Structural Magnetic Resonance Imaging Research Articles

MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET outcomes from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded highly accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study reveals current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.

MRI for Differentiation between HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma: A Systematic Review.

Human papillomavirus (HPV) is an important risk factor for oropharyngeal squamous cell carcinoma (OPSCC). HPV-positive (HPV+) cases are associated with a different pathophysiology, microstructure, and prognosis compared to HPV-negative (HPV-) cases. This review aimed to investigate the potential of magnetic resonance imaging (MRI) to discriminate between HPV+ and HPV- tumours and predict HPV status in OPSCC patients. A systematic literature search was performed on 15 December 2022 on EMBASE, MEDLINE ALL, Web of Science, and Cochrane according to PRISMA guidelines. Twenty-eight studies (n = 2634 patients) were included. Five, nineteen, and seven studies investigated structural MRI (e.g., T1, T2-weighted), diffusion-weighted MRI, and other sequences, respectively. Three out of four studies found that HPV+ tumours were significantly smaller in size, and their lymph node metastases were more cystic in structure than HPV- ones. Eleven out of thirteen studies found that the mean apparent diffusion coefficient was significantly higher in HPV- than HPV+ primary tumours. Other sequences need further investigation. Fourteen studies used MRI to predict HPV status using clinical, radiological, and radiomics features. The reported areas under the curve (AUC) values ranged between 0.697 and 0.944. MRI can potentially be used to find differences between HPV+ and HPV- OPSCC patients and predict HPV status with reasonable accuracy. Larger studies with external model validation using independent datasets are needed before clinical implementation.

Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis.

Brain development/aging is not uniform across individuals,spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological age > chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or agingmap onto specific symptom facets. Using structural MRI, we compared the brain age gap among CHR-P (n = 51), ESZ (n = 78), and unaffected comparison participants (UCP; n = 90), and examined associations with CHR-P psychosis conversion (CHR-P converters n = 10; CHR-P non-converters; n = 23) and positive and negative symptoms. ESZ showed a greater brain age gap relative to UCP and CHR-P (Ps < .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (P = .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (P = .008), but not expressive negative symptom severity. Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.

Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern

AbstractThe timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state‐of‐the‐art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane‐based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin‐GTSVM‐K) excels in CT and WM features, whereas Linear Pin‐GTSVM (Pin‐GTSVM‐L) and Linear intuitionistic fuzzy TSVM (IFTSVM‐L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection, feature based‐interpretability and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.This article is categorized under: Technologies &gt; Classification Technologies &gt; Machine Learning Application Areas &gt; Health Care

Regional structural abnormalities in thalamus in idiopathic cervical dystonia

BackgroundThe thalamus has a central role in the pathophysiology of idiopathic cervical dystonia (iCD); however, the nature of alterations occurring within this structure remain largely elusive. Using a structural magnetic resonance imaging (MRI) approach, we examined whether abnormalities differ across thalamic subregions/nuclei in patients with iCD.MethodsStructural MRI data were collected from 37 patients with iCD and 37 healthy controls (HCs). Automatic parcellation of 25 thalamic nuclei in each hemisphere was performed based on the FreeSurfer program. Differences in thalamic nuclei volumes between groups and their relationships with clinical information were analysed in patients with iCD.ResultsCompared to HCs, a significant reduction in thalamic nuclei volume primarily in central medial, centromedian, lateral geniculate, medial geniculate, medial ventral, paracentral, parafascicular, paratenial, and ventromedial nuclei was found in patients with iCD (P < 0.05, false discovery rate corrected). However, no statistically significant correlations were observed between altered thalamic nuclei volumes and clinical characteristics in iCD group.ConclusionThis study highlights the neurobiological mechanisms of iCD related to thalamic volume changes.

