Magnetic Resonance Imaging Data Research Articles

An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.

Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. This framework is applicable to learning anatomy-aware, acquisition-agnostic registration of any anatomy with any architecture, as long as label maps are available for training. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain magnetic resonance imaging (MRI) data.

Surface mapping of gastric motor functions using MRI: a comparative study between humans and rats.

The stomach's ability to store, mix, propel, and empty its content requires highly coordinated motor functions. However, current diagnostic tools cannot simultaneously assess these motor processes. This study aimed to use magnetic resonance imaging (MRI) to map multifaceted gastric motor functions, including accommodation, tonic and peristaltic contractions, and emptying, through a single noninvasive experiment for both humans and rats. Ten humans and 10 Sprague-Dawley rats consumed MRI-visible semisolid meals and underwent MRI scans. We used a surface model to analyze MRI data, capturing the deformation of the stomach wall on ingestion or during digestion. We inferred muscle activity, mapped motor processes, parcellated the stomach into functional regions, and revealed cross-species distinctions. In humans, both the fundus and antrum distended postmeal, followed by sustained tonic contractions to regulate intragastric pressure. Peristaltic contractions initiated from the distal fundus, including three concurrent wavefronts oscillating at 3.3 cycles/min and traveling at 1.7 to 2.9 mm/s. These motor functions facilitated linear gastric emptying with a 61-min half-time. In contrast, rats exhibited peristalsis from the midcorpus, showing two wavefronts oscillating at 5.0 cycles/min and traveling at 0.4 to 0.9 mm/s. For both species, motility features allowed functional parcellation of the stomach along a midcorpus division. This study maps region- and species-specific gastric motor functions. We demonstrate the value of MRI with surface modeling in understanding gastric physiology and its potential to become a new standard for clinical and preclinical investigations of gastric disorders at both individual and group levels.NEW & NOTEWORTHY A novel MRI technique can visualize how the stomach accommodates, mixes, and propels food for digestion in humans and animals alike. Digital models of gastric MRI reveal the functional maps, organization, and distinction of the stomach across individuals and species. This technique holds the unique potential to advance basic and clinical studies of functional gastric disorders.

Comprehensive benchmarking of CNN-based tumor segmentation methods using multimodal MRI data

Magnetic resonance imaging (MRI) has become an essential and a frontline technique in the detection of brain tumor. However, segmenting tumors manually from scans is laborious and time-consuming. This has led to an increasing trend towards fully automated methods for precise tumor segmentation in MRI scans. Accurate tumor segmentation is crucial for improved diagnosis, treatment, and prognosis. This study benchmarks and evaluates four widely used CNN-based methods for brain tumor segmentation CaPTk, 2DVNet, EnsembleUNets, and ResNet50. Using 1251 multimodal MRI scans from the BraTS2021 dataset, we compared the performance of these methods against a reference dataset of segmented images assisted by radiologists. This comparison was conducted using segmented images directly and further by radiomic features extracted from the segmented images using pyRadiomics. Performance was assessed using the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). EnsembleUNets excelled, achieving a DSC of 0.93 and an HD of 18, outperforming the other methods. Further comparative analysis of radiomic features confirmed EnsembleUNets as the most precise segmentation method, surpassing other methods. EnsembleUNets recorded a Concordance Correlation Coefficient (CCC) of 0.79, a Total Deviation Index (TDI) of 1.14, and a Root Mean Square Error (RMSE) of 0.53, underscoring its superior performance. We also performed validation on an independent dataset of 611 samples (UPENN-GBM), which further supported the accuracy of EnsembleUNets, with a DSC of 0.85 and an HD of 17.5. These findings provide valuable insight into the efficacy of EnsembleUNets, supporting informed decisions for accurate brain tumor segmentation.

Differential network knockoff filter with application to brain connectivity analysis.

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

Circadian functional changes of pain-processing brainstem nuclei and implications for cluster headache: A 7 Tesla imaging study.

