Brain Magnetic Resonance Imaging Data Research Articles

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.

Abstract WP135: Coexistence of Enlarged Perivascular Spaces and Other MRI Markers of Cerebral Small Vessel Diseases

Objective: Enlarged Perivascular Spaces (EPVS) have gained recognition as potential indicators of cerebrovascular pathologies. In this study, we investigated the association between the presence and severity of EPVS and other MRI Markers of cerebral small vessel diseases. Method: Our study was conducted on a subset of the Geisinger DiscovEHR Initiative Cohort. A predefined protocol was employed to evaluate each brain magnetic resonance imaging (MRI) data by two trained reviewers. The total numbers of EPVS were categorized into five grades (0-4) in centrum semiovale and basal ganglia regions. We compared patients with EPVS and an age-, gender-, body mass index-, and hypertension-propensity-score-matched cohort lacking EPVS. Results: Out of 3054 (mean age of 67.5 years, 97% white of Caucasian descent) randomly selected participants, 1465 (47.9%, mean age 69.9 years) showed evidence of EPVS in their imaging. Propensity score matching resulted in 1310 participants in each group. The presence of EPVS in basal ganglia was linked to higher white matter hyperintensity according to the Fazekas scale in deep white matter (OR, 1.4; 95% CI, 1.2-1.6), as well as global cortical atrophy (OR, 1.22; 95% CI, 1.1-1.4) on MRI scans. Other factors, such as cerebral aneurysm (OR, 2.3; 95% CI, 1.4-3.8), heart failure (OR, 1.85; 95% CI, 1.3-2.6), smoking (OR, 1.43; 95% CI, 1.1-1.8) exhibited associations. Moreover, an increased burden of EPVS correlated with higher Fazekas scales in periventricular and deep white matter. Conclusion: The presence and severity of EPVS exhibited are associated with MRI markers of white matter hyperintensity and global cortical atrophy. EPVS may be a marker of an aging brain and cerebral small vessel diseases. It might be associated with cerebrovascular risk factors.

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

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

Связь кисты эпифиза с особенностями строения головного мозга и наличием определенных черт личности

Aim. To study the psychological characteristics and microstructural changes of the brain, according to magnetic resonance imaging (MRI), in healthy volunteers with pineal cyst to determine their clinical significance. Design. A prospective case — control study. Materials and methods. The study involved conditionally healthy volunteers. Based on brain MRI data, study groups were formed: the main group consisted of 33 people with pineal cyst, the control group consisted of 30 people with normal epiphysis structure. The groups were comparable in gender and age. We conducted neurological examination, experimental psychological and neuropsychological examinations, MRI of the brain with post-processing of the data obtained by MR Voxel-based morphometry, determination of the level of melatonin in saliva using enzyme immunoassay. Results. The results of experimental psychological and neuropsychological examinations have shown the tendency of people with a pineal cyst to be absorbed in worries, thoughts about something, as well as to express anxiety. According to MR Voxel-based morphometry, it was found that during cystic transformation of the pineal gland, there are zones of increased cortical thickness in both parietal lobes, each of which is located in the central sections of the postcentral gyrus on the left and the superior parietal gyrus on the right. Conclusion. Psychological features in the presence of a pineal cyst indicate the need to include an experimental psychological examination in the protocol of patient management to identify risk groups for affective disorders. The data obtained by us on microstructural changes in the cortex of the parietal lobes during cystic transformation of the epiphysis indicate the variability of the normal structure of the brain substance and require further clarification. Keywords: brain, pineal cyst, anxiety, depression, melatonin, magnetic resonance imaging, magnetic resonance voxel-based morphometry.

Discovery of High-Risk Clinical Factors That Accelerate Brain Aging in Adults: A Population-Based Machine Learning Study.

Introduction: Brain age prediction using neuroimaging data and machine learning algorithms holds significant promise for gaining insights into the development of neurodegenerative diseases. The estimation of brain age may be influenced not only by the imaging modality but also by multidomain clinical factors. However, the degree to which various clinical factors in individuals are associated with brain structure, as well as the comprehensive relationship between these factors and brain aging, is not yet clear. Methods: In this study, multimodal brain magnetic resonance imaging data and longitudinal clinical information were collected from 964 participants in a population-based cohort with 16 years of follow-up in northern China. We developed a machine learning-based algorithm to predict multimodal brain age and compared the estimated brain age gap (BAG) differences among the 5 groups characterized by varying exposures to these high-risk clinical factors. We then estimated modality-specific brain age in the hypertension group based on hypertension-related regional imaging metrics. Results: The results revealed a significantly larger BAG estimated from multimodal neuroimaging in subjects with 4 or 5 risk factors compared to other groups, suggesting an acceleration of brain aging under cumulative exposure to multiple risk factors. The estimated T1-based BAG exhibited a significantly higher level in the hypertensive subjects compared to the normotensive individuals. Conclusion: Our study provides valuable insights into a range of health factors across lifestyle, metabolism, and social context that are reflective of brain aging and also contributes to the advancement of interventions and public health initiatives targeted at the general population aimed at promoting brain health.