P.105 Corpus callosum changes in children affected by infantile hydrocephalus

Background: Infantile hydrocephalus is characterized by an atypical accumulation of cerebrospinal fluid in the brain, diagnosed and treated before the age of 2 years. Hydrocephalus development is linked to thinning of the corpus callosum (CC), mainly due to the expansion of lateral ventricles, causing upward elevation and compression of periventricular and subcortical white matter. Methods: This study investigates structural alterations in the CC in children diagnosed with infantile hydrocephalus. We examined both macrostructural and microstructural facets of the CC, providing insights into the nature and extent of alterations associated with this condition. 18 patients with infantile hydrocephalus (mean age = 9 years), and 18 age and sex matched typically-developing healthy children, participated in the study. Structural magnetic resonance imaging and diffusion tensor imaging were utilized to assess CC volume and microstructure, respectively. Results: Our findings reveal reductions in CC volume, particularly in posterior area, and distinct microstructural disparities, notably pronounced in these same segments. Conclusions: Investigating these structural alterations provides an understanding into the mechanisms underlying the effects of infantile hydrocephalus on CC integrity, given its role as a neural bridge. This knowledge offers a more nuanced perspective on neurological disorders and underscores the significance of investigating the CC’s health in such contexts.

Effect of spaceflight experience on human brain structure, microstructure, and function: systematic review of neuroimaging studies.

Spaceflight-induced brain changes have been commonly reported in astronauts. The role of microgravity in the alteration of the brain structure, microstructure, and function can be tested with magnetic resonance imaging (MRI) techniques. Here, we aim to provide a comprehensive overview of Spaceflight studies exploring the potential role of brain alterations identified by MRI in astronauts. We conducted a search on PubMed, Web of Science, and Scopus to find neuroimaging correlates of spaceflight experience using MRI. A total of 20 studies (structural MRI n = 8, diffusion-based MRI n = 2, functional MRI n = 1, structural MRI and diffusion-weighted MRI n = 6, structural MRI and functional MRI n = 3) met our inclusion criteria. Overall, the studies showed that regardless of the MRI techniques, mission duration significantly impacts the human brain, prompting the inclusion of various brain regions as features in the analyses. After spaceflight, notable alterations were also observed in the superior occipital gyrus and the precentral gyrus which show alterations in connectivity and activation during spaceflight. The results provided highlight the alterations in brain structure after spaceflight, the unique patterns of brain remodeling, the challenges in drawing unified conclusions, and the impact of microgravity on intracranial cerebrospinal fluid volume.

Computer-aided diagnosis of Alzheimer's disease based on structural magnetic resonance imaging.

Computer-aided diagnosis of Alzheimer's disease based on structural magnetic resonance imaging.

Changes in the visual areas of the cerebral cortex in children with left-sided anisometropic amblyopia according to structural MRI and resting-state fMRI

Thanks to the development of structural and functional magnetic resonance imaging (MRI) methods, in recent decades there has been a lot of research aimed at elucidating brain abnormalities caused by amblyopia. In the cases of this prevalent visual disorder, the anomalies causing decreased visual acuity and other visual disabilities cannot be determined by standard ophthalmologic examination. Since there are several types of this disorder that are fundamentally different in etiology, it is natural to suggest the presence of different types of corresponding brain abnormalities. In this regard, before obtaining a general picture of the pathogenesis of amblyopia, studies conducted on groups of specially selected similar patients are very important. This paper presents the results of a study of school-age children with left-sided anisometropic amblyopia. In the patients investigated, MRI data revealed interhemispheric differences in the thickness of the lateral occipital cortex, and resting-state fMRI revealed interhemispheric differences in the local coherence of the hemodynamic signal within 17 Brodmann area and in the functional connectivity between 17 and 18+19 Brodmann areas. The data obtained contribute to the creation of a general MRI database on the pathophysiology of amblyopia, help clarify some controversial issues and indicate the advisability of using resting-state fMRI in ophthalmology.

The usage of a modified straight-leg raise neurodynamic test and hamstring flexibility for diagnosis of non-specific low back pain: A cross-sectional study.

