Fetal brain MRI atlases and datasets: A review.

Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.

Enhancing T1 signal of normal-appearing white matter with divided subtracted inversion recovery: A pilot study in mild traumatic brain injury.

Magnetic resonance imaging (MRI) and cognitive profiles in patients with mild traumatic brain injury (mTBI) are often discordant. Conventional MRI seldom captures the full extent of pathological changes in the normal-appearing white matter (NAWM). The divided subtracted inversion recovery (dSIR) technique may enhance T1 differences in NAWM, making them easily visible. We aimed to implement dSIR on a clinical scanner and tested results in mTBI patients. To produce dSIR images, Inversion Recovery-Turbo Spin Echo sequences were modified using six different inversion times (TI) on a 3-T scanner in healthy participants and patients with mTBI. The multiple TIs determined normal white (TIshort) and gray matter (TIlong) nulling points in healthy subjects, which were used to create dSIR images. In one patient, the protocol was repeated at 3 months to identify changes after rehabilitation. Diffusion tensor imaging (DTI)-derived mean diffusivity (MD) and fractional anisotropy (FA) maps were aligned to dSIR images to ensure that signal was not artefactual. Ten healthy participants (five females; age 24 ± 3 [95% CI: 21, 26] years) were included. TIshort and TIlong were set at 450 and 750 ms, respectively. In both patients (one male, age 17 years; one female, age 14 years), dSIR images revealed areas with increased T1 in the NAWM not visible on conventional MRI. dSIR-based hyperintensities corresponded to elevated MD and reduced FA. Substantial changes were found at follow-up with improvement in DTI-based parameters. dSIR images enhance subtle changes in the NAWM of patients with mTBI by amplifying their intrinsic T1 signal.

A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment.

Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.

Alterations in neural activation in the ventral frontoparietal network during complex magnocellular stimuli in developmental dyslexia associated with READ1 deletion.

BACKGROUND: An intronic deletion within intron 2 of the DCDC2 gene encompassing the entire READ1 (hereafter, READ1d) has been associated in both children with developmental dyslexia (DD) and typical readers (TRs), with interindividual variation in reading performance and motion perception as well as with structural and functional brain alterations. Visual motion perception -- specifically processed by the magnocellular (M) stream -- has been reported to be a solid and reliable endophenotype of DD. Hence, we predicted that READ1d should affect neural activations in brain regions sensitive to M stream demands as reading proficiency changes. METHODS: We investigated neural activations during two M-eliciting fMRI visual tasks (full-field sinusoidal gratings controlled for spatial and temporal frequencies and luminance contrast, and sensitivity to motion coherence at 6%, 15% and 40% dot coherence levels) in four subject groups: children with DD with/without READ1d, and TRs with/without READ1d. RESULTS: At the Bonferroni-corrected level of significance, reading skills showed a significant effect in the right polar frontal cortex during the full-field sinusoidal gratings-M task. Regardless of the presence/absence of the READ1d, subjects with poor reading proficiency showed hyperactivation in this region of interest (ROI) compared to subjects with better reading scores. Moreover, a significant interaction was found between READ1d and reading performance in the left frontal opercular area 4 during the 15% coherent motion sensitivity task. Among subjects with poor reading performance, neural activation in this ROI during this specific task was higher for subjects without READ1d than for READ1d carriers. The difference vanished as reading skills increased. CONCLUSIONS: Our findings showed a READ1d-moderated genetic vulnerability to alterations in neural activation in the ventral attentive and salient networks during the processing of relevant stimuli in subjects with poor reading proficiency.

Progressive Spinal Cord Degeneration in Friedreich's Ataxia: Results from ENIGMA-Ataxia.

