T1-weighted Magnetic Resonance Imaging Scans Research Articles

Comparing two MRI methods to assess fundamental sex differences in resting cerebral blood flow

Background: Cerebral blood flow (CBF) is a critical measurement for understanding healthy brain physiology prior to aging or neurologic diseases. Patterns of stroke and neurologic pathologies are often sex-specific. Therefore, it is important to understand basic sex differences in CBF, which are often associated with poor brain outcomes. Furthermore, prior literature examining sex differences in CBF is confounded by age, comorbidities, medications, lack of control of menstrual cycle and/or hormonal contraceptive use, and methodological discrepancies. The objective of this study was to compare two different magnetic resonance imaging (MRI) methods of quantifying CBF in young adults while controlling for the aforementioned confounding factors. We hypothesized that females would display higher CBF than males, and that there would be a high reliability between Arterial Spin Labeling (ASL) and 4D Flow MRI. Methods: 30 males and 26 females (23 ± 4yr) were studied on a single MRI (3T) visit. Subjects were fasted 8 hours and withheld caffeine, exercise, and NSAIDs for at least 24 hours. Females were not taking hormonal contraception and were studied on days 1-7 of the menstrual cycle. Microvascular CBF (mL/100g/min) was assessed via pseudo-continuous ASL (pcASL) while 4D Flow MRI acquired with Phase Contrast Vastly undersampled Isotropic Projection Reconstruction (PC VIPR) yielded macrovascular flow via the larger cerebral arteries, summing the internal carotid arteries and basilar artery for total CBF (mL/min). A T1-weighted MRI scan was used to provide brain volumes, allowing CBF from PC VIPR to be normalized and compared to ASL (mL/100g/min). The scans were taken consecutively, with subjects breathing normoxic gas while hemodynamics and end tidal CO2 were monitored. Regression analysis was used to determine reliability of two measures to test a sex difference in CBF. Results: Linear regression analysis demonstrated reliability between measurements of resting CBF via ASL and PC VIPR (p<0.001, R2=0.621). However, ASL measured significantly higher blood flows than PC VIPR in females (63.40 ± 8.55 vs. 56.47 ± 8.48 mL/100g/min, p<0.001) while male subjects had similar flows measured by both methods (45.75 ± 9.39 vs. 46.69 ± 8.19 mL/100g/min, p=0.464). Females displayed an increase in resting CBF compared to males, being 27.8% and 17.3% higher in the ASL and PC VIPR flow measurements (both p<0.001), respectively. Conclusions: These results demonstrate that ASL and PC VIPR CBF display a strong correlation, and that females have greater resting CBF compared to males that is detected by both MRI methods of quantification. However, ASL resulted in greater flow measurements in females, resulting in larger differences being observed (27.8% versus 17.3%). This discrepancy in CBF quantification warrants further investigation to determine the optimal mode of CBF measurement and comparison. Funding by NIH (HL150361). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

The effect of head motion on brain age prediction using deep convolutional neural networks

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age—referred to as brain-predicted age difference (brain-PAD)—is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

Machine Learning-Based Perivascular Space Volumetry in Alzheimer Disease.

Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD. A convolutional neural network was trained using manually corrected, filter-based segmentations (n = 1000) to automatically segment the PVS in the centrum semiovale from interpolated, coronal T2-weighted magnetic resonance imaging scans (n = 894). These scans were sourced from the national German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study. Convolutional neural network-based segmentations and those performed by a human rater were compared in terms of segmentation volume, identified PVS clusters, as well as Dice score. The comparison revealed good segmentation quality (Pearson correlation coefficient r = 0.70 with P < 0.0001 for PVS volume, detection rate in cluster analysis = 84.3%, and Dice score = 59.0%). Subsequent multivariate linear regression analysis, adjusted for participants' age, was performed to correlate PVS volume with clinical diagnoses, disease progression, cerebrospinal fluid biomarkers, lifestyle factors, and cognitive function. Cognitive function was assessed using the Mini-Mental State Examination, the Comprehensive Neuropsychological Test Battery, and the Cognitive Subscale of the 13-Item Alzheimer's Disease Assessment Scale. Multivariate analysis, adjusted for age, revealed that participants with AD and MCI, but not those with SCD, had significantly higher PVS volumes compared with CU participants without SCD (P = 0.001 for each group). Furthermore, CU participants who developed incident MCI within 4.5 years after the baseline assessment showed significantly higher PVS volumes at baseline compared with those who did not progress to MCI (P = 0.03). Cognitive function was negatively correlated with PVS volume across all participant groups (P ≤ 0.005 for each). No significant correlation was found between PVS volume and any of the following parameters: cerebrospinal fluid biomarkers, sleep quality, body mass index, nicotine consumption, or alcohol abuse. The very early changes of PVS volume may suggest that alterations in PVS function are involved in the pathophysiology of AD. Overall, the volumetric assessment of centrum semiovale PVS represents a very early imaging biomarker for AD.

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study.

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Mapping brain volume change across time in primary-progressive multiple sclerosis.

Detection and prediction of the rate of brain volume loss with age is a significant unmet need in patients with primary progressive multiple sclerosis (PPMS). In this study we construct detailed brain volume maps for PPMS patients. These maps compare age-related changes in both cortical and sub-cortical regions with those in healthy individuals. We conducted retrospective analyses of brain volume using T1-weighted Magnetic Resonance Imaging (MRI) scans of a large cohort of PPMS patients and healthy subjects. The volume of brain parenchyma (BP), cortex, white matter (WM), deep gray matter, thalamus, and cerebellum were measured using the robust SynthSeg segmentation tool. Age- and gender-related regression curves were constructed based on data from healthy subjects, with the 95% prediction interval adopted as the normality threshold for each brain region. We analyzed 495 MRI scans from 169 PPMS patients, aged 20-79years, alongside 563 exams from healthy subjects aged 20-86. Compared to healthy subjects, a higher proportion of PPMS patients showed lower than expected brain volumes in all regions except the cerebellum. The most affected areas were BP, WM, and thalamus. Lower brain volumes correlated with longer disease duration for BP and WM, and higher disability for BP, WM, cortex, and thalamus. Constructing age- and gender-related brain volume maps enabled identifying PPMS patients at a higher risk of brain volume loss. Monitoring these high-risk patients may lead to better treatment decisions and improve patient outcomes.

Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy

BACKGROUND CONTEXTDegenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Degeneration of spinal discs, bony osteophyte growth and ligament pathology results in physical compression of the spinal cord contributing to damage of white matter tracts and grey matter cellular populations. This results in an insidious neurological and functional decline in patients which can lead to paralysis. Magnetic resonance imaging (MRI) confirms the diagnosis of DCM and is a prerequisite to surgical intervention, the only known treatment for this disorder. Unfortunately, there is a weak correlation between features of current commonly acquired MRI scans (“community MRI, cMRI”) and the degree of disability experienced by a patient. PURPOSEThis study examines the predictive ability of current MRI sequences relative to “advanced MRI” (aMRI) metrics designed to detect evidence of spinal cord injury secondary to degenerative myelopathy. We hypothesize that the utilization of higher fidelity aMRI scans will increase the effectiveness of machine learning models predicting DCM severity and may ultimately lead to a more efficient protocol for identifying patients in need of surgical intervention. STUDY DESIGN/SETTINGSingle institution analysis of imaging registry of patients with DCM. PATIENT SAMPLEA total of 296 patients in the cMRI group and 228 patients in the aMRI group. OUTCOME MEASURESPhysiologic measures: accuracy of machine learning algorithms to detect severity of DCM assessed clinically based on the modified Japanese Orthopedic Association (mJOA) scale. METHODSPatients enrolled in the Canadian Spine Outcomes Research Network registry with DCM were screened and 296 cervical spine MRIs acquired in cMRI were compared with 228 aMRI acquisitions. aMRI acquisitions consisted of diffusion tensor imaging, magnetization transfer, T2-weighted, and T2*-weighted images. The cMRI group consisted of only T2-weighted MRI scans. Various machine learning models were applied to both MRI groups to assess accuracy of prediction of baseline disease severity assessed clinically using the mJOA scale for cervical myelopathy. RESULTSThrough the utilization of Random Forest Classifiers, disease severity was predicted with 41.8% accuracy in cMRI scans and 73.3% in the aMRI scans. Across different predictive model variations tested, the aMRI scans consistently produced higher prediction accuracies compared to the cMRI counterparts. CONCLUSIONSaMRI metrics perform better in machine learning models at predicting disease severity of patients with DCM. Continued work is needed to refine these models and address DCM severity class imbalance concerns, ultimately improving model confidence for clinical implementation.

