T1-weighted Brain MRI Research Articles

OpenMAP-T1: A Rapid Deep-Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain.

This study introduces OpenMAP-T1, a deep-learning-based method for rapid and accurate whole-brain parcellation in T1-weighted brain MRI, which aims to overcome the limitations of conventional normalization-to-atlas-based approaches and multi-atlas label-fusion (MALF) techniques. Brain image parcellation is a fundamental process in neuroscientific and clinical research, enabling a detailed analysis of specific cerebral regions. Normalization-to-atlas-based methods have been employed for this task, but they face limitations due to variations in brain morphology, especially in pathological conditions. The MALF techniques improved the accuracy of the image parcellation and robustness to variations in brain morphology, but at the cost of high computational demand that requires a lengthy processing time. OpenMAP-T1 integrates several convolutional neural network models across six phases: preprocessing; cropping; skull-stripping; parcellation; hemisphere segmentation; and final merging. This process involves standardizing MRI images, isolating the brain tissue, and parcellating it into 280 anatomical structures that cover the whole brain, including detailed gray and white matter structures, while simplifying the parcellation processes and incorporating robust training to handle various scan types and conditions. The OpenMAP-T1 was validated on the Johns Hopkins University atlas library and eight available open resources, including real-world clinical images, and the demonstration of robustness across different datasets with variations in scanner types, magnetic field strengths, and image processing techniques, such as defacing. Compared with existing methods, OpenMAP-T1 significantly reduced the processing time per image from several hours to less than 90 s without compromising accuracy. It was particularly effective in handling images with intensity inhomogeneity and varying head positions, conditions commonly seen in clinical settings. The adaptability of OpenMAP-T1 to a wide range of MRI datasets and its robustness to various scan conditions highlight its potential as a versatile tool in neuroimaging.

Comprehensive segmentation of gray matter structures on T1-weighted brain MRI: A Comparative Study of CNN, CNN hybrid-transformer or -Mamba architectures.

Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their datasets and/or the number of structures they can identify. This study evaluates the performance of six advanced deep learning models in segmenting 122 brain structures from T1-weighted MRI scans, aiming to identify the most effective model for clinical and research applications. 1,510 T1-weighted MRIs were used to compare six deep-learning models for the segmentation of 122 distinct gray matter structures: nnU-Net, SegResNet, SwinUNETR, UNETR, U-Mamba_BOT and U-Mamba_ Enc. Each model was rigorously tested for accuracy using the Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95). Additionally, the volume of each structure was calculated and compared between normal control (NC) and Alzheimer's Disease (AD) patients. U-Mamba_Bot achieved the highest performance with a median DSC of 0.9112 [IQR:0.8957, 0.9250]. nnU-Net achieved a median DSC of 0.9027 [IQR: 0.8847, 0.9205] and had the highest HD95 of 1.392[IQR: 1.174, 2.029]. The value of each HD95 (<3mm) indicates its superior capability in capturing detailed brain structures accurately. Following segmentation, volume calculations were performed, and the resultant volumes of normal controls and AD patients were compared. The volume changes observed in thirteen brain substructures were all consistent with those reported in existing literature, reinforcing the reliability of the segmentation outputs. This study underscores the efficacy of U-Mamba_Bot as a robust tool for detailed brain structure segmentation in T1-weighted MRI scans. The congruence of our volumetric analysis with the literature further validates the potential of advanced deep-learning models to enhance the understanding of neurodegenerative diseases such as AD. Future research should consider larger datasets to validate these findings further and explore the applicability of these models in other neurological conditions. AD = Alzheimer's Disease; ADNI = Alzheimer's Disease Neuroimaging Initiative; DSC = Dice Similarity Coefficient; HD95 = the 95th Percentile Hausdorff Distance; IQR = Interquartile Range; NC = Normal Control; SSMs = State-space Sequence Models.

MRI recovery with self-calibrated denoisers without fully-sampled data.