Ventral Anterior Cingulate Atrophy as a Predisposing Factor for Transient Global Amnesia.

This study aimed to evaluate the brain magnetic resonance imaging (MRI) of patients with acute transient global amnesia (TGA) using volumetric analysis to verify whether the brains of TGA patients have pre-existing structural abnormalities. We evaluated the brain MRI data from 87 TGA patients and 20 age- and sex-matched control subjects. We included brain MRIs obtained from TGA patients within 72 hours of symptom onset to verify the pre-existence of structural change. For voxel-based morphometric analyses, statistical parametric mapping was employed to analyze the structural differences between patients with TGA and control subjects. TGA patients exhibited significant volume reductions in the bilateral ventral anterior cingulate cortices (corrected p<0.05). TGA patients might have pre-existing structural changes in bilateral ventral anterior cingulate cortices prior to TGA attacks.

PDFF-CNN: An attention-guided dynamic multi-orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset.

Fetal brain magnetic resonance imaging (MRI)-based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brainmalformations. The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above-mentioned problems pose great challenges for deep learning-based fetal brain MRI gestational ageprediction. In this study, a pyramid squeeze attention (PSA)-guided dynamic feature fusion CNN (PDFF-CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF-CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi-orientation feature representations in a parallel weight-sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi-orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age predictionperformance. Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2-weighted MRI images from 157 subjects, PDFF-CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an of 0.904. Furthermore, the attention activation maps of PDFF-CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestationalstage. These results suggest that the proposed PDFF-CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.

Macular vessel density in the superficial plexus is not associated to cerebrovascular damage in non‐demented individuals: data from the NORFACE cohort

AbstractBackgroundOptical Coherence Tomography angiography (OCT‐A) is a novel tool that allows the detection of retinal vascular changes. The comparison of OCT‐A measures with brain vascular burden is essential to validate the former as a surrogate of cerebrovascular damage and biomarker of cognitive decline. We aimed to investigate the association of macular vessel density (VD) in the superficial plexus assessed by OCT‐A with measures of cerebrovascular pathology quantified by brain magnetic resonance imaging (MRI) in a cohort of non‐demented individuals.MethodClinical, demographical, APOE ε4 status, amyloid status, OCT‐A and brain MRI data from non‐demented participants of the FACEHBI and BIOFACE studies were included. We analyzed the association of macular VD in four quadrants (superior, nasal, inferior and temporal) with brain vascular burden using the Fazekas scale scores (dichotomized in two categories, 0‐1 vs 2‐3), assessed in a logistic regression analysis, and the volume of white matter hyperintensities (WMH) quantified by Freesurfer assessed in a multiple regression analysis. We additionally explored the associations of macular VD with hippocampal volume, ventricles volume and Alzheimer Disease cortical signature (ADCS) thickness assessed in a multiple regression analysis. All analyses were adjusted for age and dyslipidemia.ResultThe study cohort comprised 188 participants: 89 with subjective cognitive decline (SCD) and 99 with mild cognitive impairment (MCI). Logistic regression analysis showed no significant differences of regional macular VD between the two Fazekas groups (all, p&gt;0.05). Multiple regression analysis showed no significant association of regional macular VD with the volume of WMH (all, p&gt;0.05). Macular VD in the nasal and inferior quadrants were positively associated with hippocampal volume (p = 0.009 and p = 0.021, respectively), but no association was detected with ventricles volume or ADCS thickness (both p&gt;0.05).ConclusionOur study showed that retinal vascular measures (regional macular VD in the superficial vascular plexus) assessed by OCT‐A were not associated with brain vascular damage quantified by the Fazekas scale and the WMH burden n in a cohort of non‐demented research participants with SCD and MCI.