Altered volume of the amygdala subregions in patients with chronic low back pain.

Neuroimaging studies have suggested a pivotal role for the amygdala involvement in chronic low back pain (CLBP). However, the relationship between the amygdala subregions and CLBP has not yet been delineated. This study aimed to analyze whether the amygdala subregions were linked to the development of CLBP. A total of 45 patients with CLBP and 45 healthy controls (HCs) were included in this study. All subjects were asked to complete a three-dimensional T1-weighted magnetic resonance imaging (3D-T1 MRI) scan. FreeSurfer 7.3.2 was applied to preprocess the structural MRI images and segment the amygdala into nine subregions. Afterwards, comparisons were made between the two groups in terms of the volumes of the amygdala subregions. Correlation analysis is utilized to examine the relationship between the amygdala subregion and the scale scores, as well as the pain duration in patients with CLBP. Additionally, logistic regression was used to explore the risk of the amygdala and its subregions for CLBP. In comparison to HCs, patients with CLBP exhibited a significant enlargement of the left central nucleus (Ce) and left cortical nucleus (Co). Furthermore, the increased volume of the left Ce was associated with a higher risk of CLBP. Our study suggests that the left Ce and left Co may be involved in the pathophysiological processes of CLBP. Moreover, the volume of the left Ce may be a biomarker for detecting the risk of CLBP.

Diffusion deep learning for brain age prediction and longitudinal tracking in children through adulthood

Abstract Deep learning (DL)-based prediction of biological age in the developing human from a brain magnetic resonance imaging (MRI) (“brain age”) may have important diagnostic and therapeutic applications as a non-invasive biomarker of brain health, aging, and neurocognition. While previous deep learning tools for predicting brain age have shown promising capabilities using single-institution, cross-sectional datasets, our work aims to advance the field by leveraging multi-site, longitudinal data with externally validated and independently implementable code to facilitate clinical translation and utility. This builds on prior foundational efforts in brain age modeling to enable broader generalization and individual’s longitudinal brain development. Here, we leveraged 32,851 T1-weighted MRI scans from healthy children and adolescents aged 3 to 30 from 16 multisite datasets to develop and evaluate several DL brain age frameworks, including a novel regression diffusion DL network (AgeDiffuse). In a multisite external validation (5 datasets), we found that AgeDiffuse outperformed conventional DL frameworks, with a mean absolute error (MAE) of 2.78 years (interquartile range [IQR]: [1.2-3.9]). In a second, separate external validation (3 datasets), AgeDiffuse yielded an MAE of 1.97 years (IQR: [0.8-2.8]). We found that AgeDiffuse brain age predictions reflected age-related brain structure volume changes better than biological age (R2 = 0.48 vs. R2 = 0.37). Finally, we found that longitudinal predicted brain age tracked closely with chronological age at the individual level. To enable independent validation and application, we made AgeDiffuse publicly available and usable for the research community.

Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [18F]FET PET, and TSPO PET.

According to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively. Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied. TBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97). The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.

