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While language is expressed in multiple modalities, including sign, writing, or whistles, speech is arguably the most common. The human vocal tract is capable of producing the bewildering diversity of the 7000 or so currently spoken languages, but relatively little is known about its genetic bases, especially in what concerns normal variation. Here, we capitalize on five cohorts totaling 632 Dutch twins with structural magnetic resonance imaging (MRI) data. Two raters placed clearly defined (semi)landmarks on each MRI scan, from which we derived 146 measures capturing the dimensions and shape of various vocal tract structures, but also aspects of the head and face. We used Genetic Covariance Structure Modeling to estimate the additive genetic, common environmental or non-additive genetic, and unique environmental components, while controlling for various confounds and for any systematic differences between the two raters. We found high heritability, h2, for aspects of the skull and face, the mandible, the anteroposterior (horizontal) dimension of the vocal tract, and the position of the hyoid bone. These findings extend the existing literature, and open new perspectives for understanding the complex interplay between genetics, environment, and culture that shape our vocal tracts, and which may help explain cross-linguistic differences in phonetics and phonology.

MRI; Lipoproteins Ressonància magnètica; Lipoproteïnes Resonancia magnética; Lipoproteínas Introduction: Cerebral amyloid angiopathy (CAA) is characterized by the accumulation of amyloid-β (Aβ) in brain vessels and is a main cause of lobar intracerebral hemorrhage (ICH) in the elderly. CAA is associated with magnetic resonance imaging (MRI) markers of small vessel disease (SVD). Since Aβ is also accumulated in Alzheimer’s disease (AD) in the brain parenchyma, we aimed to study if several single nucleotide polymorphisms (SNPs) previously associated with AD were also associated with CAA pathology. Furthermore, we also studied the influence of APOE and CLU genetic variants in apolipoprotein E (ApoE) and clusterin/apolipoprotein J (ApoJ) circulating levels and their distribution among lipoproteins. Methods: The study was carried out in a multicentric cohort of 126 patients with lobar ICH and clinical suspicion of CAA. Results: We observed several SNPs associated with CAA neuroimaging MRI markers [cortical superficial siderosis (cSS), enlarged perivascular spaces in the centrum semiovale (CSO-EPVS), lobar cerebral microbleeds (CMB), white matter hyperintensities (WMH), corticosubcortical atrophy and CAA-SVD burden score]. Concretely, ABCA7 (rs3764650), CLU (rs9331896 and rs933188), EPHA1 (rs11767557), and TREML2 (rs3747742) were significantly associated with a CAA-SVD burden score. Regarding circulating levels of apolipoproteins, protective AD SNPs of CLU [rs11136000 (T) and rs9331896 (C)] were significantly associated with higher HDL ApoJ content in the lobar ICH cohort. APOEε2 carriers presented higher plasma and LDL-associated ApoE levels whereas APOEε4 carriers presented lower plasma ApoE levels. Additionally, we observed that lower circulating ApoJ and ApoE levels were significantly associated with CAA-related MRI markers. More specifically, lower LDL-associated ApoJ and plasma and HDL-associated ApoE levels were significantly associated with CSO-EPVS, lower ApoJ content in HDL with brain atrophy and lower ApoE content in LDL with the extent of cSS. Discussion: This study reinforces the relevance of lipid metabolism in CAA and cerebrovascular functionality. We propose that ApoJ and ApoE distribution among lipoproteins may be associated with pathological features related to CAA with higher ApoE and ApoJ levels in HDL possibly enhancing atheroprotective, antioxidative, and anti-inflammatory responses in cerebral β-amyloidosis. This work was funded by the Instituto de Salud Carlos III (ISCIII), (PI17/00275, PI019/00421, PI20/00465, and PI20/00334) and co-financed by the European Regional Development Fund (FEDER). The Neurovascular Research Laboratory was part of the RICORS-ICTUS-Enfermedades Vasculares Cerebrales Network, ISCIII, Spain (RD21/0006/0007). CIBERDEM (CB07/08/0016) was an ISCIII Project.

MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness., This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

IntroductionThis study aims to explore whole-brain resting-state spontaneous brain activity using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) strategies to find differences among age groups within a population ranging from middle age to older adults.MethodsThe sample comprised 112 healthy persons (M = 68.80, SD = 7.99) aged 48–89 who were split into six age groups (ResultsIncreased activity was found using fALFF in the superior temporal gyrus and inferior frontal gyrus when comparing the first group and the fifth. Regarding ReHo analysis, Group 6 showed increased ReHo in the temporal lobe (hippocampus), right and left precuneus, right caudate, and right and left thalamus depending on the age group. Moreover, significant correlations between age and fALFF and ReHo clusters, as well as with their gray matter volume were found, meaning that the higher the age, the higher the regional synchronization, the lower the fALFF activation, and the lower gray matter of the right thalamus.ConclusionBoth techniques have been shown to be valuable and usable tools for disentangling brain changes in activation in a very low interval of years in healthy aging.

Choosing how much effort to expend is critical for everyday decisions. While several neuroimaging studies have examined effort-based decision-making, results have been highly heterogeneous, leaving unclear which brain regions process effort-related costs and integrate them with rewards. We conducted two meta-analyses of functional magnetic resonance imaging data to examine consistent neural correlates of effort demands (23 studies, 15 maps, 549 participants) and net value (15 studies, 11 maps, 428 participants). The pre-supplementary motor area (pre-SMA) scaled positively with pure effort demand, whereas the ventromedial prefrontal cortex (vmPFC) showed the opposite effect. Moreover, regions that have been previously implicated in value integration in other cost domains, such as the vmPFC and ventral striatum, were consistently involved in signaling net value. The opposite response patterns of the pre-SMA and vmPFC imply that they are differentially involved in the representation of effort costs and value integration. These findings provide conclusive evidence that the vmPFC is a central node for net value computation and reveal potential brain targets to treat motivation-related disorders. Keywords: Effort; Effort-based decision making; Meta-analysis; Subjective value; Supplementary motor area; Value integration; Ventromedial prefrontal cortex; fMRI.

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

Objective: This research aims to study structural brain changes in patients with persistent olfactory dysfunctions after coronavirus disease 2019 (COVID-19). Methods: COVID-19 patients were evaluated using T1-weighted and diffusion tensor imaging (DTI) on a 3T MRI scanner, 9.94 ± 3.83 months after COVID-19 diagnosis. Gray matter (GM) voxel-based morphometry was performed using FSL-VBM. Voxelwise statistical analysis of the fractional anisotropy, mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity was carried out with the tract-based spatial statistics in the olfactory system. The smell identification test (UPSIT) was used to classify patients as normal olfaction or olfactory dysfunction groups. Intergroup comparisons between GM and DTI measures were computed, as well as correlations with the UPSIT scores. Results: Forty-eight COVID-19 patients were included in the study. Twenty-three were classified as olfactory dysfunction, and 25 as normal olfaction. The olfactory dysfunction group had lower GM volume in a cluster involving the left amygdala, insular cortex, parahippocampal gyrus, frontal superior and inferior orbital gyri, gyrus rectus, olfactory cortex, caudate, and putamen. This group also showed higher MD values in the genu of the corpus callosum, the orbitofrontal area, the anterior thalamic radiation, and the forceps minor; and higher RD values in the anterior corona radiata, the genu of the corpus callosum, and uncinate fasciculus compared with the normal olfaction group. The UPSIT scores for the whole sample were negatively associated with both MD and RD values (p-value ≤0.05 FWE-corrected). Interpretation: There is decreased GM volume and increased MD in olfactory-related regions explaining prolonged olfactory deficits in post-acute COVID-19 patients.

