Magnetic Resonance Imaging Studies Research Articles

A Novel Synonymous Variant in SQSTM1 Causes Neurodegeneration With Ataxia, Dystonia, and Gaze Palsy Revealed by Urine-Derived Cells-Based Functional Analysis.

Heterozygous variants of sequestosome-1 gene (SQSTM1) have been reported in patients with various neurological disorders, whereas biallelic pathogenic variants of SQSTM1 can cause child-onset and multisystem neurodegeneration, including cerebellar ataxia, dystonia, and vertical gaze palsy (NADGP). Here, we describe two cases of NADGP in a Japanese family. We performed clinical and genetic laboratory evaluations of the two patients and their healthy parents. By whole-exome sequencing, we identified compound heterozygous variants in SQSTM1(NM_003900.5): c.1A>G p.(Met1?) in the initial codon, and c.969G>A, located at the 3' end of exon 6, which is novel and seemingly a synonymous but is actually a truncating variant causing aberrant splicing. An SQSTM1 protein expression assay using urine-derived cells (UDCs) demonstrated that both variants (c.1A>G and c.969G>A) were unable to induce normal splicing of premessenger RNA. Cerebellar ataxia is a characteristic manifestation of this disorder; however, brain magnetic resonance imaging studies have not shown significant cerebellar atrophy. Our patients experienced chorea during adolescence. Only a few reports have highlighted the presence of chorea; however, our findings suggest that NADGP should be considered as a differential diagnosis of hereditary chorea. This study also demonstrates the utility of UDCs, obtained using noninvasive approaches, in functionally analyzing genetic diseases.

FONDUE: Robust resolution-invariant denoising of MR Images using Nested UNets

Abstract Recent human magnetic resonance imaging (MRI) studies continually push the boundaries of spatial resolution as a means to enhance levels of neuroanatomical detail and increase the accuracy and sensitivity of derived brain morphometry measures. However, acquisitions required to achieve these resolutions have a higher noise floor, potentially impacting segmentation and morphometric analysis results. This study proposes a novel, fast, robust, and resolution-invariant deep learning method to denoise structural human brain MRIs. We explore denoising of T1-weighted (T1w) brain images from varying field strengths (1.5T to 7T), voxel sizes (1.2mm to 250µm), scanner vendors (Siemens, GE, and Phillips), and diseased and healthy participants from a wide age range (young adults to aging individuals). Our proposed Fast-Optimized Network for Denoising through residual Unified Ensembles (FONDUE) method demonstrated stable denoising capabilities across multiple resolutions with performance on par or superior to the state-of-the-art methods while being several orders of magnitude faster at low relative cost when using a dedicated Graphics Processing Unit (GPU). FONDUE achieved the best performance on at least one of the four denoising-performance metrics on all the test datasets used, showing its generalization capabilities and stability. Due to its high-quality performance, robustness, fast execution times, and relatively low-GPU memory requirements, as well as its open-source public availability, FONDUE can be widely used for structural MRI denoising, especially in large-cohort studies. We have made the FONDUE repository and all training and evaluation scripts as well as the trained weights available at https://github.com/waadgo/FONDUE.

Altered resting-state connectivity of the auditory cortex in congenital amusia: A functional magnetic resonance imaging study in Mandarin speakers.

Brain imaging studies have reported that the neural deficits of congenital amusia in non-tonal language speakers are mainly in the connectivity between the auditory cortex and the inferior frontal gyrus (IFG) in the right hemisphere. However, the relationship between the functional connectivity (FC) in these regions and the music perception ability of amusia in tonal language speakers remains unclear. In this study, we investigated the FC characteristics of amusia in Mandarin speakers in resting-state functional magnetic resonance imaging data by voxel-wise connectivity analyses with seeds in left and right Heschl's gyri (HG) and region of interest (ROI)-to-ROI connectivity analyses. Our findings indicate increased connectivity between right HG and bilateral posterior superior temporal gyrus, as determined by voxel-wise connectivity analyses in amusia. Conversely, reduced connectivity was observed between bilateral HG and bilateral IFG (orbital part) as assessed through ROI-to-ROI connectivity analyses in amusia when compared to controls. Moreover, the music perception scores of amusia in Mandarin speakers were associated with diminished connectivity between the left HG and the right IFG. This study furnishes direct evidence for the link between music perception deficits and the aberrant frontotemporal connectivity of congenital amusia in tonal language speakers in resting state.