Acquiring fully sampled training data is challenging for many MRI applications. We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data. ReSiDe is inspired by plug-and-play (PnP) methods, but unlike traditional PnP approaches that utilize pre-trained denoisers, ReSiDe iteratively trains the denoiser on the image or images that are being reconstructed. We introduce two variations of our method: ReSiDe-S and ReSiDe-M. ReSiDe-S is scan-specific and works with a single set of undersampled measurements, while ReSiDe-M operates on multiple sets of undersampled measurements and provides faster inference. Studies I, II, and III compare ReSiDe-S and ReSiDe-M against other self-supervised or unsupervised methods using data from T1- and T2-weighted brain MRI, MRXCAT digital perfusion phantom, and first-pass cardiac perfusion, respectively. ReSiDe-S and ReSiDe-M outperform other methods in terms of peak signal-to-noise ratio and structural similarity index measure for Studies I and II, and in terms of expert scoring for Study III. We present a self-supervised image reconstruction method and validate it in both static and dynamic MRI applications. These developments can benefit MRI applications where the availability of fully sampled training data is limited.

Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy

BackgroundMesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults.Most atrophic hippocampi can be identified using MRI based on standard epilepsy protocols; however, difficulties can arise in cases where sclerotic changes in the hippocampus are subtle or non-epilepsy-specific protocols have been implemented. In such cases, quantitative methods, such as T1-weighted axial series MRIs, are valuable additional tools to complement epilepsy-specific protocols. In the current study, we applied machine learning (ML) techniques to the analysis of brain regions of interest (ROIs), including the hippocampus, thalamus, and cortical areas, to enhance the accuracy of lesion lateralization in MRI. MethodsThis study included 104 patients diagnosed with mTLE, including 55 with lesions on the right side and 49 with lesions on the left side. FreeSurfer software was used to extract features from high-resolution T1-weighted axial brain MRI scans for use in computing lateralization indices (LI) for various brain regions. After using feature selection to pinpoint critical ROIs, the corresponding LI values were used as parameters in training the ML model. ResultsThe proposed ML model demonstrated exceptional performance in the lateralization of mTLE, achieving test accuracy of 92.38 % with an AUROC of 0.97. ConclusionThis study demonstrated the efficacy of ML in detecting instances of MTS from thin-slice T1 images. The proposed method provides valuable insights for surgical planning and treatment. Nonetheless, additional research will be required to enhance the robustness of the model and rigorously validate its effectiveness and applicability in clinical settings.

339 - Construction of T1 Weighted MRI Brain Template Among Nepalese Population

339 - Construction of T1 Weighted MRI Brain Template Among Nepalese Population

Comparison of Explainable AI Models for MRI-based Alzheimer's Disease Classification.

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from 3D T1-weighted brain MRI scans. Here, we examine the value of adding occlusion sensitivity analysis (OSA) and gradient-weighted class activation mapping (Grad-CAM) to these models to make the results more interpretable. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) or National Alzheimer's Coordinating Center (NACC), which assess people of North American, predominantly European ancestry, so we examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort). We also evaluate the benefit of using a combined dataset to train the CNN models. Our experiments show feature localization consistent with knowledge of AD from other methods. OSA and Grad-CAM resolve features at different scales to help interpret diagnostic inferences made by CNNs.

Automated Idiopathic Normal-Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis.

Idiopathic normal pressure hydrocephalus (iNPH) is reversible dementia, that is underdiagnosed. The purpose of this study was to develop an automated diagnostic method for iNPH using artificial intelligence techniques with a T1-weighted MRI scan. We quantified iNPH, Parkinson's disease, Alzheimer's disease, and healthy control patients on T1-weighted 3D brain MRI scans using 452 scans for training and 110 scans for testing. Automatic component measurement algorithms were developed for Evans' index, Sylvian fissure enlargement, high-convexity tightness, callosal angle, and normalized lateral ventricle volume. XGBoost models were trained for both automated measurements and manual labels for iNPH prediction. A total of 452 patients (200 men; mean age ± standard deviation, 73.2 ± 6.5 years) were included in the training set. Of the 452 patients, 111 (24.6%) had iNPH. We obtained AUC values of 0.956 for automatically measured high-convexity tightness and 0.830 for Sylvian fissure enlargement. Intra-class correlation values of 0.824 for the callosal angle and 0.924 for Evans' index were measured. Using the decision tree of the XGBoost model, the model trained on manual labels obtained an average cross-validation AUC of 0.988 on the training set and 0.938 on the unseen test set, while the fully automated model obtained a cross-validation AUC of 0.983 and an unseen test AUC of 0.936. We demonstrated a machine-learning algorithm capable of diagnosing iNPH from a 3D T1-weighted MRI scan that is robust to the failure. We propose a method to scan large numbers of 3D T1-weighted MRI scans with minimal human intervention, making possible large-scale iNPH screening. iNPH = idiopathic normal-pressure hydrocephalus; PD = Parkinson's disease; AD = Alzheimer's disease; HC = healthy control; CSF = cerebrospinal fluid; DESH = disproportionately enlarged subarachnoid space hydrocephalus; 3D = three-dimensional.

