Search Results (16)

Background: The ability of Graph Neural Networks (GNNs) to analyse brain structural patterns in various kinds of neurodegenerative diseases, including Parkinson’s disease (PD), has drawn a lot of interest recently. One emerging technique in this field is brain age prediction, which estimates biological age to identify ageing patterns that may serve as biomarkers for such disorders. However, a significant problem with most of the GNNs is their depth, which can lead to issues like oversmoothing and diminishing gradients. Methods: In this study, we propose SAGEFusionNet, a GNN architecture specifically designed to enhance brain age prediction and assess PD-related brain ageing patterns using T1-weighted structural MRI (sMRI). SAGEFusionNet learns important ROIs for brain age prediction by incorporating ROI-aware pooling at every layer to overcome the above challenges. Additionally, it incorporates multi-layer feature fusion to capture multi-scale structural information across the network hierarchy and auxiliary supervision to enhance gradient flow and feature learning at multiple depths. The dataset utilised in this study was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. It included a total of 580 T1-weighted sMRI scans from healthy individuals. The brain sMRI scans were parcellated into 56 regions of interest (ROIs) using the LPBA40 brain atlas in CAT12. The anatomical graph was constructed based on grey matter (GM) volume features. This graph served as input to the GNN models, along with GM and white matter (WM) volume as node features. All models were trained using 5-fold cross-validation to predict brain age and subsequently tested for performance evaluation. Results: The proposed framework achieved a mean absolute error (MAE) of $4.24\pm 0.38$ years and a mean Pearson’s Correlation Coefficient (PCC) of $0.72\pm 0.03$ during cross-validation. We also used 215 PD patient scans from the Parkinson’s Progression Markers Initiative (PPMI) database to assess the model’s performance and validate it. The initial findings revealed that out of 215 individuals with Parkinson’s disease, 213 showed higher and 2 showed lower predicted brain ages than their actual ages, with a mean MAE of 13.36 years (95% confidence interval: 12.51–14.28). Conclusions: These results suggest that brain age prediction using the proposed method may provide important insights into neurodegenerative diseases. Full article

Background: The pathophysiology of Alzheimer’s disease (AD) may begin developing years or even decades prior to the manifestation of its first symptoms. The APOE ε4 genotype is a prominent genetic risk for AD that has been found to be associated with brain changes across the lifespan since early adulthood. Thus, studying brain changes that may occur in young adults with an APOE ε4 status is highly relevant. Objective: Examine potential differences in grey matter (GM) and functional connectivity (FC) in brains of cognitively healthy young APOE ε4 carriers and non-carriers, denoted here as ε4(+) and ε4(−), respectively. Methods: Three Tesla magnetic resonance imaging (MRI) brain scans were acquired from cognitively healthy young participants aged approximately 20 years (n = 151). Voxel-based morphometry (VBM) analysis was employed to identify potential structural differences in GM between ε4(+) and ε4(−). In a subsequent seed-based connectivity (SBC) analysis, brain regions that structurally differed in the VBM analysis were considered as seeds and correlated with all the remaining voxels across the brains to then measure the differences in FC between groups. Results: The VBM analysis suggested that ε4(+) (n = 28) had greater GM densities relative to ε4(−) (n = 123) in the left hippocampus and the left posterior insula (p_uncorr < 0.001). However, the effect did not survive the correction for multiple comparisons, suggesting minimal structural differences in this age range. In contrast, the SBC analysis indicated that ε4(+) exhibited significantly decreased FC between the left hippocampus and areas of the left middle temporal gyrus (n = 27) compared to ε4(−) (n = 102). These results remained significant after multiple comparisons (p_FDR < 0.05). Lastly, no statistically significant differences in FC between groups were observed for the left insular seed (p_FDR > 0.05). Discussion: These results suggest early structural and functional brain changes associated with the APOE ε4 genotype on young adults. Yet, they must be cautiously interpreted and contrasted with both older adults with genetic risk for AD and patients diagnosed with AD. Full article

