T1-weighted Magnetic Resonance Imaging Data Research Articles

Placental T2-weighted MRI-based radiomics-clinical nomogram to predict postpartum hemorrhage of placenta previa

Background Placenta previa is an obstetric complication related to severe maternal morbidity and mortality. Magnetic resonance imaging (MRI) can be used for the preoperative evaluation of postpartum hemorrhage. Purpose To investigate the value of MRI-based radiomics analysis in predicting postpartum hemorrhage among pregnant women with placenta previa. Material and Methods Preoperative T2-weighted MRI and related clinical data of 371 patients were retrospectively collected, and these patients were randomly allocated into two subsets: the training dataset (n = 260) and the validation dataset (n = 111). The logistic regression (LR) classifier was used for the development of the radiomics model and the calculation of the radiomics score (Radscore). Results A total of eight radiomics features and five clinical features were selected for model development. The area under the receiver operating characteristic curve (AUC) of the radiomics model in the training and validation datasets were 0.929 (95% confidence interval [CI] = 0.891–0.957) and 0.914 (95% CI = 0.846–0.959), respectively. Combined with clinical factors, nomograms demonstrated improved diagnostic efficacy, with an AUC of 0.968 (95% CI = 0.939–0.986) in the training dataset and 0.947 (95% CI = 0.888–0.981) in the validation dataset. Conclusion The MRI-based model has certain value in predicting postpartum hemorrhage in pregnant women with placenta previa.

Comparative analysis of cortical anatomy inmale participants with internet gaming disorder or tobacco use disorder: Insights fromnormative modeling.

Research on individual differences in brain structural features of internet gaming disorder (IGD) and established addictions such as tobacco use disorder (TUD) is currently limited. This study utilized normative modeling to analyze the cortical thickness (CT) development patterns of male patients with IGD and TUD, aiming to provide further insights into whether IGD qualifies as an addiction. Surface-based brain morphometry (SBM) was used to calculate CT from T1-weighted magnetic resonance imaging data of 804 male participants (665 healthy individuals, 68 IGD and 71 TUD). Gaussian process regression was employed to generate normative models of CT development. Deviation maps were produced to depict deviations of IGD and TUD participants from the typical developmental patterns. Both addiction groups exhibited widespread cortical thinning, particularly in regions such as the bilateral temporal pole and medial orbitofrontal cortex. The TUD group demonstrated a higher degree of individualization and limited spatial overlap compared to the IGD group. Opposite trends in CT changes were observed between the two groups in the bilateral pericalcarine cortex and pars triangularis. These findings regarding the similarities and differences between IGD and TUD provide support for the idea that IGD shares common features with substance-related addictions and contribute to a deeper understanding of the neural mechanisms underlyingIGD.

Multiscale heterogeneity of white matter morphometry in psychiatric disorders.

Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

Controlled Antenatal Thyroid Screening Study III: Effects of gestational thyroid status on adolescent brain morphology.

Children born to mothers with gestational hypo- or hyperthyroidism may have increased risk of adverse neurodevelopmental outcomes. However, the effects of maternal thyroid status on offspring brain development are unclear. To establish whether adolescent brain morphology is affected by suboptimal gestational thyroid function (SGTF). The Controlled Antenatal Thyroid Screening (CATS) study randomized mothers with SGTF to levothyroxine or no supplementation from ∼12 weeks' gestation. At age 9, children born to mothers who were over-treated with levothyroxine had a higher risk of conduct and hyperactivity traits. For the current CATS III study, children underwent neuroimaging studies, including T1-weighted structural magnetic resonance imaging (MRI). A total of 85 children aged 11-16 years had usable T1-weighted MRI data (exposed to untreated SGTF (n=21), normal GTF (n=24), or treated SGTF (optimally-treated (n=21), over-treated (n=20)). Primary outcome: to examine the association of SGTF and its treatment with global brain volumes. Secondary and exploratory outcomes: to investigate the association of maternal TSH and free T4 levels with global and subregional brain volumes. Results were adjusted for age, sex and pubertal scores. There were no significant differences in global brain volumetric measures between groups, including total gray matter volume (p=0.373). Weak positive correlations were found between maternal TSH, but not FT4, levels and several brain volumes, but these did not survive testing for multiple comparisons. We found no evidence that SGTF was associated with differences in adolescent brain morphology, and no impact of levothyroxine supplementation.