Cortical structure and subcortical volumes in conduct disorder: a coordinated analysis of 15 international cohorts from the ENIGMA-Antisocial Behavior Working Group

Conduct disorder is associated with the highest burden of any mental disorder in childhood, yet its neurobiology remains unclear. Inconsistent findings limit our understanding of the role of brain structure alterations in conduct disorder. This study aims to identify the most robust and replicable brain structural correlates of conduct disorder. The ENIGMA-Antisocial Behavior Working Group performed a coordinated analysis of structural MRI data from 15 international cohorts. Eligibility criteria were a mean sample age of 18 years or less, with data available on sex, age, and diagnosis of conduct disorder, and at least ten participants with conduct disorder and ten typically developing participants. 3D T1-weighted MRI brain scans of all participants were pre-processed using ENIGMA-standardised protocols. We assessed group differences in cortical thickness, surface area, and subcortical volumes using general linear models, adjusting for age, sex, and total intracranial volume. Group-by-sex and group-by-age interactions, and DSM-subtype comparisons (childhood-onset vs adolescent-onset, and low vs high levels of callous-unemotional traits) were investigated. People with lived experience of conduct disorder were not involved in this study. We collated individual participant data from 1185 young people with conduct disorder (339 [28·6%] female and 846 [71·4%] male) and 1253 typically developing young people (446 [35·6%] female and 807 [64·4%] male), with a mean age of 13·5 years (SD 3·0; range 7-21). Information on race and ethnicity was not available. Relative to typically developing young people, the conduct disorder group had lower surface area in 26 cortical regions and lower total surface area (Cohen's d 0·09-0·26). Cortical thickness differed in the caudal anterior cingulate cortex (d 0·16) and the banks of the superior temporal sulcus (d -0·13). The conduct disorder group also had smaller amygdala (d 0·13), nucleus accumbens (d 0·11), thalamus (d 0·14), and hippocampus (d 0·12) volumes. Most differences remained significant after adjusting for ADHD comorbidity or intelligence quotient. No group-by-sex or group-by-age interactions were detected. Few differences were found between DSM-defined conduct disorder subtypes. However, individuals with high callous-unemotional traits showed more widespread differences compared with controls than those with low callous-unemotional traits. Our findings provide robust evidence of subtle yet widespread brain structural alterations in conduct disorder across subtypes and sexes, mostly in surface area. These findings provide further evidence that brain alterations might contribute to conduct disorder. Greater consideration of this under-recognised disorder is needed in research and clinical practice. Academy of Medical Sciences and Economic and Social Research Council.

Artificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal Extraction.

Reducing gadolinium-based contrast agents to lower costs, the environmental impact of gadolinium-containing wastewater, and patient exposure is still an unresolved issue. Published methods have never been compared. The purpose of this study was to compare the performance of 2 reimplemented state-of-the-art deep learning methods (settings A and B) and a proposed method for contrast signal extraction (setting C) to synthesize artificial T1-weighted full-dose images from corresponding noncontrast and low-dose images. In this prospective study, 213 participants received magnetic resonance imaging of the brain between August and October 2021 including low-dose (0.02 mmol/kg) and full-dose images (0.1 mmol/kg). Fifty participants were randomly set aside as test set before training (mean age ± SD, 52.6 ± 15.3 years; 30 men). Artificial and true full-dose images were compared using a reader-based study. Two readers noted all false-positive lesions and scored the overall interchangeability in regard to the clinical conclusion. Using a 5-point Likert scale (0 being the worst), they scored the contrast enhancement of each lesion and its conformity to the respective reference in the true image. The average counts of false-positives per participant were 0.33 ± 0.93, 0.07 ± 0.33, and 0.05 ± 0.22 for settings A-C, respectively. Setting C showed a significantly higher proportion of scans scored as fully or mostly interchangeable (70/100) than settings A (40/100, P < 0.001) and B (57/100, P < 0.001), and generated the smallest mean enhancement reduction of scored lesions (-0.50 ± 0.55) compared with the true images (setting A: -1.10 ± 0.98; setting B: -0.91 ± 0.67, both P < 0.001). The average scores of conformity of the lesion were 1.75 ± 1.07, 2.19 ± 1.04, and 2.48 ± 0.91 for settings A-C, respectively, with significant differences among all settings (all P < 0.001). The proposed method for contrast signal extraction showed significant improvements in synthesizing postcontrast images. A relevant proportion of images showing inadequate interchangeability with the reference remains at this dosage.