Pain thresholds and primary headaches, including cluster headache attacks, have circadian rhythmicity. Thus, they might share a common neuronal mechanism. This study aimed to elucidate how the modulation of nociceptive input in the brainstem changes from noon to midnight. Insights into the mechanism of these fluctuations could allow for new hypotheses about the pathophysiology of cluster headache. This repeated measure observational study was conducted at the University Hospital Zurich from December 2019 to November 2022. Healthy adults between 18 and 85 years of age were eligible. All participants were examined at noon and midnight. We tested the pain threshold on both sides of the foreheads with quantitative sensory testing, assessed tiredness levels, and obtained high-field (7 Tesla) and high-resolution functional magnetic resonance imaging (MRI) at each visit. Functional connectivity was assessed at the two visits by performing a region-of-interest analysis. We defined nuclei in the brainstem implicated in processing nociceptive input as well as the thalamus and suprachiasmatic nucleus as the region-of-interest. Ten people were enrolled, and seven participants were included. First, we did not find statistically significant differences between noon and midnight of A-delta-mediated pain thresholds (median mechanical pain threshold at noon: left 9.2, right 9.2; at night: left 6.5, right 6.1). Second, after correction for a false discovery rate, we found changes in the mechanical pain sensitivity to have a statistically significant effect on changes in the functional connectivity between the left parabrachial nucleus and the suprachiasmatic nucleus (T = -40.79). The MRI data analysis suggested that brain stem nuclei and the hypothalamus modulate A-delta-mediated pain perception; however, these changes in pain perception did not lead to statistically significantly differing pain thresholds between noon and midnight. Hence, our findings shed doubt on our hypothesis that the physiologic circadian rhythmicity of pain thresholds could drive the circadian rhythmicity of cluster headache attacks.

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis

BackgroundRobust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.ObjectiveTo evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal.MethodsWe used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FW), 9-Hole Peg Test (9HPT), or Symbol Digit Modalities Test (SDMT). Different combinations of clinical and volumetric MRI measures were systematically assessed in predicting clinical outcomes. ML models were evaluated using Monte Carlo cross-validation, area under the curve (AUC), and permutation testing to assess significance.ResultsThe ML models significantly determined clinical impairment at baseline for the Amsterdam cohort, but did not reach significance for predicting clinical worsening over a follow-up of 2 and 5 years. High disability (EDSS ≥ 4) was best determined by a support vector machine (SVM) classifier using clinical and global MRI volumes (AUC = 0.83 ± 0.07, p = 0.015). Impaired cognition (SDMT Z-score ≤ −1.5) was best determined by a SVM using regional MRI volumes (thalamus, ventricles, lesions, and hippocampus), reaching an AUC of 0.73 ± 0.04 (p = 0.008).ConclusionML models could aid in classifying pwMS with clinical impairment and identify relevant biomarkers, but prediction of clinical worsening is an unmet need.

The possible impact of segmenting radiological images on students’ spatial abilities

Visuospatial ability plays a crucial role in anatomy related study success as studies have shown that students with high scores on a mental rotation test (MRT) perform better on anatomy exams. We hypothesized that segmenting anatomical structures on radiological images might impact students’ visuospatial abilities as measured by MRT scores. Second-year (bio)medical students who participated in a 10-week elective course were recruited for this study. All students completed an MRT questionnaire at the beginning of the elective course and another at the end of the elective course. During the elective course, students manually segmented radiological images (e.g., computed tomography (CT) and magnetic resonance imaging (MRI) data) to construct extended reality models. In total, twenty-eight students (17 males, 25 studied medicine) provided consent to participate in this study. A significant difference in mean pre-test scores (mean: 14.46; SD: ± 4.36) and post-test scores (mean: 17.56; SD: ± 4.57) (paired samples t-test, t(27) = −8.30, P < 0.001) was observed. Males had significantly better MRT scores, both at the pre- and at the post-test MRT. The results from this study should be interpretated with some caution due to the lack of a control group. The use of image segmentation as a teaching method to help (bio)medical students, especially those with low visuospatial insights, should be further investigated In future studies with larger cohorts and using a case–control experimental design.

Effect of patient specificity on predicting knee cartilage degeneration in obese adults: Musculoskeletal finite-element modeling of data from the CAROT trial.

Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. Inthisstudy,we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren-Lawrence (KL)and Boston-Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve(AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.

More than labels: neural representations of emotion words are widely distributed across the brain.

