Background/Objectives: Isocitrate dehydrogenase (IDH) mutation is a key prognostic indicator in diffuse gliomas; however, it is clinically determined from invasive tissue sampling. Non-invasive preoperative identification of IDH mutation from routine anatomical MRI could support treatment decision making. This study evaluated deep learning models for IDH mutation detection using routine anatomical MRI (post-contrast T1-weighted (T1c), T2-weighted, and fluid attenuated inversion recovery (FLAIR) MRI) and quantified how tumor-focused image preprocessing and different training schemes, centralized learning (CL) versus federated learning (FL) with alternative aggregation strategies, affected model performance. Methods: Anatomical MRI from 501 diffuse glioma patients in the UCSF Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset was analyzed using a deep learning classifier built on a 2D U-Net encoder, with age and sex included as covariates. Two methods of tumor-focused image preprocessing, Naïve Soft Filtering (NSF) and Gradient-Based Soft Filtering (GBSF), were compared. Centralized learning (CL) was benchmarked against federated learning (FL) using Federated Averaging (FA) and Federated Trimmed Mean (FTM) aggregation strategies. Model performance was compared in terms of accuracy, precision, recall, F1 score, specificity, and the area under the receiver operating characteristic curve (ROC-AUC). Results: The CL model with NSF achieved the best test performance (accuracy = 0.949, F1 = 0.951, ROC-AUC = 0.971), with NSF consistently outperforming GBSF. FL’s performance decreased relative to CL’s, but the FA strategy outperformed FTM (FTM accuracy = 0.915 vs. FA accuracy = 0.949), which indicates that the FL aggregation strategy has an influence on model performance. Conclusions: Deep learning applied to routine anatomical MRI could classify IDH mutation status with high accuracy. Context-preserving image preprocessing with NSF substantially improved performance across training schemes. FL provides a privacy-preserving alternative to CL, but incurs a measurable performance degradation that is sensitive to the choice of aggregation strategy.

Background Preoperative and noninvasive detection of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations in glioma is critical for prognosis and treatment planning. This study aims to develop deep learning classifiers to identify IDH and TERTp mutations using proton magnetic resonance spectroscopy (1H-MRS) and a one-dimensional convolutional neural network (1D-CNN) architecture. Methods This study included 1H-MRS data from 225 adult patients with hemispheric diffuse glioma (117 IDH mutants and 108 IDH wild-type; 99 TERTp mutants and 100 TERTp wild-type). The spectra were processed using the LCModel, and multiple deep learning models, including a baseline, a deep-shallow network, and an attention deep-shallow network (ADSN), were trained to classify mutational subgroups of gliomas. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was used to interpret the models’ decision-making process. Results The ADSN model was the most effective for IDH mutation detection, achieving F1-scores of 93 % on the validation set and 88 % on the test set. For TERTp mutation detection, the ADSN model achieved F1-scores of 80 % in the validation set and 81 % in the test set, whereas TERTp-only gliomas were detected with F1-scores of 88 % in the validation set and 86 % in the test set using the same architecture. Conclusion Deep learning models accurately predicted the IDH and TERTp mutational subgroups of hemispheric diffuse gliomas by extracting relevant information from 1H-MRS spectra without the need for manual feature extraction.

S100 protein expression is a relevant indicator of prognosis in meningiomas and it is more common in benign meningiomas. To our knowledge, a clinically feasible non-invasive method that preoperatively identifies S100 protein expression is not available. In this study, we proposed registration-free deep learning models to predict S100 expression non-invasively using T2-w MRI. The proposed hybrid deep learning model could predict S100 protein expression in meningiomas using T2-w MRI, with 91% accuracy on the validation set, and 83% accuracy on the test set.

Meningiomas are the most common primary intracranial tumors in adults. S100 protein expression (S100+) in meningiomas is a marker of neural crest origin. Eighty-four patients with preoperative MRI were included in this IRB approved study. The whole tumor volumes were segmented from FLAIR, followed by co-registration onto SWI. Supervised machine and deep learning methods were employed to categorize meningiomas into S100+ and S100- groups based on SWI histogram values. Ensemble bagged trees resulted in an accuracy of 85.7% (sensitivity=87.0 % and specificity=84.4 %), while a Resnet 50 architecture had 70.5% accuracy (sensitivity=80%, specificity=57.1%) for predicting S100 protein expression in meningiomas.