Background/Objective: Neuroimaging studies have shown brain abnormalities in Down syndrome (DS) but have not clarified the underlying mechanisms of dysfunction. Here, we investigated the degree centrality (DC) abnormalities found in the DS group compared with the control group, and we conducted seed-based functional connectivity (FC) with the significant clusters found in DC. Moreover, we used the significant clusters of DC and the seed-based FC to elucidate differences between brain networks in DS compared with controls. Method: The sample comprised 18 persons with DS (M = 28.67, SD = 4.18) and 18 controls (M = 28.56, SD = 4.26). Both samples underwent resting-state functional magnetic resonance imaging. Results: DC analysis showed increased DC in the DS in temporal and right frontal lobe, as well as in the left caudate and rectus and decreased DC in the DS in regions of the left frontal lobe. Regarding seed-based FC, DS showed increased and decreased FC. Significant differences were also found between networks using Yeo parcellations, showing both hyperconnectivity and hypoconnectivity between and within networks. Conclusions: DC, seed-based FC and brain networks seem altered in DS, finding hypo- and hyperconnectivity depending on the areas. Network analysis revealed between- and within-network differences, and these abnormalities shown in DS could be related to the characteristics of the population.

Becoming aware of one's own states is a fundamental aspect for self-monitoring, allowing us to adjust our beliefs of the world to the changing context. Previous evidence points out to the key role of the anterior insular cortex (aIC) in evaluating the consequences of our own actions, especially whenever an error has occurred. In the present study, we propose a new multimodal protocol combining electrical stimulation mapping (ESM) and functional magnetic resonance imaging (fMRI) to explore the functional role of the aIC for self-monitoring in patients undergoing awake brain surgery. Our results using a modified version of the Stroop task tackling metacognitive abilities revealed new direct evidence of the involvement of the aIC in monitoring our performance, showing increased difficulties in detecting action-outcome mismatches when stimulating a cortical site located at the most posterior part of the aIC as well as significant BOLD activations at this region during outcome incongruences for self-made actions. Based on these preliminary results, we highlight the importance of assessing the aIC's functioning during tumor resection involving this region to evaluate metacognitive awareness of the self in patients undergoing awake brain surgery. In a similar vein, a better understanding of the aIC's role during self-monitoring may help shed light on action/outcome processing abnormalities reported in several neuropsychiatric disorders such as schizophrenia, anosognosia for hemiplegia or major depression.

Background Patients with obsessive-compulsive disorder (OCD) present difficulties in the cognitive regulation of emotions, possibly because of inefficient recruitment of distributed patterns of frontal cortex regions. The aim of the present study is to characterize the brain networks, and their dysfunctions, related to emotion regulation alterations observed during cognitive reappraisal in OCD. Methods Adult patients with OCD (n = 31) and healthy controls (HC; n = 30) were compared during performance of a functional magnetic resonance imaging cognitive reappraisal protocol. We used a free independent component analysis approach to analyze network-level alterations during emotional experience and regulation. Correlations with behavioral scores were also explored. Results Analyses were focused on six networks encompassing the frontal cortex. OCD patients showed decreased activation of the frontotemporal network in comparison with HC (F(1,58) = 7.81, p = 0.007) during cognitive reappraisal. A similar trend was observed in the left frontoparietal network. Conclusions The present study demonstrates that patients with OCD show decreased activation of specific networks implicating the frontal cortex during cognitive reappraisal. These outcomes should help to better characterize the psychological processes modulating fear, anxiety, and other core symptoms of patients with OCD, as well as the associated neurobiological alterations, from a system-level perspective.

Age has important implications for health, and understanding how age manifests in the human body is the first step for a potential intervention. This becomes especially important for cardiac health, since age is the main risk factor for development of cardiovascular disease. Data-driven modeling of age progression has been conducted successfully in diverse applications such as face or brain aging. While longitudinal data is the preferred option for training deep learning models, collecting such a dataset is usually very costly, especially in medical imaging. In this work, a conditional generative adversarial network is proposed to synthesize older and younger versions of a heart scan by using only cross-sectional data. We train our model with more than 14,000 different scans from the UK Biobank. The induced modifications focused mainly on the interventricular septum and the aorta, which is consistent with the existing literature in cardiac aging. We evaluate the results by measuring image quality, the mean absolute error for predicted age using a pre-trained regressor, and demonstrate the application of synthetic data for counter-balancing biased datasets. The results suggest that the proposed approach is able to model realistic changes in the heart using only cross-sectional data and that these data can be used to correct age bias in a dataset.

Although Down syndrome (DS) is the most common genetic cause of neurodevelopmental delay, few neuroimaging studies have explored this population. This investigation aimed to study whole-brain resting-state spontaneous brain activity using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) strategies to find differences in spontaneous brain activity among young people with DS and controls and to correlate these results with cognitive outcomes. The sample comprised 18 persons with DS (age mean = 28.67, standard deviation = 4.18) and 18 controls (age mean = 28.56, standard deviation = 4.26). fALFF and ReHo analyses were performed, and the results were correlated with other cognitive variables also collected (KBIT-2 and verbal fluency test). Increased activity was found in DS using fALFF in areas involving the frontal and temporal lobes and left cerebellum anterior lobe. Decreased activity in DS was found in the left parietal and occipital lobe, the left limbic lobe and the left cerebellum posterior lobe. ReHo analysis showed increased activity in certain DS areas of the left frontal lobe and left rectus, as well as the inferior temporal lobe. The areas with decreased activity in the DS participants were regions of the frontal lobe and the right limbic lobe. Altered fALFF and ReHo were found in the DS population, and this alteration could predict the cognitive abilities of the participants. To our knowledge, this is the first study to explore regional spontaneous brain activity in a population with DS. Moreover, this study suggests the possibility of using fALFF and ReHo as biomarkers of cognitive function, which is highly important given the difficulties in cognitively evaluating this population to assess dementia. More research is needed, however, to demonstrate its utility.

