Brain Magnetic Resonance Imaging Data Research Articles

Diagnosing the MRI brain tumour images through RNN-LSTM

According to the World Health Organization, cancer is the second most common cause of death worldwide (WHO). Death from cancer may sometimes be avoided through early diagnosis, although this is not always practicable. A tumour, unlike cancer, may be benign, pre-cancerous, or malignant. Contrary to malignant tumours, benign tumours may frequently be surgically removed and seldom metastasize to other organs or tissues. The method most often used to differentiate between various types of tumours is magnetic resonance imaging (MRI) (MRI). But because it depends on human subjectivity, it could be challenging for one person to see a lot of data. The radiologist's ability to identify brain tumours early mostly depends on their level of training. Before determining whether the tumour is benign or malignant, the diagnostic process for it could not be finished. A biopsy is often carried out to determine if the tissue is benign or cancerous. The biopsy of a brain tumour is often delayed until the last brain operation, in contrast to tumours discovered elsewhere in the body. An efficient diagnostics tool for tumour segmentation and classification from MRI images is crucial to obtaining accurate diagnoses, avoiding surgery, and eliminating subjectivity. The classification and segmentation of the tumour part of the MRI brain picture is the goal of this study. Prior to feature extraction in the first study, pre-processing is done using the Gray Level Co-occurrence Matrix (GLCM). Based on these features, the data is then separated into normal, glioma, meningioma, and pituitary groups. In prior studies, classification was handled by traditional Deep Neural Networks (DNN), with the best weights selected using optimization methods. Accordingly, a recent study recommends including Long Short-Term Memory based on Recurrent Neural Networks (RNN) (LSTM). The Local Direction Ternary Pattern (LDTP), the Gray Level Co-occurrence Matrix (GLCM), and the Le-Net features are all combined in the proposed Hybrid GLCM-LDTP-Le-Net Feature extraction approach. The hybrid parameters produced by the feature extraction process and the brain MRI data are used by the RNN-LSTM model to categorise the data as benign or malignant. By removing mixed features from the input data, the suggested hybrid feature extraction improves the learnt models' accuracy. The accuracy of 98.99 % of the suggested hybrid feature extraction approach was higher than the current CNN-based model methodologies.

Association of accelerated biological aging with brain volumes: A cross-sectional study

BackgroundMultiple epidemiological studies have observed the connection between aging and brain volumes. The concept of accelerated biological aging (BA) is more powerful for observing the degree of aging of an individual than chronologic age (CA). The objective of this study is to explore the relationship between BA and brain volumes. MethodsBA was measured from clinical traits using two blood-chemistry algorithms, the Klemera-Doubal method (KDM) and the PhenoAge. The two age acceleration biomarkers were calculated by the residuals from regressing CA, termed “KDM-acceleration” and “PhenoAge-acceleration”. Brain volumes were from brain magnetic resonance imaging (MRI) data. After adjustment for confounding factors, general linear regression models were used to examine associations between KDM-acceleration and PhenoAge-acceleration and brain volumes, respectively. Additionally, we stratified participants by sex, age, and the four quartiles of the Townsend Deprivation Index (TDI) for extra subgroup analysis. Results14,725 participants with available information were enrolled. After full adjustment, we observed negative associations between KDM-acceleration and brain volumes, such as gray matter (β = −0.029), white matter (β = −0.021), gray and white matter (β = −0.026), and hippocampus (β = −0.011 for left and β = −0.014 for right). There were also negative associations between PhenoAge-acceleration and brain volumes, such as white matter (β = −0.008), gray and white matter (β = −0.010), thalamus (β = −0.012 for left and β = −0.012 for right). In the subgroup analysis stratified by sex, age, and the four quartiles of TDI, the association between KDM-acceleration and PhenoAge-acceleration and brain volumes still existed. In subgroup analyses, the variation in associations suggests that socioeconomic and biological factors may differentially influence brain aging. ConclusionsOur research indicated that more advanced BA was associated with less brain tissue.

Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data

Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other outcomes, especially when impacted by co-morbidities such as HIV infection. Brain magnetic resonance imaging (MRI) characterises the extent and severity of disease and may enable more accurate prediction of complications and poor outcomes. We analysed clinical and brain MRI data from a prospective longitudinal study of 216 adults with TBM; 73 (34%) were HIV-positive, a factor highly correlated with mortality. We implemented an end-to-end framework to model clinical and imaging features to predict disease progression. Our model used state-of-the-art machine learning models for automatic imaging feature encoding, and time-series models for forecasting, to predict TBM progression. The proposed approach is designed to be robust to missing data via a novel tailored model optimisation framework. Our model achieved a 60% balanced accuracy in predicting the prognosis of TBM patients over the six different classes. HIV status did not alter the performance of the models. Furthermore, our approach identified brain morphological lesions caused by TBM in both HIV and non-HIV-infected, associating lesions to the disease staging with an overall accuracy of 96%. These results suggest that the lesions caused by TBM are analogous in both populations, regardless of the severity of the disease. Lastly, our models correctly identified changes in disease symptomatology and severity in 80% of the cases. Our approach is the first attempt at predicting the prognosis of TBM by combining imaging and clinical data, via a machine learning model. The approach has the potential to accurately predict disease progression and enable timely clinical intervention.

Risk of type 2 diabetes, MASLD and cardiovascular disease in people living with polycystic ovary syndrome.

Abstract Background Polycystic ovary syndrome (PCOS) is associated with adverse clinical outcomes that may differ according to PCOS phenotype. Methods Using UK Biobank data, we compared the incidence of type 2 diabetes (T2D), metabolic dysfunction associated steatotic liver disease, cardiovascular disease (CVD), hormone-dependent cancers, and dementia between PCOS participants and age- and body mass index-matched controls. We also compared multiorgan (liver, cardiac, and brain) magnetic resonance imaging (MRI) data and examined the impact of PCOS phenotype (hyperandrogenic and normoandrogenic) on these outcomes. Results We included 1008 women with PCOS (defined by diagnostic codes, self-reported diagnoses, or clinical/biochemical features of hyperandrogenism and a/oligoCmenorrhoea) and 5017 matched controls (5:1 ratio); median age, 61 years, body mass index, 28.4 kg/m². Adjusted Cox proportional hazard modeling demonstrated PCOS participants had greater incident T2D [hazard ratio (HR) 1.47; 95% confidence interval (CI), 1.11-1.95] and all-cause CVD (1.76; 1.35-2.30). No between-group differences existed for cancers or dementia. Liver MRI confirmed more PCOS participants had hepatic steatosis (proton density fat fraction >5.5%: 35.9 vs 23.9%; P = .02) and higher fibroinflammation (corrected T1 721.4 vs 701.5 ms; P = <.01) vs controls. No between-group difference existed for cardiac (biventricular/atrial structure and function) or brain (grey and white matter volumes) imaging. Normoandrogenic (but not hyperandrogenic) PCOS participants had greater incident all-cause CVD (1.82; 1.29-2.56) while hyperandrogenic (but not normoandrogenic) PCOS participants were more likely to have hepatic steatosis (8.96 vs 6.04 vs 5.23%; P = .03) with greater fibroinflammation (776.3 vs 707.7 vs 701.9 ms; P=<.01). Conclusion Cardiometabolic disease may be increased in PCOS patients with a disease phenotype-specific pattern.

Neuroprotective Effects of Inhaled Xenon Gas on Brain Structural Gray Matter Changes After Out-of-Hospital Cardiac Arrest Evaluated by Morphometric Analysis: A Substudy of the Randomized Xe-Hypotheca Trial.

