T1-weighted Brain Research Articles

Choroid plexus enlargement in patients with obstructive sleep apnea

ObjectivesThe function of choroid plexus is to produce cerebrospinal fluid, which is critical for the glymphatic system function. In this study, we aimed to analyze the differences in choroid plexus volume between patients with obstructive sleep apnea (OSA) and healthy controls, with the goal of discovering the glymphatic system dysfunction in patients with OSA. MethodsWe prospectively enrolled 40 patients with OSA confirmed by polysomnography and 38 age- and sex-matched healthy controls. All participants underwent three-dimensional T1-weighted brain imaging, which was suitable for volumetric analysis. We compared choroid plexus volumes between patients with OSA and healthy controls, and analyzed the association between choroid plexus volume and polysomnographic findings in patients with OSA. ResultsChoroid plexus volumes were significantly larger in patients with OSA than in healthy controls (2.311 % vs. 2.096 %, p = 0.005). However, no significant association was detected between choroid plexus volume and polysomnographic findings. ConclusionThis study demonstrated enlargement of the choroid plexus in patients with OSA compared with healthy controls. This finding could be related with glymphatic system dysfunction in patients with OSA.

Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation

The emergence of clinical data warehouses (CDWs), which contain the medical data of millions of patients, has paved the way for vast data sharing for research. The quality of MRIs gathered in CDWs differs greatly from what is observed in research settings and reflects a certain clinical reality. Consequently, a significant proportion of these images turns out to be unusable due to their poor quality. Given the massive volume of MRIs contained in CDWs, the manual rating of image quality is impossible. Thus, it is necessary to develop an automated solution capable of effectively identifying corrupted images in CDWs. This study presents an innovative transfer learning method for automated quality con- trol of 3D gradient echo T1-weighted brain MRIs within a CDW, leveraging artefact sim- ulation. We first intentionally corrupt images from research datasets by inducing poorer contrast, adding noise and introducing motion artefacts. Subsequently, three artefact- specific models are pre-trained using these corrupted images to detect distinct types of artefacts. Finally, the models are generalised to routine clinical data through a transfer learning technique, utilising 3660 manually annotated images. The overall image quality is inferred from the results of the three models, each designed to detect a specific type of artefact. Our method was validated on an independent test set of 385 3D gradient echo T1-weighted MRIs. Our proposed approach achieved excellent results for the detection of bad quality MRIs, with a balanced accuracy of over 87%, surpassing our previous approach by 3.5 percent points. Additionally, we achieved a satisfactory balanced accuracy of 79% for the detection of moderate quality MRIs, outperforming our previous performance by 5 percent points. Our framework provides a valuable tool for exploiting the potential of MRIs in CDWs.

Pilot study of circulating cell-free mitochondrial DNA in relation to brain structure in youth bipolar disorder

BackgroundMitochondrial dysfunction is implicated in the neuropathology of bipolar disorder (BD). Higher circulating cell-free mitochondrial DNA (ccf-mtDNA), generally reflecting poorer mitochondrial health, has been associated with greater symptoms severity in BD. The current study examines the association of serum ccf-mtDNA and brain structure in relation to youth BD. We hypothesized that higher ccf-mtDNA will be associated with measures of lower brain structure, particularly in the BD group.MethodsParticipants included 40 youth (BD, n = 19; Control group [CG], n = 21; aged 13–20 years). Serum ccf-mtDNA levels were assayed. T1-weighted brain images were acquired using 3T-MRI. Region of interest (ROI) analyses examined prefrontal cortex (PFC) and whole brain gray matter, alongside exploratory vertex-wise analyses. Analyses examined ccf-mtDNA main-effects and ccf-mtDNA-by-diagnosis interaction effects controlling for age, sex, and intracranial volume.ResultsThere was no significant difference in ccf-mtDNA levels between BD and CG. In ROI analyses, higher ccf-mtDNA was associated with higher PFC surface area (SA) (β = 0.32 p < 0.001) and PFC volume (β = 0.32 p = 0.002) in the overall sample. In stratified analyses, higher ccf-mtDNA was associated with higher PFC SA within both subgroups (BD: β = 0.39 p = 0.02; CG: β = 0.24 p = 0.045). Higher ccf-mtDNA was associated with higher PFC volume within the BD group (β = 0.39 p = 0.046). In vertex-wise analyses, higher ccf-mtDNA was associated with higher SA and volume in frontal clusters within the overall sample and within the BD group. There were significant ccf-mtDNA-by-diagnosis interactions in three frontal and parietal clusters, whereby higher ccf-mtDNA was associated with higher neurostructural metrics in the BD group but lower neurostructural metrics in CG.ConclusionsContrasting our hypothesis, higher ccf-mtDNA was consistently associated with higher, rather than lower, regional neuralstructural metrics among youth with BD. While this finding may reflect a compensatory mechanism, future repeated-measures prospective studies evaluating the inter-relationship among ccf-mtDNA, mood, and brain structure across developmental epochs and illness stages are warranted.

