Search

Contains fulltext : 292574.pdf (Publisher’s version ) (Open Access) Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnection-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace of white matter disconnections (disconnectome) to predict neuropsychological scores 1 year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights that are available as the first comprehensive atlas of disconnectome-deficit relations across 86 scores - a Neuropsychological White Matter Atlas. Our novel predictive framework, the Disconnectome Symptoms Discoverer, achieved better predictivity performances than six other models, including functional disconnection, lesion topology and volume modelling. Out-of-sample prediction derived from this atlas presented a mean absolute error below 20% and allowed personalize neuropsychological predictions. Prediction on an external cohort achieved an R2 = 0.201 for semantic fluency. In addition, training and testing were replicated on two external cohorts achieving an R2 = 0.18 for visuospatial performance.This framework is available as an interactive web application (http://disconnectomestudio.bcblab.com) to provide the foundations for a new and practical approach to modelling cognition in stroke. We hope our atlas and web application will help to reduce the burden of cognitive deficits on patients, their families and wider society while also helping to tailor future personalized treatment programmes and discover new targets for treatments. We expect our framework’s range of assessments and predictive power to increase even further through future crowdsourcing. 16 p.

Thirty years of functional neuroimaging have been accumulating a wealth of evidence of the intricate relationship between structure and function. However, potential overlap, independence, granularity of and gaps between functions remain poorly understood at the global level. In this study, we extracted the latent structure of the current brain-cognition knowledge and revealed its organisational structure. This knowledge has been derived from the most comprehensive meta-analytic fMRI database (Neurosynth) that was used to compute a two-dimensional embedding space – the brain-cognition space – capturing the relationship between brain functions as we know it. The structure of the space allowed us to test the degree of logic in the relationship between functions statistically – a rationality index – and to predict the activation pattern of new and still undetermined functions. The organisation and predictive framework defined by the brain-cognition space will foster the investigation of novel functions and guide the exploration of the fabric of human cognition.

Over the past two decades, the study of resting-state functional magnetic resonance imaging (fMRI) has revealed the existence of multiple brain areas displaying synchronous functional blood oxygen level-dependent signals (BOLD) - resting-state networks (RSNs). The variation in functional connectivity between the different areas of a resting-state network or between multiple networks, have been extensively studied and linked to cognitive states and pathologies. However, the white matter connections supporting each network remain only partially described. In this work, we developed a data-driven method to systematically map the white and grey matter contributing to resting-state networks. Using the Human Connectome Project, we generated an atlas of 30 resting-state networks, each with two maps: white matter and grey matter. By integrating structural and functional neuroimaging data, this method builds an atlas that unlocks the joint anatomical exploration of white and grey matter to resting-state networks. The method also allows highlighting the overlap between networks, which revealed that most (89%) of the brain's white matter is shared amongst multiple networks, with 16% shared by at least 7 resting-state networks. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the correlations and the communication within resting-state networks. We provide an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage on RSNs.Competing Interest StatementThe authors have declared no competing interest.

Objective The purpose of this study was to investigate correlations between brain proton magnetic resonance spectroscopy (1H‐MRS) findings with serum biomarkers and heteroplasmy of mitochondrial DNA (mtDNA) mutations. This study enrolled patients carrying mtDNA mutations associated with Mitochondrial Encephalomyopathy, Lactic Acidosis, and Stroke‐like episodes (MELAS), and MELAS‐Spectrum Syndrome (MSS). Methods Consecutive patients carrying mtDNA mutations associated with MELAS and MSS were recruited and their serum concentrations of lactate, alanine, and heteroplasmic mtDNA mutant load were evaluated. The brain protocol included single‐voxel 1H‐MRS (1.5T) in the medial parieto‐occipital cortex (MPOC), left cerebellar hemisphere, parieto‐occipital white matter (POWM), and lateral ventricles. Relative metabolite concentrations of N‐acetyl‐aspartate (NAA), choline (Cho), and myo‐inositol (mI) were estimated relative to creatine (Cr), using LCModel 6.3. Results Six patients with MELAS (age 28 ± 13 years, 3 [50%] female) and 17 with MSS (age 45 ± 11 years, 7 [41%] female) and 39 sex‐ and age‐matched healthy controls (HC) were enrolled. These patients demonstrated a lower NAA/Cr ratio in MPOC compared to HC (p = 0.006), which inversely correlated with serum lactate (p = 0.021, rho = −0.68) and muscle mtDNA heteroplasmy (p