Major depressive disorder (MDD) is a prevalent mental illness globally, yet its etiology remains largely elusive. Recent interest in the scientific community has focused on the correlation between the disruption of iron homeostasis and MDD. Prior studies have revealed anomalous levels of iron in both peripheral blood and the brain of MDD patients; however, these findings are not consistent. This study involved 95 MDD patients aged 18–35 and 66 sex- and age-matched healthy controls (HCs) who underwent 3D-T1 and quantitative susceptibility mapping (QSM) sequence scans to assess grey matter volume (GMV) and brain iron concentration, respectively. Plasma ferritin (pF) levels were measured in a subset of 49 MDD individuals and 41 HCs using the Enzyme-linked immunosorbent assay (ELISA), whose blood data were simultaneously collected. We hypothesize that morphological brain changes in MDD patients are related to abnormal regulation of iron levels in the brain and periphery. Multimodal canonical correlation analysis plus joint independent component analysis (MCCA+jICA) algorithm was mainly used to investigate the covariation patterns between the brain iron concentration and GMV. The results of “MCCA+jICA” showed that the QSM values in bilateral globus pallidus and caudate nucleus of MDD patients were lower than HCs. While in the bilateral thalamus and putamen, the QSM values in MDD patients were higher than in HCs. The GMV values of these brain regions showed a significant positive correlation with QSM. The GMV values of bilateral putamen were found to be increased in MDD patients compared with HCs. A small portion of the thalamus showed reduced GMV values in MDD patients compared to HCs. Furthermore, the region of interest (ROI)-based comparison results in the basal ganglia structures align with the outcomes obtained from the “MCCA+jICA” analysis. The ELISA results indicated that the levels of pF in MDD patients were higher than those in HCs. Correlation analysis revealed that the increase in pF was positively correlated with the iron content in the left thalamus. Finally, the covariation patterns obtained from “MCCA+jICA” analysis as classification features effectively differentiated MDD patients from HCs in the support vector machine (SVM) model. Our findings indicate that elevated peripheral ferritin in MDD patients may disrupt the normal metabolism of iron in the brain, leading to abnormal changes in brain iron levels and GMV. Full article

With people living with HIV (PLWH) reaching the senium, the importance of aging-related comorbidities such as metabolic syndrome (MS) becomes increasingly important. This study aimed to determine the additive effect of MS on brain atrophy in PLWH. This prospective study included 43 PLWH, average age of 43.02 ± 10.93 years, and 24 healthy controls, average age of 36.87 ± 8.89 years. PLWH were divided into two subgroups: without MS and with MS, according to NCEP ATP III criteria. All patients underwent brain magnetic resonance imaging (MRI) on a 3T clinical scanner with MR volumetry, used for defining volumes of cerebrospinal fluid (CSF) spaces and white and grey matter structures, including basal ganglia. A Student’s t-test was used to determine differences in brain volumes between subject subgroups. The binary classification was performed to determine the sensitivity and specificity of volumetry findings and cut-off values. Statistical significance was set at p < 0.05. PLWH presented with significantly lower volumes of gray matter, putamen, thalamus, globus pallidus, and nc. accumbens compared to healthy controls; cut-off values were: for gray matter 738.130 cm³, putamen 8.535 cm³, thalamus 11.895 cm³, globus pallidus 2.252 cm³, and nc. accumbens 0.715 cm³. The volumes of CSF and left lateral ventricles were found to be higher in PLWH with MS compared to those without MS, where, with a specificity of 0.310 and sensitivity of 0.714, it can be assumed that PLWH with a CSF volume exceeding 212.83 cm³ are likely to also have MS. This suggests that PLWH with metabolic syndrome may exhibit increased CSF volume above 212.83 cm³ as a consequence of brain atrophy. There seems to be an important connection between MS and brain volume reduction in PLWH with MS, which may add to the accurate identification of persons at risk of developing HIV-associated cognitive impairment. Full article

Age-related myelination decrease is considered one of the likely mechanisms of cognitive decline. The present preliminary study is based on the longitudinal assessment of global and regional myelination of the normal adult human brain using fast macromolecular fraction (MPF) mapping. Additional markers were age-related changes in white matter (WM) hyperintensities on FLAIR-MRI and the levels of anti-myelin autoantibodies in serum. Eleven healthy subjects (33–60 years in the first study) were scanned twice, seven years apart. An age-related decrease in MPF was found in global WM, grey matter (GM), and mixed WM–GM, as well as in 48 out of 82 examined WM and GM regions. The greatest decrease in MPF was observed for the frontal WM (2–5%), genu of the corpus callosum (CC) (4.0%), and caudate nucleus (5.9%). The age-related decrease in MPF significantly correlated with an increase in the level of antibodies against myelin basic protein (MBP) in serum (r = 0.69 and r = 0.63 for global WM and mixed WM–GM, correspondingly). The volume of FLAIR hyperintensities increased with age but did not correlate with MPF changes and the levels of anti-myelin antibodies. MPF mapping showed high sensitivity to age-related changes in brain myelination, providing the feasibility of this method in clinics. Full article