Mapping common and distinct brain correlates among cognitive flexibility tasks: concordant evidence from meta-analyses.

Cognitive flexibility allows individuals to switch between different tasks, strategies, or ideas; an ability that is important for everyday life. The Wisconsin card sorting test (WCST) and task switching paradigm (TSP) are popular measures of cognitive flexibility. Although both tasks require switching, the TSP requires participants to memorize switching rules and retrieve them when they view a cue (rule-retrieval), whereas the classic WCST requires participants to discover the switching rule via trial-and-error (rule-discovery). Many functional magnetic resonance imaging studies have examined brain responses to these tasks. Extant meta-analyses show concordance in activation in a widespread set of areas including frontal, parietal, and cingulate cortices. Critically, past meta-analyses have not specifically examined brain correlates associated with rule derivation (i.e., rule-discovery vs. rule-retrieval) in cognitive flexibility tasks. We examine for the first time common and distinct concordance in brain responses to rule-discovery (i.e., WCST) and rule-retrieval (i.e., TSP), as well as TSP subtypes using quantitative meta-analyses. We analyzed data from 69 eligible articles with a total of 1617 young-adult participants. Conjunction results show concordance in common fronto-parietal areas predominantly in the left hemisphere. Contrast analyses show that rule-discovery required increased involvement in multiple cortical and subcortical regions such as frontopolar (Brodmann Area 10), parietal, insular cortex, thalamus and caudate nucleus predominantly in the right hemisphere. No significant differences in concordance were observed among the three, task switching paradigm sub-types. We propose a neuroanatomical model of cognitive flexibility and discuss theoretical and practical applications.

Targeted Magnetic Nanoparticles for Beta-Amyloid Detection.

Background/Objectivities: The presence of beta-amyloid plaques is a part of the pathogenesis of Alzheimer's disease, but there is currently no universally accepted method for magnetic resonance (MR) imaging of the disease. However, it is known that magnetic nanoparticles (MNPs) can improve the T2 contrast in MR images of various targets. Methods: We used cubic MNPs, which were produced by thermal decomposition and then it was covalently bonded to a modified fluorescently labeled tetrapeptide, HAEE-Cy5, for visualizing beta-amyloid plaques. The interaction of MNPs-HAEE-Cy5 and beta-amyloid was determinate by confocal microscopy using SH-SY5Y cell line. Results: MNPs exhibit relatively high relaxivity (approximately 200 mM-1s-1), which is crucial for enhancing target visibility in MR imaging. HAEE provides targeted delivery of MNPs by specifically interacting with beta-amyloid, while the fluorescent label Cy5 enables monitoring the efficacy of the interaction through confocal microscopy. Conclusions: The MNPs modified with HAEE-Cy5 demonstrated excellent binding to beta-amyloid plaques in vitro, as shown by experiments on the SH-SY5Y cell line. These results suggest that the proposed method has potential for use in future MR imaging studies of Alzheimer's disease.

The epicardial adipose tissue volume is in patients with type 2 diabetes mellitus associated with a lowered myocardial perfusion reserve