We have earlier reported that inhaled xenon combined with hypothermia attenuates brain white matter injury in comatose survivors of out-of-hospital cardiac arrest (OHCA). A predefined secondary objective was to assess the effect of inhaled xenon on the structural changes in gray matter in comatose survivors after OHCA. Patients were randomly assigned to receive either inhaled xenon combined with target temperature management (33°C) for 24h (n = 55, xenon group) or target temperature management alone (n = 55, control group). A change of brain gray matter volume was assessed with a voxel-based morphometry evaluation of high-resolution structural brain magnetic resonance imaging (MRI) data with Statistical Parametric Mapping. Patients were scheduled to undergo the first MRI between 36 and 52h and a second MRI 10days after OHCA. Of the 110 randomly assigned patients in the Xe-Hypotheca trial, 66 patients completed both MRI scans. After all imaging-based exclusions, 21 patients in the control group and 24 patients in the xenon group had both scan 1 and scan 2 available for analyses with scans that fulfilled the quality criteria. Compared with the xenon group, the control group had a significant decrease in brain gray matter volume in several clusters in the second scan compared with the first. In a between-group analysis, significant reductions were found in the right amygdala/entorhinal cortex (p = 0.025), left amygdala (p = 0.043), left middle temporal gyrus (p = 0.042), left inferior temporal gyrus (p = 0.008), left parahippocampal gyrus (p = 0.042), left temporal pole (p = 0.042), and left cerebellar cortex (p = 0.005). In the remaining gray matter areas, there were no significant changes between the groups. In comatose survivors of OHCA, inhaled xenon combined with targeted temperature management preserved gray matter better than hypothermia alone. ClinicalTrials.gov: NCT00879892.

Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.

Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. This framework is applicable to learning anatomy-aware, acquisition-agnostic registration of any anatomy with any architecture, as long as label maps are available for training. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain magnetic resonance imaging (MRI) data.

Interactions between age, sex and visceral adipose tissue on brain ageing.

To examine the associations between visceral adipose tissue (VAT) and brain structural measures at midlife and explore how these associations may be affected by age, sex and cardiometabolic factors. We used abdominal and brain magnetic resonance imaging data from a population-based cohort of people at midlife in the UK Biobank. Regression modelling was applied to study associations of VAT volume with total brain volume (TBV), grey matter volume (GMV), white matter volume, white matter hyperintensity volume (WMHV) and total hippocampal volume (THV), and whether these associations were altered by age, sex or cardiometabolic factors. Complete data were available for 17 377 participants (mean age 63 years, standard deviation = 12, 53% female). Greater VAT was associated with lower TBV, GMV and THV (P < .001). We found an interaction between VAT and sex on TBV (P < .001), such that the negative association of VAT with TBV was greater in men (β = -2.89, 95% confidence interval [CI] -2.32 to -10.15) than in women (β = -1.32, 95% CI -0.49 to -3.14), with similar findings for GMV. We also found an interaction between VAT and age (but not sex) on WMHV (P < .001). The addition of other cardiometabolic factors or measures of physical activity resulted in little change to the models. VAT volume is associated with poorer brain health in midlife and this relationship is greatest in men and those at younger ages.

Distinct attentional and executive profiles in neurofibromatosis type 1: Is there difference with primary attention deficit-hyperactivity disorder?

