Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment.

OBJECTIVE: The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection. METHODS: Seventeen patients with diffuse nonenhancing glioma (ages 22-56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery. RESULTS: Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10-3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10-4, respectively). CONCLUSIONS: Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients' memory recovery.We thank all patients for generous involvement in the study. We also thank to Luca Villa, Jessica Ingham, Alexa Mcdonald for their contribution to the study. This research was supported by The Brain Tumour Charity and the Guarantors of Brai

Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

Glioblastoma widely affects brain structure and function, and remodels neural connectivity. Characterizing the neural connectivity in glioblastoma may provide a tool to understand tumor invasion. Here, using a structural connectome approach based on diffusion MRI, we quantify the global and regional connectome disruptions in individual glioblastoma patients and investigate the prognostic value of connectome disruptions and topological properties. We show that the disruptions in the normal-appearing brain beyond the lesion could mediate the topological alteration of the connectome (P &lt;0.001), associated with worse patient performance (P &lt;0.001), cognitive function (P &lt;0.001), and survival (overall survival: HR: 1.46, P = 0.049; progression-free survival: HR: 1.49, P = 0.019). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of remodeling, where increased connectivity is associated with better overall survival (log-rank P = 0.005). Our approach reveals the glioblastoma invasion invisible on conventional MRI, promising to benefit patient stratification and precise treatment

Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

Glioblastoma widely affects brain structure and function, and remodels neural connectivity. Characterizing the neural connectivity in glioblastoma may provide a tool to understand tumor invasion. Here, using a structural connectome approach based on diffusion MRI, we quantify the global and regional connectome disruptions in individual glioblastoma patients and investigate the prognostic value of connectome disruptions and topological properties. We show that the disruptions in the normal-appearing brain beyond the lesion could mediate the topological alteration of the connectome (P &lt;0.001), associated with worse patient performance (P &lt;0.001), cognitive function (P &lt;0.001), and survival (overall survival: HR: 1.46, P = 0.049; progression-free survival: HR: 1.49, P = 0.019). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of remodeling, where increased connectivity is associated with better overall survival (log-rank P = 0.005). Our approach reveals the glioblastoma invasion invisible on conventional MRI, promising to benefit patient stratification and precise treatment

Strength of spatial correlation between gray matter connectivity and patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival

IntroductionLike other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics.MethodsWe retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes.ResultsWe found that all 17 genes were significantly co-localized with brain connectivity (p &lt; 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/− 0.01) and significantly (p &lt; 0.001) more so for a random forest survival model (mean AUC = 0.97 +/− 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p &lt; 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings.DiscussionOur findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival

Assessment of hemodynamic and connectivity alterations in brain gliomas through sparse DCM

The Master thesis is focused on the assessment of the alterations in the hemodynamic response function in 12 patients with brain gliomas through sparse Dynamic Causal Model, after it has been explored the brain connectivity through the Effective connectivity matrices for each subject.The Master thesis is focused on the assessment of the alterations in the hemodynamic response function in 12 patients with brain gliomas through sparse Dynamic Causal Model, after it has been explored the brain connectivity through the Effective connectivity matrices for each subject

Outcome prediction in aneurysmal subarachnoid haemorrhage: a comparison of machine learning methods and established clinico-radiological scores