A brain tumor in the left hemisphere can decrease language laterality as assessed through fMRI. However, it remains unclear whether or not this decreased language laterality is associated with a structural reshaping of the grey matter, particularly within the language network. Here, we examine if the disruption of the language hubs exclusively affects the macrostructural properties of the contralateral homologues or whether it affects both hemispheres. This study uses voxel-based morphometry applied to high-resolution MR T1-weighted MPRAGE images from 31 adult patients’ left hemisphere, which is dominant for language. Eighteen patients had brain tumors in the left hemisphere, and thirteen had tumors in the right hemisphere. A cohort of 71 healthy individuals matched with respect to age and sex was used as a baseline. We defined 10 ROIs per hemisphere involved in language function. Two separate repeated-measure ANOVAs were conducted with the volume per region as the dependent variable. For the patients, tumor lateralization (right versus left) served as a between-subject factor. The current study demonstrated that the presence of a brain tumor generates global volumetric changes affecting the left language regions and their contralateral homologues. These changes are mediated by the lateralization of the lesion. Our findings suggest that functional mechanisms are supported by the rearrangement of the grey matter. Full article

Cohort studies that quantify volumetric brain data among individuals with different levels of COVID-19 severity are presently limited. It is still uncertain whether there exists a potential correlation between disease severity and the effects of COVID-19 on brain integrity. Our objective was to assess the potential impact of COVID-19 on measured brain volume in patients with asymptomatic/mild and severe disease after recovery from infection, compared with healthy controls, using artificial intelligence (AI)-based MRI volumetry. A total of 155 participants were prospectively enrolled in this IRB-approved analysis of three cohorts with a mild course of COVID-19 (n = 51, MILD), a severe hospitalised course (n = 48, SEV), and healthy controls (n = 56, CTL) all undergoing a standardised MRI protocol of the brain. Automated AI-based determination of various brain volumes in mL and calculation of normalised percentiles of brain volume was performed with mdbrain software, using a 3D T1-weighted magnetisation-prepared rapid gradient echo (MPRAGE) sequence. The automatically measured brain volumes and percentiles were analysed for differences between groups. The estimated influence of COVID-19 and demographic/clinical variables on brain volume was determined using multivariate analysis. There were statistically significant differences in measured brain volumes and percentiles of various brain regions among groups, even after the exclusion of patients undergoing intensive care, with significant volume reductions in COVID-19 patients, which increased with disease severity (SEV > MILD > CTL) and mainly affected the supratentorial grey matter, frontal and parietal lobes, and right thalamus. Severe COVID-19 infection, in addition to established demographic parameters such as age and sex, was a significant predictor of brain volume loss upon multivariate analysis. In conclusion, neocortical brain degeneration was detected in patients who had recovered from SARS-CoV-2 infection compared to healthy controls, worsening with greater initial COVID-19 severity and mainly affecting the fronto-parietal brain and right thalamus, regardless of ICU treatment. This suggests a direct link between COVID-19 infection and subsequent brain atrophy, which may have major implications for clinical management and future cognitive rehabilitation strategies. Full article

Background and Objectives: Subcortical grey matter structures play essential roles in cognitive, affective, social, and motoric functions in humans. Their volume changes with age, and decreased volumes have been linked with many neuropsychiatric disorders. The aim of our study was to examine the heritability of six subcortical brain volumes (the amygdala, caudate nucleus, pallidum, putamen, thalamus, and nucleus accumbens) and four general brain volumes (the total intra-cranial volume and the grey matter, white matter, and cerebrospinal fluid (CSF) volume) in twins. Materials and Methods: A total of 118 healthy adult twins from the Hungarian Twin Registry (86 monozygotic and 32 dizygotic; median age 50 ± 27 years) underwent brain magnetic resonance imaging. Two automated volumetry pipelines, Computational Anatomy Toolbox 12 (CAT12) and volBrain, were used to calculate the subcortical and general brain volumes from three-dimensional T1-weighted images. Age- and sex-adjusted monozygotic and dizygotic intra-pair correlations were calculated, and the univariate ACE model was applied. Pearson’s correlation test was used to compare the results obtained by the two pipelines. Results: The age- and sex-adjusted heritability estimates, using CAT12 for the amygdala, caudate nucleus, pallidum, putamen, and nucleus accumbens, were between 0.75 and 0.95. The thalamus volume was more strongly influenced by common environmental factors (C = 0.45−0.73). The heritability estimates, using volBrain, were between 0.69 and 0.92 for the nucleus accumbens, pallidum, putamen, right amygdala, and caudate nucleus. The left amygdala and thalamus were more strongly influenced by common environmental factors (C = 0.72−0.85). A strong correlation between CAT12 and volBrain (r = 0.74−0.94) was obtained for all volumes. Conclusions: The majority of examined subcortical volumes appeared to be strongly heritable. The thalamus was more strongly influenced by common environmental factors when investigated with both segmentation methods. Our results underline the importance of identifying the relevant genes responsible for variations in the subcortical structure volume and associated diseases. Full article