PurposeAttentional and executive dysfunctions are the most frequent cognitive disorders in neurofibromatosis type 1 (NF1), with a high prevalence of attention deficit-hyperactivity disorder (ADHD). We (i) compared attentional profiles between NF1 children with and without ADHD and children with primary ADHD criteria and (ii) investigated the possible relationship between attentional disorders and “unidentified bright objects” (UBOs) in NF1. MethodsThis retrospective study included 47 NF1 children, 25 with ADHD criteria (NF1+adhd group), matched for age, sex, and cognitive level with 47 children with primary ADHD (ADHD group). We collected computer task (sustained-attention, visuomotor-decision, inhibition, and cognitive-flexibility tasks) scores normalized for age and sex, and brain magnetic resonance imaging data. Results(i) Working memory was impaired in all groups. (ii) Omissions (p < 0.002) and response-time variability (p < 0.05) in sustained-attention and visuomotor-decision tasks and errors (p < 0.02) in the cognitive-flexibility task were lower for the NFI+adhd and ADHD groups than for the NF1-no-adhd group. (iii) The NF1+adhd group had slower response times (p ≤ 0.02) for inhibition and visuomotor-decision tasks than the other groups. (iv) We found no relevant association between cognitive performance and UBOs. ConclusionsNF1 children with ADHD have an attentional and executive functions deficit profile similar to that of children with primary ADHD, but with a slower response-time, increasing learning difficulties. The atypical connectivity of fronto-striatal pathways, poorer dopamine homeostasis, and increased GABA inhibition observed in NF1 renders vulnerable the development of the widely distributed neural networks that support attentional, working-memory, and executive functions.

Association of Gaseous Ambient Air Pollution and Dementia-Related Neuroimaging Markers in the ARIC Cohort, Comparing Exposure Estimation Methods and Confounding by Study Site.

Evidence linking gaseous air pollution to late-life brain health is mixed. We explored associations between exposure to gaseous pollutants and brain magnetic resonance imaging (MRI) markers among Atherosclerosis Risk in Communities (ARIC) Study participants, with attention to the influence of exposure estimation method and confounding by site. We considered data from 1,665 eligible ARIC participants recruited from four US sites in the period 1987-1989 with valid brain MRI data from Visit 5 (2011-2013). We estimated 10-y (2001-2010) mean carbon monoxide (CO), nitrogen dioxide (), nitrogen oxides (), and 8- and 24-h ozone () concentrations at participant addresses, using multiple exposure estimation methods. We estimated site-specific associations between pollutant exposures and brain MRI outcomes (total and regional volumes; presence of microhemorrhages, infarcts, lacunes, and severe white matter hyperintensities), using adjusted linear and logistic regression models. We compared meta-analytically combined site-specific associations to analyses that did not account for site. Within-site exposure distributions varied across exposure estimation methods. Meta-analytic associations were generally not statistically significant regardless of exposure, outcome, or exposure estimation method; point estimates often suggested associations between higher and smaller temporal lobe, deep gray, hippocampal, frontal lobe, and Alzheimer disease signature region of interest volumes and between higher CO and smaller temporal and frontal lobe volumes. Analyses that did not account for study site more often yielded significant associations and sometimes different direction of associations. Patterns of local variation in estimated air pollution concentrations differ by estimation method. Although we did not find strong evidence supporting impact of gaseous pollutants on brain changes detectable by MRI, point estimates suggested associations between higher exposure to CO, , and and smaller regional brain volumes. Analyses of air pollution and dementia-related outcomes that do not adjust for location likely underestimate uncertainty and may be susceptible to confounding bias. https://doi.org/10.1289/EHP13906.

Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)

Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.

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.

Dissecting unique and common variance across body and brain health indicators using age prediction

Dissecting unique and common variance across body and brain health indicators using age prediction

Music and the aging brain - Exploring the role of long-term Carnatic music training on cognition and gray matter volumes.