Cortical volumetric changes after cochlear implantation in postlingually deaf adults: correlation with speech perception abilities

This study aims to analyse the volumetric changes in brain MRI after cochlear implantation (CI), focusing on the speech perception in postlingually deaf adults. We conducted a prospective cohort study with 16 patients who had bilateral hearing loss and received unilateral CI. Based on the surgical side, patients were categorized into left and right CI groups. Volumetric T1-weighted brain MRI were obtained before and one year after the surgery. To overcome the artifact caused by the internal device in post-CI scan, image reconstruction method was newly devised and applied using the contralateral hemisphere of the pre-CI MRI data, to run FreeSurfer. We conducted within-subject template estimation for unbiased longitudinal image analysis, based on the linear mixed effect models. When analyzing the contralateral cerebral hemisphere before and after CI, a substantial increase in superior frontal gyrus and superior temporal gyrus (STG) volumes was observed in the left CI group. A positive correlation was observed in the STG and post-CI word recognition score in both groups. As far as we know, this is the first study attempting longitudinal brain volumetry based on post-CI MRI scans. We demonstrate that better auditory performance after CI is associated with structural restoration in central auditory structures.

Involvement of the cingulate cortex and insula in patients with trigeminal neuralgia: A clinical and volumetric study

AimAdvanced neuroimaging strategies may provide new insights into the underlying mechanisms of trigeminal neuralgia (TN). The objective of this study is to measure central pain centers in patients with long-standing trigeminal neuralgia and compare them to those of normal individuals. The findings of this study could improve the understanding of central region changes related to pain and improve the diagnosis and management of chronic trigeminal pain. Material and methodsWe examined radiologic data from 20 patients with trigeminal neuralgia and 28 healthy controls who underwent 3D iso T1-weighted brain MRI at our university hospital between 2018 and 2023. Patients with a minimum pain duration of 5 years were included and compared with healthy controls. Additionally, patients were categorized into groups based on the presence of vascular compression. The pain-related subcortical structures, such as the cingulate cortex and insula, were analyzed volumetrically using volBrain software. The results were evaluated statistically. ResultsSignificant differences were observed in the measurement of the posterior insula (p = 0.014) when comparing patients with trigeminal neuralgia and healthy subjects. Additionally, group comparisons based on the presence of vascular compression revealed significant differences in the Middle Cingulate Cortex (0.036) and Posterior Cingulate Cortex (0.031) between groups, which may be related to the etiological factor. ConclusionUnderstanding changes in central regions related to pain can aid in the diagnosis and management of chronic trigeminal pain.

Brain Connectivity Networks Constructed Using MRI for Predicting Patterns of Atrophy Progression in Parkinson Disease.