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by polysomnography-confirmed REM sleep without atonia and dream-enacting behaviors. This disorder is considered a prodromal syndrome of alpha-synucleinopathies like Parkinson's disease (PD), where it affects more than 50% of PD patients. The underlying pathology of RBD has been generally understood to involve the pontine nuclei within the brainstem. However, the complete pathophysiology beyond the brainstem remains unclear as does its relationship with PD pathology. Therefore, this review aims to survey the neuroimaging literature involving PET, SPECT, and MR imaging techniques to provide an updated understanding of the neuro-chemical, structural, and functional changes in both RBD and PD patients comorbid with RBD. This review found neuroimaging evidence that indicate alterations to the dopaminergic and cholinergic system, blood perfusion, and glucose metabolism in both RBD patients and PD patients with RBD. Beyond the brainstem, structural and functional changes were found to involve the nigrostriatal system, limbic system, and the cortex-suggesting that RBD is a multi-systemic neurodegenerative process. Future investigations are encouraged to follow RBD patients longitudinally using multimodal imaging techniques to enhance our understanding of this parasomnia disorder. Uncovering which individuals are most likely to develop an alpha-synuclein disorder in the prodromal phase will improve patient outcomes and potentially aid in the development of novel treatments for patients affected by RBD.

Music's ability to induce feelings of pleasure has been the subject of intense neuroscientific research lately. Prior neuroimaging studies have shown that music-induced pleasure engages cortico-striatal circuits related to the anticipation and receipt of biologically relevant rewards/incentives, but these reports are necessarily correlational. Here, we studied both the causal role of this circuitry and its temporal dynamics by applying Transcranial Magnetic Stimulation (TMS) over the left dorsolateral prefrontal cortex combined with functional Magnetic Resonance Imaging (fMRI) in 17 male and female participants. Behaviorally, we found that, in accord with previous findings, excitation of fronto-striatal pathways enhanced subjective reports of music-induced pleasure and motivation; whereas inhibition of the same circuitry led to the reduction of both. fMRI activity patterns indicated that these behavioral changes were driven by bidirectional TMS-induced alteration of fronto-striatal function. Specifically, changes in activity in the nucleus accumbens (NAcc) predicted modulation of both hedonic and motivational responses, with a dissociation between pre-experiential vs. experiential components of musical reward. In addition, TMS-induced changes in the fMRI functional connectivity between the NAcc and frontal and auditory cortices predicted the degree of modulation of hedonic responses. These results indicate that the engagement of cortico-striatal pathways and the NAcc, in particular, is indispensable to experience rewarding feelings from music.SIGNIFICANCE STATEMENT:Neuroimaging studies have shown that music-induced pleasure engages cortico-striatal circuits involved in the processing of biologically relevant rewards. Yet, these reports are necessarily correlational. Here, we studied both the causal role of this circuitry and its temporal dynamics by combining brain stimulation over the frontal cortex with functional imaging. Behaviorally, we found that excitation and inhibition of fronto-striatal pathways enhanced and disrupted, respectively, subjective reports of music-induced pleasure and motivation. These changes were associated with changes in nucleus accumbens (NAcc) activity and NAcc coupling with frontal and auditory cortices, dissociating between pre-experimental vs. experiential components of musical reward. These results indicate that the engagement of cortico-striatal pathways, and the NAcc in particular, is indispensable to experience rewarding feeling from music.

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Ignasi Cos Aguilera i Mihaela Breaban, [en] Functional Magnetic resonance Imaging (fMRI) is a non invasive tool used to examine the cognitive and affective processes in the brain. It is important to note that, although fMRI is indirect, it has been used with success to corroborate the results from the animal into the human brain and improve models to include more common human characteristics that influence behavior. This work aims to answer to the question "Which cortical and subcortical areas are involved in anticipation of the consequence?" by doing a Activation Likelihood Estimation Meta-Analysis over reported results of several papers from literature. These reports used fMRI to track the brain hemodynamic response of the subjects in different tasks and reported the peak coordinates of activation. The prefrontal cortex was observed to contain a consequence network that was modulated by being rewarded with a high incentive in a decision-making task. Another concordant regional activation in the frontal lobe, insula and putamen was found during "n-back" task for working memory and decision-making tasks.

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Oliver Díaz, [en] Currently, multiple machine learning models have been implemented to assist radiology staff in the decision-making process during the analysis of medical images for cancerous lesions, both benign and malignant. Despite this, such models mostly cannot be trained with sufficient images, due to the ethical and legal complications in obtaining the images. For this purpose, the objective of this study has been defined as the implementation of a deep learning model system, more specifically a Progressively Growing GAN, by means of which two-dimensional medical images of breast MRIs can be generated from a set of other two-dimensional medical images that were provided by the Consorci Corporació Sanitària Parc Taulí. Despite the lack of radiological knowledge and computational capacity, the results of the model can be seen as satisfactory, because the structure of the breasts is preserved, together with an approximation of the breast tissues. However, if these images are intended to be used in another project, the model should be trained on a machine with higher computational capacity by which the model can be trained for a longer time.

Rapid eye movement sleep behavior disorder (RBD) is associated with high likelihood of prodromal Parkinson’s disease (PD) and is common in de novo PD. It is associated with greater cognitive impairment and brain atrophy. However, the relation between structural brain characteristics and cognition remains poorly understood. We aimed to investigate subcortical and cortical atrophy in de novo PD with probable RBD (PD-pRBD) and to relate it with cognitive impairment. We analyzed volumetry, cortical thickness, and cognitive measures from 79 PD-pRBD patients, 126 PD without probable RBD patients (PD-non pRBD), and 69 controls from the Parkinson’s Progression Markers Initiative (PPMI). Regression models of cognition were tested using magnetic resonance imaging measures as predictors. We found lower left thalamus volume in PD-pRBD compared with PD-non pRBD. Compared with controls, PD-pRBD group showed atrophy in the bilateral putamen, left hippocampus, left amygdala, and thinning in the right superior temporal gyrus. Specific deep gray matter nuclei volumes were associated with impairment in global cognition, phonemic fluency, processing speed, and visuospatial function in PD-pRBD. In conclusion, cognitive impairment and gray matter atrophy are already present in de novo PD-pRBD. Thalamus, hippocampus, and putamen volumes were mainly associated with these cognitive deficits.

Multi-site MRI datasets are crucial for big data research. However, neuroimaging studies must face the batch effect. Here, we propose an approach that uses the predictive probabilities provided by Gaussian processes (GPs) to harmonize clinical-based studies. A multi-site dataset of 216 Parkinson's disease (PD) patients and 87 healthy subjects (HS) was used. We performed a site GP classification using MRI data. The outcomes estimated from this classification, redefined like Weighted HARMonization PArameters (WHARMPA), were used as regressors in two different clinical studies: A PD versus HS machine learning classification using GP, and a VBM comparison (FWE-p < .05, k = 100). Same studies were also conducted using conventional Boolean site covariates, and without information about site belonging. The results from site GP classification provided high scores, balanced accuracy (BAC) was 98.39% for grey matter images. PD versus HS classification performed better when the WHARMPA were used to harmonize (BAC = 78.60%; AUC = 0.90) than when using the Boolean site information (BAC = 56.31%; AUC = 0.71) and without it (BAC = 57.22%; AUC = 0.73). The VBM analysis harmonized using WHARMPA provided larger and more statistically robust clusters in regions previously reported in PD than when the Boolean site covariates or no corrections were added to the model. In conclusion, WHARMPA might encode global site-effects quantitatively and allow the harmonization of data. This method is user-friendly and provides a powerful solution, without complex implementations, to clean the analyses by removing variability associated with the differences between sites.