Introduction: The mitochondrial DNA (mtDNA) m.3243A > G mutation in the MT-TL1 gene results in a multi-systemic disease, that is commonly associated with neurodegenerative changes in the brain. Methods: Seventeen patients harboring the m3243A > G mutation were enrolled (age 43.1 ± 11.4 years, 10 M/7F). A panel of plasma biomarkers including lactate acid, alanine, L-arginine, fibroblast growth factor 21 (FGF-21), growth/differentiation factor 15 (GDF-15) and circulating cell free -mtDNA (ccf-mtDNA), as well as blood, urine and muscle mtDNA heteroplasmy were evaluated. Patients also underwent a brain standardized MR protocol that included volumetric T1-weighted images and diffusion-weighted MRI. Twenty sex- and age-matched healthy controls were included. Voxel-wise analysis was performed on T1-weighted and diffusion imaging, respectively with VBM (voxel-based morphometry) and TBSS (Tract-based Spatial Statistics). Ventricular lactate was also evaluated by 1H-MR spectroscopy. Results: A widespread cortical gray matter (GM) loss was observed, more severe (p < 0.001) in the bilateral calcarine, insular, frontal and parietal cortex, along with infratentorial cerebellar cortex. High urine mtDNA mutation load, high levels of plasma lactate and alanine, low levels of plasma arginine, high levels of serum FGF-21 and ventricular lactate accumulation significantly (p < 0.05) correlated with the reduced brain GM density. Widespread microstructural alterations were highlighted in the white matter, significantly (p < 0.05) correlated with plasma alanine and arginine levels, with mtDNA mutation load in urine, with high level of serum GDF-15 and with high content of plasma ccf-mtDNA. Conclusions: Our results suggest that the synergy of two pathogenic mechanisms, mtDNA-related mitochondrial respiratory deficiency and defective nitric oxide metabolism, contributes to the brain neurodegeneration in m.3243A > G patients.

Endoscopic endonasal surgery has gained a prominent role in the management of complex skull base tumors. It allows the resection of a large group of benign and malignant lesions through a natural anatomical extra-cranial pathway, represented by the nasal cavities, avoiding brain retraction and neurovascular manipulation. This is reflected by the patients' prompt clinical recovery and the low risk of permanent neurological sequelae, representing the main caveat of conventional skull base surgery. This surgery must be tailored to each specific case, considering its features and relationship with surrounding neural structures, mostly based on preoperative neuroimaging. Advanced MRI techniques, such as tractography, have been rarely adopted in skull base surgery due to technical issues: lengthy and complicated processes to generate reliable reconstructions for inclusion in the neuronavigation system. This paper aims to present the protocol implemented in the institution and highlights the synergistic collaboration and teamwork between neurosurgeons and the neuroimaging team (neurologists, neuroradiologists, neuropsychologists, physicists, and bioengineers) with the final goal of selecting the optimal treatment for each patient, improving the surgical results and pursuing the advancement of personalized medicine in this field.

Differential diagnosis between primary progressive aphasia (PPA) and Alzheimer’s disease (AD) could be difficult if based on clinical grounds alone. We evaluated the combination of proton MR spectroscopy of posterior cingulate cortex (PCC) and quantitative structural imaging asymmetries to differentiate PPA from AD patients. A greater left-lateralized temporo-parietal atrophy (higher accuracy for the PCC, 81.4%) and metabolic neurodegenerative changes in PCC (accuracy 76.8%) was demonstrated in PPA versus AD. The combined multiparametric approach increased the accuracy to 94%in the differential diagnosis between these two neurodegenerative diseases.