Abstract Introduction The epicardial adipose tissue (EAT) has unobstructed vascular connection to the myocardium and has a secretome very different from that of other fat depots. The EAT fat pad and its paracrine and vasocrine secretion of pro-inflammatory and profibrotic cytokines is suggested to contribute to heart failure with preserved ejection fraction (HFpEF) pathogenesis albeit via unknown mechanisms. Animal studies and magnetic resonance imaging studies in patients with type 2 diabetes mellitus (T2DM) have documented that important myocardial changes linking diabetic cardiomyopathy to systolic and diastolic dysfunction are myocardial microvascular dysfunction and myocardial fibrosis. Purpose To investigate whether a high volume of EAT is associated with impaired myocardial perfusion reserve (MPR) or disperse fibrosis in patients with T2DM. Methods 198 patients with T2DM but without ischemic heart disease were identified from a cross-sectional cardiovascular magnetic resonance imaging cohort study. 25 healthy controls were included for characterization of baseline characteristics. With gadolinium for contrast, MPR was determined by perfusion sequences and disperse fibrosis was determined from the myocardial extracellular volume (ECV). Volumes of EAT and pericardial adipose tissue (PAT) were determined from cine images. Results The T2DM patients (69% male) had a median age 59 IQR [50, 67] yrs, BMI 30.7 IQR [27.8, 33.7] kg/m2, and EAT of 17.3 IQR [14.2, 22.4] cm2. All patients had normal left ventricle ejection fraction. Compared to the healthy controls, patients with T2DM had higher BMI, abdominal circumference, ECV, EAT, and PAT volumes, and lower MPR (all p<0.001). In univariable regression analysis, EAT and PAT were both associated with higher BMI, abdominal circumference, and age (all p<0.02). In multivariable regression analysis, however, with adjustments for age, sex, BMI, and T2DM-duration, only EAT and MPR were significantly associated (P<0.01), whereas EAT and ECV were not. The PAT volume was not associated with MPR nor ECV. High-sensitive CRP was associated with the EAT volume (p<0.001), but hs-CRP was not associated with MPR or ECV. Conclusion An increased epicardial adipose tissue volume is in patients with T2DM associated with myocardial microvascular dysfunction. Irrespective of a generally heightened inflammatory level, the mechanism by which EAT contribute to development of HFpEF is therefore possibly via an EAT-induced impairment of the myocardial perfusion reserve. Randomized studies targeting a decreased epicardial adipose tissue volume are needed to demonstrate if this will improve myocardial microvascular dysfunction and hence limit development of HFpEF in patients with T2DM.Univariable analysis of EAT and ECVUnivariable analysis of EAT and MPR

Standardized weighted low-resolution electromagnetic tomography study of the amygdala activity in patients with comorbid major depressive disorder and anxiety symptoms

Major depressive disorder (MDD) often coexists with anxiety disorders or symptoms, as identified by previous functional magnetic resonance imaging (fMRI) studies. These studies have found abnormal amplitudes of low-frequency fluctuations (ALFF) in the amygdala, which serve as traits and state markers of MDD. This study used standardized weighted low-resolution electromagnetic tomography (swLORETA) technology to explore amygdala markers in patients with comorbid MDD and anxiety. Participants included patients with MDD comorbid with anxiety symptoms (MDD group) and healthy controls (HC group) who completed the Beck Depression Inventory-II (BDI-II) and the Beck Anxiety Inventory (BAI). EEG data collected under resting state, happiness recall, and depressive recall tasks were converted into current-source density (CSD) values using swLORETA to assess amygdala activation. The results indicated higher beta2, beta3, and high beta levels in both the left and right amygdalae during the resting state in the MDD group than in the HC group. Similarly, elevated levels of beta2, beta3, and high beta were observed in the left and right amygdalae of the MDD group during happiness and depressive recall tasks. These findings support the presence of hyperactivity in the amygdala under resting state and emotional tasks in patients with comorbid MDD and anxiety symptoms.

Prognostic impact of the concept of late gadolinium enhancement granularity in non-ischaemic dilated cardiomyopathy

Abstract Background The prognostic stratification of non-ischaemic dilated cardiomyopathy (DCM) to predict the risk of death is mainly driven by the left ventricular ejection fraction (LVEF). Indeed, LVEF<35% is currently the only indication for primary prevention with implantable cardioverter-defibrillator (ICD) in these patients. Beyond LVEF, several cardiac magnetic resonance imaging (MRI) studies have suggested the strong prgnostic impact of late gadolinium enhancement (LGE), and particularly its extent and location. Therefore, we assumed that a more granular approach combining detailed LGE findings including extent, location and pattern could improve risk stratification in this population. Purpose To assess the additional prognostic value of the concept of "Late Gadolinium Enhancement (LGE) Granularity" that includes extent, location, and pattern in non-ischaemic dilated cardiomyopathy (DCM) patients to predict all-cause death. Methods Between 2008-2021, all consecutive patients with DCM without ICD or history of sustained ventricular arrythmia referred for cardiac MRI were included in two French centres. In line with the current ESC and AHA guidelines, DCM was defined by the LV dilation and LVEF< 50% using cardiac MRI. All patients with history of coronary artery disease or clinical history of myocarditis were excluded. The primary outcome was all-cause death using the French National Registry of Death. Cox regressions were performed to determine the prognostic value of each LGE findings. The additional prognostic value of the LGE granularity was assessed by the C-statistic increment, the continuous net reclassification improvement (NRI), and the integrative discrimination index (IDI). Results Of 1,668 DCM patients (age 52±8 years, 54% males), 268 (16%) died after a median (interquartile range) follow-up of 9 (7-12) years. With a median LVEF value of 39% (30-46), 472 (28%) patients had non-ischemic LGE. Using survival curves, patients with LVEF>35% but the LGE septal location showed a higher risk of death compared to all patients with LVEF≤35% with or without LGE (except for the septal location, all p<0.001). In DCM patients with LGE (N=472), LGE extent (adjusted HR: 4.27, 95% CI: 2.22-8.22), septal location (HR: 5.74, 95% CI: 3.35-9.85) and multiple areas of LGE (HR: 4.38, 95% CI: 2.08-9.22; all p<0.001) were all independently associated with death after adjustment for prognosticators. The concept of "LGE granularity" combining all these LGE features showed the best improvement in model discrimination and reclassification over traditional prognosticators including LVEF (C-statistic improvement: 0.14; net reclassification improvement=64.3%; integrative discrimination index=29.0%; all p<0.05). Conclusion In a large cohort of DCM patients, the concept of "LGE granularity" combining LGE extent, location and the presence of multiple areas had an additional prognostic value above traditional prognosticators including LVEF to predict all-cause death.