Background/Objective: Neuroimaging studies have reported abnormalities in the examination of functional connectivity in late-life depression (LLD) in the default mode network (DMN). The present study aims to study resting-state functional connectivity within the DMN in people diagnosed with late-life major depressive disorder (MDD) compared to healthy controls (HCs). Moreover, we would like to differentiate these same connectivity patterns between participants with high vs. low anxiety levels. Method: The sample comprised 56 participants between the ages of 60 and 75; 27 of them were patients with a diagnosis of MDD. Patients were further divided into two samples according to anxiety level: the four people with the highest anxiety level and the five with the lowest anxiety level. Clinical aspects were measured using psychological questionnaires. Each participant underwent functional magnetic resonance imaging (fMRI) acquisition in different regions of interest (ROIs) of the DMN. Results: There was a greater correlation between pairs of ROIs in the control group than in patients with LLD, being this effect preferentially observed in patients with higher anxiety levels. Conclusions: There are differences in functional connectivity within the DMN depending on the level of psychopathology. This can be reflected in these correlations and in the number of clusters and how the brain lateralizes (clustering).

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Oliver Díaz, [en] Breast cancer is the most widely diagnosed cancer, if it is detected in their early stages, it has a hight survival rate, 85 % if it is in the first stage. That’s why it is so important to act by early detection and choosing the best treatment to give to the patient. The aim of this study is to predict the complete pathological response of the patient, the prediction of their total care. To achieve it, a deep learning model will be developed, which by patient data and the magnetic ressonance image that is taken to him when the tumor is detected, will try to predict his pathological response. These data have been obtained thanks to the collaboration of the Hospital Parc Taulí in Sabadell, which provided it. The results obtained show potential although the model developed has not been as complex as it was intended from the beginning due to the lack of greater computing power. That’s why it would be a great idea to continue working in this field when having the necessary tools to be able to develop a model with greater complexity.

The brain and skeletal systems are intimately integrated during development through common molecular pathways. This is evidenced by genetic disorders where brain and skull dysmorphologies are associated. However, the mechanisms underlying neural and skeletal interactions are poorly understood. Using the Ts65Dn mouse model of Down syndrome (DS) as a case example, we performed the first longitudinal assessment of brain, skull and neurobehavioral development to determine alterations in the coordinated morphogenesis of brain and skull. We optimized a multimodal protocol combining in vivo micro-computed tomography (μCT) and magnetic resonance imaging (μMRI) with morphometric analyses and neurodevelopmental tests to longitudinally monitor the different systems' development trajectories during the first postnatal weeks. We also explored the impact of a perinatal treatment with green tea extracts enriched in epigallocatechin-3-gallate (GTE-EGCG), which can modulate cognition, brain and craniofacial development in DS. Our analyses quantified alterations associated with DS, with skull dysmorphologies appearing before brain anomalies, reduced integration and delayed acquisition of neurodevelopmental traits. Perinatal GTE-EGCG induced disparate effects and disrupted the magnitude of integration and covariation patterns between brain and skull. Our results exemplify how a longitudinal research approach evaluating the development of multiple systems can reveal the effect of morphological integration modulating the response of pathological phenotypes to treatment, furthering our understanding of complex genetic disorders. This research was funded by research grants from CRG Awards BrainFace, the Jerome Lejeune Foundation (3M180477), KU Leuven BOF (C24/17/061 and STG/15/024), Grup de Recerca Consolidat en Antropologia Biològica, GREAB (2017 SGR 1630) and a 2-year doctoral fellowship from the Marie-Marguerite Delacroix Foundation to SL

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2021-2022. Director: Saül Pascual-Díaz i Tutor: Alberto Prats Galino, Acquisition parameters play a crucial role in Diffusion Tensor Imaging (DTI), having such an impact on white matter (WM) scalar measures values such as Fractional Anisotropy (FA), Signal to Noise Ratio (SNR) or even when it comes to whole brain tractography studies. Among those acquisitions parameters we find the b-values (e.g. b-value is a factor that reflects the strength and timing of those gradients used to generate Diffusion-Weighted Images; the higher the b-value, the stronger the diffusion effects), voxel size (the smaller the voxel size the higher the quality) and diffusion directions (as the number of directions increases the acquisition time increases as well). This project, places all of them of the focus of several clinical MRI studies performed on healthy subjects in the IDIBAPS’ MRI core facility. Those studies have been grouped into 5 datasets according to different acquisition protocols; in addition, 8 new acquisition proposal are first undergone and analysed. Results show that as expected those parameters play a crucial role in image quality; however, each one of them has a different weight over those quality measurements. This will be discussed by splitting the results into datasets. Furthermore, the analysis performed on the protocol proposals confirm that they have reached what was their goal, to demonstrate that depending on the purpose of the scanner (emergency MRI, clinical MRI and research MRI) a specifical acquisition sequence can be used.

Background: Late-life depression (LLD) is characterized by cognitive and social impairments. Determining neurobiological alterations in connectivity in LLD by means of fMRI may lead to a better understanding of the neural basis underlying this disorder and more precise diagnostic markers. The primary objective of this paper is to identify a structural model that best explains the dynamic effective connectivity (EC) of the default mode network (DMN) in LLD patients compared to controls. Methods: Twenty-seven patients and 29 healthy controls underwent resting-state fMRI during a period of eight minutes. In both groups, jackknife correlation matrices were generated with six ROIs of the DMN that constitute the posterior DMN (pDMN). The different correlation matrices were used as input to estimate each structural equation model (SEM) for each subject in both groups incorporating dynamic effects. Results: The results show that the proposed LLD diagnosis algorithm achieves perfect accuracy in classifying LLD patients and controls. This differentiation is based on three aspects: the importance of ROIs 4 and 6, which seem to be the most distinctive among the subnetworks; the shape that the specific connections adopt in their networks, or in other words, the directed connections that are established among the ROIs in the pDMN for each group; and the number of dynamic effects that seem to be greater throughout the six ROIs studied [t = 54.346; df = 54; p < .001; 95 % CI difference = 5.486-5.906]. Limitations: The sample size was moderate, and the participants continued their current medications. Conclusions: The network models that we developed describe a pattern of dynamic activation in the pDMN that may be considered a possible biomarker for LLD, which may allow early diagnosis of this disorder.

Altres ajuts: This study is also supported in part by grants from the CIBER-Consorcio Centro de Investigación Biomédica en Red-(CB 2021), Ministerio de Ciencia e Innovación and Unión Europea, NextGenerationEU. The diagnosis of the post-COVID condition is usually achieved by excluding other diseases; however, cognitive changes are often found in the post-COVID disorder. Therefore, monitoring and treating the recovery from the post-COVID condition is necessary to establish biomarkers to guide the diagnosis of symptoms, including cognitive impairment. Our study employs a prospected cohort and nested case-control design with mixed methods, including statistical analyses, interviews, and focus groups. Our main aim is to identify biomarkers (functional and structural neural changes, inflammatory and immune status, vascular and vestibular signs and symptoms) easily applied in primary care to detect cognitive changes in post-COVID cases. The results will open up a new line of research to inform diagnostic and therapeutic decisions with special considerations for cognitive impairment in the post-COVID condition.