Introduction: The neuroanatomical substrate of stridor associated with Multiple System Atrophy (MSA) remains unclear. We evaluated stridor-related gray matter (GM) changes in MSA. Methods: 36 MSA patients underwent standardized nocturnal video-polysomnography and brain MRI. Differences in GM density between MSA patients with and without stridor and a sample of 22 matched healthy controls were evaluated with Voxel Based Morphometry protocol supplemented by a specific tool (SUIT) for analysing infratentorial structures. Results: Stridor was confirmed in 14 patients (10 MSA-cerebellar variant; 10 M; mean ± SD age = 61.6 ± 8.9years; disease duration = 5.2 ± 2.9years) and absent in 22 (11 MSA-cerebellar variant; 18 M; age = 61.4 ± 9.9years; disease duration = 4.8 ± 3.4years). Compared to MSA without stridor, patients with stridor showed higher GM density in the cerebellum (p < 0.05, corrected for the MSA-cerebellar variant and uncorrected when considering both MSA-variants) and lower in the striatum (p < 0.05, uncorrected). Conclusions: This preliminary study has demonstrated for the first time in MSA stridor-related GM changes in striatal and cerebellar regions. Abnormalities in these regions were previously reported in dystonic disorders affecting laryngeal muscles, suggesting the hypothesis that stridor pathophysiology is dystonia-related. These results need however to be confirmed in a larger sample of patients.

Stroke significantly impacts quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need for a better prediction of long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnections-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace to predict neuropsychological scores one year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights available as the first comprehensive atlas of disconnectome-deficit relations across 86 neuropsychological scores. Out-of-sample prediction derived from this atlas achieved average accuracy over 80%, which is higher than any other framework. Our novel predictive framework is available as an interactive web application, the disconnectome symptoms discoverer (http://disconnectomestudio.bcblab.com), to provide the foundations for a new and practical approach to modelling cognition in stroke. Our atlas and web application will reduce the burden of cognitive deficits on patients, their families, and wider society while also helping to tailor personalized treatment programs and discover new targets for treatments. We expect the range of assessments and the predictive power of our framework to increase even further through future crowdsourcing.

Autosomal-dominant leukodystrophy (ADLD) is a rare fatal neurodegenerative disorder with overexpression of the nuclear lamina component, Lamin B1 due to LMNB1 gene duplication or deletions upstream of the gene. The molecular mechanisms responsible for driving the onset and development of this pathology are not clear yet. Vacuolar demyelination seems to be one of the most significant histopathological observations of ADLD. Considering the role of oligodendrocytes, astrocytes, and leukemia inhibitory factor (LIF)-activated signaling pathways in the myelination processes, this work aims to analyze the specific alterations in different cell populations from patients with LMNB1 duplications and engineered cellular models overexpressing Lamin B1 protein. Our results point out, for the first time, that astrocytes may be pivotal in the evolution of the disease. Indeed, cells from ADLD patients and astrocytes overexpressing LMNB1 show severe ultrastructural nuclear alterations, not present in oligodendrocytes overexpressing LMNB1. Moreover, the accumulation of Lamin B1 in astrocytes induces a reduction in LIF and in LIF-Receptor (LIF-R) levels with a consequential decrease in LIF secretion. Therefore, in both our cellular models, Jak/Stat3 and PI3K/Akt axes, downstream of LIF/LIF-R, are downregulated. Significantly, the administration of exogenous LIF can partially reverse the toxic effects induced by Lamin B1 accumulation with differences between astrocytes and oligodendrocytes, highlighting that LMNB1 overexpression drastically affects astrocytic function reducing their fundamental support to oligodendrocytes in the myelination process. In addition, inflammation has also been investigated, showing an increased activation in ADLD patients’ cells.