Altered cerebral oxygen extraction and metabolism in preterm neonates and the relationship to anemia: A noncontrast MRI study

AbstractObjectiveThe preterm brain is susceptible to structural injuries, which may be related to an imbalance between blood supply and oxygen metabolism. However, the effect of preterm birth on cerebral oxygen metabolism and its underlying mechanism have not been fully elucidated. The present study measured cerebral oxygen extraction and metabolism using noncontrast magnetic resonance imaging (MRI) methods in preterm neonates and examined its relationship with anemia of prematurity.MethodsFifty neonates with a gestational age of 28–42 weeks were enrolled. Cerebral oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2) were measured with T2‐relaxation‐under‐spin‐tagging (TRUST) MRI, together with cerebral blood flow (CBF).ResultsWe showed that CBF (p = 0.00021) and CMRO2 (p < 0.0001) increased with gestational age while OEF increased with postnatal age (p = 0.0013). Higher OEF was also associated with a higher Apgar score at birth (p = 0.039). Furthermore, hematocrit significantly mediates the increase of OEF with postnatal age (p < 0.001). Structural equation modeling analysis suggested a bidirectional relationship between CBF and CMRO2; both contributed to the changes in OEF.InterpretationThese findings demonstrated an altered cerebral oxygen metabolism in preterm brain, suggesting a potential role of MRI–based oxygenation measurement in the assessment of transfusion and intervention for preterm neonates.

Schizophrenia and magnetic resonance imaging research: A scientometric analysis during 2014 to 2023.

Recently, magnetic resonance imaging (MRI) has emerged as a leading technique for investigating schizophrenia (SZ) pathological mechanisms, prompting an increase in related studies. This study aims to examine the field's research status and trends via bibliometric analysis. The publications on SZ and MRI over the past decade were retrieved from the Web of Science Core Collection (WOSCC) On October 15, 2023. CiteSpace and VOSviewer were used to conduct scientometric and visualized analysis, covering countries, institutions, authors, journals, co-cited literature, and keywords. A total of 4840 publications were retrieved from 2014 to 2023. The United States leads with 1863 articles, followed by China with 1127 articles. King's College London had the highest number of publications, with 332 articles. Schizophrenia Research ranks first in the journal that published the research on schizophrenia and MRI, the most published journal, Neuroimage is the most cited journal. Calhoun is the most prolific author with 145 articles, and Fischl is the most cited author, receiving 1188 citations. The literature co-citation network (2014 to 2023) revealed 16 clusters with robust structure (Q = 0.8719) and high confidence (S = 0.9421) involving MRI studies of SZ, genetic imaging and treatment of schizophrenia. Keywords include MRI, psychosis and functional magnetic resonance imaging (fMRI), MRI and neuroimaging, MRI and neuroimaging and white matter and diffusion tensor imaging. This study offers an overview of the research status and trends of publications on SZ and MRI, aiming to inspire future research directions.