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.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p &lt; 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings.

Magnetic resonance imaging of intracranial anomalies in pregnancies complicated by twin anemia-polycythemia sequence

PurposeTo describe fetal brain Magnetic Resonance Imaging (MRI) findings in a large series of monochorionic (MC) pregnancies complicated by Twin Anemia-Polycythemia Sequence (TAPS) prenatally diagnosed, so to characterize the potential intracranial complications associated with this condition, their frequency and potential treatment options.MethodsThis is a retrospective study of MC twin pregnancies complicated by TAPS and undergone fetal MRI in a single institution from 2006 to 2023.MRI control was performed and post-natal ultrasound (US) or MRI were available.Results1250 MC pregnancies were evaluated in our institution. 50 pregnancies (4%) were diagnosed with TAPS, 29 underwent a fetal brain MRI.13/29 pregnancies (44.8%) demonstrated brain findings at MRI in at least a twin. Neuroradiological findings were detected in 14/57 twins (24.6%).We detected four main categories of findings: hemorrhagic lesions, T2-weighted white-matter hyperintensities (WMH), brain edema-swelling and venous congestion. Nineteen findings were present in the anemic and three in the polycythemic twins, with a statistically significant ratio between the two groups (p-value = 0.01).Intrauterine MRI follow-up demonstrated the sequalae of hemorrhagic lesions. A complete regression of brain swelling, veins prominence and T2-WMHs was demonstrated after treatment. Postnatal imaging confirmed prenatal features.ConclusionsOur work demonstrates that TAPS-related MRI anomalies consisted in edematous/hemorrhagic lesions that occur mostly in anemic rather than in polycythemic twins. Fetoscopic laser surgery could have a potential decongestant role.Therefore, prenatal MRI may help in counselling and management in TAPS pregnancies, especially for the planning of therapy and the monitoring of its efficacy.

Lifestyle management and brain MRI metrics in female Australian adults living with multiple sclerosis: a feasibility and acceptability study

BackgroundLimited studies of multiple sclerosis (MS) exist whereby magnetic resonance imaging (MRI) of the brain with consistent imaging protocols occurs at the same time points as collection of healthy lifestyle measures. The aim of this study was to test the feasibility, acceptability and preliminary efficacy of acquiring MRI data as an objective, diagnostic and prognostic marker of MS, at the same time point as brain-healthy lifestyle measures including diet.MethodsParticipants living with relapsing remitting MS partook in one structural MRI scanning session of the brain, completed two online 24-hour dietary recalls and demographic and self-reported lifestyle questionnaires (e.g. self-reported disability, comorbidities, physical activity, smoking status, body mass index (BMI), stress). Measures of central tenancy and level of dispersion were calculated for feasibility and acceptability of the research protocols. Lesion count was determined by one radiologist and volumetric analyses by a data analysis pipeline based on FreeSurfer software suite. Correlations between white matter lesion count, whole brain volume analyses and lifestyle measures were assessed using Spearman’s rank-order correlation coefficient.ResultsThirteen female participants were included in the study: eligibility rate 90.6% (29/32), recruitment rate 46.9% (15/32) and compliance rate 87% (13/15). The mean time to complete all required tasks, including MRI acquisition was 115.86 minutes (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$($$\\end{document}± 23.04), over 4 days. Conversion to usual dietary intake was limited by the small sample. There was one strong, negative correlation between BMI and brain volume (rs = −0.643, p = 0.018) and one strong, positive correlation between physical activity and brain volume (rs = 0.670, p = 0.012) that were both statistically significant.ConclusionsAcquiring MRI brain scans at the same time point as lifestyle profiles in adults with MS is both feasible and accepted among adult females living with MS. Quantification of volumetric MRI data support further investigations using semi-automated pipelines among people living with MS, with pre-processing steps identified to increase automated feasibility. This protocol may be used to determine relationships between elements of a brain-healthy lifestyle, including dietary intake, and measures of disease burden and brain health, as assessed by T1-weighted and T2-weighted lesion count and whole brain volume, in an adequately powered sample.Trial registrationThe study protocol was retrospectively registered in the Australia New Zealand Clinical Trials Registry (ACTRN12624000296538).