Genome‐Wide Mediation Analysis Reveals the Genetic Underpinnings of the Neuromorphometric Endophenotypes of Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease (AD) is a common neurodegenerative disorder. AD has high heritability, estimated to be 58%‐79% from twin studies. The strong genetic basis motivates the investigation of AD etiology. AD progression can be captured, in part, by brain volumetric variations, quantified by magnetic resonance imaging (MRI). Brain volumetric variations have been used to capture in vivo imaging biomarkers of AD. Advances in recent brain imaging genetics studies provide an opportunity to elucidate the neuropathogenesis of AD.MethodWe performed a genome‐wide mediation study (GWMS) investigating genetic effects on AD classification that are mediated by regional brain volumes. Specifically, we downloaded the genotyping and raw T1‐weighted brain MRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We extracted 145 regions of interest (ROIs) using a multi‐atlas parcellation method (MUSE) from voxel‐wise regional volumetric maps (RAVENS) generated from the MRI scans. We downloaded, integrated, aligned, imputed, and annotated the genotyping data from ADNI 1, GO, 2, and 3 studies. After quality control that excluded low‐quality SNP variants and genetically similar subjects, we performed a genome‐wide association study (GWAS) on 18,366,758 variants and 145 ROI‐level volumetric QTs with 1,382 individuals adjusted for age, sex, and population structure. We prioritized 12,733 significant SNP‐ROI GWAS signals and performed GWMS of AD on those prioritized SNPs with ROIs as mediators.ResultWe found that 35 unique SNPs significantly influenced AD classification mediated by 13 ROI‐level volumetric QTs after false discovery rate correction – in total 218 significant mediation signals. Among all the significant mediation signals, there were 214 partial mediations and 4 full mediations. Chromosome 19 showed the most significant mediation signals. The hippocampus and amygdala in both hemispheres had the most (n = 30) mediation signals, followed by the left entorhinal area with 28 signals (Figure 1).ConclusionOur GWMS of AD identified various SNP variants influencing AD classification via the mediation of ROI‐level volumetric QTs. Complementary to the traditional GWAS analyses, the GWMS analyses can help to explain the genetic underpinnings of the neuromorphometric endophenotypes of AD and provide valuable guidance for AD precision medicine.

Cholinergic white matter hyperintensity volume, cognitive decline, and incident dementia in older adults

AbstractBackgroundWhite matter hyperintensities (WMHs) are frequently observed in the elderly and their severity tends to increase with age. Previous studies have revealed that WMHs are closely related to cognitive dysfunction. However, as inter‐individual variations and a clinicoradiological discrepancy exist, not all individuals with severe WMH burden experience worse cognitive performance. Furthermore, the location of WMHs appears to be a critical factor in the relationship between WMHs and cognitive function. Few studies take both volume and location of white matter hyperintensities (WMHs) into account to explore the association between WMH burden and cognitive impairment. We therefore investigated associations of cholinergic WMH volume (WMHV) with global cognitive function, cognitive decline, and incident dementia in older adults.MethodA total of 752 older adults (mean age 60 years, 50% female) from the Taizhou Imaging Study were involved. Brain magnetic resonance imaging data at baseline and repeated measures of cognition over 5 years were collected and analyzed. WMHV in whole brain, the cholinergic pathways, and different tract classes in the Montreal Neurologic Institute standard space was obtained using the Lesion Segmentation Toolbox and tools from FMRIB Software Library. Multivariable linear regression, Cox regression, and partial correlation tests were performed.ResultOver a median 5.67 (IQR: 2.88‐6.84) years of follow‐up, cholinergic WMHV was associated with annual decline of Mini‐Mental State Examination (MMSE) (β = ‐0.206; P = 0.006), and incident dementia (HR = 3.45; 95%CI: 2.08‐5.74). Within the cholinergic pathways, WMHs in commissural fibers were related to incident dementia. However, greater global WMHV only slightly increased the risk of incident dementia (HR = 1.03; 95%CI: 1.01‐1.06). Neither global nor cholinergic WMHV was associated with MMSE in cross‐sectional analysis.ConclusionCholinergic WMHV is associated with longitudinal cognitive decline and incident dementia in older adults. Within the cholinergic pathways, commissural fiber tracts were strategic locations correlated to incident dementia. Our findings suggest cholinergic WMHV might be a potential indicator of cognitive deterioration.

Linking brain structure, cognition, and sleep: insights from clinical data.

To use relatively noisy routinely collected clinical data (brain magnetic resonance imaging (MRI) data, clinical polysomnography (PSG) recordings, and neuropsychological testing), to investigate hypothesis-driven and data-driven relationships between brain physiology, structure, and cognition. We analyzed data from patients with clinical PSG, brain MRI, and neuropsychological evaluations. SynthSeg, a neural network-based tool, provided high-quality segmentations despite noise. A priori hypotheses explored associations between brain function (measured by PSG) and brain structure (measured by MRI). Associations with cognitive scores and dementia status were studied. An exploratory data-driven approach investigated age-structure-physiology-cognition links. Six hundred and twenty-three patients with sleep PSG and brain MRI data were included in this study; 160 with cognitive evaluations. Three hundred and forty-two participants (55%) were female, and age interquartile range was 52 to 69 years. Thirty-six individuals were diagnosed with dementia, 71 with mild cognitive impairment, and 326 with major depression. One hundred and fifteen individuals were evaluated for insomnia and 138 participants had an apnea-hypopnea index equal to or greater than 15. Total PSG delta power correlated positively with frontal lobe/thalamic volumes, and sleep spindle density with thalamic volume. rapid eye movement (REM) duration and amygdala volume were positively associated with cognition. Patients with dementia showed significant differences in five brain structure volumes. REM duration, spindle, and slow-oscillation features had strong associations with cognition and brain structure volumes. PSG and MRI features in combination predicted chronological age (R2 = 0.67) and cognition (R2 = 0.40). Routine clinical data holds extended value in understanding and even clinically using brain-sleep-cognition relationships.