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p‐hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left–right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta‐analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an “ideal publishing environment,” that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically‐used sample sizes., Region‐wise effect sizes and reproducibility rates of hemispheric asymmetry effects. In general, effects with higher effect sizes showed higher reproducibility, given the same conditions (e.g., sample size and data heterogeneity).

We developed a novel interpretable biological heart age estimation model using cardiovascular magnetic resonance radiomics measures of ventricular shape and myocardial character. We included 29,996 UK Biobank participants without cardiovascular disease. Images were segmented using an automated analysis pipeline. We extracted 254 radiomics features from the left ventricle, right ventricle, and myocardium of each study. We then used Bayesian ridge regression with tenfold cross-validation to develop a heart age estimation model using the radiomics features as the model input and chronological age as the model output. We examined associations of radiomics features with heart age in men and women, observing sex-differential patterns. We subtracted actual age from model estimated heart age to calculate a “heart age delta”, which we considered as a measure of heart aging. We performed a phenome-wide association study of 701 exposures with heart age delta. The strongest correlates of heart aging were measures of obesity, adverse serum lipid markers, hypertension, diabetes, heart rate, income, multimorbidity, musculoskeletal health, and respiratory health. This technique provides a new method for phenotypic assessment relating to cardiovascular aging; further studies are required to assess whether it provides incremental risk information over current approaches.

Included in the neurotrophins family, the Neuritin 1 gene (NRN1) has emerged as an attractive candidate gene for schizophrenia (SZ) since it has been associated with the risk for the disorder and general cognitive performance. In this work, we aimed to further investigate the association of NRN1 with SZ by exploring its role on age at onset and its brain activity correlates. First, we developed two genetic association analyses using a family-based sample (80 early-onset (EO) trios (offspring onset ≤ 18 years) and 71 adult-onset (AO) trios) and an independent case–control sample (120 healthy subjects (HS), 87 EO and 138 AO patients). Second, we explored the effect of NRN1 on brain activity during a working memory task (N-back task; 39 HS, 39 EO and 39 AO; matched by age, sex and estimated IQ). Different haplotypes encompassing the same three Single Nucleotide Polymorphisms(SNPs, rs3763180–rs10484320–rs4960155) were associated with EO in the two samples (GCT, TCC and GTT). Besides, the GTT haplotype was associated with worse N-back task performance in EO and was linked to an inefficient dorsolateral prefrontal cortex activity in subjects with EO compared to HS. Our results show convergent evidence on the NRN1 association with EO both from genetic and neuroimaging approaches, highlighting the role of neurotrophins in the pathophysiology of SZ., This study received funding provided by: (i) Fundación Alicia Koplowitz; (ii) Acadèmia de les Ciències Mèdiques i de la Salut de Catalunya i de Balears (predoctoral contract to C.A.-P.); (iii) the Instituto de Salud Carlos III through a PFIS predoctoral contract to M.G.-R. (FI19/0352) and a Miguel Servet contract to M.F.-V. (CP20/00072), co-funded by European Regional Development Fund (ERDF)/European Social Fund “Investing in your future”; (iv) the Comissionat per a Universitats i Recerca del DIUE of the Generalitat de Catalunya (Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), 2017SGR1271 and 2017SGR1577).

This research was supported by grant PSI2017-84556-P funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe,” and the European Union’s Horizon 2020 Research and Innovation Program under grant agreement number 825903 (euCanSHare project)., Structural magnetic resonance imaging (sMRI) studies have shown that children that differ in some mathematical abilities show differences in gray matter volume mainly in parietal and frontal regions that are involved in number processing, attentional control, and memory. In the present study, a structural neuroimaging analysis based on radiomics and machine learning models is presented with the aim of identifying the brain areas that better predict children’s performance in a variety of mathematical tests. A sample of 77 school-aged children from third to sixth grade were administered four mathematical tests: Math fluency, Calculation, Applied problems and Quantitative concepts as well as a structural brain imaging scan. By extracting radiomics related to the shape, intensity, and texture of specific brain areas, we observed that areas from the frontal, parietal, temporal, and occipital lobes, basal ganglia, and limbic system, were differentially related to children’s performance in the mathematical tests. sMRI-based analyses in the context of mathematical performance have been mainly focused on volumetric measures. However, the results for radiomics-based analysis showed that for these areas, texture features were the most important for the regression models, while volume accounted for less than 15% of the shape importance. These findings highlight the potential of radiomics for more in-depth analysis of medical images for the identification of brain areas related to mathematical abilities., PSI2017-84556-P funded by MCIN/AEI/10.13039/501100011033, “ERDF A way of making Europe, European Union’s Horizon 2020 Research and Innovation Program under grant agreement number 825903 (euCanSHare project)

BackgroundIn mental health, comorbidities are the norm rather than the exception. However, current meta-analytic methods for summarizing the neural correlates of mental disorders do not consider comorbidities, reducing them to a source of noise and bias rather than benefitting from their valuable information.ObjectivesWe describe and validate a novel neuroimaging meta-analytic approach that focuses on comorbidities. In addition, we present the protocol for a meta-analysis of all major mental disorders and their comorbidities.MethodsThe novel approach consists of a modification of Seed-based d Mapping—with Permutation of Subject Images (SDM-PSI) in which the linear models have no intercept. As in previous SDM meta-analyses, the dependent variable is the brain anatomical difference between patients and controls in a voxel. However, there is no primary disorder, and the independent variables are the percentages of patients with each disorder and each pair of potentially comorbid disorders. We use simulations to validate and provide an example of this novel approach, which correctly disentangled the abnormalities associated with each disorder and comorbidity. We then describe a protocol for conducting the new meta-analysis of all major mental disorders and their comorbidities. Specifically, we will include all voxel-based morphometry (VBM) studies of mental disorders for which a meta-analysis has already been published, including at least 10 studies. We will use the novel approach to analyze all included studies in two separate single linear models, one for children/adolescents and one for adults.DiscussionThe novel approach is a valid method to focus on comorbidities. The meta-analysis will yield a comprehensive atlas of the neuroanatomy of all major mental disorders and their comorbidities, which we hope might help develop potential diagnostic and therapeutic tools.