BACKGROUND: Spinal cord damage is a hallmark of Friedreich's ataxia (FRDA), but its progression and clinical correlates remain unclear. OBJECTIVE: The objective of this study was to perform a characterization of cervical spinal cord structural damage in a large multisite FRDA cohort. METHODS: We performed a cross-sectional analysis of cervical spinal cord (C1-C4) cross-sectional area (CSA) and eccentricity using magnetic resonance imaging data from eight sites within the ENIGMA-Ataxia initiative, including 256 individuals with FRDA and 223 age- and sex-matched control subjects. Correlations and subgroup analyses within the FRDA cohort were undertaken based on disease duration, ataxia severity, and onset age. RESULTS: Individuals with FRDA, relative to control subjects, had significantly reduced CSA at all examined levels, with large effect sizes (d > 2.1) and significant correlations with disease severity (r < -0.4). Similarly, we found significantly increased eccentricity (d > 1.2), but without significant clinical correlations. Subgroup analyses showed that CSA and eccentricity are abnormal at all disease stages. However, although CSA appears to decrease progressively, eccentricity remains stable over time. CONCLUSIONS: Previous research has shown that increased eccentricity reflects dorsal column (DC) damage, while decreased CSA reflects either DC or corticospinal tract (CST) damage, or both. Hence our data support the hypothesis that damage to the DC and damage to CST follow distinct courses in FRDA: developmental abnormalities likely define the DC, while CST alterations may be both developmental and degenerative. These results provide new insights about FRDA pathogenesis and indicate that CSA of the cervical spinal cord should be investigated further as a potential biomarker of disease progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Onset of Chiari type 1 malformation: insights from a small series of intrauterine MR imaging cases.

PURPOSE: Morphometric studies on idiopathic Chiari malformation type 1 (CM1) pathogenesis have been mainly based on post-natal neuroimaging. Prenatal clues related to CM1 development are lacking. We present pre- and post-natal imaging time course in idiopathic CM1 and assess fetal skull and brain biometry to establish if clues about CM1 development are present at fetal age. METHODS: Multicenter databases were screened to retrieve intrauterine magnetic resonance (iuMR) of children presenting CM1 features at post-natal scan. Syndromes interfering with skull-brain growth were excluded. Twenty-two morphometric parameters were measured at fetal (average 24.4 weeks; range 21 to 32) and post-natal (average 15.4 months; range 1 to 45) age; matched controls were included. RESULTS: Among 7000 iuMR cases, post-natal scans were available for 925, with postnatal CM1 features reported in seven. None of the fetuses presented CM1 features. Tonsillar descent was clear at a later post-natal scan in all seven cases. Six fetal parameters resulted to be statistically different between CM1 and controls: basal angle (p = 0.006), clivo-supraoccipital angle (p = 0.044), clivus' length (p = 0.043), posterior cranial fossa (PCF) width (p = 0.009), PCF height (p = 0.045), and PCFw/BPDb (p = 0.013). Postnatally, only the clivus' length was significant between CM1 cases and controls. CONCLUSION: Pre- and post-natal CM1 cases did not share striking common features, making qualitative prenatal assessment not predictive; however, our preliminary results support the view that some of the pathogenetic basis of CM1 may be embedded to some extent already in intrauterine life.

Congenital isolated unilateral third nerve palsy in children: the diagnostic contribution of high-resolution MR imaging.

PURPOSE: To describe the neuroanatomical correlates of unilateral congenital isolated oculomotor palsy by means of high-resolution MRI. METHODS: Children with a clinical diagnosis of congenital isolated oculomotr palsy and with a high-resolution MRI acquisition targeted on the orbits and cranial nerves were selected and included in the study. An experienced pediatric neuroradiologist evaluated all the exams, assessing the integrity and morphology of extraocular muscles, oculomotor, trochlear and abducens nerves as well as optic nerves and globes. Clinical data and ophthalmologic evaluations were also collected. RESULTS: Six children (age range: 1-16 years; males: 3) were selected. All patients showed, on the affected side (left:right = 5:1), anomalies of the III nerve and extraocular muscles innervated by the pathological nerve. One patient had complete nerve agenesis, two patients showed a diffuse thinning of the nerve, from the brainstem to the orbit and 3 patients showed a distal thinning of the oculomotor nerve, starting at the level of the cavernous sinus. In all cases atrophy of corresponding muscles was noticed, but the involvement of the affected muscles varied with the nervous pattern of injury. CONCLUSIONS: High-resolution MRI represents a valuable tool for the diagnosis of III nerve anomalies in unilateral congenital IOP, showing different patterns of nerve involvement and muscular atrophy.

DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients.

T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.

Brain Structure and Degeneration Staging in Friedreich Ataxia: Magnetic Resonance Imaging Volumetrics from the ENIGMA-Ataxia Working Group.