The main purpose of this research study was to compare mean modified straight-leg raise test (mSLR) and hamstring muscle length (HL) between chronic non-specific low back pain (LBP) and healthy subjects to understand the possibility of neuropathic causes in LBP population as it may impact the diagnosis and treatment of LBP. Another purpose was to compare mean mSLR between those with lumbar nerve root impingement and those without as determine by magnetic resonance imaging (MRI). The design of the study is cross sectional and included 32 subjects with ages ranging from 18-50 years old. Clinical exam objective measures were collected such as patient questionnaires, somatosensory tests, HL range of motion, and a mSLR test, and were compared to the findings from a structural lumbar spine MRI. There were no significant differences in mean HL angulation and mSLR angulation between LBP and healthy subjects (p>0.05). There was no significant difference in mean HL by impingement by versus no impingement (38.3±15.6 versus 44.8±9.4, p = 0.08, Cohen's d = 0.50). On the other hand, there was a significant difference in mean mSLR angulation by impingement (57.6.3±8.7 versus 63.8±11.6, p = 0.05, Cohen's d = 0.60). The mSLR test was found to be associated with lumbar nerve root compression, regardless of the existence of radiating leg symptoms, and showed no association solely with the report of LBP. The findings highlight the diagnostic dilemma facing clinicians in patients with chronic nonspecific LBP with uncorrelated neuroanatomical image findings. Clinically, it may be necessary to reevaluate the common practice of exclusively using the mSLR test for patients with leg symptoms. This study may impact the way chronic LBP and neuropathic symptoms are diagnosed, potentially improving treatment methods, reducing persistent symptoms, and ultimately improving disabling effects.

Subclinical structural atypicality of retinal thickness and its association with gray matter volume in the visual cortex of maltreated children

Childhood maltreatment is reportedly associated with atypical gray matter structures in the primary visual cortex (V1). This study explores the hypothesis that retinal structures, the sensory organs of vision, are associated with brain atypicality and child maltreatment and examines their interrelation. General ophthalmologic examinations, visual cognitive tasks, retinal imaging, and structural magnetic resonance imaging (MRI) were conducted in children and adolescents aged 9–18 years with maltreatment experiences (CM) and typically developing (TD) children. The retinal nerve fiber layer (RNFL), the most superficial of the ten distinct retinal layers, was found to be significantly thinner in both eyes in CM. While whole-brain analysis using Voxel-based morphometry revealed a significantly larger gray matter volume (GMV) in the thalamus in CM, no significant correlation with RNFL thickness was observed. However, based on region-of-interest analysis, a thinner RNFL was associated with a larger GMV in the right V1. Although it cannot be ruled out that this outcome resulted from maltreatment alone, CM demonstrated subclinical structural atypicality in the retina, which may also correlate with the immaturity of V1 development. Examination of retinal thickness offers a novel clinical approach to capturing characteristics associated with childhood maltreatment.

Changes in the central nervous system in football players: an MRI study.

Football (soccer) is the world's most popular team sport. To comprehensively examine the brain in football (soccer) players, with the use of magnetic resonance imaging (MRI) techniques. The study involved 65 football players and 62 controls. The MR examinations were performed using MR 1.5-T system (Optima MR 360; GE Medical Systems). The examinations were carried out in the 3D Bravo, CUBE, FSEpropeller, and diffusion-weighted imaging (DWI) sequences. The 1HMRS signal was obtained from the volume of interest in the frontal and occipital lobes on both sides. The present study, based on structural MRI, shows some changes in the brains of the group of football players. The findings show asymmetry of the ventricular system in four football players, arachnoid cysts in the parieto-occipital region, and pineal cysts. NAA/Cr concentration in the right frontal lobe was lower in the football players than in the controls, and the Glx/Cr concentration in the right occipital lobe was higher. The apparent diffusion coefficient value is lower in football players in the occipital lobes. Playing football can cause measurable changes in the brain, known to occur in patients diagnosed with traumatic brain injury. The present findings fill the gap in the literature by contributing evidence showing that playing football may lead to changes in the brain, without clinical symptoms of concussion.

