Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

• MeSH
• dospělí MeSH
• konektom * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mladý dospělý MeSH
• mozek patologie   patofyziologie   MeSH
• mozková kůra patologie   patofyziologie   MeSH
• nervová síť patologie   patofyziologie   diagnostické zobrazování   MeSH
• nervové dráhy patofyziologie   patologie   MeSH
• schizofrenie * patologie   patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients' cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling.

• MeSH
• bílá hmota * diagnostické zobrazování   MeSH
• cévní mozková příhoda * komplikace   diagnostické zobrazování   MeSH
• difuzní magnetická rezonance MeSH
• kognitivní dysfunkce * diagnostické zobrazování   etiologie   psychologie   MeSH
• lidé MeSH
• senioři MeSH
• stárnutí MeSH
• zobrazování difuzních tenzorů metody   MeSH
• Check Tag
• lidé MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics. To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.

• MeSH
• Bayesova věta MeSH
• elektrická stimulace metody   MeSH
• epilepsie * MeSH
• evokované potenciály * fyziologie   MeSH
• lidé MeSH
• mapování mozku metody   MeSH
• mozek MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Schizophrenia is a severe neuropsychiatric disease whose diagnosis, unfortunately, lacks an objective diagnostic tool supporting a thorough psychiatric examination of the patient. We took advantage of today's computational abilities, structural magnetic resonance imaging, and modern machine learning methods, such as stacked autoencoders (SAE) and 3D convolutional neural networks (3D CNN), to teach them to classify 52 patients with schizophrenia and 52 healthy controls. The main aim of this study was to explore whether complex feature extraction methods can help improve the accuracy of deep learning-based classifiers compared to minimally preprocessed data. Our experiments employed three commonly used preprocessing steps to extract three different feature types. They included voxel-based morphometry, deformation-based morphometry, and simple spatial normalization of brain tissue. In addition to classifier models, features and their combination, other model parameters such as network depth, number of neurons, number of convolutional filters, and input data size were also investigated. Autoencoders were trained on feature pools of 1000 and 5000 voxels selected by Mann-Whitney tests, and 3D CNNs were trained on whole images. The most successful model architecture (autoencoders) achieved the highest average accuracy of 69.62% (sensitivity 68.85%, specificity 70.38%). The results of all experiments were statistically compared (the Mann-Whitney test). In conclusion, SAE outperformed 3D CNN, while preprocessing using VBM helped SAE improve the results.

• Publikační typ
• časopisecké články MeSH

Better understanding of GBM signalling networks in-vivo would help develop more physiologically relevant ex vivo models to support therapeutic discovery. A "functional proteomics" screen was undertaken to measure the specific activity of a set of protein kinases in a two-step cell-free biochemical assay to define dominant kinase activities to identify potentially novel drug targets that may have been overlooked in studies interrogating GBM-derived cell lines. A dominant kinase activity derived from the tumour tissue, but not patient-derived GBM stem-like cell lines, was Bruton tyrosine kinase (BTK). We demonstrate that BTK is expressed in more than one cell type within GBM tissue; SOX2-positive cells, CD163-positive cells, CD68-positive cells, and an unidentified cell population which is SOX2-negative CD163-negative and/or CD68-negative. The data provide a strategy to better mimic GBM tissue ex vivo by reconstituting more physiologically heterogeneous cell co-culture models including BTK-positive/negative cancer and immune cells. These data also have implications for the design and/or interpretation of emerging clinical trials using BTK inhibitors because BTK expression within GBM tissue was linked to longer patient survival.

• MeSH
• glioblastom enzymologie   mortalita   patologie   MeSH
• kokultivační techniky metody   MeSH
• lidé MeSH
• míra přežití MeSH
• nádorové buněčné linie MeSH
• nádorové kmenové buňky enzymologie   MeSH
• nádory mozku enzymologie   mortalita   patologie   MeSH
• proteinkinasa BTK metabolismus   MeSH
• proteom metabolismus   MeSH
• proteomika metody   MeSH
• signální transdukce * MeSH
• transkripční faktory SOXB1 metabolismus   MeSH
• viabilita buněk MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