Effects of α1-antitrypsin on motor function in mice with immature brain white matter injury

To investigate the effects of α1-antitrypsin (AAT) on motor function in adult mice with immature brain white matter injury. Five-day-old C57BL/6J mice were randomly assigned to the sham surgery group (n=27), hypoxia-ischemia (HI) + saline group (n=27), and HI+AAT group (n=27). The HI white matter injury mouse model was established using HI methods. The HI+AAT group received intraperitoneal injections of AAT (50 mg/kg) 24 hours before HI, immediately after HI, and 72 hours after HI; the HI+saline group received intraperitoneal injections of the same volume of saline at the corresponding time points. Brain T2-weighted magnetic resonance imaging scans were performed at 7 and 55 days after modeling. At 2 months of age, adult mice were evaluated for static, dynamic, and coordination parameters using the Catwalk gait analysis system. Compared to the sham surgery group, mice with HI injury showed high signal intensity on brain T2-weighted magnetic resonance imaging at 7 days after modeling, indicating significant white matter injury. The white matter injury persisted at 55 days after modeling. In comparison to the sham surgery group, the HI+saline group exhibited decreased paw print area, maximum contact area, average pressure, maximum pressure, paw print width, average velocity, body velocity, stride length, swing speed, percentage of gait pattern AA, and percentage of inter-limb coordination (left hind paw → left front paw) (P<0.05). The HI+saline group showed increased inter-paw distance, percentage of gait pattern AB, and percentage of phase lag (left front paw → left hind paw) compared to the sham surgery group (P<0.05). In comparison to the HI+saline group, the HI+AAT group showed increased average velocity, body velocity, stride length, and swing speed (right front paw) (P<0.05). The mice with immature brain white matter injury may exhibit significant motor dysfunction in adulthood, while the use of AAT can improve some aspects of their motor function.

Computational simulation of transcranial magnetic stimulation-induced electric fields in the dorsolateral prefrontal cortex of heavy cannabis using individuals

We aimed to investigate the influence of demographic and clinical modulators on the strength of transcranial magnetic stimulation (TMS)-induced electric fields (EFs) in the left dorsolateral prefrontal cortex (lDLPFC) in heavy cannabis using individuals. Structural T1-weighted magnetic resonance imaging scans of 20 heavy cannabis using individuals and 22 non-cannabis users (the controls) in the age range of 18–25 were retrieved. Computational simulations of TMS-induced EFs in the lDLPFC were performed. No significant difference in the strength of TMS-induced EFs was observed between heavy cannabis using individuals and the controls. A negative correlation between the scalp-to-cortex distance demonstrated and the strength of the induced EFs. The severity of cannabis use related problems did not correlate with the induced EFs in the lDLPFC of heavy cannabis using individuals. However, the severity of alcohol use related problems was negatively correlated with the induced EF in the lDLPFC localized by the 5-cm method in the whole sample. Early adulthood seems related to an increase in the induced EFs in the lDLPFC. In conclusion, the dominant factor influencing TMS-induced EFs was the scalp-to-cortex distance. In early adulthood, the interaction between age and comorbid substance use may influence with the magnitude of TMS-induced EFs, thereby complicating the treatment effect of TMS in young people with substance use disorders.

Differential longitudinal changes of hippocampal subfields in patients with anorexia nervosa.

Anorexia nervosa (AN) is a mental disorder characterized by dietary restriction, fear of gaining weight, and distorted body image. Recent studies indicate that the hippocampus, crucial for learning and memory, may be affected in AN, yet subfield-specific effects remain unclear. We investigated hippocampal subfield alterations in acute AN, changes following weight restoration, and their associations with leptin levels. T1-weighted magnetic resonance imaging scans were processed using FreeSurfer. We compared 22 left and right hemispheric hippocampal subfield volumes cross-sectionally and longitudinally in females with acute AN (n = 165 at baseline, n = 110 after partial weight restoration), healthy female controls (HCs; n = 271), and females after long-term recovery from AN (n = 79) using linear models. We found that most hippocampal subfield volumes were significantly reduced in patients with AN compared with HCs (~-3.9%). Certain areas such as the subiculum exhibited no significant reduction in the acute state of AN, while other areas, such as the hippocampal tail, showed strong decreases (~-9%). Following short-term weight recovery, most subfields increased in volume. Comparisons between participants after long-term weight-recovery and HC yielded no differences. The hippocampal tail volume was positively associated with leptin levels in AN independent of body mass index. Our study provides evidence of differential volumetric differences in hippocampal subfields between individuals with AN and HC and almost complete normalization after weight rehabilitation. These alterations are spatially inhomogeneous and more pronounced compared with other major mental disorders (e.g. major depressive disorder and schizophrenia). We provide novel insights linking hypoleptinemia to hippocampal subfield alterations hinting towards clinical relevance of leptin normalization in AN recovery.

