Search

Background: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative condition that increasingly impairs cognitive functions and daily activities. Given the incurable nature of AD and its profound impact on the elderly, early diagnosis (at the mild cognitive impairment (MCI) stage) and intervention are crucial, focusing on delaying disease progression and improving patients' quality of life. Methods: This work aimed to develop an automatic sMRI-based method to detect AD in three different stages, namely healthy controls (CN), mild cognitive impairment (MCI), and AD itself. For such a purpose, brain sMRI images from the ADNI database were pre-processed, and a set of 22 texture statistical features from the sMRI gray-level co-occurrence matrix (GLCM) were extracted from various slices within different anatomical planes. Different combinations of features and planes were used to feed classical machine learning (cML) algorithms to analyze their discrimination power between the groups. Results: The cML algorithms achieved the following classification accuracy: 85.2% for AD vs. CN, 98.5% for AD vs. MCI, 95.1% for CN vs. MCI, and 87.1% for all vs. all. Conclusions: For the pair AD vs. MCI, the proposed model outperformed state-of-the-art imaging source studies by 0.1% and non-imaging source studies by 4.6%. These results are particularly significant in the field of AD classification, opening the door to more efficient early diagnosis in real-world settings since MCI is considered a precursor to AD. [ABSTRACT FROM AUTHOR]

Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by increasing prevalence, diverse impairments, and unclear origins and mechanisms. To gain a better grasp of the origins of ASD, it is essential to identify the most distinctive structural brain abnormalities in individuals with ASD. Methods: A Multi-Stage Progressive Feature Refinement Approach was employed to identify the most pivotal structural magnetic resonance imaging (MRI) features that distinguish individuals with ASD from typically developing (TD) individuals. The study included 175 individuals with ASD and 69 TD individuals, all aged between 7 and 18 years, matched in terms of age and gender. Both cortical and subcortical features were integrated, with a particular focus on hippocampal subfields. Results: Out of 317 features, 9 had the most significant impact on distinguishing ASD from TD individuals. These structural features, which include a specific hippocampal subfield, are closely related to the brain areas associated with the reward system. Conclusion: Structural irregularities in the reward system may play a crucial role in the pathophysiology of ASD, and specific hippocampal subfields may also contribute uniquely, warranting further investigation. [ABSTRACT FROM AUTHOR]

Background: For over three decades, the concomitance of cortical neurodegeneration and white matter hyperintensities (WMH) has sparked discussions about their coupled temporal dynamics. Longitudinal studies supporting this hypothesis nonetheless remain scarce. Methods: We applied global and regional bivariate latent growth curve modelling to determine the extent to which WMH and cortical thickness were interrelated over a four-year period. For this purpose, we leveraged longitudinal MRI data from 451 cognitively unimpaired participants (DELCODE; median age 69.71 [IQR 65.51, 75.50] years; 52.32% female). Participants underwent MRI sessions annually over a four-year period (1815 sessions in total, with roughly four MRI sessions per participant). We adjusted all models for demographics and cardiovascular risk. Results: Our findings were three-fold. First, larger WMH volumes were linked to lower cortical thickness (σ = -0.165, SE = 0.047, Z = -3.515, P < 0.001). Second, individuals with higher WMH volumes experienced more rapid cortical thinning (σ = -0.226, SE = 0.093, Z = -2.443, P = 0.007), particularly in temporal, cingulate, and insular regions. Similarly, those with lower initial cortical thickness had faster WMH progression (σ = -0.141, SE = 0.060, Z = -2.336, P = 0.009), with this effect being most pronounced in temporal, cingulate, and insular cortices. Third, faster WMH progression was associated with accelerated cortical thinning (σ = -0.239, SE = 0.139, Z = -1.710, P = 0.044), particularly in frontal, occipital, and insular cortical regions. Conclusions: Our study suggests that cortical thinning and WMH progression could be mutually reinforcing rather than parallel, unrelated processes, which become entangled before cognitive deficits are detectable. Trial registration: German Clinical Trials Register (DRKS00007966, 04/05/2015). [ABSTRACT FROM AUTHOR]