Objective: Juvenile fibromyalgia (FM) is a prevalent chronic pain condition affecting children and adolescents worldwide during a critical period of brain development. To date, no published studies have addressed the pathophysiology of juvenile FM. This study was undertaken to characterize gray matter volume (GMV) alterations in juvenile FM patients for the first time, and to investigate their functional and clinical relevance. Methods: Thirty-four female adolescents with juvenile FM and 38 healthy adolescents underwent a structural magnetic resonance imaging examination and completed questionnaires assessing core juvenile FM symptoms. Using voxel-based morphometry, we assessed between-group GMV differences and associations between GMV and functional disability, fatigue, and pain interference in juvenile FM. We also studied whether validated brain patterns predicting pain, cognitive control, or negative emotion were amplified/attenuated in juvenile FM patients and whether structural alterations reported in adult FM were replicated in adolescents with juvenile FM. Results: Compared to controls, juvenile FM patients showed GMV reductions in the anterior midcingulate cortex (aMCC) region (family-wise error corrected P [PFWE-corr ] = 0.04; estimated with threshold-free cluster enhancement [TFCE]; n = 72) associated with pain. Within the juvenile FM group, patients reporting higher functional disability had larger GMV in inferior frontal regions (PFWE-corr = 0.006; TFCE estimated; n = 34) linked to affective, self-referential, and language-related processes. Last, GMV reductions in juvenile FM showed partial overlap with findings in adult FM, specifically for the anterior/posterior cingulate cortices (P = 0.02 and P = 0.03, respectively; n = 72). Conclusion: Pain-related aMCC reductions may be a structural hallmark of juvenile FM, whereas alterations in regions involved in emotional, self-referential, and language-related processes may predict disease impact on patients' well-being. The partial overlap between juvenile and adult FM findings strengthens the importance of early symptom identification and intervention to prevent the transition to adult forms of the disease.

Combining non-invasive brain stimulation (NIBS) with resting-state functional magnetic resonance imaging (rs-fMRI) is a promising approach to characterize and potentially optimize the brain networks subtending cognition that changes as a function of age. However, whether multifocal NIBS approaches are able to modulate rs-fMRI brain dynamics in aged populations, and if these NIBS-induced changes are consistent with the simulated electric current distribution on the brain remains largely unknown. In the present investigation, thirty-one cognitively healthy older adults underwent two different multifocal real transcranial direct current stimulation (tDCS) conditions (C1 and C2) and a sham condition in a crossover design during a rs-fMRI acquisition. The real tDCS conditions were designed to electrically induce two distinct complex neural patterns, either targeting generalized frontoparietal cortical overactivity (C1) or a detachment between the frontal areas and the posteromedial cortex (C2). Data revealed that the two tDCS conditions modulated rs-fMRI differently. C1 increased the coactivation of multiple functional couplings as compared to sham, while a smaller number of connections increased in C1 as compared to C2. At the group level, C1-induced changes were topographically consistent with the calculated electric current density distribution. At the individual level, the extent of tDCS-induced rs-fMRI modulation in C1 was related with the magnitude of the simulated electric current density estimates. These results highlight that multifocal tDCS procedures can effectively change rs-fMRI neural functioning in advancing age, being the induced modulation consistent with the spatial distribution of the simulated electric current on the brain. Moreover, our data supports that individually tailoring NIBS-based interventions grounded on subject-specific structural data might be crucial to increase tDCS potential in future studies amongst older adults.

Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ($$\hbox {MAE}=5.23$$ MAE = 5.23 ), Association ($$\hbox {MAE}=5.24$$ MAE = 5.24 ), and Projection ($$\hbox {MAE}=5.28$$ MAE = 5.28 ) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value $$< 5\hbox {E}{-}8$$ < 5 E - 8 ) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes.

IMPORTANCE The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological markers of psychosis risk.OBJECTIVE To investigate baseline structural neuroimaging differences between individuals at CHR and healthy controls as well as between participants at CHR who later developed a psychotic disorder (CHR-PS+) and those who did not (CHR-PS-).DESIGN, SETTING, AND PARTICIPANTS In this case-control study, baseline T1-weighted magnetic resonance imaging (MRI) data were pooled from 31 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. CHR status was assessed using the Comprehensive Assessment of At-Risk Mental States or Structured Interview for Prodromal Syndromes. MRI scans were processed using harmonized protocols and analyzed within a mega-analysis and meta-analysis framework from January to October 2020.MAIN OUTCOMES AND MEASURES Measures of regional cortical thickness (CT), surface area, and subcortical volumes were extracted from T1-weighted MRI scans. Independent variables were group (CHR group vs control group) and conversion status (CHR-PS+ group vs CHR-PS- group vs control group).RESULTS Of the 3169 included participants, 1428 (45.1%) were female, and the mean (SD; range) age was 21.1 (4.9; 9.5-39.9) years. This study included 1792 individuals at CHR and 1377 healthy controls. Using longitudinal clinical information, 253 in the CHR-PS+ group, 1234 in the CHR-PS- group, and 305 at CHR without follow-up data were identified. Compared with healthy controls, individuals at CHR exhibited widespread lower CT measures (mean [range] Cohen d = -0.13 [-0.17 to -0.09]), but not surface area or subcortical volume. Lower CT measures in the fusiform, superior temporal, and paracentral regions were associated with psychosis conversion (mean Cohen d = -0.22; 95% CI, -0.35 to 0.10). Among healthy controls, compared with those in the CHR-PS+ group, age showed a stronger negative association with left fusiform CT measures (F = 9.8; P < .001; q < .001) and left paracentral CT measures (F = 5.9; P = .005; q = .02). Effect sizes representing lower CT associated with psychosis conversion resembled patterns of CT differences observed in ENIGMA studies of schizophrenia (rho = 0.35; 95% CI, 0.12 to 0.55; P = .004) and individuals with 22q11.2 microdeletion syndrome and a psychotic disorder diagnosis (rho = 0.43; 95% CI, 0.20 to 0.61; P = .001).CONCLUSIONS AND RELEVANCE This study provides evidence for widespread subtle, lower CT measures in individuals at CHR. The pattern of CT measure differences in those in the CHR-PS+ group was similar to those reported in other large-scale investigations of psychosis. Additionally, a subset of these regions displayed abnormal age associations. Widespread disruptions in CT coupled with abnormal age associations in those at CHR may point to disruptions in postnatal brain developmental processes.Question How are brain morphometric features associated with later psychosis conversion in individuals at clinical high risk (CHR) for developing psychosis?Findings In this case-control study including 3169 participants, lower cortical thickness, but not cortical surface area or subcortical volume, was more pronounced in individuals at CHR in a manner highly consistent with thinner cortex in individuals with established psychosis. Regions that displayed lower cortical thickness in individuals at CHR who later developed a psychotic disorder additionally displayed abnormal associations with age.Meaning In this study, CHR status and later transition to psychosis was robustly associated with lower cortical thickness; abnormal age associations and specificity to cortical thickness may point to aberrant postnatal brain development in individuals at CHR, including pruning and myelination.This case-control study investigates baseline structural magnetic resonance imaging (MRI) differences between individuals at clinical high risk and healthy controls as well as between participants at clinical high risk who later developed a psychotic disorder and those who did not.