Unenhanced Breast MRI With Diffusion-Weighted Imaging for Breast Cancer Detection: Effects of Training on Performance and Agreement of Subspecialty Radiologists.

To investigate whether reader training improves the performance and agreement of radiologists in interpreting unenhanced breast magnetic resonance imaging (MRI) scans using diffusion-weighted imaging (DWI). A study of 96 breasts (35 cancers, 24 benign, and 37 negative) in 48 asymptomatic women was performed between June 2019 and October 2020. High-resolution DWI with b-values of 0, 800, and 1200 sec/mm² was performed using a 3.0-T system. Sixteen breast radiologists independently reviewed the DWI, apparent diffusion coefficient maps, and T1-weighted MRI scans and recorded the Breast Imaging Reporting and Data System (BI-RADS) category for each breast. After a 2-h training session and a 5-month washout period, they re-evaluated the BI-RADS categories. A BI-RADS category of 4 (lesions with at least two suspicious criteria) or 5 (more than two suspicious criteria) was considered positive. The per-breast diagnostic performance of each reader was compared between the first and second reviews. Inter-reader agreement was evaluated using a multi-rater κ analysis and intraclass correlation coefficient (ICC). Before training, the mean sensitivity, specificity, and accuracy of the 16 readers were 70.7% (95% confidence interval [CI]: 59.4-79.9), 90.8% (95% CI: 85.6-94.2), and 83.5% (95% CI: 78.6-87.4), respectively. After training, significant improvements in specificity (95.2%; 95% CI: 90.8-97.5; P = 0.001) and accuracy (85.9%; 95% CI: 80.9-89.8; P = 0.01) were observed, but no difference in sensitivity (69.8%; 95% CI: 58.1-79.4; P = 0.58) was observed. Regarding inter-reader agreement, the κ values were 0.57 (95% CI: 0.52-0.63) before training and 0.68 (95% CI: 0.62-0.74) after training, with a difference of 0.11 (95% CI: 0.02-0.18; P = 0.01). The ICC was 0.73 (95% CI: 0.69-0.74) before training and 0.79 (95% CI: 0.76-0.80) after training (P = 0.002). Brief reader training improved the performance and agreement of interpretations by breast radiologists using unenhanced MRI with DWI.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease.

A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aβ) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aβ-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aβ-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls. The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively. The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.

Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study

Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often ‘older’ than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21–24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = −.21; 95% CI −2.00, 1.58; p = .82; Cohen's d = −.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.

Neural Correlates of Trait Impulsivity among Adult Healthy Individuals.

: Impulsivity can be observed in individuals with or without mental illness. The discovery of neural correlates responsible for trait impulsivity can therefore help to understand the severity of psychiatric symptoms, personality characteristics and social adjustment. In this study, we aimed to identify the gray matter substrates of trait impulsivity in healthy individuals. : Seventy-five healthy individuals were enrolled. At baseline, trait impulsivity was assessed using the Barratt Impulsiveness Scale (BIS) and all participants underwent T1-weighted magnetic resonance imaging scan. Beck Anxiety Inventory (BAI), World Health Organization Quality of Life (WHOQOL-BREF) and Connor-Davidson Resilience Scale (CD-RISC) were also assessed. Mean cortical thickness (CT) and the local gyrification index (LGI) were calculated to perform whole-brain vertex-wise correlation analysis, which were performed to investigate the relationship between BIS scores and CT or LGI in each brain region. We also revealed the relationship between brain regions and psychological measurements. : Total BIS scores were significantly and negatively correlated with mean CT values in the left lateral occipital cortex (OC) and LGIs in the inferior frontal gyrus (IFG). Correlation analyses revealed that the lateral OC's mean CT values were negatively correlated with BAI scores and positively correlated with WHOQOL-BREF scores, while LGI in the IFG was positively correlated with CD-RISC scores. : Our study showed that trait impulsivity might be associated with the lateral OC and IFG in healthy individuals. Understanding the neural correlates of trait impulsivity could provide ways to expect high impulsivity, anxiety, and poor resilience in healthy adults.