Introduction: Cognitive impairment in patients with end-stage renal disease (ESRD) is associated with brain structural damage. However, no prior studies have investigated the relationship between brain texture features and the cognitive function in ESRD patients. This study aimed to investigate changes in brain texture features in ESRD patients and their relationships with cognitive function using voxel-based 3D brain texture analysis (TA), and further predict individual cognitive-related brain damage in ESRD patients. Methods: Forty-seven ESRD patients and 45 control subjects underwent wholebrain high-resolution 3D T1-weighted imaging scans and neuropsychological assessments. The voxel-based 3D brain TA was performed to examine intergroup differences in brain texture features. Additionally, within the ESRD group, the relationships of altered texture features with neuropsychological function and clinical indicators were analyzed. Finally, receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of brain texture features for cognitive-related brain damage in ESRD patients. Results: Compared to the control group, the ESRD group exhibited altered texture features in several brain regions, including the insula, temporal lobe, striatum, cerebellum, and fusiform gyrus (p < 0.05, Gaussian random-field correction). Some of these altered texture features were associated with scores from the Digit Symbol Substitution Test and the Trail Making Test Parts A (p < 0.05), and showed significant correlations with serum creatinine and calcium levels within the ESRD group (p < 0.05). Notably, ROC curve analysis revealed that the texture features in the right insula and left middle temporal gyrus could accurately predict cognitive-related brain damage in ESRD patients, with the area under the curve values exceeding 0.90. Conclusion: Aberrant brain texture features may be involved in the neuropathological mechanism of cognitive decline, and have high accuracy in predicting cognitive-related brain damage in ESRD patients. TA offers a novel neuroimaging marker to explore the neuropathological mechanisms of cognitive impairment in ESRD patients, and may be a valuable tool to predict cognitive decline. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate alterations in gray matter structure among individuals diagnosed with diabetic retinopathy (DR). This study included a cohort of 32 diabetic patients with retinopathy (DR group, n = 32) and 38 healthy adults (HC group, n = 38). Both cohorts underwent comprehensive psychological and cognitive assessments alongside structural magnetic resonance imaging. The brain's gray matter volume and morphology were analyzed using voxel-based morphometry (VBM) and surface-based morphometry (SBM). Partial correlation analysis was employed to investigate the associations between differences in gray matter volume (GMV) across diverse brain regions and the outcomes of cognitive psychological tests as well as clinical indicators. The VBM results revealed that, in comparison to the healthy control (HC) group, patients with diabetic retinopathy (DR) exhibited reduced gray matter volume (GMV) in the right fusiform gyrus, inferior frontal gyrus, opercular part, and left hippocampus; conversely, an increase in GMV was observed in the right thalamus. The SBM results indicated cortical thinning in the left caudal anterior cingulate cortex, left superior frontal gyrus, left parahippocampal gyrus, and bilateral lingual gyrus in the DR group. Sulcal depth (SD) exhibited increased values in the bilateral rostral middle frontal gyrus, superior frontal gyrus, frontal pole, left precentral gyrus, postcentral gyrus, lateral orbitofrontal gyrus, and right paracentral gyrus. Local gyrification indices (LGIs) decreased in the left caudal middle frontal gyrus and superior frontal gyrus. The fractal dimension (FD) decreased in the posterior cingulate gyrus and isthmus of the cingulate gyrus. The left hippocampal gray matter volume (GMV) in patients with diabetic retinopathy was negatively correlated with disease duration (r = -0.478, p = 0.008) and self-rating depression scale (SAS) score (r = -0.381, p = 0.038). The structural alterations in specific brain regions of individuals with DR, which may contribute to impairments in cognition, emotion, and behavior, provide valuable insights into the neurobiological basis underlying these dysfunctions. [ABSTRACT FROM AUTHOR]

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD. [ABSTRACT FROM AUTHOR]

Reduced cerebral blood flow (CBF) in the temporoparietal region and gray matter volumes (GMVs) in the temporal lobe were previously reported in patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). However, the temporal relationship between reductions in CBF and GMVs requires further investigation. This study sought to determine if reduced CBF is associated with reduced GMVs, or vice versa. Data came from 148 volunteers of the Cardiovascular Health Study Cognition Study (CHS-CS), including 58 normal controls (NC), 50 MCI, and 40 AD who had perfusion and structural MRIs during 2002-2003 (Time 2). Sixty-three of the 148 volunteers had follow-up perfusion and structural MRIs (Time 3). Forty out of the 63 volunteers received prior structural MRIs during 1997-1999 (Time 1). The relationships between GMVs and subsequent CBF changes, and between CBF and subsequent GMV changes were investigated. At Time 2, we observed smaller GMVs (p<0.05) in the temporal pole region in AD compared to NC and MCI. We also found associations between: (1) temporal pole GMVs at Time 2 and subsequent declines in CBF in this region (p=0.0014) and in the temporoparietal region (p=0.0032); (2) hippocampal GMVs at Time 2 and subsequent declines in CBF in the temporoparietal region (p=0.012); and (3) temporal pole CBF at Time 2 and subsequent changes in GMV in this region (p = 0.011). Therefore, hypoperfusion in the temporal pole may be an early event driving its atrophy. Perfusion declines in the temporoparietal and temporal pole follow atrophy in this temporal pole region. [ABSTRACT FROM AUTHOR]