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.

Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality.

Deep learning reconstruction (DLR) technologies are the latest methods attempting to solve the enduring problem of reducing MRI acquisition times without compromising image quality. The clinical utility of this reconstruction technique is yet to be fully established. This study aims to assess whether a commercially available DLR technique applied to 2D T2-weighted FLAIR brain images allows a reduction in scan time, without compromising image quality and thus diagnostic accuracy. 47 participants (24 male, mean age 55.9 ± 18.7 SD years, range 20-89 years) underwent routine, clinically indicated brain MRI studies in March 2022, that included a standard-of-care (SOC) T2-weighted FLAIR sequence, and an accelerated acquisition that was reconstructed using the DLR denoising product. Overall image quality, lesion conspicuity, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artefacts for each sequence, and preferred sequence on direct comparison, were subjectively assessed by two readers. There was a strong preference for SOC FLAIR sequence for overall image quality (P = 0.01) and head-to-head comparison (P < 0.001). No difference was observed for lesion conspicuity (P = 0.49), perceived SNR (P = 1.0), and perceived CNR (P = 0.84). There was no difference in motion (P = 0.57) nor Gibbs ringing (P = 0.86) artefacts. Phase ghosting (P = 0.038) and pseudolesions were significantly more frequent (P < 0.001) on DLR images. DLR algorithm allowed faster FLAIR acquisition times with comparable image quality and lesion conspicuity. However, an increased incidence and severity of phase ghosting artefact and presence of pseudolesions using this technique may result in a reduction in reading speed, efficiency, and diagnostic confidence.

Distinct Volume Alterations of Thalamic Nuclei Across the Schizophrenia Spectrum.

Abnormal thalamic nuclei volumes and their link to cognitive impairments have been observed in schizophrenia. However, whether and how this finding extends to the schizophrenia spectrum is unknown. We hypothesized a distinct pattern of aberrant thalamic nuclei volume across the spectrum and examined its potential associations with cognitive symptoms. We performed a FreeSurfer-based volumetry of T1-weighted brain MRIs from 137 healthy controls, 66 at-risk mental state (ARMS) subjects, 89 first-episode psychosis (FEP) individuals, and 126 patients with schizophrenia to estimate thalamic nuclei volumes of six nuclei groups (anterior, lateral, ventral, intralaminar, medial, and pulvinar). We used linear regression models, controlling for sex, age, and estimated total intracranial volume, both to compare thalamic nuclei volumes across groups and to investigate their associations with positive, negative, and cognitive symptoms. We observed significant volume alterations in medial and lateral thalamic nuclei. Medial nuclei displayed consistently reduced volumes across the spectrum compared to controls, while lower lateral nuclei volumes were only observed in schizophrenia. Whereas positive and negative symptoms were not associated with reduced nuclei volumes across all groups, higher cognitive scores were linked to lower volumes of medial nuclei in ARMS. In FEP, cognition was not linked to nuclei volumes. In schizophrenia, lower cognitive performance was associated with lower medial volumes. Results demonstrate distinct thalamic nuclei volume reductions across the schizophrenia spectrum, with lower medial nuclei volumes linked to cognitive deficits in ARMS and schizophrenia. Data suggest a distinctive trajectory of thalamic nuclei abnormalities along the course of schizophrenia.