Occupational burnout, manifested by emotional exhaustion, lack of a sense of personal achievement, and depersonalization, is not a new phenomenon, but thusfar, there is no clear definition or diagnostic guidelines. The aim of this article wasto summarize all empirical studies to date that have used medical neuroimaging techniques to provide evidence or links regarding changes in brain function in occupational burnout syndrome from a neuroscientific perspective, and then use these to propose a fuzzy-based computational model of burnout.A comprehensive literature search was conducted in two major databases (PubMed and Medline Complete). The search period was 2006–2021, and searches were limited to the English language. Each article was carefully reviewed and appropriately selected on the basis of raw data, validity of methods used, clarity of results, and scales for measuring burnout. The results showed that the brain structures of patients with job burnout that are associated with emotion, motivation, and empathy weresignificantly different from healthy controls. These altered brain regions included the thalamus, hippocampus, amygdala, caudate, striatum, dorso-lateral prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, anterior insula, inferior frontal cingulate cortex, middle frontal cingulate cortex, temporoparietal junction, and grey matter. Deepening our understanding of how these brain structures are related to burnout will pave the way for better approaches fordiagnosis and intervention. As an alternative to the neuroimaging approach, the paper presents a late proposal of the PLUS (personal living usual satisfaction) parameter. It is based on a fuzzy model, wherein the data source is psychological factors—the same or similar to the neuroimaging approach. As the novel approach to searching for neural burnout mechanisms, we have shown that computational models, including those based on fuzzy logic and artificial neural networks, can play an important role in inferring and predicting burnout. Effective computational models of burnout are possible but need further development to ensure accuracy across different populations. There is also a need to identify mechanisms and clinical indicators of chronic fatigue syndrome, stress, burnout, and natural cognitive changes associated with, for example, ageing, in order to introduce more effective differential diagnosis and screening. Full article

In this study, we used magnetic resonance imaging (MRI) to identify the different brain phenotypes within apparently healthy children and to evaluate whether these phenotypes had different prenatal characteristics. We included 65 healthy children (mean age, 10 years old) with normal neurological examinations and without structural abnormalities. We performed cluster analyses to identify the different brain phenotypes in the brain MRI images. We performed descriptive analyses, including demographic and perinatal characteristics, to assess the differences between the clusters. We identified two clusters: Cluster 1, or the “small brain phenotype” (n = 44), which was characterized by a global reduction in the brain volumes, with smaller total intracranial volumes (1044.53 ± 68.37 vs. 1200.87 ± 65.92 cm³ (p < 0.001)), total grey-matter volumes (644.65 ± 38.85 vs. 746.79 ± 39.37 cm³ (p < 0.001)), and total white-matter volumes (383.68 ± 40.17 vs. 443.55 ± 36.27 cm³ (p < 0.001)), compared with Cluster 2, or the “normal brain phenotype” (n = 21). Moreover, almost all the brain areas had decreased volumes, except for the ventricles, caudate nuclei, and pallidum areas. The risk of belonging to “the small phenotype” was 82% if the child was preterm, 76% if he/she was born small for his/her gestational age and up to 80% if the mother smoked during the pregnancy. However, preterm birth appears to be the only substantially significant risk factor associated with decreased brain volumes. Full article

Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI < 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippocampal volume (beta −0.16 cm³, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm³, 95% CI −7.68, −1.01) in participants aged ≥60 years but not <60 years. Healthy lifestyle was associated with higher total brain, grey matter and hippocampal volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk. Full article

The aim of the study was to evaluate changes in the central visual pathways during the early and advanced stages of bilateral normal-tension glaucoma (NTG). Methods: The studied groups constituted patients with bilateral normal-tension glaucoma of the same stage (n = 45) and age-matched healthy volunteers (n = 17). All patients underwent ophthalmic examination and examination on a 1.5 Tesla Magnetic Resonance Scanner (Optima 360, GE Healthcare). Volume and cortical thickness analyses were performed using the open-source automated software package FreeSurfer. Results: There was a significant difference in lateral geniculate nuclei volume between the control and advanced glaucoma groups in the right hemisphere (p = 0.03) and in the left hemisphere between the early and advanced glaucoma patients (p = 0.026). The optic chiasm volume differed significantly between the control and advanced NTG groups (p = 0.0003) and between early and advanced glaucoma patients (p = 0.004). Mean cortical thickness analysis revealed a significant increase in values in the advanced glaucoma group in the right Brodmann area 17 (BA17) (p = 0.007) and right BA18 (p = 0.049) as compared to early NTG. In the left BA18 area, the mean thickness of the cortex in the early glaucoma group was significantly lower than in the control group (p = 0.03). Conclusions: The increase in the grey matter thickness in the V1 region with more-advanced glaucoma stages may reflect compensatory hypertrophy. Additionally, the regions of the brain early affected during glaucoma with reduced thickness were the right lateral occipital gyrus and left lingual gyrus. The most prominent change during the course of glaucoma was the increase in grey matter thickness in the right cuneus. Full article