Inflammatory mechanisms may play crucial roles in the pathophysiology of major depressive disorder (MDD), and cytokine concentrations are correlated with brain alterations. Adolescents and young adults with MDD have higher recurrence and suicide rates than adults, but there has been limited research on the underlying mechanisms. In this study, we aimed to investigate the potential correlations among cytokines, depression severity, and the volumes of the amygdala, hippocampus, and nucleus accumbens in Han Chinese adolescents and young adults with first-episode MDD. Nineteen patients with MDD aged 10–21 years were enrolled from the Psychiatry Department of the First Affiliated Hospital of Chongqing Medical University, along with 18 age-matched healthy controls from a local school. We measured the concentrations of interleukin (IL)-4, IL-6, IL-8, and IL-10 in the peripheral blood, along with the volumes of the amygdala, hippocampus, and nucleus accumbens, as determined by magnetic resonance imaging. We observed that patients with MDD had higher concentrations of IL-6 and a trend towards reduced left amygdala and bilateral hippocampus volumes than healthy controls. Additionally, the concentration of IL-6 was correlated with the left amygdala volume and depression severity, while the left hippocampus volume was correlated with depression severity. This study suggests that inflammation is an underlying neurobiological change and implies that IL-6 could serve as a potential biomarker for identifying early stage MDD in adolescents and young adults. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the lives of many children due to its hidden symptoms. Early detection of ASD is challenging because of its complex and heterogeneous nature. Magnetic resonance imaging (MRI) has emerged as a crucial tool for early detection, offering non-invasive imaging with detailed soft tissue information. However, existing approaches face limitations such as overfitting, underfitting, class imbalance, control, domain shift, and behavioral issues. To address these challenges, this paper proposes a novel ASD detection and classification model called the Autism Spectrum Disorder-based Attention Graph Neural Network and Crossover Boosted Meerkat Optimization (ASD-AttGCBMO) algorithm. The proposed method utilizes structural Magnetic Resonance Imaging (sMRI) data from the ABIDE 1 dataset. The data undergoes preprocessing to remove artifacts and noise, ensuring high image quality and consistency. Node feature extraction employs voxel-based morphometry (VBM) and surface-based analysis, which extract relevant features such as surface area, cortical thickness, shape descriptors, and brain volumes. The ASD-AttGCBMO model is trained using preprocessed sMRI images, employing the Adam and Stochastic Gradient Descent (SGD) optimizers to prevent overfitting, reduce classification loss, and improve convergence. The model is designed to enhance the learning process and capture complex patterns for accurate feature classification between ASD and control subjects. To optimize the hyperparameters in the attention-based neural network model, the CBMO algorithm is employed. Experimental validation is conducted using essential performance evaluation measures. The proposed method achieves impressive results, with accuracy, precision, recall, specificity, F1-score, Area under Receiver Operating curve (AUC/ROC), and computational time values of 98.8%, 99%, 98.5%, 98.6%, 98.2%, 0.989, and 3.05 s, respectively. Comparative analysis demonstrates that the proposed method outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background and Hypothesis The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). Study Design We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n  = 541). The brain-PAD was analyzed in independent HCs (n  = 209) and patients (n  = 233). Correlations between the brain-PAD and clinical measures were investigated. Study Results The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs. Conclusions Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age. [ABSTRACT FROM AUTHOR]

