Altered cortical myelination based on gray-to-white matter signal intensity contrast in shift workers.

Predicting diagnosis and cognition with 18F-AV-1451 tau PET and structural MRI in Alzheimer's disease

Recurring exposure to head impacts in American football has garnered public and scientific attention, yet neurobiological associations in adolescent football players remain unclear. To examine cortical structure and neurophysiological characteristics in adolescent football players. This cohort study included adolescent football players and control athletes (swimming, cross country, and tennis) from 5 high school athletic programs, who were matched with age, sex (male), and school. Neuroimaging assessments were conducted May to July of the 2021 and 2022 seasons. Data were analyzed from February to November 2023. Playing tackle football or noncontact sports. Structural magnetic resonance imaging (MRI) data were analyzed for cortical thickness, sulcal depth, and gyrification, and cortical surface-based resting state (RS)-functional MRI analyses examined the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and RS-functional connectivity (RS-FC). Two-hundred seventy-five male participants (205 football players; mean [SD] age, 15.8 [1.2] years; 5 Asian [2.4%], 8 Black or African American [3.9%], and 189 White [92.2%]; 70 control participants; mean [SD] age 15.8 [1.2] years, 4 Asian [5.7], 1 Black or African American [1.4%], and 64 White [91.5%]) were included in this study. Relative to the control group, the football group showed significant cortical thinning, especially in fronto-occipital regions (eg, right precentral gyrus: t = -2.24; P = .01; left superior frontal gyrus: -2.42; P = .002). Elevated cortical thickness in football players was observed in the anterior and posterior cingulate cortex (eg, left posterior cingulate cortex: t = 2.28; P = .01; right caudal anterior cingulate cortex 3.01; P = .001). The football group had greater and deeper sulcal depth than the control groups in the cingulate cortex, precuneus, and precentral gyrus (eg, right inferior parietal lobule: t = 2.20; P = .004; right caudal anterior cingulate cortex: 4.30; P < .001). Significantly lower ALFF was detected in the frontal lobe and cingulate cortex of the football group (t = -3.66 to -4.92; P < .01), whereas elevated ALFF was observed in the occipital regions (calcarine and lingual gyrus, t = 3.20; P < .01). Similar to ALFF, football players exhibited lower ReHo in the precentral gyrus and medial aspects of the brain, such as precuneus, insula, and cingulum, whereas elevated ReHo was clustered in the occipitotemporal regions (t = 3.17; P < .001; to 4.32; P < .01). There was no group difference in RS-FC measures. In this study of adolescent athletes, there was evidence of discernible structural and physiological differences in the brains of adolescent football players compared with their noncontact controls. Many of the affected brain regions were associated with mental health well-being.

Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders. We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data. A systematic review and diagnostic meta-analysis. Sixty-seven studies with 6,285 patients were included. Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included. The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures. Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias. sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies. The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance. 1 TECHNICAL EFFICACY: Stage 2.

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (“Which change in voxels would change the outcome most?”), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (“Why does this person have AD?”) with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual “fingerprints” of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data.

Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

Working in shifts, especially if night shifts are included, is associated with many adverse health effects varying from gastrointestinal disturbances to cardiovascular diseases, metabolic syndrome, and type 2 diabetes (1-4). The exact mechanisms for these associations are not firmly established (5). Short-term sleep restriction is known to induce insulin resistance and, for example, disturbances in fat metabolism (6), and shift workers are known to suffer from sleep loss. It is plausible that lifestyle factors related to shift work play a role as well. The accumulation of body fat leading to overweight and obesity is one of the key factors behind the development of metabolic disturbances in general population, and we can assume it to be so also among shift workers (7). There seems to be even a dose-response association, with an increasing trend in weight with increasing duration of shift work (8). But what exactly is it in shift work that predisposes those who do it to weight gain? And can we do something about it?In their systematic review of longitudinal studies, Van Drongelen et al (9) found a strong evidence for a crude association between shift work exposure and body weight increase; however the confounder-adjusted associations were found to be inconclusive. This indicates that it is not shift work per se but other factors related to or associated with shift work that lead to body fat accumulation.On the other hand, in a recent systematic review, Bonham et al (10) concluded that energy intake does not differ between day and shift workers, and suggest that other factors such as circadian misalignment, meal timing, food choice and diurnal variation of energy metabolism at night may be responsible for the increased rates of obesity observed among shift workers. In general, only minor differences in nutrient intake between day and shift workers have been identified, and the results have been somewhat conflicting. It has been suggested that shift workers have more eating events per day than day workers (11), and it seems that shift workers make more unfavorable food choices than day workers do, preferring snack foods and sugary drinks (11, 12). In a recent study, shift working men were less likely to consume vegetables and fruits daily and women had higher intake of saturated fat compared with day workers (13).In a paper by Hulsegge et al (14) in this issue, shift workers were found to have a higher energy intake than day workers (56 kcal/d) and a slightly higher consumption of grains, dairy products, meat and fish and lower consumption of cakes and biscuits. No difference in savory snacks, sweets, soft drinks or juices were found. The difference in energy intake was largest for shift workers with >5 night shifts/month, who consumed 103 kcal/d more than day workers. The authors conclude that shift and day workers' dietary quality is the same, but suggest that a higher energy intake among shift workers may be one of the causes of shift-work-induced overweight, obesity, and other adverse health outcomes; a reasonable suggestion if we acknowledge as fact that overweight indeed develops as a result of positive energy balance.The study included a relatively large population-based sample of 683 and 7173 shift and day workers, respectively from the original cohort of 40 010 men and women. The analyses were cross-sectional, with dietary data collection based on a 178-item semi-quantitative food frequency questionnaire for the past year diet and retrospective data collection on work schedule 14-20 years earlier. The analyses were adjusted for several confounders, including age, sex, smoking, physical activity, and body mass index (BMI). No differences were presented for clinical risk factors, except that shift workers had slightly higher BMI compared with day workers. BMI is a crude measure of body adiposity, but does not tell anything about the actual body size, which is the most important predictor of energy expenditure (15). …

Background: Congenital amusia (CA) is a rare disorder characterized by deficits in pitch perception, and many structural and functional magnetic resonance imaging studies have been conducted to better understand its neural bases. However, a structural magnetic resonance imaging analysis using a surface-based morphology method to identify regions with cortical features abnormalities at the vertex-based level has not yet been performed.Methods: Fifteen participants with CA and 13 healthy controls underwent structural magnetic resonance imaging. A surface-based morphology method was used to identify anatomical abnormalities. Then, the surface parameters' mean value of the identified clusters with statistically significant between-group differences were extracted and compared. Finally, Pearson's correlation analysis was used to assess the correlation between the Montreal Battery of Evaluation of Amusia (MBEA) scores and surface parameters.Results: The CA group had significantly lower MBEA scores than the healthy controls (p = 0.000). The CA group exhibited a significant higher fractal dimension in the right caudal middle frontal gyrus and a lower sulcal depth in the right pars triangularis gyrus (p < 0.05; false discovery rate-corrected at the cluster level) compared to healthy controls. There were negative correlations between the mean fractal dimension values in the right caudal middle frontal gyrus and MBEA score, including the mean MBEA score (r = −0.5398, p = 0.0030), scale score (r = −0.5712, p = 0.0015), contour score (r = −0.4662, p = 0.0124), interval score (r = −0.4564, p = 0.0146), rhythmic score (r = −0.5133, p = 0.0052), meter score (r = −0.3937, p = 0.0382), and memory score (r = −0.3879, p = 0.0414). There was a significant positive correlation between the mean sulcal depth in the right pars triangularis gyrus and the MBEA score, including the mean score (r = 0.5130, p = 0.0052), scale score (r = 0.5328, p = 0.0035), interval score (r = 0.4059, p = 0.0321), rhythmic score (r = 0.5733, p = 0.0014), meter score (r = 0.5061, p = 0.0060), and memory score (r = 0.4001, p = 0.0349).Conclusion: Individuals with CA exhibit cortical morphological changes in the right hemisphere. These findings may indicate that the neural basis of speech perception and memory impairments in individuals with CA is associated with abnormalities in the right pars triangularis gyrus and middle frontal gyrus, and that these cortical abnormalities may be a neural marker of CA.