OBJECTIVE: Friedreich ataxia (FRDA) is an inherited neurological disease defined by progressive movement incoordination. We undertook a comprehensive characterization of the spatial profile and progressive evolution of structural brain abnormalities in people with FRDA. METHODS: A coordinated international analysis of regional brain volume using magnetic resonance imaging data charted the whole-brain profile, interindividual variability, and temporal staging of structural brain differences in 248 individuals with FRDA and 262 healthy controls. RESULTS: The brainstem, dentate nucleus region, and superior and inferior cerebellar peduncles showed the greatest reductions in volume relative to controls (Cohen d = 1.5-2.6). Cerebellar gray matter alterations were most pronounced in lobules I-VI (d = 0.8), whereas cerebral differences occurred most prominently in precentral gyri (d = 0.6) and corticospinal tracts (d = 1.4). Earlier onset age predicted less volume in the motor cerebellum (rmax  = 0.35) and peduncles (rmax  = 0.36). Disease duration and severity correlated with volume deficits in the dentate nucleus region, brainstem, and superior/inferior cerebellar peduncles (rmax  = -0.49); subgrouping showed these to be robust and early features of FRDA, and strong candidates for further biomarker validation. Cerebral white matter abnormalities, particularly in corticospinal pathways, emerge as intermediate disease features. Cerebellar and cerebral gray matter loss, principally targeting motor and sensory systems, preferentially manifests later in the disease course. INTERPRETATION: FRDA is defined by an evolving spatial profile of neuroanatomical changes beyond primary pathology in the cerebellum and spinal cord, in line with its progressive clinical course. The design, interpretation, and generalization of research studies and clinical trials must consider neuroanatomical staging and associated interindividual variability in brain measures. ANN NEUROL 2021;90:570-583.

The spectrum of brainstem malformations associated to mutations of the tubulin genes family: MRI and DTI analysis.

OBJECTIVES: To describe the spectrum of brainstem malformations associated to mutations in the tubulin genes taking advantage of magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). METHODS: Fifteen patients (six males; median age, 1.25 years; range, 1 month to 31 years) with mutations in the tubulin genes (TUBA1A = 8, TUBB2B = 4, TUBB3 = 3) studied with MRI and DTI were included in the study. Brain MR exams were reviewed to describe the malformative aspects of the brainstem. Malformations of the supratentorial brain and cerebellum were also recorded. Tractography was performed in seven selected cases. RESULTS: Fourteen patients (93%) showed complex malformations of the brainstem. Most common findings, apparent on anatomical MR sequences, were brainstem asymmetry (12 cases, 5 of which with a crossed pattern characterised by a hypertrophic right medulla oblongata and hypertrophic left pons), short and small pons on midline (10 cases) and anterior brainstem clefting (6 cases). DTI revealed abnormal transverse pontine fibres (13 cases), fusion of corticospinal tracts and medial lemnisci (9 cases) and a small decussation of the superior cerebellar peduncles (7 cases). CONCLUSIONS: Conventional/anatomical MRI and DTI reveal a complex pattern of brainstem malformations associated with tubulin genes mutations. KEY POINTS: • Brainstem malformations affect 93% patients with mutated tubulin genes • MRI shows homolateral and crossed brainstem asymmetries, clefts and pons hypoplasia • DTI demonstrates irregular representation of transverse pontine fibres and fusion of corticospinal tracts.

Stable spline deconvolution for dynamic susceptibility contrast MRI.

PURPOSE: To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI. METHODS: The SS method was compared with both the block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR) methods. oSVD is one of the most popular deconvolution methods in dynamic susceptibility contrast MRI (DSC-MRI). NSR is an alternative approach that we proposed previously. The three methods were compared using simulated data and two clinical data sets. RESULTS: The SS method correctly reconstructed the dispersed residue function and its peak in presence of dispersion, regardless of the delay. In absence of dispersion, SS performs similarly to oSVD and does not correctly reconstruct the residue function and its peak. SS and NSR better differentiate healthy and pathologic CBF values compared with oSVD in all simulated conditions. Using acquired data, SS and NSR provide more clinically plausible and physiological estimates of the residue function and CBF maps compared with oSVD. CONCLUSION: The SS method overcomes some of the limitations of oSVD, such as unphysiological estimates of the residue function and NSR, the latter of which is too computationally expensive to be applied to large data sets. Thus, the SS method is a valuable alternative for CBF quantification using DSC-MRI data. Magn Reson Med 78:1801-1811, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Aberrant supracallosal longitudinal bundle: MR features, pathogenesis and associated clinical phenotype.