This study investigated the ability of magnetic resonance spectroscopy ((1)H-MRS) of posterior cingulate cortex (PCC) and brain volumetry to predict the progression from mild cognitive impairment (MCI) to Alzheimer's Disease (AD) on the basis of clinical classification at 2 years follow-up. Thirty-eight MCI patients, eighteen healthy older adults and twenty-three AD patients were included in this study. All participants underwent a brain-MR protocol (1.5 T GE scanner) including high-resolution T1-weighted volumetric sequence (isotropic 1mm(3)). Voxel-wise differences in brain volumetry were evaluated using FreeSurfer software and all volumes were normalized by the total intracranial volume (TIV). Careful localization of (1)H-MRS volume of PCC was performed and data were processed with the LCModel program. MCI patients underwent a complete neuropsychological assessment at baseline and were clinically re-evaluated after a mean of 28 months; twenty-six MCI patients (68.4%) converted to AD and twelve remained stable. At baseline these two MCI subgroups did not differ in the global cognitive level (Mini Mental State Examination, MMSE) or in any of the other cognitive domains; the NAA/ mI ratio in the PCC was able to differentiate MCI converters from those MCI that did not develop AD (p = 0.022) with a level of accuracy (AUC area) of 0.779. A significantly reduced volume of parahippocampal gyrus (p = 0.010) and fusiform gyrus (p = 0.026) were found in the converter MCI subgroup compared to the stable MCI subgroup. The combined use of both N- acetyl-aspartate (NAA)/myo-Inositol (mI) ratio and volume of parahippocampal gyrus, increases the overall accuracy (AUC = 0.910) in predicting the conversion to AD two years before the development of clinical symptoms. Additional longitudinal studies with a broader representative sample of MCI patients and longer follow-up might be helpful to confirm these results and to elucidate the role of each parameter in predicting the possible progression to AD, and also to all the other non-AD dementia subtypes.

Purpose We propose a new along-tract algorithm to compare different tractography algorithms in tract curvature mapping and along-tract analysis of the arcuate fasciculus (AF). In particular, we quantified along-tract diffusion parameters and AF spatial distribution evaluating hemispheric asymmetries in a group of healthy subjects. Methods The AF was bilaterally reconstructed in a group of 29 healthy subjects using the probabilistic ball-and-sticks model, and both deterministic and probabilistic constrained spherical deconvolution. We chose cortical ROIs as tractography targets and the developed along-tract algorithm used the Laplacian operator to parameterize the volume of the tract, allowing along-tract analysis and tract curvature mapping independent of the tractography algorithm used. Results The Laplacian parameterization successfully described the tract geometry underlying hemispheric asymmetries in the AF curvature. Using the probabilistic tractography methods, we found more tracts branching towards cortical terminations in the left hemisphere. This influenced the left AF curvature and its diffusion parameters, which were significantly different with respect to the right. In particular, we detected projections towards the middle temporal and inferior frontal gyri bilaterally, and towards the superior temporal and precentral gyri in the left hemisphere, with a significantly increased volume and connectivity. Conclusions The approach we propose is useful to evaluate brain asymmetries, assessing the volume, the diffusion properties and the quantitative spatial localization of the AF.

Background and purpose In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. Methods We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. Results When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. Conclusions The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis.

Major cerebral vessels have been proposed as a target of defective mitochondrial metabolism in patients with mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes syndrome (MELAS). Cerebral angiographic techniques are not routinely performed in MELAS patients. A systematic literature review was performed to identify studies describing major vessel caliber alterations in MELAS. Twenty-three studies reporting on 46 MELAS patients were included. Alterations in major caliber vessels were present in 59% (27/46) of patients. Dilation occurred in 37% (17/46) of patients, and in 88% (15/17) of them during a stroke-like episode (SLE). Stenosis was reported in 24% (11/46) of patients: 36% (4/11) related to an SLE and 64% (7/11) to dissections or degenerative changes. During an SLE, identification of intracranial vessels dilation or stenosis could be a selection tool for new treatment protocols. Outside SLE, identification of major cerebral vessels dissections and degenerative changes may help to prevent subsequent complications. (c) 2021 Elsevier Masson SAS. All rights reserved.