Objective To explore the longitudinally changed patterns in the structure of mild cognitive impairment (MCI) due to Alzheimer's disease (AD) during the process of converting to AD dementia and further investigate the structural features at the baseline of MCI converters with the purpose of finding the potential structure l biomarkers which may predict the conversion in a short term. Methods Twenty-seven patients with MCI, collected from September 2009 to March 2011, and 31 normal controls (NC) were enrolled in this study; neuropsychological assessment and structural magnetic resonance imaging (sMRI) data were acquired respectively at baseline and follow-up. Voxel-based morphometry (VBM) was used to calculate the whole brain gray matter volume. According to the follow up results, patients were divided into MCI converters group (MCI-c, n=16) and MCI non-converters group (MCI-nc, n=11); the trajectory of longitudinal sMRI among the three groups was compared and the correlation between sMRI and neuropsychological assessment was analyzed. Results As compared with MCI-nc group, there was obvious brain atrophy in the bilateral ventromedial prefrontal cortices, bilateral middle frontal gyri and bilateral superior temporal gyri in the MCI-c group. In the comparison of trajectory of sMRI longitudinal changes in the three groups, the degree of brain atrophy in NC group was much lower than the other two groups, and there was no significant difference between MCI-c and MCI-nc group. There were positive correlations between scores of mini-mental state examination and Montreal cognitive assessment and decreased volume of gray matter in the medial temporal lobe, hippocampus, parahippocampal gyrus, precuneus, inferior parietal lobule, lateral temporal lobe and dorsolateral frontal lobe. Conclusions No essential difference in the pattern of encephalatrophy is noted between MCI-c and MCI-nc groups. The decline of brain gray matter volume in medial temporal lobe, hippocampus, parahippocampal gyrus, precuneus and inferior parietal lobule may be one of the potential structural biomarkers which will predict the conversion of MCI to AD dementia. Key words: Alzheimer's disease; Mild cognitive impairment; Longitudinal study; Magnetic resonance imaging

Attention deficit hyperactivity disorder (ADHD) is a common mental-health disorder in adolescent groups. Successful automatic diagnosis of ADHD based on features extracted from magnetic resonance imaging (MRI) data, would provide reference information for treating. Previous researches have shown gray matter (GM) of some anatomical brain structures will increase in ADHD subjects. Fractal analysis has been widely used in texture image processing and fractal dimension is capable of representing intrinsic structural information of images. With large-scale MRI data becoming publicly available, deep-learning methods for ADHD diagnosis become feasible. This paper proposes a novel classification approach using 3D fractal dimension complexity map (FDCM) for ADHD automatic diagnosis. We calculate the Hausdorff fractal dimension of GM density data extracted from structural MRI data. Subsequently, we design a 3 dimensional convolutional neural network (3D-CNN) for extracting features from FDCM then judging ADHD and TDC. Our model is evaluated on the hold-out testing data of the ADHD-200 global competition and performance outperforms previous approaches based on structural MRI data.

ObjectiveEmerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS.MethodsWe measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks.ResultsThe cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models.InterpretationOur findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.