Background Whether connectome mapping of structural and functional connectivity across the brain could be used to predict patterns of atrophy progression in patients with mild Parkinson disease (PD) has not been well studied. Purpose To assess the structural and functional connectivity of brain regions in healthy controls and its relationship with the spread of gray matter (GM) atrophy in patients with mild PD. Materials and Methods This prospective study included participants with mild PD and controls recruited from a single center between January 2012 and December 2023. Participants with PD underwent three-dimensional T1-weighted brain MRI, and the extent of regional GM atrophy was determined at baseline and every year for 3 years. The structural and functional brain connectome was constructed using diffusion tensor imaging and resting-state functional MRI in healthy controls. Disease exposure (DE) indexes-indexes of the pathology of each brain region-were defined as a function of the structural or functional connectivity of all the connected regions in the healthy connectome and the severity of atrophy of the connected regions in participants with PD. Partial correlations were tested between structural and functional DE indexes of each GM region at 1- or 2-year follow-up and atrophy progression at 2- or 3-year follow-up. Prediction models of atrophy at 2- or 3-year follow-up were constructed using exhaustive feature selection. Results A total of 86 participants with mild PD (mean age at MRI, 60 years ± 8 [SD]; 48 male) and 60 healthy controls (mean age at MRI, 62 years ± 9; 31 female) were included. DE indexes at 1 and 2 years were correlated with atrophy at 2 and 3 years (r range, 0.22-0.33; P value range, .002-.04). Models including DE indexes predicted GM atrophy accumulation over 3 years in the right caudate nucleus and some frontal, parietal, and temporal brain regions (R2 range, 0.40-0.61; all P < .001). Conclusion The structural and functional organization of the brain connectome plays a role in atrophy progression in the early stages of PD. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yamada in this issue.

Volumetric Analysis of Pain Centers in Migraine Patients

Purpose: To investigate patterns of abnormalities in pain centers among patients with chronic pain, particularly those with migraines. The study aims to explore the potential correlation with pain duration and migraine types, and to propose new interventions for managing chronic pain &#x0D; Methods: Radiologic data of 32 migraine patients and 28 healthy controls underwent three-dimensional iso T1-weighted brain MRI between 2019 and 2023 at our university hospital were examined. Structured questionnaires collected data on age, gender, disease duration since migraine diagnosis, average pain intensity, and medication use. Patients with a minimum migraine duration of 3 years were included and divided into two groups based on the absence (Group 1) or presence (Group 2) of aura. Furthermore, patients were categorized into groups according to migraine attacks (episodic and chronic). Volumetric analysis, including cortical and subcortical pain-related structures, was performed using volBrain software. &#x0D; Results: Significant differences were observed in grey matter (p=0.037), cortical grey matter (p=0.022), cerebrum grey matter (p=0.026), anterior cingulate cortex (ACC) (p=0.017), middle cingulate cortex (MCC) (p=0.014), and posterior cingulate cortex (PCC) (p=0.008) volumes among the groups. Group comparisons revealed significant differences in the ACC, MCC, and PCC between Groups 0 and 1 (p=0.047, p=0.040, and p=0.047, respectively) and PCC between Groups 0 and 2 (p=0.026), possibly related to aura pathogenesis. Patients without aura exhibited non-significantly thinner postcentral gyrus (p=0.079), suggesting potential cortical involvement.&#x0D; Conclusions: This study provides insights into pain center abnormalities in migraine patients and their potential relevance to pain duration and migraine type.

Insular cortex involvement in migraine patients with chronic pain: A volumetric radiological and clinical study

BackgroundThis study aimed to assess abnormalities in the insular cortex of individuals suffering from migraines and examine their associations with pain duration, medication usage, and clinical symptoms. MethodsWe analyzed radiological data from 38 migraine patients who had undergone 3D iso T1-weighted brain MRI at our university hospital between 2019 and 2023. Structured questionnaires were used to collect information on participants' age, migraine type, disease duration, clinical symptoms, and medication use. Volumetric analysis was performed on the insular regions using Volbrain and 3DSlicer. The results were statistically analyzed. ResultsComparing groups with chronic pain to normal groups revealed significant differences in several insular regions, including the posterior insula (p = 0.034), parietal operculum (p = 0.04), and the entire insular cortex (p = 0.023). Further group comparisons (Group 1, 2, and 3) showed significant differences in specific insular regions. For instance, the anterior insula (p = 0.032) was associated with taste changes, the posterior insula (p = 0.010) with smell-related changes, and the central operculum (p = 0.046) with sensations of nausea. Additionally, significant changes were observed in the parietal operculum concerning nausea, photophobia, phonophobia, and changes in smell. ConclusionTo the best of our knowledge, there have been no studies investigating the relationship between clinical manifestations and volumetric correlation. This study provides insights into abnormalities in the insular cortex among migraine patients and their potential relevance to pain duration, severity, and migraine type. The results suggest that understanding alterations in insular regions possibly linked to pain could contribute to the development of innovative approaches to managing chronic pain.

Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI

BackgroundClinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse.MethodsWe propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area.ResultsValidation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images.ConclusionWe showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Atypical case of hypermanganesemia with dystonia type 1: a treatable primary dystonia caused by inborn error of manganese metabolism

Dystonia is one of the most common movement disorders in the pediatric age group. Dystonia with hypermanganesemia have polycythemia with deranged iron profile, chronic liver disease and basal ganglia hyperintensity on T1 weighted MRI brain image as characteristic finding. We are reporting one case of dystonia, lower limb weakness, polycythemia, and characteristic basal ganglia hyperintensity with a normal iron profile without liver involvement which diagnosed as inherited manganese (Mn) transporter defect due to homozygous mutations of SLC30A10 gene. Most of the reported cases of this mutation have reported decreased iron profile in their blood report and liver involvement contrary to our case. Intravenous disodium calcium edetate chelation and oral iron therapy led to a decrease in whole blood Mn level as well as clinical improvement in the patient. This is a rare disorder and is one of the potentially treatable inherited metal storage disorders and fatal if left untreated.

Structural indices of brain aging in methamphetamine use disorder

BackgroundMethamphetamine use is surging globally. It has been linked to premature stroke, Parkinsonism, and dementia, suggesting that it may accelerate brain aging. MethodsWe performed a retrospective study to determine if structural indices of brain aging were more prevalent prior to old age (26 – 54 years) in individuals with Methamphetamine Use Disorder (MUD), who were in early abstinence (M ± SD = 22.1 ± 25.6 days) than in healthy control (HC) participants. We compared T1-weighted MRI brain scans in age- and sex-matched groups (n = 89/group) on three structural features of brain aging: the brain volume/cerebrospinal fluid (BV/CSF) index, volume of white matter hypointensities/lesions, and choroid plexus volume. ResultsThe MUD group had a lower mean BV/CSF index and larger volumes of white matter hypointensities and choroid plexus (p-values < 0.01). Regression analyses showed significant age-by-group effects, indicating different age trajectories of the BV/CSF index and choroid plexus volume, consistent with abnormal global brain atrophy and choroid plexus pathology in the MUD group. Significant age and group main effects reflected a larger volume of white matter hypointensities for older participants across groups and for the MUD group irrespective of age. None of the three measures of brain aging correlated significantly with recent use or duration of recent abstinence from methamphetamine. ConclusionsPremature brain pathology, which may reflect cerebrovascular damage and dysfunction of the choroid plexus, occurs in people with MUD. Such pathology may affect cognition and thereby efficacy of behavioral treatments for MUD.

Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome.

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

The effect of inflammation markers on cortical thinning in major depressive disorder: A possible mediator of depression and cortical changes

BackgroundMajor depressive disorder (MDD) is a prevalent mental health condition with significant societal impact. Owing to the intricate biological diversity of MDD, treatment efficacy remains limited. Immune biomarkers have emerged as potential predictors of treatment response, underscoring the interaction between the immune system and the brain. This study investigated the relationship between cytokine levels and cortical thickness in patients with MDD, focusing on the corticolimbic circuit, to elucidate the influence of neuroinflammation on structural brain changes and contribute to a deeper understanding of the pathophysiology of MDD. MethodA total of 114 patients with MDD and 101 healthy controls (HC) matched for age, sex, and body mass index (BMI) were recruited. All participants were assessed for depression severity using the Hamilton Depression Rating Scale (HDRS), and 3.0 T T1 weighted brain MRI data were acquired. Additionally, cytokine levels were measured using a highly sensitive bead-based multiplex immunosorbent assay. ResultsPatients diagnosed with MDD exhibited notably elevated levels of interleukin-6 (p = 0.005) and interleukin-8 (p = 0.005), alongside significant cortical thinning in the left anterior cingulate gyrus and left superior frontal gyrus, with these findings maintaining significance even after applying Bonferroni correction. Furthermore, increased interleukin-6 and interleukin-8 levels in patients with MDD are associated with alterations in the left frontomarginal gyrus and right anterior cingulate cortex (ACC). ConclusionsThis suggests a potential influence of neuroinflammation on right ACC function in MDD patients, warranting longitudinal research to explore interleukin-6 and interleukin-8 mediated neurotoxicity in MDD vulnerability and brain morphology changes.