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.

Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.

While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel-level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs. -0.1, p = .01). Brain-PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = -153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

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.

Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer’s disease on routinely acquired T1-weighted imaging-based brain network

Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer’s disease on routinely acquired T1-weighted imaging-based brain network

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.

Brain artery diameters and risk of dementia and stroke.

We tested the association of brain artery diameters with dementia and stroke risk in three distinct population-based studies using conventional T2-weighted brain magnetic resonance imaging (MRI) images. We included 8420 adults> 40 years old from three longitudinal population-based studies with brain MRI scans. We estimated and meta-analyzed the hazard ratios (HRs) of the brain and carotids and basilar diameters associated with dementia and stroke. Overall and carotid artery diameters>95th percentile increased the risk for dementia by 1.74 (95% confidence interval [CI], 1.13-2.68) and 1.48 (95% CI, 1.12-1.96) fold, respectively. For stroke, meta-analyses yielded HRs of 1.59 (95% CI, 1.04-2.42) for overall arteries and 2.11 (95% CI, 1.45-3.08) for basilar artery diameters> 95th percentile. Individuals with dilated brain arteries are at higher risk for dementia and stroke, across distinct populations. Our findings underline the potential value of T2-weighted brain MRI-based brain diameter assessment in estimating the risk of dementia and stroke.

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.

Regional decreases of cortical thickness in major depressive disorder and their correlation with illness duration: a case-control study.

Alterations in brain structure and function in major depressive disorder (MDD) have been identified in a number of studies, but findings regarding cortical thickness were various and inconsistent. Our current study aims to explore the differences in cortical thickness between individuals with MDD and healthy controls (HC) in a Chinese population. We investigated T1-weighted brain magnetic resonance imaging data from 61 participants (31 MDD and 30 HC). The cortical thickness between the two groups and analyzed correlations between cortical thickness and demographic variables in the MDD group for regions with significant between-group differences were conducted. Compared with the HC group, patients with MDD had significantly decreased cortical thickness, in left pars triangularis, left pars orbitalis, left rostral middle frontal gyrus, left supramarginal gyrus, right parahippocampal gyrus, right lingual gyrus, right fusiform and right inferior parietal gyrus. The cortical thickness of left rostral middle frontal gyrus was negatively correlated (r = -0.47, p = 0.028) with the illness duration in patients with MDD. Our study distinguished that cortical thickness decreases in numerous brain regions both in the left and right hemisphere in individuals with MDD, and the negative correlation between the cortical thickness of left rostral middle frontal gyrus illness duration. Our current findings are valuable in providing neural markers to identify MDD and understanding the potential pathophysiology of mood disorders.

Brain morphometric changes in children born as small for gestational age without catch up growth.

Most infants born as small for gestational age (SGA) demonstrate catch up growth by 2-4 years, but some fail to do so. This failure is associated with several health risks, including neuropsychological development issues. However, data on the morphological characteristics of the brains of infants born as SGA without achieving catch up growth are lacking. This study aims to determine the structural aspects of the brains of children born as SGA without catch up growth. We conducted voxel- and surface-based morphometric analyses of 1.5-T T1-weighted brain images scanned from eight infants born as SGA who could not achieve catch up growth by 3 years and sixteen individuals with idiopathic short stature (ISS) to exclude body size effects. Growth hormone (GH) secretion stimulation tests were used to rule out GH deficiency in all SGA and ISS cases. The magnetic resonance imaging data were assessed using Levene's test for equality of variances and a two-tailed unpaired t-test for equality of means. The Benjamini-Hochberg procedure was used to apply discovery rate correction for multiple comparisons. Morphometric analyses of both t-statical map and surface-based analyses using general linear multiple analysis determined decreased left insula thickness and volume in SGA without catch up growth compared with ISS. The brain scans of patients with SGA who lack catch up growth indicated distinct morphological disparities when compared to those with ISS. The discernible features of brain morphology observed in patients born as SGA without catch up growth may improve understanding of the association of SGA without catch up growth with both intellectual and psychological outcomes.