OBJECTIVE: To describe the MRI and structural features of a peculiar malformation of the corpus callosum (CC) in a group of young patients with intellectual disability. METHODS: We studied with conventional MRI and DTI a group of subjects showing an aberrant supracallosal bundle, characterized by the presence of a triangle-shaped bulging above the dorsal surface of CC on the midline. Clinical evaluations, CGH-array and instrumental analysis were also collected. RESULTS: Among 85 patients with malformed CC, we identified 15 subjects that showed the supracallosal bundle. The CC was thickened in five cases, long and thinned in three cases, short and thinned in three cases and it had a "ribbon-like" appearance in four subjects. Additional brain anomalies were present in eight cases. DTI colour maps and tractography showed that the bundle had an antero-posterior longitudinal orientation and that the tract bifurcated posteriorly, ending in the posterior hippocampi. Patients had different combinations of neurological symptoms, but all showed mild or severe intellectual disability. CONCLUSIONS: Combining radiological and genetic data with embryological knowledge of the development of cerebral commissures, we hypothesize that the supracallosal bundle represents a vestigial structure, the dorsal fornix, present during fetal life. Its persistence is associated with intellectual disability. KEY POINTS: • An aberrant longitudinal bundle can be detected above corpus callosum. • The presence of the supracallosal bundle is associated with intellectual disability. • The supracallosal bundle may represent a persistent dorsal fornix.

Estimation of arterial arrival time and cerebral blood flow from QUASAR arterial spin labeling using stable spline.

PURPOSE: QUASAR arterial spin labeling (ASL) permits the application of deconvolution approaches for the absolute quantification of cerebral perfusion. Currently, oscillation index regularized singular value decomposition (oSVD) combined with edge-detection (ED) is the most commonly used method. Its major drawbacks are nonphysiological oscillations in the impulse response function and underestimation of perfusion. The aim of this work is to introduce a novel method to overcome these limitations. METHODS: A system identification method, stable spline (SS), was extended to address ASL peculiarities such as the delay in arrival of the arterial blood in the tissue. The proposed framework was compared with oSVD + ED in both simulated and real data. SS was used to investigate the validity of using a voxel-wise tissue T1 value instead of using a single global value (of blood T1 ). RESULTS: SS outperformed oSVD + ED in 79.9% of simulations. When applied to real data, SS exhibited a physiologically realistic range for perfusion and a higher mean value with respect to oSVD + ED (55.5 ± 9.5 SS, 34.9 ± 5.2 oSVD + ED mL/100 g/min). CONCLUSION: SS can represent an alternative to oSVD + ED for the quantification of QUASAR ASL data. Analysis of the retrieved impulse response function revealed that using a voxel wise tissue T1 might be suboptimal.

Classification of first-episode psychosis: a multi-modal multi-feature approach integrating structural and diffusion imaging.

Currently, most of the classification studies of psychosis focused on chronic patients and employed single machine learning approaches. To overcome these limitations, we here compare, to our best knowledge for the first time, different classification methods of first-episode psychosis (FEP) using multi-modal imaging data exploited on several cortical and subcortical structures and white matter fiber bundles. 23 FEP patients and 23 age-, gender-, and race-matched healthy participants were included in the study. An innovative multivariate approach based on multiple kernel learning (MKL) methods was implemented on structural MRI and diffusion tensor imaging. MKL provides the best classification performances in comparison with the more widely used support vector machine, enabling the definition of a reliable automatic decisional system based on the integration of multi-modal imaging information. Our results show a discrimination accuracy greater than 90 % between healthy subjects and patients with FEP. Regions with an accuracy greater than 70 % on different imaging sources and measures were middle and superior frontal gyrus, parahippocampal gyrus, uncinate fascicles, and cingulum. This study shows that multivariate machine learning approaches integrating multi-modal and multisource imaging data can classify FEP patients with high accuracy. Interestingly, specific grey matter structures and white matter bundles reach high classification reliability when using different imaging modalities and indices, potentially outlining a prefronto-limbic network impaired in FEP with particular regard to the right hemisphere.

Comparative analysis of algorithms for whole-genome assembly of pyrosequencing data.