• Publikační typ
• časopisecké články MeSH

BACKGROUND: About every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis. METHODS: This study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n = 110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n = 1058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n = 1143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three consistency criteria were defined to appraise robustness and replicability of results across estimation methods, network bootstrapping, and samples. RESULTS: Network analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our consistency criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two consistency criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods. CONCLUSIONS: Genetic predisposition to higher systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue, consistent with previous studies based on circulating levels of inflammatory markers. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

• MeSH
• antidepresiva terapeutické užití   MeSH
• C-reaktivní protein analýza   MeSH
• deprese * genetika   MeSH
• depresivní porucha unipolární * farmakoterapie   genetika   MeSH
• lidé MeSH
• multifaktoriální dědičnost MeSH
• zánět farmakoterapie   genetika   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Research Support, N.I.H., Extramural MeSH

Cíl: Cílem naší studie bylo odlišit glioblastom (GBM) od solitární metastázy mozku za pomoci strojových modelů vyvinutých na základě radiomických dat získaných automatickou segmentací nádoru z konvenčích MR skenů pacientů pomocí umělé inteligence. Metody: Naše studie byla prováděna na jednom pracovišti a byla retrospektivní. Do studie bylo zařazeno 35 pacientů s GBM a 25 pacientů se solitární metastázou na mozku, u nichž byla před operací provedena MR mozku s kontrastní látkou. Do programu BraTumIA byly nahrány T1 vážené obrazy, T1 vážené obrazy po podání kontrastní látky, T2 vážené obrazy a T2 vážené obrazy s využitím sekvence fluid attenuated inversion recovery (FLAIR). V programu byly léze pacienta pomocí umělé inteligence rozděleny do čtyř různých segmentů: nekróza, nesytící se solidní oblast, sytící se solidní oblast a peritumorózní edém. Z T1 obrazů po podání kontrastní látky a T2 FLAIR obrazů bylo extrahováno 856 znaků. Pro výběr znaků, optimalizaci modelu a validaci byl použit vnořený (nested) přístup. Byly modelovány umělé neuronové sítě, podpůrný vektorový stroj, náhodný les a naivní bayesovský klasifikátor. Funkce modelu byla hodnocena pomocí přesnosti, senzitivity, specificity a plochy pod křivkou (area under the curve; AUC). Výsledky: Mezi skupinami s GBM a s metastázou nebyly rozdíly ve věku a pohlaví. Nejúspěšnější výsledky byly získány pomocí algoritmu neuronové sítě – byla získána hodnota AUC 0,970. U algoritmů za použití podpůrného vektorové stroje, naivního bayesovského klasifikátoru, logistické regrese či náhodného lesu byly získány hodnoty AUC 0,959, 0,955, 0,955, respektive 0,917. Závěr: V diferenciální diagnostice GBM a solitárních metastáz mozku mohou modely umělé inteligence založené na radiomických datech pomocí automatické segmentace objektivně a s vysokou přesností odlišovat tak, že závislost na prostředku a osobě udržují na nejnižší úrovni za použití prostých konvenčních sekvencí.

Aim: Our study aimed to distinguish glioblastoma (GBM) from solitary brain metastasis with machine models developed with radiomics data obtained by artificial intelligence-based automatic tumour segmentation over conventional MRI of the patients. Methods: Our study was conducted as single-centre and retrospective. Thirty-five GBM and 25 solitary brain metastasis patients who had pre-operative contrast-enhanced brain MRI were included in the study. T1-weighted, postcontrast T1-weighted, T2-weighted and fluid attenuated inversion recovery (FLAIR) T2-weighted images of the patients were uploaded to the program named BraTumIA. With the program, the patient‘s lesions were divided into four different segments by artificial intelligence as necrosis, non-enhancing solid area, enhancing solid area and peritumorous oedema. 856 features were extracted from T1 post-contrast and T2 FLAIR images. A nested approach was used for feature selection, model optimization and validation. Artificial neural networks, support vector machine, random forest and naive bayes were modelled. Accuracy, sensitivity, specificity and area under the curve (AUC) parameters were used to evaluate the model performance. Results: There was no difference between GBM and metastasis groups in terms of age and gender. The most successful results were obtained in the neural network algorithm; 0.970 AUC was found. Other support vector machine, naive bayes, logistic regression and random forest algorithms also found 0.959, 0.955, 0.955, 0.917 AUC values, respectively. Conclusion: In the differential diagnosis of GBM and solitary brain metastasis, radiomics-based artificial intelligence models obtained by automatic segmentation can distinguish objectively and with high accuracy by keeping device and person dependency at the lowest level with only conventional sequences.