Alterations in Brain Morphometric Networks and Their Relationship with Memory Dysfunction in Patients with Type 2 Diabetes Mellitus.

Cognitive dysfunction, a significant complication of type 2 diabetes mellitus (T2DM), can potentially manifest even from the early stages of the disease. Despite evidence of global brain atrophy and related cognitive dysfunction in early-stage T2DM patients, specific regions vulnerable to these changes have not yet been identified. The study enrolled patients with T2DM of less than five years' duration and without chronic complications (T2DM group, n=100) and demographically similar healthy controls (control group, n=50). High-resolution T1-weighted magnetic resonance imaging data were subjected to independent component analysis to identify structurally significant components indicative of morphometric networks. Within these networks, the groups' gray matter volumes were compared, and distinctions in memory performance were assessed. In the T2DM group, the relationship between changes in gray matter volume within these networks and declines in memory performance was examined. Among the identified morphometric networks, the T2DM group exhibited reduced gray matter volumes in both the precuneus (Bonferroni-corrected p=0.003) and insular-opercular (Bonferroni-corrected p=0.024) networks relative to the control group. Patients with T2DM demonstrated significantly lower memory performance than the control group (p=0.001). In the T2DM group, reductions in gray matter volume in both the precuneus (r=0.316, p=0.001) and insular-opercular (r=0.199, p=0.047) networks were correlated with diminished memory performance. Our findings indicate that structural alterations in the precuneus and insular-opercular networks, along with memory dysfunction, can manifest within the first 5 years following a diagnosis of T2DM.

Altered brain morphology and functional connectivity in postmenopausal women: automatic segmentation of whole-brain and thalamic subnuclei and resting-state fMRI.

The transition to menopause is associated with various physiological changes, including alterations in brain structure and function. However, menopause-related structural and functional changes are poorly understood. The purpose of this study was not only to compare the brain volume changes between premenopausal and postmenopausal women, but also to evaluate the functional connectivity between the targeted brain regions associated with structural atrophy in postmenopausal women. Each 21 premenopausal and postmenopausal women underwent magnetic resonance imaging (MRI). T1-weighted MRI and resting-state functional MRI data were used to compare the brain volume and seed-based functional connectivity, respectively. In statistical analysis, multivariate analysis of variance, with age and whole brain volume as covariates, was used to evaluate surface areas and subcortical volumes between the two groups. Postmenopausal women showed significantly smaller cortical surface, especially in the left medial orbitofrontal cortex (mOFC), right superior temporal cortex, and right lateral orbitofrontal cortex, compared to premenopausal women (p < 0.05, Bonferroni-corrected) as well as significantly decreased functional connectivity between the left mOFC and the right thalamus was observed (p < 0.005, Monte-Carlo corrected). Although postmenopausal women did not show volume atrophy in the right thalamus, the volume of the right pulvinar anterior, which is one of the distinguished thalamic subnuclei, was significantly decreased (p < 0.05, Bonferroni-corrected). Taken together, our findings suggest that diminished brain volume and functional connectivity may be linked to menopause-related symptoms caused by the lower sex hormone levels.

Predicting the occurrence of mild cognitive impairment in Parkinson's disease using structural MRI data.