Previous evidence suggests that transcranial direct current stimulation (tDCS) to the left dorsolateral prefrontal cortex (l-DLPFC) can enhance episodic memory in subjects with subjective cognitive decline (SCD), known to be at risk of dementia. Our main goal was to replicate such findings in an independent sample and elucidate if baseline magnetic resonance imaging (MRI) characteristics predicted putative memory improvement. Thirty-eight participants with SCD (aged: 60–65 years) were randomly assigned to receive active (N = 19) or sham (N = 19) tDCS in a double-blind design. They underwent a verbal learning task with 15 words (DAY-1), and 24 h later (DAY-2) stimulation was applied for 15 min at 1.5 mA targeting the l-DLPFC after offering a contextual reminder. Delayed recall and recognition were measured 1 day after the stimulation session (DAY-3), and at 1-month follow-up (DAY-30). Before the experimental session, structural and functional MRI were acquired. We identified a group∗time interaction in recognition memory, being the active tDCS group able to maintain stable memory performance between DAY-3 and DAY-30. MRI results revealed that individuals with superior tDCS-induced effects on memory reconsolidation exhibited higher left temporal lobe thickness and greater intrinsic FC within the default-mode network. Present findings confirm that tDCS, through the modulation of memory reconsolidation, is capable of enhancing performance in people with self-perceived cognitive complaints. Results suggest that SCD subjects with more preserved structural and functional integrity might benefit from these interventions, promoting maintenance of cognitive function in a population at risk to develop dementia.

Background: Greater red and processed meat consumption has been linked to adverse cardiovascular outcomes. However, the impact of these exposures on cardiovascular magnetic resonance (CMR) phenotypes has not been adequately studied.Objective: We describe novel associations of meat intake with cardiovascular phenotypes and investigate underlying mechanisms through consideration of a range of covariates.Design: We studied 19,408 UK Biobank participants with CMR data available. Average daily red and processed meat consumption was determined through food frequency questionnaires and expressed as a continuous variable. Oily fish was studied as a comparator, previously associated with favourable cardiac outcomes. We considered associations with conventional CMR indices (ventricular volumes, ejection fraction, stroke volume, left ventricular mass), novel CMR radiomics features (shape, first-order, texture), and arterial compliance measures (arterial stiffness index, aortic distensibility). We used multivariable linear regression to investigate relationships between meat intake and cardiovascular phenotypes, adjusting for confounders (age, sex, deprivation, educational level, smoking, alcohol intake, exercise) and potential covariates on the causal pathway (hypertension, hypercholesterolaemia, diabetes, body mass index).Results: Greater red and processed meat consumption was associated with an unhealthy pattern of biventricular remodelling, worse cardiac function, and poorer arterial compliance. In contrast, greater oily fish consumption was associated with a healthier cardiovascular phenotype and better arterial compliance. There was partial attenuation of associations between red meat and conventional CMR indices with addition of covariates potentially on the causal pathway, indicating a possible mechanistic role for these cardiometabolic morbidities. However, other associations were not altered with inclusion of these covariates, suggesting importance of alternative biological mechanisms underlying these relationships. Radiomics analysis provided deeper phenotyping, demonstrating association of the different dietary habits with distinct ventricular geometry and left ventricular myocardial texture patterns.Conclusions: Greater red and processed meat consumption is associated with impaired cardiovascular health, both in terms of markers of arterial disease and of cardiac structure and function. Cardiometabolic morbidities appeared to have a mechanistic role in the associations of red meat with ventricular phenotypes, but less so for other associations suggesting importance of alternative mechanism for these relationships.

Abnormalities of cardiac rhythm are associated with substantial morbidity and economic costs. Atrial Fibrillation is the most common cardiac electrophysiology disorder worldwide affecting around 2.3 million people just in the United States alone and is associated with an increased risk of stroke and mortality. Atrial fibrillation commonly originates in the pulmonary veins from the left atrium, a complex anatomical structure with high variation in shape and size among the population. Circumferential pulmonary veins ablation and other strategies are widely performed to treat Atrial Fibrillation. These interventional procedures can largely benefit from prior assessment of the patient LA anatomy for accurate surgical planning and intraoperative guidance. The 3D LA anatomy can be noninvasively visualized with different imaging techniques such as late gadolinium enhancement magnetic resonance imaging. In clinical practice, obtaining the complex 3D geometry of the LA is challenging even for the most experienced clinicians. Automated computer methods to segment the LA structure are highly desirable. In this work, we proposed a patch-based network methodology to segment the left atrium geometry from late gadolinium enhancement magnetic resonance images. Due to the class imbalance problem, our strategy uses what we call positive percentage parameter during training, which simply forces the network to take patches that contain at least some target segmentation. We studied the effect on the segmentation when using both region-based and contour-based loss functions with different strategies. Using a database obtained from the Department of Arrhythmias of the Hospital Clínic de Barcelona, named ClínicLA dataset, we studied the performance of our model with different hyper-parameter combinations. To compare our strategy with other approaches, we used a public LA benchmark database. We evaluated the segmentation performance in terms of Dice Similarity Coefficient and Hausdorff distance to the ground truth. Our model architecture, after the hyper-parameter fine-tuning process, enabled the segmentation of the LA in 3D with a Dice Similarity Coefficient of 88.4% in the Large ClínicLA dataset, and dice score of 91.0% in the public dataset. Both results with accurate Hausdorff distance metrics.

Purpose: Magnetic resonance imaging-guided transurethral ultrasound ablation uses directional thermal ultrasound under magnetic resonance imaging thermometry feedback control for prostatic ablation. We report 12-month outcomes from a prospective multicenter trial (TACT). Materials and methods: A total of 115 men with favorable to intermediate risk prostate cancer across 13 centers were treated with whole gland ablation sparing the urethra and apical sphincter. The co-primary 12-month endpoints were safety and efficacy. Results: In all, 72 (63%) had grade group 2 and 77 (67%) had NCCN® intermediate risk disease. Median treatment delivery time was 51 minutes with 98% (IQR 95-99) thermal coverage of target volume and spatial ablation precision of ±1.4 mm on magnetic resonance imaging thermometry. Grade 3 adverse events occurred in 9 (8%) men. The primary endpoint (U.S. Food and Drug Administration mandated) of prostate specific antigen reduction ≥75% was achieved in 110 of 115 (96%) with median prostate specific antigen reduction of 95% and nadir of 0.34 ng/ml. Median prostate volume decreased from 37 to 3 cc. Among 68 men with pretreatment grade group 2 disease, 52 (79%) were free of grade group 2 disease on 12-month biopsy. Of 111 men with 12-month biopsy data, 72 (65%) had no evidence of cancer. Erections (International Index of Erectile Function question 2 score 2 or greater) were maintained/regained in 69 of 92 (75%). Multivariate predictors of persistent grade group 2 at 12 months included intraprostatic calcifications at screening, suboptimal magnetic resonance imaging thermal coverage of target volume and a PI-RADS™ 3 or greater lesion at 12-month magnetic resonance imaging (p

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Mireia Ribera, [ca] La meta del meu treball de fi de grau ha estat fer una contribució a democratitzar la intel·ligència artificial. El projecte ha consistit en crear el front-end d’una pàgina web per al projecte europeu-canadenc "EuCanSHare". Aquest projecte pretén entrenar màquines d'intel·ligència artificial que puguin diagnosticar malalties a pacients a partir d'una ressonància magnètica. Els membres del projecte, cardiòlegs principalment, poden triar descarregar màquines existents o crear-ne de noves amb els propis MRIs. A la pàgina web s’expliquen els algorismes de reducció dimensional i els algorismes de machine learning en llenguatge planer i es guia a l’usuari a través del procés de crear o descarregar una màquina que s’adigui a les seves necessitats i a la mostra de què disposen. El desenvolupament s’ha fet amb una orientació centrada en l’usuari.