Loss of neurofibromatosis 2 (NF2-L) is a well-known genetic alteration of meningiomas and causes meningiomas to evolve into more aggressive and infiltrating form. This study aims to investigate single-voxel proton magnetic resonance spectroscopy ( 1 H-MRS) correlations of NF2-L in meningiomas and to develop machine learning and deep learning models to identify NF2-L in meningiomas. NF2-L meningiomas had significantly higher Ins, Lac, and Ins+Glyc, and lower tNAA than tumors with no copy number loss. While a subspace discriminant model achieved a classification accuracy of 77.25%, a 1D-CNN model obtained a classifcation accuracy of 88.9% for identifying NF2-L meningiomas.

Proton magnetic resonance spectroscopic imaging ( 1 H‐MRSI) provides a noninvasive, spatially resolved evaluation of brain metabolism. However, there are some limitations of 1 H‐MRSI preventing its wider use in the clinics, including the spectral quality issues, partial volume effect and chemical shift artifact. Additionally, it is necessary to create metabolite maps for analyzing spectral data along with other MRI modalities. In this study, a MATLAB‐based open‐source data analysis software for three‐dimensional 1 H‐MRSI, called Oryx‐MRSI, which includes modules for visualization of raw 1 H‐MRSI data and LCModel outputs, chemical shift correction, tissue fraction calculation, metabolite map production, and registration onto standard MNI152 brain atlas while providing automatic spectral quality control, is presented. Oryx‐MRSI implements region of interest analysis at brain parcellations defined on MNI152 brain atlas. All generated metabolite maps are stored in NIfTI format. Oryx‐MRSI is publicly available at https://github.com/Computational-Imaging-LAB/Oryx-MRSI along with six example datasets.

Farklı nörolojik hastalıkların neden olduğu beyinde oluşan anormal durumlar dünya çapında birçok insanı etkilemektedir. Bu anormal durumlardan birisi de Amyotrofik lateral skleroz (ALS)’dur. ALS, beyin sapı adı verilen bölgede motor sinir hücrelerinin zarar görmesiyle ilerleyen fiziksel bozukluklara neden olan genellikle motor nöron hastalığı olarak bilinen bir hastalıktır. Beyin, dışarıdan gelen uyarıları algılar ve algılanan çok sayıda uyarıdan ilgili olanları dikkat mekanizması sayesinde seçer. Dikkat, çeşitli bilgi türlerinin, duygu ve düşünceler gibi aktivitelerin bir bölgeye yoğunlaştırılıp gerekli sürede ilgili uyarıcıların beyin tarafından seçilmesiyle oluşan bilişsel bir süreçtir. Elektroensefalogram (EEG) beynin dikkat mekanizmasında oluşan bu tür aktiviteleri ölçmek ve analiz etmek için önemli bir yere sahiptir. Dikkat analizi için son yıllarda yapılan çalışmalar Olaya İlişkin Potansiyel (OİP) sinyalleri üzerinedir. OİP sinyalleri, EEG sinyallerinde net olarak gözükmeyen P100, N200, P300 ve N400 gibi bileşenlere sahip olan küçük genlikli sinyallerdir. Bu nedenle OİP sinyallerini elde edebilmek için hedef uyaranın tekrarlanması, birçok kez EEG kaydının alınması gerekmektedir. Kayıt alınan hedef uyarana ait EEG sinyallerinin ortalamasının alınması sonucunda OİP sinyalleri elde edilmektedir. Gerçekleştirilen çalışmada, ALS hastaları ile sağlıklı kişilerin OİP sinyallerinden bir takım özelliklerin elde edilip ve görsel uyaranlara karşı dikkat analizinin k-ortalamalar kümeleme yöntemi ile incelenmesi amaçlanmıştır. K-ortalamalar kümeleme yöntemi ile yapılan inceleme sonucunda veriler 2 kümeye ayrılmış ve en yüksek başarı oranı %77.78 olarak hesaplanmıştır.