Few sex differences in regional gray matter volume growth trajectories across early childhood

Abstract Sex-specific developmental differences in brain structure have been documented in older children and adolescents, with females generally showing smaller overall brain volumes and earlier peak ages than males. However, sex differences in gray matter structural development in early childhood are less studied. We characterized sex-specific trajectories of gray matter volume development in children aged 2–8 years. We acquired anatomical magnetic resonance imaging (MRI) of the brain at the Alberta Children's Hospital in 123 typically developing children. Most children were scanned multiple times, for a total of 393 scans (mean = 3.2 scans/subject). We segmented T1-weighted structural MRI with MaCRUISE to define 116 regions and measured both absolute volumes (mm3) and proportional volumes (percent of intracranial volume). We characterized growth trajectories of gray matter volume for these brain regions between 2 and 8 years using mixed-effects models, showing volume increases, with most posterior and temporo-parietal regions peaking before 8 years. We found widespread main effects of sex, with males having larger volumes in 86% of brain regions. However, there were no significant sex differences in trajectories (age or age2 terms) for absolute volume. Proportional volumes of the right occipital fusiform gyrus and left medial postcentral gyrus showed significant age-by-sex interactions where females had steeper volume decreases than males. This study also confirms regional patterns observed in previous studies of older children, such as posterior-to-anterior timing of brain maturation. These results provide a comprehensive picture of gray matter volume development across early childhood, and suggest that sex differences do not emerge until later in development.

Major brain injuries at term continue to influence DTI parameters in adolescents born very preterm: a 13-year follow-up study.

Major brain injuries in structural brain magnetic resonance imaging (MRI) at term affect concurrent diffusion tensor imaging (DTI) parameters in very preterm infants. White matter is known to gradually maturate along with increasing gestational age, which is characterized by increasing fractional anisotropy (FA) and decreasing mean diffusivity (MD). To study the difference between DTI parameters at term and 13 years in adolescents born very preterm with and without major pathologies in structural brain MRI at term. Adolescents born very preterm (gestational age <32 weeks and/or birth weight ≤1500 g) in 2004-2006 at Turku University Hospital, Finland were included. We evaluated FA and MD at term and 13 years in 18 regions of interest using the JHU-neonate-SS atlas to compare the differences in these parameters between adolescents with and without major injuries identified on MRI at term. A total of 24 adolescents underwent brain MRI including DTI both at term and 13 years. Adolescents with major brain injury pathologies (n = 6) in structural MRI at term had decreased FA in the left corpus callosum and right cingulate gyrus part, and increased MD in the left corpus callosum, right anterior limb of internal capsule, and right posterior limb of the internal capsule at 13 years, in comparison with adolescents without major brain injuries (n = 18) in structural MRI at term. Our findings suggest that major brain injuries identified on structural MRI at term affect brain maturation, with adverse effects in FA and MD still during adolescence.

P75 neurotrophin receptor modulation in mild to moderate Alzheimer disease: a randomized, placebo-controlled phase 2a trial

p75 neurotrophin receptor (p75NTR) signaling pathways substantially overlap with degenerative networks active in Alzheimer disease (AD). Modulation of p75NTR with the first-in-class small molecule LM11A-31 mitigates amyloid-induced and pathological tau-induced synaptic loss in preclinical models. Here we conducted a 26-week randomized, placebo-controlled, double-blinded phase 2a safety and exploratory endpoint trial of LM11A-31 in 242 participants with mild to moderate AD with three arms: placebo, 200 mg LM11A-31 and 400 mg LM11A-31, administered twice daily by oral capsules. This trial met its primary endpoint of safety and tolerability. Within the prespecified secondary and exploratory outcome domains (structural magnetic resonance imaging, fluorodeoxyglucose positron-emission tomography and cerebrospinal fluid biomarkers), significant drug–placebo differences were found, consistent with the hypothesis that LM11A-31 slows progression of pathophysiological features of AD; no significant effect of active treatment was observed on cognitive tests. Together, these results suggest that targeting p75NTR with LM11A-31 warrants further investigation in larger-scale clinical trials of longer duration. EU Clinical Trials registration: 2015-005263-16; ClinicalTrials.gov registration: NCT03069014.