Aging is a natural process and is often associated with an increased incidence of cognitive impairment. Physical exercise, diet, and leisure activities (music, dance, and art) are some of the lifestyle factors that contribute to healthy aging. The present study aims to explore the differences in cognitive functioning between aging individuals involved in musical activity throughout their lifetime and the ones who were not. Fifty-one healthy elderly individuals (50-80 years of age) residing in an urban locality were selected for the study from the Tata Longitudinal Study of Aging cohort. Participants were divided into two groups: Active musicians trained in Carnatic music for more than five years (n = 18) and age-matched non-musicians (n = 33). Addenbrooke cognitive examination-III, Hindi mental status examination, and trail-making test-B (TMT-B) were used to assess cognitive functioning. A Generalized Linear Regression Model was performed including covariates such as gender, age, and years of education. We also looked at the available brain magnetic resonance imaging data of a subset of our study population to inspect the volumetric differences between musicians and non-musicians. Our results showed that musicians had significantly better visuospatial abilities as compared to non-musicians (P = 0.043). Musicians (130.89 ± 45.16 s) also took less time to complete the TMT-B task than non-musicians (148.73 ± 39.65 s), although it was not a statistically significant difference (P =0.150). In addition, brain imaging data suggested that musicians had increased gray matter volumes in the right precuneus, right post-central gyrus, right medial and superior frontal gyrus, right orbital gyrus, left middle temporal gyrus, left cuneus, left fusiform gyrus, and bilateral cingulate gyrus. Our findings are indicative of music being an important attribute in improving cognitive reserve and predicting cognitive resilience. These findings pave the way to explore the utility of non-pharmacological interventions, such as Music Therapy (especially Carnatic music in the Indian context), as a potential factor for improving cognitive reserve in elderly individuals.

Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images

This study investigates the application of convolutional neural networks (CNNs) and traditional machine learning algorithms for the early detection and classification of Alzheimer's disease (AD) using brain Magnetic Resonance Imaging (MRI) data. We compare the performance of CNNs with Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) on a dataset comprising MRI images from AD patients and healthy controls. Results show that CNNs achieved the highest accuracy (90.2%) and area under the receiver operating characteristic curve (AUC-ROC) of 0.95, outperforming SVM, RF, and GBM. The CNN model also exhibited high sensitivity (87.5%) and specificity (92.6%) in distinguishing between AD patients and healthy controls. These findings highlight the effectiveness of CNN-based approaches in leveraging raw MRI images for accurate and early detection of AD.

Autism-associated brain differences can be observed in utero using MRI.

Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.

Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance

Post-operative brain magnetic resonance imaging (MRI) segmentation is inherently challenging due to the diverse patterns in brain tissue, which makes it difficult to accurately identify resected areas. Therefore, there is a crucial need for a precise segmentation model. Due to the scarcity of post-operative brain MRI scans, it is not feasible to use complex models that require a large amount of training data. This paper introduces an innovative approach for accurately segmenting and quantifying post-operative brain resection cavities in MRI scans. The proposed model, named Attention-Enhanced VGG-U-Net, integrates VGG16 initial weights in the encoder section and incorporates a self-attention module in the decoder, offering improved accuracy for postoperative brain MRI segmentation. The attention mechanism enhances its accuracy by concentrating on a specific area of interest. The VGG16 model is comparatively lightweight, has pre-trained weights, and allows the model to extract incredibly detailed information from the input. The model is trained on publicly available post-operative brain MRI data and achieved a Dice coefficient value of 0.893. The model is then assessed using a clinical dataset of postoperative brain MRIs. The model facilitates the quantification of the resected regions and enables comparisons with each brain region based on pre-operative images. The capabilities of the model assist radiologists in evaluating surgical success and directing follow-up procedures.

Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images

It has been shown that AI models can learn race on medical images, leading to algorithmic bias. Our aim in this study was to enhance the fairness of medical image models by eliminating bias related to race, age, and sex. We hypothesise models may be learning demographics via shortcut learning and combat this using image augmentation. This study included 44,953 patients who identified as Asian, Black, or White (mean age, 60.68 years±18.21; 23,499 women) for a total of 194,359 chest X-rays (CXRs) from MIMIC-CXR database. The included CheXpert images comprised 45,095 patients (mean age 63.10 years±18.14; 20,437 women) for a total of 134,300 CXRs were used for external validation. We also collected 1195 3D brain magnetic resonance imaging (MRI) data from the ADNI database, which included 273 participants with an average age of 76.97 years±14.22, and 142 females. DL models were trained on either non-augmented or augmented images and assessed using disparity metrics. The features learned by the models were analysed using task transfer experiments and model visualisation techniques. In the detection of radiological findings, training a model using augmented CXR images was shown to reduce disparities in error rate among racial groups (-5.45%), age groups (-13.94%), and sex (-22.22%). For AD detection, the model trained with augmented MRI images was shown 53.11% and 31.01% reduction of disparities in error rate among age and sex groups, respectively. Image augmentation led to a reduction in the model's ability to identify demographic attributes and resulted in the model trained for clinical purposes incorporating fewer demographic features. The model trained using the augmented images was less likely to be influenced by demographic information in detecting image labels. These results demonstrate that the proposed augmentation scheme could enhance the fairness of interpretations by DL models when dealing with data from patients with different demographic backgrounds. National Science and Technology Council (Taiwan), National Institutes of Health.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Macular vessel density in the superficial plexus is not a proxy of cerebrovascular damage in non-demented individuals: data from the NORFACE cohort