Functional magnetic resonance imaging of depression: a bibliometrics and meta-analysis

ObjectivesThis study aims to reveal the current knowledge map, research hotspots of functional magnetic resonance imaging (fMRI) studies on depression, as well as identify the brain regions associated with depression.MethodsCiteSpace was conducted to analyze the publication outputs, country, institution, cited journals, author and cited author, references, keyword cocurrence and burst keywords of fMRI studies in depression from 2010 to 2024. And a meta-analysis of fMRI was used to identify brain regions associated with depression using Neurosynth.ResultsA total of 4,049 publications were included, and Gong Qiyong was the most prolific authors. Neuroimage, Biological Psychiatry, and Human Brain Mapping were prominent journals. Default mode network (DMN), prefrontal cortex, amygdala, and anterior cingulate cortex were the popular keywords. The fMRI studies on depression have mainly focused on major depression, especially the DMN. Functional connectivity and regional homogeneity of brain regions were research hotspots. The meta-analysis revealed significant differences in brain regions between patients with depression and healthy controls, including the Amygdala_L, Insula_R, Frontal_Inf_Oper_R, Cingulum_Post_L, Putamen_L, Thalamus_R, Angular_L, Precuneus_R, Frontal_Sup_R, Occipital_Inf_L.ConclusionsThis study sheds light on key issues and future directions in fMRI research on depression, elucidating the brain regions related to depression.Graphical

Preventive treatment effects on brain structures and functions in patients with chronic migraine: A multimodel magnetic resonance imaging study.

Patients with chronic migraine (CM) often exhibit structural and functional alterations in pain-matrix regions, but it remains unclear how preventive treatment affects these changes. Therefore, this study aimed to investigate the structural and functional changes in pain-matrix regions in CM patients after 6-month treatment. A total of 24 patients with CM and 15 healthy controls were recruited for this study. Patients were divided into responder group (N = 9) and non-responder group (N = 15). After completing the Migraine Disability Assessment (MIDAS) questionnaire, all patients underwent whole-brain high-resolution T1-weighted images, diffusion-weighted imaging, and resting-state functional magnetic resonance imaging at baseline and 6-month follow-up. Whole brain gray matter volume and white matter diffusion indices were analyzed using voxel-based analysis. Structural and functional connectivity analyses were performed to understand brain changes in patients after 6-month preventive treatment. The responder group exhibited significantly higher MIDAS scores than the non-responder group at baseline, but no significant difference between the two groups at follow-up. No significant interval change was noted in gray matter volume, white matter diffusion indices, and structural connectivity in CM patients after 6-month treatment. Nonetheless, the functional connectivity was significantly increased between occipital, temporal lobes and cerebellum, and was significantly decreased between parietal and temporal lobes after 6-month preventive treatment. We concluded that resting-state functional connectivity was suitable for investigating the preventive treatment effect on CM patients.

The Association of Prenatal Alcohol Exposure With Brain Development During the First 1000Days of Life: A Systematic Review.

Prenatal exposure to alcohol (PAE) can impact short- and long-term offspring health. However, knowledge on PAE and brain development in early life is limited. This systematic review investigated associations between PAE and brain development during the first 1000days of life, and was registered in PROSPERO at CRD42022355144. The literature search was performed from inception until February 2024 in EMBASE, MEDLINE, Web of Science, PsycINFO, and the Cochrane Library. Studies investigating PAE and brain development during the first 1000days using ultrasound, magnetic resonance imaging (MRI) or diffusion tensor imaging (DTI) were included. Our search initially identified 3618 articles, of which 16 were included. The findings suggest inconsistent associations between PAE and early brain development. Ultrasound studies on PAE and brain size report no clear relationship. Some postnatal MRI studies reported smaller thalami, amygdalae and hippocampi in alcohol exposed neonates. Postnatal DTI studies (n=5) examining network integrity and connectivity reported bidirectional results in multiple brain networks. Our results highlight the need for further research on first trimester brain development, timing and quantity of alcohol exposure using a core set of validated instruments. Longitudinal assessments and standardized procedures for neuroimaging are crucial to understand the impact of PAE on early brain development.

Modulating subjective pain perception with decoded Montreal Neurological Institute-space neurofeedback: a proof-of-concept study.