MSCLK: Multi-scale fully separable convolution neural network with large kernels for early diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) is identified as a central nervous system disease that exhibits irreversible degeneration, while mild cognitive impairment (MCI) is viewed as the preliminary stage of AD, and its pathogenesis is notably intricate. MCI contains two stages: early MCI (EMCI), and late MCI (LMCI). EMCI diagnosis can prevent EMCI from progressing to LMCI, and then to AD. Therefore, accurate diagnosis of EMCI/LMCI is crucial for developing the early intervention and treatment strategies of AD. Currently, most existing EMCI/LMCI diagnostic methods use single modality images, while different modality images carry different complementary information that helps for accurate diagnosis of EMCI/LMCI, and the lesion area is usually not limited to a single brain area, which involves multiple regions. In this case, conventional convolution operations cannot be able to accurately extract the pathological features of AD. In this work, we propose a novel Multi-scale fully Separable Convolution neural network with Large Kernels (MSCLK) method to diagnose early Alzheimer’s disease with structural Magnetic Resonance Imaging (sMRI) images. MSCLK mainly consists of the multi-scale 3D fully separable convolution modules and the deep metric learning module. The multi-scale convolution that contains both small and large kernels is used to effectively capture the discrimination features of different scale acceptance domains. 3D fully separable convolution is used to reduce parameters and overfitting. The deep metric learning is used to learn hard samples that are similar but belong to different classes. We also propose a variant method of MSCLK (called MSCLK-Fusion MRI and PET, MSCLK-FMP) by adding the pixel-level fusion module and feature-level fusion module into the MSCLK framework to integrate the sMRI image and the Positron Emission Computed Tomography (PET) image for further improving the accuracy of EMCI vs. LMCI classification task. The pixel-level fusion is used to achieve early pixel-level fusion of sMRI and PET images, and the feature-level fusion is used to achieve high-dimensional feature-level fusion of sMRI and PET images. Experimental results on the ADNI database show that the performance of our MSCLK and MSCLK-FMP are superior to other state-of-the-art methods. The accuracy of MSCLK achieves 98.89%, 95.97%, 96.39% and 98.76% for AD vs. EMCI, AD vs. LMCI, EMCI vs. NC and LMCI vs. NC classification tasks, respectively, and MSCLK-FMP achieves 93.93% for EMCI vs. LMCI classification task, indicating that MSCLK/MSCLK-FMP can be effectively used for diagnosing MCI patients. Moreover, our MSCLK-FMP is capable of pinpointing key brain areas involved in the pathological progression of MCI, such as the Temporal_Inf, the Hippocampus, the Precuneus, the Precentral, and the Thalamus. These findings contribute to uncovering the early onset of AD pathogenesis.

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

A comprehensive review on role of magnetic resonance imaging in the precise measurement of corpus callosum

Magnetic Resonance Imaging (MRI) has emerged as a powerful and non-invasive imaging technique, playing a pivotal role in neuroimaging research and clinical diagnosis. This comprehensive review explores the significance of MRI in the precise measurement of the corpus callosum, a key structure connecting the two hemispheres of the brain. The corpus callosum plays a crucial role in inter hemispheric communication and is implicated in various neurological and psychiatric disorders. The review begins by elucidating the anatomy and functions of the corpus callosum, emphasizing its importance in facilitating seamless communication between the cerebral hemispheres. Subsequently, the paper delves into the historical evolution of MRI techniques and how advancements have enhanced the visualization and measurement capabilities of the corpus callosum. A detailed examination of various MRI modalities, including structural MRI, diffusion tensor imaging (DTI), and functional MRI, is presented in the context of corpus callosum analysis. Structural MRI provides high-resolution images for accurate morphological assessments, while DTI offers insights into white matter microstructure and fiber tractography, allowing for a more comprehensive understanding of the corpus callosum's integrity. The review also highlights the role of MRI in investigating alterations in the corpus callosum associated with neurological disorders such as multiple sclerosis, epilepsy, and neurodevelopmental disorders. Additionally, the paper explores how functional MRI contributes to understanding the functional connectivity and inter hemispheric interactions mediated by the corpus callosum. Challenges and limitations in the measurement of the corpus callosum using MRI are discussed, including methodological considerations, image artifacts, and potential sources of variability. The review concludes by outlining future directions in corpus callosum imaging research, including the integration of advanced imaging techniques, artificial intelligence, and large-scale multi-center studies to enhance the precision and reliability of measurements.In summary, this comprehensive review underscores the pivotal role of MRI in advancing our understanding of the corpus callosum, providing valuable insights into both normal brain function and pathological conditions. The continual evolution of MRI techniques holds promise for further refining the assessment of the corpus callosum, ultimately contributing to improved diagnostic accuracy and therapeutic interventions in neurological and psychiatric 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.