Evidence suggests that oral intake of medium-chain triglycerides (MCTs), which promote the production of ketone bodies, may improve cognitive functions in elderly people; however, the underlying brain mechanisms remain elusive. We tested the hypothesis that cognitive improvement accompanies physiological changes in the brain and reflects the use of ketone bodies as an extra energy source. To this end, by using functional magnetic resonance imaging, cerebral blood oxygenation level-dependent (BOLD) signals were measured while 20 healthy elderly subjects (14 females and 6 males; mean age: 65.7 ± 3.9 years) were engaged in executive function tasks (N-back and Go-Nogo) after ingesting a single MCT meal (Ketonformula^®) or placebo meal in a randomized, double-blind placebo-controlled design (UMIN000031539). Morphological characteristics of the brain were also examined in relation to the effects of an MCT meal. The MCT meal improved N-back task performance, and this was prominent in subjects who had reduced grey matter volume in the dorsolateral prefrontal cortex (DLPFC), a region known to promote executive functions. When the participants were dichotomized into high/low level groups of global cognitive function at baseline, the high group showed improved N-back task performance, while the low group showed improved Go-Nogo task performance. This was accompanied by decreased BOLD signals in the DLPFC, indicative of the consumption of ketone bodies as an extra energy source. Full article

The purpose of this study is to investigate possible differences in brain structure, as measured by T1-weighted MRI, between patients with systemic lupus erythematosus (SLE) and healthy controls (HC), and whether any observed differences were in turn more severe in SLE patients with neuropsychiatric manifestations (NPSLE) than those without (non-NPSLE). Structural T1-weighted MRI was performed on 69 female SLE patients (mean age = 35.8 years, range = 18–51 years) and 24 age-matched female HC (mean age = 36.8 years, range = 23–52 years) in conjunction with neuropsychological assessment using the CNS Vital Signs test battery. T1-weighted images were preprocessed and analyzed by FSL-VBM. The results show that SLE patients had lower grey matter probability values than the control group in the VIIIa of the cerebellum bilaterally, a region that has previously been implied in sensorimotor processing in human and non-human primates. No structural differences for this region were found between NPSLE and non-NPSLE patients. VBM values from the VIIIa region showed a weak positive correlation with the psychomotor speed domain from CNS Vital Signs (p = 0.05, r = 0.21), which is in line with its presumed role as a sensorimotor processing area. Full article

(1) Purpose: Quantitative magnetic resonance imaging (qMRI) measurements can be used to sensitively estimate brain morphological alterations and may support clinical diagnosis of neurodegenerative diseases (ND). We aimed to establish a normative reference database for a clinical applicable quantitative MR morphologic measurement on neurodegenerative changes in patients; (2) Methods: Healthy subjects (HCs, n = 120) with an evenly distribution between 21 to 70 years and amyotrophic lateral sclerosis (ALS) patients (n = 11, mean age = 52.45 ± 6.80 years), as an example of ND patients, underwent magnetic resonance imaging (MRI) examinations under routine diagnostic conditions. Regional cortical thickness (rCTh) in 68 regions of interest (ROIs) and subcortical grey matter volume (SGMV) in 14 ROIs were determined from all subjects by using Computational Anatomy Toolbox. Those derived from HCs were analyzed to determine age-related differences and subsequently used as reference to estimate ALS-related alterations; (3) Results: In HCs, the rCTh (in 49/68 regions) and the SGMV (in 9/14 regions) in elderly subjects were less than those in younger subjects and exhibited negative linear correlations to age (p < 0.0007 for rCTh and p < 0.004 for SGMV). In comparison to age- and sex-matched HCs, the ALS patients revealed significant decreases of rCTh in eight ROIs, majorly located in frontal and temporal lobes; (4) Conclusion: The present study proves an overall grey matter decline with normal ageing as reported previously. The provided reference may be used for detection of grey matter alterations in neurodegenerative diseases that are not apparent in standard MR scans, indicating the potential of using qMRI as an add-on diagnostic tool in a clinical setting. Full article