Pain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e. brain decoders), but it is still unclear whether these observations reflect causal mechanisms. To address this question, we devised a new neurofeedback approach using real-time decoding of fMRI data to test if modulating pain-related multivoxel fMRI patterns could lead to changes in subjective pain experience. We first showed that subjective pain ratings can indeed be accurately predicted using a real-time decoding approach based on the stimulus intensity independent pain signature (SIIPS) and the neurologic pain signature (NPS). Next, we trained participants (n = 16) in a double-blinded decoded fMRI neurofeedback experiment to up- or downregulate the SIIPS. Our results indicate that participants can learn to downregulate the expression of SIIPS independently from NPS expression. Importantly, the success of this neurofeedback training was associated with the perceived intensity of painful stimulation following the intervention. Taken together, these results indicate that closed-loop brain imaging can be efficiently conducted using a priori fMRI decoders of pain, potentially opening up a new range of applications for decoded neurofeedback, both for clinical and basic science purposes. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Delay discounting in adolescence depends on whom you wait for: Evidence from a functional neuroimaging study

With age, adolescents increasingly demonstrate the ability to forgo immediate, smaller rewards in favor of larger delayed rewards, indicating reduced delay discounting. Adolescence is also a time of social reorientation, where decisions not only involve weighing immediate against future outcomes, but also consequences for self versus those for others. In this functional Magnetic Resonance Imaging study, we examined the neural correlates of immediate and delayed reward choices where the delayed outcomes could benefit self, friends, or unknown others. A total of 196 adolescent twins aged 14–17 completed a social delay discounting task, with fMRI data acquired from 174 participants. Out of these, 156 adolescents had valid fMRI data, and 138 adolescents had observations in every condition. Adolescents more often chose the immediate reward when it was larger, and when the delay was longer. Area-under-the-curve (AUC) comparisons revealed that behavior differed across delay-beneficiaries, with AUC being highest for the self, followed by friends, and lowest for unknown others. This suggests that adolescents are more willing to wait for rewards for self. Neuroimaging analyses showed increased activity in the midline areas medial prefrontal cortex (MPFC) and precuneus, as well as bilateral temporal parietal junction (TPJ) when considering delayed reward for unknown others and friends compared to self. A whole-brain interaction with choice showed that the bilateral insula and right dorsolateral prefrontal cortex (DLPFC) were more active for delayed choices for unknown others and for immediate choices for friends and self. This underscores that the neuro-cognitive processing of how delays reduce the value of rewards depends on closeness of the beneficiary.

Predictors of breast cancer HER2-receptor positivity by MRI intuitive imaging features

BackgroundToday, breast cancer is the most diagnosed cancer worldwide. There are many different clinical presentations, radiological characteristics, and histological types of breast cancer. HER2 is overexpressed in a significant number of breast cancer cases reaching 20% of all breast cancers, and its overexpression is seen directly proportionate with a poor outcome and prognosis.MethodsWe started this cross-sectional research from January 2022–December 2023 on 202 breast cancer patients who had 220 lesions. The molecular subtypes of the different lesions were determined in all the included cases. Magnetic resonance imaging (MRI) studies were conducted in all included cases. The MRI parameters included conventional MRI, diffusion-weighted analysis, and dynamic post-contrast T1-weighted imaging.ResultsThe prevalence of irregular margins (P < 0.001), linear and segmental distribution (P = 0.044), heterogeneous pattern (P < 0.001), and type 2 curve was statistically significantly higher in the HER2-positive lesions. Nipple infiltration incidence showed statistically significant elevation in the HER2-positive lesions (P = 0.017). The lesions’ ADC and perilesional ADC in the HER2-positive lesions were also statistically significantly elevated. The best cutoff point of ADC to detect lesions with positive HER2 expression was > 0.885 × 10–3 mm2/s, with 65.7% sensitivity and 60% specificity, with a statistically significant value (p = 0.005).ConclusionsMagnetic resonance imaging of breast imaging is a promising noninvasive method for identifying breast tumors with the HER2 molecular subtype. Combining various radiological features by MRI may provide a conclusion for recognizing positive HER2 lesions.