Mild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson's disease (PD) and a main risk factor for progressing to dementia. Our objective was to identify early anatomical brain changes that precede the transition from healthy cognition to MCI in PD. Structural T1-weighted magnetic resonance imaging data of PD patients with healthy cognition at baseline were downloaded from the Parkinson's Progression Markers Initiative database. Patients were divided into two groups based on the annual cognitive assessments over a 5-year time span: (i) PD patients with unstable healthy cognition who developed MCI over a 5-year follow-up (PD-UHC, n = 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC, n = 52). These 52 PD-SHC were selected among 192 PD-SHC patients using propensity score matching method to have similar demographic and clinical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier. The average accuracy of classifying PD-UHC vs. PD-SHC was 80.76%, with 82.05% sensitivity and 79.48% specificity using 10-fold cross-validation. The performance was similar in the hold-out test sets with all accuracy, sensitivity, and specificity at 76.92%. SHAP analysis showed that the most influential brain regions in the prediction model were located in the frontal, occipital, and cerebellar regions as well as midbrain. Our machine learning-based analysis yielded promising results in identifying PD individuals who are at risk of cognitive decline from the earliest disease stage and revealed the brain regions which may be linked to the prospective cognitive decline in PD before clinical symptoms emerge.

Towards privacy-preserving Alzheimer's disease classification: Federated learning on T1-weighted magnetic resonance imaging data.

This study aims to address the challenge of privacy-preserving Alzheimer's disease classification using federated learning across various data distributions, focusing on real-world applicability. The goal is to improve the efficiency of classification by minimizing communication rounds between clients and the central server. The proposed approach leverages two key strategies: increasing parallelism by utilizing more clients in each communication round and increasing computation per client during the intervals between rounds. To reflect real-world scenarios, data is divided into three distributions: identical and independently distributed, non-identical and independently distributed equal, and non-identical and independently distributed unequal. The impact of extreme quantity distribution skew is also examined. A convolutional neural network is used to evaluate the performance across these setups. The empirical study demonstrates that the proposed federated learning approach achieves a maximum accuracy of 84.75%, a precision of 86%, a recall of 85%, and an F1-score of 84%. Increasing the number of local epochs improves classification performance and reduces communication needs. The experiments show that federated learning is effective in handling heterogeneous datasets when all clients participate in each round of training. However, the results also indicate that extreme quantity distribution skew negatively impacts classification performance. The study confirms that federated learning is a viable solution for Alzheimer's disease classification while preserving data privacy. Increasing local computation and client participation enhances classification performance, though extreme distribution imbalances present a challenge. Further investigation is needed to address these limitations in real-world scenarios.

Interaction of Alzheimer Disease and Traumatic Brain Injury on Cortical Thickness.

Traumatic brain injury (TBI) is associated with an accelerated course of dementia, although biological relationships are incompletely understood. The study examined 1124 participants, including 343 with Alzheimer disease (AD), 127 with AD with TBI, 266 cognitively normal adults with TBI, and 388 cognitively normal adults without TBI. Cortical thickness was quantified from T1-weighted magnetic resonance imaging data. Multiple linear regression was used to determine the interaction between AD and TBI on cortical thickness. Among those with AD, TBI was associated with an earlier age of AD onset but, counterintuitively, less cortical thinning in frontotemporal regions relative to non-AD controls. AD with TBI represents a distinct group from AD, likely with distinct pathologic contributions beyond gray matter loss. This finding has important implications for the diagnosis and treatment of AD in the presence of TBI and indicates that models of AD, aging, and neural loss should account for TBI history.

GHA-DenseNet prediction and diagnosis of malignancy in femoral bone tumors using magnetic resonance imaging