BackgroundShift work is increasingly common and associated with numerous adverse health effects. Although studies show that shift work affects brain structure and neurological stress, its direct impact on brain aging remains unclear. Therefore, this study aims to investigate the association between shift work and brain aging using the brain age gap (BAG)—a neuroimaging biomarker calculated by comparing predicted brain age derived from structural magnetic resonance imaging (MRI) scans to chronological age.MethodsStructural MRI data (T1-weighted and T2-weighted) were collected from 113 healthcare workers, including 33 shift workers and 80 fixed daytime workers. Brain age was estimated using seven validated machine learning models. BAG was calculated as the difference between predicted brain age and chronological age. Statistical analyses, including ANCOVA, adjusted for chronological age, sex, intracranial volume (ICV), education level, and occupational type.ResultsThe association between BAG and shift work duration was also evaluated. Model performance varied (maximum R2 = 0.79) and showed systematic age-related bias, typically underestimating brain age in older participants. Unadjusted analyses initially indicated lower BAG values in younger shift workers. However, after covariate adjustments, shift workers consistently exhibited significantly higher BAG values, suggesting accelerated brain aging. Two models retained statistical significance despite adjustment for potential confounders. Longer shift work duration correlated with a decreasing BAG trend, suggesting potential neuroadaptive changes or selective retention of resilient workers.ConclusionThese findings demonstrate that shift work is associated with accelerated apparent brain aging, even after controlling for systematic model bias and demographic covariates. The observed reduction in BAG with extended shift work exposure may reflect adaptive or selective effects, emphasizing the need for longitudinal studies to clarify these mechanisms. This research highlights the importance of incorporating occupational exposures in neuroimaging and brain health investigations.

Cortical thinning across Parkinson's disease stages and clinical correlates

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.

Identifying major depressive disorder based on cerebral blood flow and brain structure: An explainable multimodal learning study.

BackgroundSchizophrenia (SCZ) is a severe mental illness that causes a substantial burden for individuals, families, and society. Early identification and intervention are crucial for improving patient outcomes. Prior to the onset of psychotic disorders, such as SCZ, individuals may go through a prodromal phase, known as clinical high risk for psychosis (CHR). Among those identified as CHR, the subtype termed attenuated psychosis syndrome (APS) is particularly significant, as it is associated with an increased risk of progression to psychosis and is commonly encountered in clinical practice. Although previous studies had reported structural brain differences in APS patients, no wide consensus has been reached.Aims & ObjectivesThis study aimed to elucidate structural brain differences among APS, SCZ, and healthy controls (HCs) using structural magnetic resonance imaging (MRI) data, thereby advancing our understanding of the neurophysiological mechanisms underlying in APS.MethodA total of 39 HCs, 38 APS, and 35 SCZ patients were finally included. T1-weighted MRI data were processed using SPM12 and CAT12 to quantify cortical thickness and gray matter volume (GMV) among the three groups were evaluated using analysis of covariance (ANCOVA), in which age, gender, years of education were regarded as covariates. The analysis was conducted using the Desikan-Killiany 40 template and the third version of the Automated Anatomical Labeling atlas. Voxel-level significance was set at P < 0.001, and False Discovery Rate correction was applied, yielding a cluster-level significance threshold of P < 0.05.ResultsCompared with HCs group, the APS and SCZ groups demonstrated a reduction in the cortical thickness in the left lateral occipital lobe. Compared with SCZ group, the APS and HCs groups exhibited a significant increase in the cortical thickness in some regions of the frontal and parietal lobes. Correlation analysis revealed that in the APS group, the thickness of the left triangular part of the inferior frontal gyrus and the right supramarginal gyrus exhibited significant negative correlations with reasoning and problem-solving ability scores. In the SCZ group, the cortical thickness of the left cuneus, inferior parietal lobule, and superior parietal lobule exhibited positive correlations with information processing speed scores. Additionally, compared with the SCZ group, the GMV in the frontal and insular regions was significantly increased in the APS and HCs groups.Discussion & ConclusionsThis study revealed significant differences in cortical thickness and GMV across specific brain regions among the HCs, APS, and SCZ patients, which may reflect underlying pathophysiological mechanisms of SCZ. Specifically, the reduced cortical thickness in the left occipital lobe may be associated with individual susceptibility to SCZ, while thinning cortical thickness in the frontal and parietal lobes, along with reduced GMV in the frontal and insular lobes, may be linked to the disease state of SCZ. The more widespread and severe damage to cortical thickness and GMV in SCZ patients compared with the APS, suggested a gradient pattern of brain structural impairment between the APS and SCZ patients.