Language performance and brain volumes, asymmetry, and cortical thickness in children born extremely preterm

BackgroundChildren born preterm are more prone to have language difficulties. Few studies focus on children born extremely preterm (EPT) and the structural differences in language-related regions between these children and children born at term.MethodsOur study used T1-weighted magnetic resonance imaging (MRI) scans to calculate the brain volumetry, brain asymmetry, and cortical thickness of language-related regions in 50 children born EPT and 37 term-born controls at 10 years of age. The language abilities of 41 of the children born EPT and 29 term-born controls were then assessed at 12 years of age, using the Wechsler Intelligence Scale for Children, Fifth Edition and the Clinical Evaluations of Language Fundamentals, Fourth Edition. The differences between MRI parameters and their associations with language outcomes were compared in the two groups.ResultsBrain volume and cortical thickness of language-related regions were reduced in children born EPT, but volumetric asymmetry was not different between children born EPT and at term. In children born EPT the brain volume was related to language outcomes, prior to adjustments for full-scale IQ.ConclusionsThese findings expand our understanding of the structural correlates underlying impaired language performance in children born with EPT.ImpactThe article expands understanding of the structure-function relationship between magnetic resonance imaging measurements of language-related regions and language outcomes for children born extremely preterm beyond infancy.Most literature to date has focused on very preterm children, but the focus in this paper is on extreme prematurity and language outcomes.While the brain volume and cortical thickness of language-related regions were reduced in children born EPT only the volume, prior to adjustment for full-scale IQ, was associated with language outcomes.We found no differences in volumetric asymmetry between children born EPT and at term.

Posterior reversible encephalopathy syndrome in a child following hypovolemic shock: a case report.

Posterior reversible encephalopathy syndrome (PRES) is a condition that causes a wide range of clinical neurological manifestations like headache, seizures, visual changes, and altered mental sensations. It is diagnosed with the help of sequential neuroimaging findings. Manifestations may occur a few hours to months after the initial precipitating cause. In the pediatric population, the most common cause is hypertension caused by renal disease or different drugs. Here, the authors present the case of a 4-year-old boy with a significant medical history of acute gastroenteritis following hypovolemic shock that later developed white matter edema of the brain on T2-weighted MRI scans along with symptoms such as headache and vomiting. Here, the patient was managed symptomatically with antiepileptic medication as prophylaxis. PRES is a rare neurological diagnosis made in the child that presents with headache, vomiting, blurring of vision, and abnormal body movements, which have several etiology like hypertension, glomerulonephritis, organ transplant, drugs, and very rarely with hypovolemic shock. It is an acute reversible condition in which a person presents with visual disturbances, headaches, and seizures. Seizures present as a life-threatening situation, so antiepileptic drugs are used as early prophylaxis. PRES is a reversible neurological condition, and prognosis is typically favorable if recognized and treated early, with symptom improvement or resolution in a few days to several weeks. Complications of PRES develop if the disease is not treated promptly. Complications include focal neurologic deficits from ischemic injury and epilepsy.