Background and objectiveDue to their aggressive nature and poor prognosis, malignant femoral bone tumors present considerable hurdles. Early treatment commencement is essential for enhancing vital and practical outcomes. In this investigation, deep learning algorithms will be used to analyze magnetic resonance imaging (MRI) data to identify bone tumors that are malignant. MethodologyThe study cohort included 44 patients, with ages ranging from 17 to 78 (22 women and 22 males). To categorize T1 and T2 weighted MRI data, this paper presents an improved DenseNet network model for the classification of bone tumor MRI, which is named GHA-DenseNet. Based on the original DenseNet model, the attention module is added to solve the problem that the deep convolutional model can reduce the loss of key features when capturing the location and content information of femoral bone tumor tissue due to the limitation of local receptive field. In addition, the sparse connection mode is used to prune the connection mode of the original model, so as to remove unnecessary and retain more useful fast connection mode, and alleviate the overfitting problem caused by small dataset size and image characteristics. In a clinical model designed to anticipate tumor malignancy, the utilization of T1 and T2 classifier output values, in combination with patient-specific clinical information, was a crucial component. ResultsThe T1 classifier's accuracy during the training phase was 92.88% whereas the T2 classifier's accuracy was 87.03%. Both classifiers demonstrated accuracy of 95.24% throughout the validation phase. During training and validation, the clinical model's accuracy was 82.17% and 81.51%, respectively. The clinical model's receiver operating characteristic (ROC) curve demonstrated its capacity to separate classes. ConclusionsThe proposed method does not require manual segmentation of MRI scans because it makes use of pretrained deep learning classifiers. These algorithms have the ability to predict tumor malignancy and shorten the diagnostic and therapeutic turnaround times. Although the procedure only needs a little amount of radiologists' involvement, more testing on a larger patient cohort is required to confirm its efficacy.

Hippocampal volume mediates the relationship of parental rejection in childhood with social cognition in healthy adults

Childhood abuse reduces hippocampal and amygdala volumes and impairs social cognition, including the ability to recognize facial expressions. However, these associations have been studied primarily in individuals with a history of severe abuse and psychiatric symptoms; researchers have not determined whether these associations can also be observed in healthy adults. In the present study, we analyzed data from 400 healthy adults (208 men and 192 women) at Tamagawa University. Parental rejection reflecting childhood abuse was assessed using the short form of Egna Minnen Beträffande Uppfostran, while social cognition was assessed using the “Fake Smile Detection Task.” Hippocampal and amygdala volumes were extracted from T1-weighted magnetic resonance imaging data using FreeSurfer. We found that greater parental rejection resulted in smaller hippocampal and amygdala volumes and poorer performance in the Fake Smile Detection Task. Structural equation modeling analysis supported the model that hippocampal volume mediates maternal rejection effect on performance on the Fake Smile Detection Task, with involvement of the amygdala. These findings are in line with the structural and functional connectivity found between the hippocampus and amygdala and their joint involvement in social cognition. Therefore, parental rejection may affect hippocampal and amygdala volumes and social cognitive function even in symptom-free adults.

Considerations on brain age predictions from repeatedly sampled data across time.

Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls. We used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model. We found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality. The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation. We report dice scores higher than the agreement between two human raters reported in previous studies, highlighting the ability to remove individual human bias and standardize the process across research studies and centers.

A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma

The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen’s Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.

Morphologic brain network predicts levodopa responsiveness in Parkinson disease.

The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson's Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS. A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient's neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen-Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups. The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased. Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

IntroductionDementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI). MethodsAtlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification). ResultsThe binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration. DiscussionResults suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease.

PhiPipe: A multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments.

Magnetic resonance imaging (MRI) has been one of the primary instruments to measure the properties of the human brain non-invasively in vivo. MRI data generally needs to go through a series of processing steps (i.e., a pipeline) before statistical analysis. Currently, the processing pipelines for multi-modal MRI data are still rare, in contrast to single-modal pipelines. Furthermore, the reliability and validity of the output of the pipelines are critical for the MRI studies. However, the reliability and validity measures are not available or adequate for almost all pipelines. Here, we present PhiPipe, a multi-modal MRI processing pipeline. PhiPipe could process T1-weighted, resting-state BOLD, and diffusion-weighted MRI data and generate commonly used brain features in neuroimaging. We evaluated the test-retest reliability of PhiPipe's brain features by computing intra-class correlations (ICC) in four public datasets with repeated scans. We further evaluated the predictive validity by computing the correlation of brain features with chronological age in three public adult lifespan datasets. The multivariate reliability and predictive validity of the PhiPipe results were also evaluated. The results of PhiPipe were consistent with previous studies, showing comparable or better reliability and validity when compared with two popular single-modality pipelines, namely DPARSF and PANDA. The publicly available PhiPipe provides a simple-to-use solution to multi-modal MRI data processing. The accompanied reliability and validity assessments could help researchers make informed choices in experimental design and statistical analysis. Furthermore, this study provides a framework for evaluating the reliability and validity of image processing pipelines.