BACKGROUND AND OBJECTIVE:Understanding the effect of gender differences on the brain can provide important information to characterize normal changes throughout life and to increase the likelihood of sex-specific approaches for neurological and psychiatric diseases. In this study, Functional Connectivity (FC), Amplitude of Low-Frequency Fluctuations (ALFF) and fractional ALFF (fALFF) analyzes will be compared between female and male brains between the ages of 7 and 18 years using resting state-functional magnetic resonance imaging (rs-fMRI). METHODS:The rs-fMRI data in this study has been provided by The New York University (NYU) Child Study Center of the publicly shared ADHD200 database. From the NYU dataset, 68 (34 females, 34 males) healthy subjects in the age range of 7-18 years were selected. The female group (mean age: 12.3271±3.1380) and male group (mean age: 11.8766±2.9697) consisted of right-handed, small head motion and similar IQ values. FC was obtained by seed voxel analysis and the effect of low-frequency fluctuations on gender was examined by ALFF and fALFF analyses. Two-sample t-test was used to compare female and male groups with the significance thresholds set to FDR-corrected p<0.05. RESULTS:In the results of our study, both in the ALFF, fALFF analyses and the seed regions belonging to many network regions, higher FC rates were found in girls than boys. Our results show that the females' language functions, visual functions such as object detection and recognition, working memory, executive functions, and episodic memory are more developed than males in this age range. In addition, as another result of our study, the seed regions are statistically stronger where the higher activation of female participants than male participants has concentrated in the left hemisphere. CONCLUSIONS:Gender differences in brain networks should be taken into consideration when examining childhood cognitive and neuropsychiatric disorders and the results should also be evaluated according to gender. Evaluation of gender differences in childhood can increase the likelihood of early and definitive diagnosis and correct treatment for neurological diseases and can help doctors and scientists find new diagnostic tools to discover brain differences.

Motivation: There is a need to preoperatively assess the isocitrate dehydrogenase (IDH) mutational status in gliomas, which highly affects the treatment planning and patient prognosis. Goal(s): To develop a robust deep learning pipeline for noninvasively assessing the IDH mutational status of gliomas based on anatomical MRIApproach: Post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) MRI of 501 adult diffuse gliomas (103 IDH-mutant, 308 IDH-wildtype) of the UCSF-PGDM dataset were evaluated with a 2D UNet architecture using synthetic attention. Results: The model utilizing all three anatomical modalities achieved an accuracy of 93.31% (sensitivity=93.33%, specificity=93.24%). Impact: IDH mutational status in gliomas was identified with over 93% accuracy utilizing a 2D UNet architecture with synthetic attention for the evaluation of whole tumor slices of three standard anatomical MRI modalities.

Intelligent Radiological Imaging Systems (IRIS)-DL is a deep learning software tool that includes libraries for segmenting tumor regions and identifying several genetic mutations in gliomas and meningiomas. The tool has three modules, which are “Model Library”, “Trainer”, and “Plotter”. In the “Model Library”, the users could run pre-trained models on their local data. The “Trainer” module is for creating custom AI (conventional machine learning, artificial neural networks, and deep learning) models on the user data. Lastly, “Plotter” module is for data visualization and explorative data analysis.

NF2-L in meningiomas is a relevant indicator of prognosis. NF2-L is one of the most common genetic mutations in meningiomas. As a result of that situation, developing a non-invasive approach may assist the current clinical procedures. To our knowledge, some studies try to predict NF2-L using MRI modalities but either they use registration or performed using the modalities of MRI that are not in default MRI scanning procedures. Hence, the solutions that they provide are not clinically feasible. In this study, we aim to develop new approaches that are capable to implement directly into clinical procedures.[1]

Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase promoter (TERTp) mutations affect the clinical behavior and survival rate of diffuse gliomas. According to the latest WHO 2021 brain tumor classification, IDH mutation is an important factor for grouping adult-type diffuse gliomas. The preoperative detection of these mutations is very critical for treatment planning. In this study, we propose enhanced 1D-CNN models by adding an attention mechanism as a prior network to focus on relevant spectral frequencies of 1H-MRS to identify IDH-mutant (IDH-mut), TERTp-mutant (TERTp-mut), and IDH-wt, TERTp-mut (TERTp-only) gliomas using three binary models