high MD values were assigned to healthy brain images according to the intensity, form and location of lesion observed in real TBI cases; (ii) severe TBI patients (n = 12 patients) who underwent MR imaging within 10 days after injury. Results: In realistic TBI phantoms, no statistical differences in Dice similarity coefficient, precision and brain lesion volumes were found between AQP, the rater consensus and the ground truth lesion delineations. Similar findings were obtained when comparing AQP and manual annotations for TBI patients. The intra-class correlation coefficient between AQP and manual delineation was 0.70 in realistic phantoms and 0.92 in TBI patients. The volume of brain lesions detected in TBI patients was 59 ml (19-84 ml) (median; 25-75th centiles). Conclusions: Our results support the feasibility of using an automated quantification procedure to determine, with similar accuracy to manual delineation, the volume of low and high MD brain lesions after trauma, and thus allow the determination of the type and volume of edematous brain lesions. This approach had comparable performance with manual delineation by a panel of experts. It will be tested in a large cohort of patients enrolled in the multicenter OxyTC trial (NCT02754063).