El objetivo de este estudio es describir las alteraciones interictales de perfusión en RM por técnica de arterial spin labelled (ASL) y difusión, en pacientes con epilepsia focal y analizar su posible valor lateralizador/localizador del foco epileptógeno. Se trata de un estudio transversal de 53 pacientes adultos con epilepsia focal diagnosticados por semiología, RM y EEG. En todos ellos se realizó una RM de 3 TESLA con protocolo de epilepsia, que incluía secuencias de ASL. Las imágenes fueron sometidas a un análisis visual por un neurorradiólogo, clasificándolas en alteraciones de perfusión hemisféricas o focales. La muestra tenía un 51% de hombres y una edad media de 42.9 años (±16.5). El 60% tenían epilepsias sintomáticas. El 64% eran fármacorresistentes. Las etiologías más frecuentes fueron vascular (15%), malformaciones del desarrollo cortical (15%) y tumoral (13%). Un 45% se clasificaron como epilepsia temporal, 32% frontal y 13% temporal posterior, 8% occipital y 2% parietal. El 45% presentaban crisis parciales complejas, entre las que la semiología automotora era la más frecuente (36%). El ASL mostró, en un 74% de los pacientes, alteraciones de la perfusión interhemisféricas, observándose un valor lateralizador de éstas, especialmente cuando se observa hiperperfusión en la localización del foco y cuando se trata de epilepsia sintomática. Se observaron alteraciones focales en ASL que a pesar de encontrarse en un bajo porcentaje podrían tener valor localizador del área epileptógena.

Emerging evidence suggests that an effective or functional connectivity network does not use a static process over time but incorporates dynamic connectivity that shows changes in neuronal activity patterns. Using structural equation models (SEMs), we estimated a dynamic component of the effective network through the effects (recursive and nonrecursive) between regions of interest (ROIs), taking into account the lag 1 effect. The aim of the paper was to find the best structural equation model (SEM) to represent dynamic effective connectivity in people with Down syndrome (DS) in comparison with healthy controls. Twenty-two people with DS were registered in a functional magnetic resonance imaging (fMRI) resting-state paradigm for a period of six minutes. In addition, 22 controls, matched by age and sex, were analyzed with the same statistical approach. In both groups, we found the best global model, which included 6 ROIs within the default mode network (DMN). Connectivity patterns appeared to be different in both groups, and networks in people with DS showed more complexity and had more significant effects than networks in control participants. However, both groups had synchronous and dynamic effects associated with ROIs 3 and 4 related to the upper parietal areas in both brain hemispheres as axes of association and functional integration. It is evident that the correct classification of these groups, especially in cognitive competence, is a good initial step to propose a biomarker in network complexity studies.

Cognitive maturation during adolescence is modulated by brain maturation. However, it is unknown how these processes intertwine in early onset psychosis (EOP). Studies examining longitudinal brain changes and cognitive performance in psychosis lend support for an altered development of high-order cognitive functions, which parallels progressive gray matter (GM) loss over time, particularly in fronto-parietal brain regions. We aimed to assess this relationship in a subsample of 33 adolescents with first-episode EOP and 47 matched controls over 2 years. Backwards stepwise regression analyses were conducted to determine the association and predictive value of longitudinal brain changes over cognitive performance within each group. Fronto-parietal GM volume loss was positively associated with decreased working memory in adolescents with psychosis (frontal left (B = 0.096, p = 0.008); right (B = 0.089, p = 0.015); parietal left (B = 0.119, p = 0.007), right (B = 0.125, p = 0.015)) as a function of age. A particular decrease in frontal left GM volume best predicted a significant amount (22.28%) of the variance of decreased working memory performance over time, accounting for variance in age (14.9%). No such association was found in controls. Our results suggest that during adolescence, EOP individuals seem to follow an abnormal neurodevelopmental trajectory, in which fronto-parietal GM volume reduction is associated with the differential age-related working memory dysfunction in this group.

Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate—on 2 different cohorts—that a temporal difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena., Statistical learning is the ability to extract regularities from the environment; in the domain of language, this ability is fundamental in the learning of words and structural rules. This study uses a combination of computational modelling and functional MRI to reveal a fundamental link between online language statistical learning and reinforcement learning at the algorithmic and implementational levels.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a harbinger for developing clinical synucleinopathies. Magnetic resonance imaging (MRI) has been suggested as a tool for understanding the brain bases of iRBD and its evolution. This review systematically analyzed original full text articles on structural and functional MRI in patients with video-polysomnography-confirmed iRBD according to systematic procedures suggested by Reviews and Meta-analyses (PRISMA). The literature search was conducted via the PubMed database for articles related to structural and functional MRI in iRBD from 2000 to 2020. Investigations to date have been diverse in terms of methodology, but most agree that patients with iRBD have structural changes in deep gray matter nuclei, cortical gray matter atrophy, and disrupted functional connectivity within the basal ganglia, the cortico-striatal and cortico-cortical networks. Furthermore, there is evidence that MRI detects structural and functional brain changes associated with the motor and non-motor symptoms of iRBD. The current review highlights the need for larger multicenter and longitudinal studies, using complex approaches based on data-driven and unsupervised machine learning that will help to identify structural and functional patterns of brain degeneration. In turn, this may even allow for the prediction of subsequent phenoconversion from iRBD to the clinically defined synucleinopathies.

Background Down syndrome (DS) is a chromosomal disorder that causes intellectual disability. Few studies have been conducted on functional connectivity using resting‐state fMRI (functional magnetic resonance imaging) signals or more specifically, on the relevant structure and density of the default mode network (DMN). Although data on this issue have been reported in adult DS individuals (age: >45 years), the DMN properties in young DS individuals have not been studied. The aim of this study was to describe the density and structure of the DMN network from fMRI signals in young DS (age, DMN in Down syndrome. Network indicators in DS. Functional connectivity.

In multiple sclerosis, lesions with a peripheral paramagnetic rim is a negative prognostic imaging biomarker and represents a potential outcome measure in MRI-based clinical trials. Nowadays, the presence or absence of paramagnetic rims is determined through visual inspection by medical experts, which is tedious, time consuming and prone to observer variability. So far, few solutions to the automated classification of rims problem have been proposed. These studies present limitations that represent an obstacle to full automation of the rim analysis process and its large-scale application. Our goal is to implement and assess a fully automated algorithm capable of identifying rim lesions in MRI. In this work, we explore a Region-proposal CNN deep learning approach to solve the fully automated rim lesions classification problem that perform instance segmentation by object detection and have shown promising results in recent challenges, particularly in medical imaging. After different approaches focus on implifying the task, Mask RCNN with MobileNet v2 as backbone using attention gaussian filtering to the input images showed better performance than the rest with rates of 0.42 TPR and 0.61 FPR for the test set. However, the achieved results reveal the weaknesses of our approach and the difficulty of our classification problem.