Temporal encephaloceles and coexisting epileptogenic lesions.

This study was performed to identify coexisting structural lesions in patients with epilepsy and known temporal encephaloceles (TEs). Forty-seven structural magnetic resonance imaging (MRI) scans of patients with epilepsy and radiologically diagnosed TEs were retrospectively reviewed visually and using an automated postprocessing software, the Morphometric Analysis Program v2018 (MAP18), to depict additional subtle, potentially epileptogenic lesions in the 3D T1-weighted MRI data. All imaging findings were evaluated in the context of clinical and electroencephalographical findings. The study population consisted of 47 epilepsy patients (38.3% female, n=18). The median age at the time of the scan was 40 years (range 12-81 years). Twenty-one out of 47 MRI scans (44.7%) showed coexisting lesions in the initial MRI evaluation; in 38.3% (n=18) of patients, those lesions were considered probably epileptogenic. After postprocessing, probable epileptogenic lesions were identified in 53.2% (n=25) of patients. Malformations of cortical development had initially been reported in 17.0% (n=8) of patients with TEs, which increased to 38.3% (n=18) after postprocessing. TEs and other epileptogenic lesions were considered equally epileptogenic in 21.3% (n=10) of the cases in the initial MR reports and 25.5% (n=12) of the cases after postprocessing. Temporal encephaloceles are a potential cause of MRI-negative temporal lobe epilepsy. According to our data, TEs can occur with other lesions, suggesting that increased awareness is also required in patients with lesional epilepsy. TEs may not always be epileptogenic; hence, their occurrence with other structural pathologies may influence the presurgical evaluation and surgical approach. Finally, TEs can be associated with malformations of cortical development, which may indicate a common developmental etiology of those lesions.

Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.

The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG. T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction. The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers. In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.

Multimodal multi-center analysis of electroconvulsive therapy effects in depression: Brainwide gray matter increase without functional changes

BackgroundElectroconvulsive therapy (ECT) is an effective treatment for severe depression and induces gray matter (GM) increases in the brain. Small-scale studies suggest that ECT also leads to changes in brain functioning, but findings are inconsistent. In this study, we investigated the influence of ECT on changes in both brain structure and function and their relation to clinical improvement using multicenter neuroimaging data from the Global ECT-MRI Research Collaboration (GEMRIC). MethodsWe analyzed T1-weighted structural magnetic resonance imaging (MRI) and functional resting-state MRI data of 88 individuals (49 male) with depressive episodes before and within one week after ECT. We performed voxel-based morphometry on the structural data and calculated fractional amplitudes of low-frequency fluctuations, regional homogeneity, degree centrality, functional connectomics, and hippocampus connectivity for the functional data in both unimodal and multimodal analyses. Longitudinal effects in the ECT group were compared to repeated measures of healthy controls (n = 27). ResultsWide-spread increases in GM volume were found in patients following ECT. In contrast, no changes in any of the functional measures were observed, and there were no significant differences in structural or functional changes between ECT responders and non-responders. Multimodal analysis revealed that volume increases in the striatum, supplementary motor area and fusiform gyrus were associated with local changes in brain function. ConclusionThese results confirm wide-spread increases in GM volume, but suggest that this is not accompanied by functional changes or associated with clinical response. Instead, focal changes in brain function appear related to individual differences in brain volume increases.