Next-generation sequencing technologies have fostered an unprecedented proliferation of high-throughput sequencing projects and a concomitant development of novel algorithms for the assembly of short reads. In this context, an important issue is the need of a careful assessment of the accuracy of the assembly process. Here, we review the efficiency of a panel of assemblers, specifically designed to handle data from GS FLX 454 platform, on three bacterial data sets with different characteristics in terms of reads coverage and repeats content. Our aim is to investigate their strengths and weaknesses in the reconstruction of the reference genomes. In our benchmarking, we assess assemblers' performance, quantifying and characterizing assembly gaps and errors, and evaluating their ability to solve complex genomic regions containing repeats. The final goal of this analysis is to highlight pros and cons of each method, in order to provide the final user with general criteria for the right choice of the appropriate assembly strategy, depending on the specific needs. A further aspect we have explored is the relationship between coverage of a sequencing project and quality of the obtained results. The final outcome suggests that, for a good tradeoff between costs and results, the planned genome coverage of an experiment should not exceed 20-30 ×.

Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model.

Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against traditional convolutional neural networks (CNNs) in distinguishing between healthy individuals, Becker muscular dystrophy (BMD), and limb-girdle muscular Dystrophy type 2 (LGMD2) patients. Moreover, 3T MRI scans from a retrospective dataset of 75 scans (from 54 subjects) were utilized, with multiparametric protocols capturing various MRI contrasts, including T1-weighted and Dixon sequences. The dataset included 17 scans from healthy volunteers, 27 from BMD patients, and 31 from LGMD2 patients. SwinT and CNNs were trained and validated using a subset of the dataset, with the performance evaluated based on accuracy and F-score. Results indicate the superior accuracy of SwinT (0.96), particularly when employing fat fraction (FF) images as input; it served as a valuable parameter for enhancing classification accuracy. Despite limitations, including a modest cohort size, this study provides valuable insights into the application of AI-driven approaches for precise neuromuscular disorder classification, with potential implications for improving patient care.

Brain Alteration Patterns in Children with Duchenne Muscular Dystrophy: A Machine Learning Approach to Magnetic Resonance Imaging.

Background: Duchenne Muscular Dystrophy (DMD) is a genetic disease in which lack of the dystrophin protein causes progressive muscular weakness, cardiomyopathy and respiratory insufficiency. DMD is often associated with other cognitive and behavioral impairments, however the correlation of abnormal dystrophin expression in the central nervous system with brain structure and functioning remains still unclear. Objective: To investigate brain involvement in patients with DMD through a multimodal and multivariate approach accounting for potential comorbidities. Methods: We acquired T1-weighted and Diffusion Tensor Imaging data from 18 patients with DMD and 18 age- and sex-matched controls with similar cognitive and behavioral profiles. Cortical thickness, structure volume, fractional anisotropy and mean diffusivity measures were used in a multivariate analysis performed using a Support Vector Machine classifier accounting for potential comorbidities in patients and controls. Results: the classification experiment significantly discriminates between the two populations (97.2% accuracy) and the forward model weights showed that DMD mostly affects the microstructural integrity of long fiber bundles, in particular in the cerebellar peduncles (bilaterally), in the posterior thalamic radiation (bilaterally), in the fornix and in the medial lemniscus (bilaterally). We also reported a reduced cortical thickness, mainly in the motor cortex, cingulate cortex, hippocampal area and insula. Conclusions: Our study identified a small pattern of alterations in the CNS likely associated with the DMD diagnosis.

Review on deep learning fetal brain segmentation from Magnetic Resonance images.

Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.

Context expectation influences the gait pattern biomechanics.

Beyond classical aspects related to locomotion (biomechanics), it has been hypothesized that walking pattern is influenced by a combination of distinct computations including online sensory/perceptual sampling and the processing of expectations (neuromechanics). Here, we aimed to explore the potential impact of contrasting scenarios ("risky and potentially dangerous" scenario; "safe and comfortable" scenario) on walking pattern in a group of healthy young adults. Firstly, and consistently with previous literature, we confirmed that the scenario influences gait pattern when it is recalled concurrently to participants' walking activity (motor interference). More intriguingly, our main result showed that participants' gait pattern is also influenced by the contextual scenario when it is evoked only before the start of walking activity (motor expectation). This condition was designed to test the impact of expectations (risky scenario vs. safe scenario) on gait pattern, and the stimulation that preceded walking activity served as prior. Noteworthy, we combined statistical and machine learning (Support-Vector Machine classifier) approaches to stratify distinct levels of analyses that explored the multi-facets architecture of walking. In a nutshell, our combined statistical and machine learning analyses converge in suggesting that walking before steps is not just a paradox.