58 Right Anterior Temporal Lobe Atrophy is Associated with Informant-Reported Socioemotional Dysfunction in Patients with Frontotemporal and Early Onset Alzheimer's Dementia

Objective:Abnormalities in social and emotional behavior are the major diagnostic criteria for behavioral variant frontotemporal dementia (bvFTD). Investigators have attributed their behavioral disturbances to disease in mesial prefrontal and related networks, such as the salience network. This study examined the main neural correlates of informant-reported socioemotional dysfunction among patients with bvFTD compared to those with early-onset (before age 65) Alzheimer's Disease (EOAD).Participants and Methods:Participants included 13 patients with bvFTD and their caregivers and 18 patients with EOAD and their caregivers. The caregivers consisted of a spouse, family member, or other informant who resided with the patient. They completed the informant-based Socioemotional Dysfunction Scale (SDS), a 40-item scale which rates common disturbances in social and emotional behavior on a five-point Likert scale (1-5). The patients underwent magnetic resonance imaging (MRI) with tensor-based morphometry (TBM) analysis of the 3D T1-weighted MRI scans. Computations of mean Jacobian values within select regions of interest (ROIs) in frontal and temporal lobes generated numerical summaries of regional volumes, and voxel-wise regressions created 3D statistical maps of the association between tissue volume and SDS total scores. Statistical analyses included independent samples t-tests group differences in ROIs and SDS scores, Pearson correlations between SDS scores and brain volumes, and multiple regression of ROIs with SDS scores and group as predictor variables.Results:Compared to the EOAD group, the bvFTD group had significantly higher SDS scores (p &lt; .001; d = 2.24), smaller frontal lobe volumes (specifically dorsolateral-prefrontal cortex, p = .003; d = 1.24), and larger temporal lobe volumes (specifically hippocampus, p = .014; d = 0.979). Within the bvFTD group, higher SDS scores were associated with a smaller right anterior temporal lobe (ATL; p = .005; r = -.729), especially the lateral ATLs (p = .002; r = -.776), and a smaller bilateral orbitofrontal cortex (OFC; p = .016; r = -.650). In contrast, within the EOAD group, higher SDS scores were associated with a smaller right parietal cortex (p = .030; r = .542). In the entire sample (both bvFTD and EOAD), higher SDS scores was associated with a smaller lateral ATL volumes (p = .019; r = -.431). Regression analyses confirmed that SDS score predicted lateral ATL volume (p = .041; b = -.262) after controlling for diagnosis (p &lt; .001; b = -.692).Conclusions:These findings are consistent with greater socioemotional dysfunction, smaller frontal, and larger mesial temporal regions in bvFTD, when compared to EOAD. The findings, however, suggest that positively disturbed socioemotional behavior in bvFTD, as reported by caregivers, results from involvement of the right temporal lobe and the lateral temporal region, with further contribution from disease in OFC. The association of SDS scores and ATL volume across diagnostic groups suggests that this region is instrumental in socioemotional functioning and that the SDS may have diagnostic value in distinguishing the "right-temporal variant" of bvFTD.

Verbal memory depends on structural hippocampal subfield volume.

To investigate correlates in hippocampal subfield volume and verbal and visual memory function in patients with temporal lobe epilepsy (TLE), mild amnestic cognitive impairment (MCI) and heathy participants (HP). 50 right-handed participants were included in this study; 11 patients with temporal lobe epilepsy (TLE), 18 patients with mild amnestic cognitive impairment (MCI) and 21 healthy participants (HP). Verbal memory performance was evaluated via the verbal memory test (VLMT) and visual memory performance via the diagnosticum for cerebral damage (DCM). Hippocampal subfield volumes of T1-weighted Magnetic Resonance Imaging (MRI) scans were computed with FreeSurfer version 7.1. Stepwise correlation analyses were performed between the left hippocampal subfield volumes and learning, free recall, consolidation and recognition performance scores of the VLMT as well as between right hippocampal subfield volumes and visual memory performance. The volume of the left subicular complex was highly correlated to learning performance (β = 0.284; p = 0.042) and free recall performance in the VLMT (β = 0.434; p = 0.001). The volume of the left CA3 subfield showed a significant correlation to the consolidation performance in the VLMT (β = 0.378; p = 0.006) and recognition performance in the VLMT (β = 0.290; p = 0.037). There was no significant correlation identified between the right hippocampal subfields and the visual memory performance. The results of this study show verbal memory correlates with hippocampal subfields and support the role of left subiculum and left CA2/CA3 in verbal memory performance.