Deep learning‐based robust hybrid approaches for brain tumor classification in magnetic resonance images

AbstractThe classification of brain images is an immensely challenging task and one of the most valuable and frequently employed procedures in the field of medical diagnostics. Deep Learning (DL), which falls under the umbrella of artificial intelligence, has introduced novel techniques for automated analysis of medical images. The objective of this study was to develop two hybrid DL models for the segmentation and classification of brain magnetic resonance imaging (MRI) data. The first hybrid model combines fully convolutional networks (FCNs) and residual networks (ResNets) for both segmentation and classification purposes. The second model is a hybrid of SegNet for segmentation and MobileNet for classification. Rest assured, it will provide you with a dependable and promising performance. The analysis was conducted on a labeled dataset comprising images of glioma, meningioma, pituitary, and no‐tumor cases. Python was utilized to implement the aforementioned hybrid models. The proposed models achieved remarkable accuracies of 93.9% and 91.3%, respectively, when evaluated on a brain tumor MRI dataset. Additionally, a comprehensive evaluation of the proposed models was conducted, comparing them with other existing hybrid models in terms of metrics such as the dice score, Jaccard index, positive predictive value (PPV), false predictive value (FPV), testing time, precision, recall, specificity, and F1 score. The results demonstrated that the proposed DL technique can significantly assist doctors and radiologists in the early detection of brain tumor cells.

Segmentation of brain tissue using improved kernelized rough-fuzzy c-means technique

&lt;p&gt;&lt;span&gt;Brain magnetic resonance imaging (MRI) data is a hot topic in the domains of biomedical engineering and machine learning. Without locating anomalies, such as tumors and edema, radiologists and other medical experts cannot effectively recommend or administer therapy for patients. Having three different magnetic resonance techniques (T1 weighted, T2 weighted, and T3 weighted), MRI can produce detailed multimodal scans of different human brain tissues with varying contrast, which can help pinpoint the source of any abnormalities. The cerebrospinal fluid (CSF), white matter (WM), and grey matter (GM) are all components of the brain, and their boundaries are sometimes hazy and difficult to nail down. In light of the problems above, this paper makes an effort to tackle issues like: i) the noise that exists in the brain datasets for MRI, ii) the fuzziness, uncertainty, overlap, indiscernibility of complex brain tissue regions, iii) the inability of traditional unsupervised methods to reliably distinguish between various brain tissue locations, and iv) ineffective performance. We propose some robust techniques by utilise spatial contextual data, a rough set, a fuzzy set, and ultimately a fuzzy set to steer the clustering process in a better direction, allowing it to deal with likely noise, outliers, and other artifacts.&lt;/span&gt;&lt;/p&gt;

Association of Cerebral Oximetry With Brain Ischemic Lesions and Functional Outcomes in Arch Repair

BackgroundThis exploratory analysis of the randomized controlled Aortic Surgery Cerebral Protection Evaluation CardioLink-3 trial sought to determine if cerebral oximetry desaturation during elective proximal arch repair is associated with detrimental postoperative neuroradiologic and neurofunctional outcomes. MethodsCerebral oximetry and pre- and postoperative brain magnetic resonance imaging data from 101 participants were analyzed. Oximetry data from the trial allocation groups were compared; the relationships between cerebral oximetry indices and new ischemic cerebral lesions on magnetic resonance imaging and neurologic outcomes were also evaluated. ResultsTotal cerebral desaturation events (>20% decrease from baseline) on the left (median [interquartile range], 1 [1-3] vs 1.5 [0.5-3] with innominate and axillary cannulation; P = .80) were comparable to those on the right (1 [1-3] vs 1 [0-3]; P = .75) as were the total area under the curve of desaturation (left, P = .61; right, P = .84). Seventy patients had new ischemic lesions, among whom 36 had new severe lesions. Total desaturation events and area under the curve of desaturation were similar in patients with and without new ischemic lesions or severe lesions. The nadir regional cerebral saturation was lower on the left (49% [41-56]) than the right (53% [44-59]); left desaturation episodes were associated with lower postoperative cognitive test scores (P = .004). ConclusionsThe innominate and axillary cannulation techniques for elective proximal arch repair with unilateral antegrade cerebral perfusion were associated with similar occurrences of cerebral oximetry desaturation and neither were associated with new ischemic lesions.

Brain age prediction across the human lifespan using multimodal MRI data.

Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.