Introduction: Up to now, clinical outcome prediction of aneurysmal subarachnoid haemorrhage (aSAH) has mainly been based on clinical and radiographic scoring systems. They may help as a rough indicator of outcome, but their precise predictive abilities are limited. Their limitations are particularly salient with regard to advocating for one treatment strategy over another, which – in an age of scarcity of medical resources in the midst of a global pandemic—is of critical importance. In the present study, we aimed to examine whether an approach based on Machine Learning (ML) algorithms, using variables readily available on patient admission, may improve outcome prediction compared to the status quo based on current clinical and radiological scoring systems. Methods: Using a consecutive single-centre database of 388 patients suffering from aSAH, we implemented an analysis of combined clinical and radiographic features as well as classic scoring systems, such as Hunt and Hess, WFNS, BNI, Fisher, modified Fischer and Vasograde. We trained a total of seven different ML-algorithms for scores, single features and combined features. The algorithms included a tree boosting algorithm, a Naïve Bayes classifier, a support vector machine classifier, a multilayer perceptron artificial neural net (MLP) as well as three different types of generalized linear models. A random split into training and test-sets was implemented according to a four to one ratio. We proceeded with ten-fold cross-validations and fifty shuffles. We calculated feature importance for combined features. Results: Our findings show that there is no performative difference between traditional and more modern ML models using clinico-radiografic features. No significant difference was found when comparing a set of clinico-radiological features available on admission and the Glasgow Coma Scale (GCS) – the best performing clinical score (highest AUC 0,76, running a Tree Boosting model). Discussion: The GCS and patient age turned out to be the most relevant variables within the feature combination. Functional outcome prediction by advanced ML-techniques did not outperform established scores in our cohort of 388 aSAH Patients. In order to reap the benefits and the full power of data mining models such as ML-algorithms to improve functional outcome prediction in aSAH, future research efforts will need to examine input variables that have not yet been taken into account.Einleitung: Verlässliche Vorhersagen über das zu erwartende Therapieergebnis bei aneurysmatischen Subarachnoidalblutungen (aSAB) auf der Grundlage von Merkmalen, die bei der Patient*innenaufnahme zur Verfügung stehen, könnten positiven Einfluss ausüben auf Therapieentscheidungen und die Verteilung von Ressourcen. Radiographische und klinische Scores helfen Kliniker*innen bereits, den Schweregrad einer SAB einzuschätzen. Ihre prädiktiven Fähigkeiten sind jedoch begrenzt, insbesondere als Grundlage für Therapieentscheidungen. In dieser Studie wurde untersucht, ob Machine Learning (ML) Algorithmen basierend auf Merkmalen/Variablen, die bei Patient*innenaufnahme vorhanden sind, die Outcome-Vorhersage bei Patient*innen mit aSAB im Vergleich zu bereits bestehenden klinischen Scores verbessern. Methoden: Kombinierte klinische und radiographische Merkmale, sowie klassische Scores (Hunt and Hess Score, World Federation of Neurosurgical Societies Score, Fisher Score, modified Fisher Score, Barrow National Index, VASOGRADE Score) wurden für 388 Patient*innen mit aSAB (n=388) an der Charité – Universitätsmedizin Berlin bei der Patient*innenaufnahme erhoben und ausgewertet. Verschiedene ML-Modelle (insgesamt sieben Algorithmen, davon drei Typen des traditionellen generalisierten Linearen Modells, ein Tree-Boosting Algorithmus, ein Naive Bayes Classifier, ein Support Vector Machine Classifier und ein Multilayer Perceptron) wurden in drei Modellen antrainiert für (i) Scores, (ii) kombinierte Merkmalsets (‚combined features‘) sowie (iii) einzelne Merkmale (‚single features‘) mit einer zufallsbestimmten Teilung von Trainings- und Testset (4:1), zehnfacher Kreuzvalidierung und 50 Durchgängen. Für kombinierte Merkmalssets wurde die Merkmalsrelevanz errechnet. Resultate: Es zeigte sich kein Unterschied zwischen ML-Algorithmen und etablierten Scores in der Vorhersagegenauigkeit bezüglich des Outcome einer aSAB. Es war ebenfalls kein Unterschied festzustellen zwischen einem Set kombinierter klinisch-radiologischer Merkmale bei Aufnahme (höchste AUC 0,78, Tree Boosting) und der Glasgow Coma Scale (GCS) bei Aufnahme - dem klinischen Score mit der besten Performance (höchste AUC 0,76, Tree Boosting). Der GCS-Wert und das Alter waren die Variablen mit der besten Outcomeprädiktion in der Merkmalskombination. Schlussfolgerungen: In dieser Patient*innenkohorte mit aSAB war die Vorhersage des Therapieergebnisses durch ML-Algorithmen vergleichbar mit den traditionellen, etablierten klinischen Scores. Weitere Studien sind nötig, um zusätzliche Inputvariablen zu untersuchen, die nicht bereits Teil der traditionellen klinisch-radiologischen Merkmale sind, um zu prüfen, ob eine bessere Performance der Outcome-Vorhersage für aSAB-Patient*innen erreicht werden kann