Although emotion words such as "anger," "disgust," "happiness," or "pride" are often thought of as mere labels, increasing evidence points to language as being important for emotion perception and experience. Emotion words may be particularly important for facilitating access to the emotion concepts. Indeed, deficits in semantic processing or impaired access to emotion words interfere with emotion perception. Yet, it is unclear what these behavioral findings mean for affective neuroscience. Thus, we examined the brain areas that support processing of emotion words using representational similarity analysis of functional magnetic resonance imaging data (N = 25). In the task, participants saw 10 emotion words (e.g. "anger," "happiness") while in the scanner. Participants rated each word based on its valence on a continuous scale ranging from 0 (Pleasant/Good) to 1 (Unpleasant/Bad) scale to ensure they were processing the words. Our results revealed that a diverse range of brain areas including prefrontal, midline cortical, and sensorimotor regions contained information about emotion words. Notably, our results overlapped with many regions implicated in decoding emotion experience by prior studies. Our results raise questions about what processes are being supported by these regions during emotion experience.

Spatial and temporal pattern of structure-function coupling of human brain connectome with development.

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7-21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.

Spatial and temporal pattern of structure–function coupling of human brain connectome with development

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7–21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC–FC coupling. Our findings revealed that SC–FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC–FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC–FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC–FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC–FC coupling in typical development.

Interactions between age, sex and visceral adipose tissue on brain ageing.

To examine the associations between visceral adipose tissue (VAT) and brain structural measures at midlife and explore how these associations may be affected by age, sex and cardiometabolic factors. We used abdominal and brain magnetic resonance imaging data from a population-based cohort of people at midlife in the UK Biobank. Regression modelling was applied to study associations of VAT volume with total brain volume (TBV), grey matter volume (GMV), white matter volume, white matter hyperintensity volume (WMHV) and total hippocampal volume (THV), and whether these associations were altered by age, sex or cardiometabolic factors. Complete data were available for 17 377 participants (mean age 63 years, standard deviation = 12, 53% female). Greater VAT was associated with lower TBV, GMV and THV (P < .001). We found an interaction between VAT and sex on TBV (P < .001), such that the negative association of VAT with TBV was greater in men (β = -2.89, 95% confidence interval [CI] -2.32 to -10.15) than in women (β = -1.32, 95% CI -0.49 to -3.14), with similar findings for GMV. We also found an interaction between VAT and age (but not sex) on WMHV (P < .001). The addition of other cardiometabolic factors or measures of physical activity resulted in little change to the models. VAT volume is associated with poorer brain health in midlife and this relationship is greatest in men and those at younger ages.

Quantitative MRI approach in the preoperative evaluation of a rare large-mass abdominal GIST tumor

Gastrointestinal stromal tumors (GISTs) are rare cancers originating from the gastrointestinal tract's interstitial cells of Cajal. This study explores the utility of quantitative Magnetic Resonance Imaging (MRI), particularly volumetric lesion segmentation, in the preoperative evaluation of large-mass abdominal GISTs. MRI offers high-resolution imaging without ionizing radiation, which is crucial for oncology patients requiring repeated scans. Advanced semi-automatic segmentation techniques were employed in this study from different MRI sequences to enhance precision in tumor boundary delineation and volumetric analysis, and aid personalized surgical planning. In addition, this study demonstrates that using different clinical imaging protocols and comparing volumetric data from CT and various MRI sequences yields consistent results with minimal volumetric error. Finally, integrating MRI data into real-time surgical navigation systems underscored the synergy between radiology, surgical oncology, and computer science, improving tumor margin assessment and reducing recurrence risk. This approach highlights a pivotal shift towards technology-enhanced healthcare, promising better patient outcomes.

Reduced resting-state functional connectivity between insula and inferior frontal gyrus and superior temporal gyrus in hoarding disorder.

Hoarding disorder (HD) is characterized by cognitive control impairments and abnormal brain activity in the insula and anterior cingulate cortex (ACC) during disposal of personal items or certain executive function tasks. However, whether there are any changes in resting-state functional connectivity of the insula and ACC remains unclear. A total of 55 subjects, including 24 patients with HD and 31 healthy controls (HCs), participated in the study. We acquired resting-state functional magnetic resonance imaging data and examined group differences in functional connectivity from the insula and ACC in whole-brain voxels. In patients with HD, functional connectivity was significantly lower between the right insula and right inferior frontal gyrus (IFG) and left superior temporal gyrus (STG) compared to HCs. There was no correlation between these connectivities and HD symptoms. Although the clinical implication is uncertain, our results suggest that patients with HD have resting-state functional alterations between the insula and IFG and STG, corresponding with the results of previous fMRI studies. These findings provide new insight into the neurobiological basis of HD.