Low-field MRI study of the 1H distribution and migration in porous sediments during natural gas conversion from methane hydrate

Gas hydrates, crystalline compounds formed from water and guest molecules like methane, are gaining attention as a promising clean energy source. While research has extensively focused on the extraction and transport of natural gas hydrates from offshore marine sediments, the dynamic processes in the porous sediments remain under explored. This study employs low-field Magnetic Resonance Imaging (MRI) to investigate the in-situ formation and dissociation of methane hydrate within a partially saturated stack of glass beads. By integrating advanced MRI techniques, including 1D dhk SPRITE, bulk T1 distribution, and 2D spatial T2 imaging, this research provides a comprehensive analysis of fluid distribution and migration during phase transitions in the porous sediments. Three key findings emerged from the study. First, SE-SPI measurements successfully quantified hydrate and residual water saturation, aligning with previous X-ray CT and high-field MRI studies and validating low-field MRI for hydrate research. Secondly, this study introduced a volumetric approach to T1 and T2 measurements, distinguishing between free and bound water, with characteristic relaxation times for each, confirming the technique's capability to differentiate types of residual water. Finally, spatially resolved T2 relaxation time distributions revealed vertical fluid migration within the pore spaces, identifying an equilibrium zone where inflow and outflow rates were balanced. Overall, this study underscores the effectiveness of low-field MRI as a noninvasive tool for analyzing hydrate-bearing sediments. The findings lay the groundwork for further exploration of hydrate in porous sediments, enhancing our understanding of gas production dynamics in hydrate-rich environments.

Adverse childhood experiences and left hippocampal volumetric reductions: A structural magnetic resonance imaging study

BackgroundAdverse childhood experiences (ACEs) have been associated with volume alterations of stress-related brain structures among aging and clinical populations, however, existing studies have predominantly assessed only one type of ACE, with small sample sizes, and it is less clear if these associations exist among a general population of young adults. ObjectiveThe aims were to describe structural hippocampal volumetric differences by ACEs exposure and investigate the association between ACEs exposure and left and right hippocampal volume in a student sample of young adults. Methods959 young adult students (18–24 years old) completed an online questionnaire on ACEs, mental health conditions, and sociodemographic characteristics. Magnetic resonance imaging (MRI) was used to measure left and right hippocampal volume (mm3). We used linear regression to explore the differences of hippocampal volumes in university students with and without ACEs. ResultsTwo thirds of students (65.9%) reported ACEs exposure. As ACEs exposure increased there were significant volumetric reductions in left (p < 0.0001) and right hippocampal volume (p = 0.001) and left (p = 0.0023) and right (p = 0.0013) amygdala volume. After adjusting for intracranial brain volume, sex, age, and depression diagnosis there was a negative association between ACEs exposure and left (β = −22.6, CI = −44.5, −0.7, p = 0.0412) but not right hippocampal volume (β = −18.3, CI = −39.2, 2.6, p = 0.0792). After adjusting for intracranial volume there were no associations between ACEs exposure and left (β = −9.2, CI = −26.2, 7.9 p = 0.2926) or right (β = −5.6, CI = −19.9,8.8 p = 0.4466) amygdala volume. ConclusionsHippocampal volume varied by ACEs exposure in young adult students. ACEs appear to contribute to neuroanatomic differences in young adults from the general population.

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression.

The subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders. In this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects. Post-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively. To our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.

Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine.

Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP using T2 weighted MRI image of the lumbar spine. This was a prospective case-control study. A total of 200 MRI patients (100 cases and controls each) referred for lumbar spine and whole spine screening were included. The scans were performed using 3.0 Tesla MRI (United Imaging Healthcare). T2 weighted images of the lumbar spine were segmented to extract radiomic features. Machine learning (ML) models, such as random forest, decision tree, logistic regression, K-nearest neighbors, adaboost, and deep learning methods (DL), such as ResNet and GoogleNet, were used, and performance measures were calculated. Our study showed that Random forest and AdaBoost are the most reliable ML models for predicting LBP. Random forest showed high performance with area under curve (AUC) values from 0.83 to 0.88 across all lumbar vertebrae and L2-L3, L3-L4, and L4-L5 intervertebral discs (IVDs), with AUCs of 0.88 the highest at L5-S1 IVD (0.92). Adaboost demonstrated high performance at the L2-L5 vertebrae with AUC values of 0.82 to 0.90, with the highest AUC (0.97) at the L5-S1 IVD. Among the DL models, GoogleNet outperformed the other models at 30 epochs with an accuracy of 0.85, followed by ResNet 18 (30 epochs) with an accuracy of 0.84. The study demonstrated that ML and DL models can effectively predict LBP from MRI T2 weighted image of the lumbar spine. ML and DL models could also enhance the diagnostic accuracy of LBP, potentially leading to better patient management and outcomes.