• Klíčová slova
• automatická segmentace,
• MeSH
• diagnóza počítačová MeSH
• glioblastom * diagnostické zobrazování   diagnóza   MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• metastázy nádorů MeSH
• nádory mozku * diagnostické zobrazování   diagnóza   sekundární   MeSH
• počítačové zpracování obrazu MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH

Zánět mozku představuje jeden z hlavních substrátů farmakorezistentní epilepsie různé etiologie a může přímo ovlivnit neuronální excitabilitu. Neuromodulační schopnosti ně kterých prozánětlivých molekul (cytokinů, chemokinů) mohou být odpovědné za hyperexcitabilitu v neuronálních sítích. Vztah zánětu a epilepsie je reciproční. Zánětlivé procesy v mozku se mohou účastnit na spouštění záchvatové aktivity a zároveň mohou být následkem pokračujících záchvatů. Farmakologické studie na zvířecích modelech cílené na systémy IL-1/IL-1R1, HMGB1/TLR4 a COX-2/prostaglandiny prokazují, že tyto zánětlivé kaskády mají významný podíl na spouštění a opakování záchvatové aktivity. Status epilepticus (SE) vede k rozvoji zánětlivých procesů, které mohou být detekovány v mozkové tkáni, mozkomíšním moku i séru. Prolongované záchvaty a SE vedou k rychlé a dlouhotrvající aktivaci specifických zánětlivých kaskád v těch oblastech mozku, které odpovídají epileptogenní zóně. Pochopení komplexní role zánětu při vzniku a exacerbaci epilepsie a rozvoji farmakorezistence je zásadním předpokladem možnosti identifikace nových molekulárních cílů, které by se mohly uplatnit v léčbě těchto pacientů.

Brain inflammation represents a common substrate of pharmacoresistant epilepsy of different etiologies and it can directly affect neuronal excitability. Neuromodulatory properties of some proinflammatory molecules (cytokines, chemokines) may be responsible for hyperexcitability in neuronal networks. The relation between inflammation and epilepsy is reciprocal. The inflammatory processes in the brain may participate in initiating seizure activity and simultaneously they may be a consequence of the recurrence of the seizures. Pharmacological studies on experimental models focused on IL-1β/ IL-1R1, HMGB1/ TLR4 and COX-2/ prostaglandin systems demostrate that these inflammatory pathways significantly in triggering and recurring seizure activity. Status epilepticus (SE) leads to development of inflammatory processes which can be detected in brain tissue, cerebrospinal fluid and blood serum. Prolonged seizures and SE lead to fast and prolonged activation of specific inflammatory pathways in brain areas accordant with the epileptogenic zone. Understanding the complex role of inflammation in the generation and exacerbation of epilepsy and development of pharmacoresistance in epilepsy is crucial for the identification of new molecular targets for therapeutic intervention in these patients.

• MeSH
• cytokiny fyziologie   MeSH
• imunomodulace MeSH
• lidé MeSH
• modely u zvířat MeSH
• mozek patologie   MeSH
• refrakterní epilepsie * farmakoterapie   imunologie   patologie   MeSH
• status epilepticus etiologie   farmakoterapie   patologie   MeSH
• zánět * etiologie   farmakoterapie   patologie   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• práce podpořená grantem MeSH
• přehledy MeSH

• MeSH
• nádory radioterapie   MeSH
• nukleární lékařství metody   MeSH
• radioterapie metody   MeSH
• Publikační typ
• učebnice MeSH

• Konspekt
• Učební osnovy. Vyučovací předměty. Učebnice
• Lékařské vědy. Lékařství
• NLK Obory
• radiologie, nukleární medicína a zobrazovací metody
• onkologie
• NLK Publikační typ
• kolektivní monografie