IntroductionOptical coherence tomography angiography (OCT-A) is a novel tool that allows the detection of retinal vascular changes. We investigated the association of macular vessel density (VD) in the superficial plexus assessed by OCT-A with measures of cerebrovascular pathology and atrophy quantified by brain magnetic resonance imaging (MRI) in non-demented individuals.MethodsClinical, demographical, OCT-A, and brain MRI data from non-demented research participants were included. We analyzed the association of regional macular VD with brain vascular burden using the Fazekas scale assessed in a logistic regression analysis, and the volume of white matter hyperintensities (WMH) assessed in a multiple linear regression analysis. We also explored the associations of macular VD with hippocampal volume, ventricle volume and Alzheimer disease cortical signature (ADCS) thickness assessed in multiple linear regression analyses. All analyses were adjusted for age, sex, syndromic diagnosis and cardiovascular variables.ResultsThe study cohort comprised 188 participants: 89 with subjective cognitive decline and 99 with mild cognitive impairment. No significant association of regional macular VD with the Fazekas categories (all, p > 0.111) and WMH volume (all, p > 0.051) were detected. VD in the nasal quadrant was associated to hippocampal volume (p = 0.007), but no other associations of macular VD with brain atrophy measures were detected (all, p > 0.05).DiscussionRetinal vascular measures were not a proxy of cerebrovascular damage in non-demented individuals, while VD in the nasal quadrant was associated with hippocampal atrophy independently of the amyloid status.

Depression, anxiety and brain volume after hearing loss and tinnitus: cohort study in the UK Biobank.

Hearing loss and tinnitus have been proposed as potential indicators of impaired mental health and brain morphological changes. To assess the associations of hearing loss and tinnitus with the risk of depression and anxiety and with brain volume. We conducted a community-based cohort study including 129 610 participants aged 40-69 years at recruitment to the UK Biobank with a follow-up period during 2006-2021 to estimate the risk of depression and anxiety after detection of hearing loss and reported tinnitus. We also assessed the associations of hearing loss and tinnitus with brain volume in a subsample with available brain magnetic resonance imaging data (N = 5222). We observed an increased risk of depression among individuals with hearing loss (hazard ratio [HR] 1.14, 95% CI 1.03-1.26), tinnitus (HR 1.30, 95% CI 1.21-1.41) or both (HR 1.32, 95% CI 1.15-1.52), compared with individuals with neither hearing loss nor tinnitus. Similar results were noted for anxiety (HR 1.18, 95% CI 1.07-1.30 for hearing loss; HR 1.32, 95% CI 1.22-1.43 for tinnitus; and HR 1.48, 95% CI 1.30-1.68 for both). Hearing loss was associated with decreased overall brain volume as well as decreased volume of different brain regions. The latter associations disappeared after adjustment for whole intracranial volume. Tinnitus was associated with greater left accumbens and right occipital pole volume after adjustment for the whole intracranial volume. Individuals with tinnitus are at increased risk of depression and anxiety. Hearing loss, on the other hand, is associated with both mood disorders and altered brain morphology.