Computational fluid dynamics in carbonate rock wormholes using magnetic resonance images as structural information

Computational fluid dynamics (CFD) is an essential tool with growing applications in many fields. In petrophysics, it is common to use computed tomography in those simulations, but in medicine, magnetic resonance imaging (MRI) is also being used as a basis for structural information. Wormholes are high-permeability structures created by the acidification of carbonate reservoirs and can impact reservoir production. CFD combined with MRI can benefit the study of wormholes in petrophysics, but combining both techniques is still a challenge. The objective of this study is to develop a pipeline for performing CFD in wormholes with MRI data. Using three samples of carbonate rocks acidified with 1.5% hydrochloric acid at 0.1, 1, and 10 ml/min, we acquired 300μm resolution T2-weighted images and experimental measurements of pressure data within flow rates of 5 to 50 ml/min. We applied cropping, bias field correction, non-local means denoising, and segmentation in the image processing step. For the 3D reconstruction, we used marching cubes to generate the surface mesh, the Taubin filter for surface smoothing, and boundary modeling with Blender. Finally, for the CFD, we generated volumetric meshes with cfMesh and used the OpenFOAM simpleFoam solver to simulate an incompressible, stationary, and laminar flow. We analyzed the effect of surface smoothing, estimating edge displacements, and measured the simulation pressure at the same flow rates as the experiments. Surface smoothing had a negligible impact on the overall edge position. For most flow rates, the simulation and experimental pressure measurements matched. A possible reason for the discrepancies is that we did not consider the surrounding porous media in the simulations. In summary, our work had satisfactory results, demonstrating CFD’s feasibility in studying wormholes using MRI.

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning.

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

Clinical and neuroimaging aspects of formal thought disorder in schizophrenia: a brief narrative review

Aim. To analyze current sources regarding the clinical and neuroimaging aspects of formal thought disorder (FTD) in patients with schizophrenia to create an up-to-date pathogenetic model of its main forms. Materials and methods. English-language publications in the Medline database (PubMed) were analyzed for this review. We analyzed only structural magnetic resonance imaging (MRI) studies in which a clear clinical assessment of FTD in schizophrenia is provided and the neuroimaging protocol meets generally accepted standards (as in the ENIGMA Schizophrenia Working Group). For the clinical division of FTD, positive and negative FTD were distinguished according to the positive and negative syndrome scale (PANSS). Results. From a clinical point of view, FTD includes at least 30 phenomena. For clinical and neuroimaging studies, division into positive and negative FTD is used according to the PANSS. Positive FTD is manifested by the disorganization of thinking processes and exhibits mainly in violations of its purposeful sequence. Negative FTD is manifested by violations of the abstract-symbolic way of thinking, lack of spontaneity, and stereotyping. According to morphometric MRI data, atrophic changes in brain regions related to neuronal networks of cognition and impulse control (prefrontal and anterior cingulate cortex), emotional processing (amygdala), abstract thinking, and imagination (lateral occipital cortex) are important for the development of both forms of FTD. Negative FTD is mainly associated with damage to the prefronto-cingulate circles, which are the anatomical and functional substrates of executive functions. A unique feature of positive FTD is atrophy of the structures of the left temporal lobe, which leads to language disorders at the semantic level. Using the method of virtual histology, it was established that both forms of FTD are associated with bilateral changes in astrocytes and dendritic spines in the involved anatomical regions. A positive FTD is also associated with pathological changes in microglia in two hemispheres, while with a negative FTD, microglial damages are present only in the right hemisphere. Conclusions. Positive FTD in schizophrenia is mainly associated with atrophic (astroglial-microglial) processes of cognitive control networks, negative – with the atrophy of networks of semantic processing of verbal information. In both forms, networks of emotional processing, abstract thinking, and imagination are involved. Treatment strategies for FTD should include effects on astroglial and microglial dysfunction.

Differentiation of malignant from benign soft tissue tumors using radiomics based on pharmacokinetic parameter maps obtained from dynamic contrast-enhanced magnetic resonance imaging data

Differentiation of malignant from benign soft tissue tumors using radiomics based on pharmacokinetic parameter maps obtained from dynamic contrast-enhanced magnetic resonance imaging data

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling.

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.