Copyright of Fudan University Journal of Medical Sciences is the property of Fudan University Journal of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The brain structure–function relationship is crucial to how the human brain works under normal or diseased conditions. Exploring such a relationship is challenging when using the 3-dimensional magnetic resonance imaging (MRI) functional dataset which is temporal dynamic and the structural MRI which is static. Partial Least Squares Correlation (PLSC) is one of the classical methods for exploring the joint spatial and temporal relationship. The goal of PLSC is to identify covarying patterns via linear voxel-wise combinations in each of the structural and functional data sets to maximize the covariance. However, existing PLSC cannot adequately deal with the unmatched temporal dimensions between structural and functional data sets. We proposed a new alternative variant of the PLSC, termed within-subject, voxel-wise, and constant-block PLSC, to address this problem. To validate our method, we used two data sets with weak and strong relationships in simulated data. Additionally, the analysis of real brain data was carried out based on gray matter volume hubs derived from sMRI and whole-brain voxel-wise measures from resting-state fMRI for aging effect based on healthy subjects aged 16–85 years. Our results showed that our constant-block PLSC can detect weak structure–function relationships and has better robustness to noise. In fact, it adequately unearthed the true simulated number of significant and more accurate latent variables for the simulated data and more meaningful LVs for the real data, with covariance improvement from 16.19 to 41.48% (simulated) and 13.29–53.68% (real data), respectively. More interestingly in the real data analysis, our method identified simultaneously the well-known brain networks such as the default mode, sensorimotor, auditory, and dorsal attention networks both functionally and structurally, implying the hubs we derived from gray matter volumes are the basis of brain function, supporting diverse functions. Constant-block PLSC is a feasible tool for analyzing the brain structure–function relationship. [ABSTRACT FROM AUTHOR]

Background and Objective: Spinal muscular atrophy (SMA) is one of the most common monogenic neuromuscular diseases, and the pathogenesis mechanisms, especially the brain network topological properties, remain unknown. This study aimed to use individual‐level morphological brain network analysis to explore the brain neural network mechanisms in SMA. Methods: Individual‐level gray matter (GM) networks were constructed by estimating the interregional similarity of GM volume distribution using both Kullback–Leibler divergence‐based similarity (KLDs) and Jesen‐Shannon divergence‐based similarity (JSDs) measurements based on Automated Anatomical Labeling 116 and Hammersmith 83 atlases for 38 individuals with SMA types 2 and 3 and 38 age‐ and sex‐matched healthy controls (HCs). The topological properties were analyzed by the graph theory approach and compared between groups by a nonparametric permutation test. Additionally, correlation analysis was used to assess the associations between altered topological metrics and clinical characteristics. Results: Compared with HCs, although global network topology remained preserved in individuals with SMA, brain regions with altered nodal properties mainly involved the right olfactory gyrus, right insula, bilateral parahippocampal gyrus, right amygdala, right thalamus, left superior temporal gyrus, left cerebellar lobule IV–V, bilateral cerebellar lobule VI, right cerebellar lobule VII, and vermis VII and IX. Further correlation analysis showed that the nodal degree of the right cerebellar lobule VII was positively correlated with the disease duration, and the right amygdala was negatively correlated with the Hammersmith Functional Motor Scale Expanded (HFMSE) scores. Conclusions: Our findings demonstrated that topological reorganization may prioritize global properties over nodal properties, and disrupted topological properties in the cortical–limbic‐cerebellum circuit in SMA may help to further understand the network pathogenesis underlying SMA. [ABSTRACT FROM AUTHOR]

Background: The thalamus has a central role in the pathophysiology of idiopathic cervical dystonia (iCD); however, the nature of alterations occurring within this structure remain largely elusive. Using a structural magnetic resonance imaging (MRI) approach, we examined whether abnormalities differ across thalamic subregions/nuclei in patients with iCD. Methods: Structural MRI data were collected from 37 patients with iCD and 37 healthy controls (HCs). Automatic parcellation of 25 thalamic nuclei in each hemisphere was performed based on the FreeSurfer program. Differences in thalamic nuclei volumes between groups and their relationships with clinical information were analysed in patients with iCD. Results: Compared to HCs, a significant reduction in thalamic nuclei volume primarily in central medial, centromedian, lateral geniculate, medial geniculate, medial ventral, paracentral, parafascicular, paratenial, and ventromedial nuclei was found in patients with iCD (P < 0.05, false discovery rate corrected). However, no statistically significant correlations were observed between altered thalamic nuclei volumes and clinical characteristics in iCD group. Conclusion: This study highlights the neurobiological mechanisms of iCD related to thalamic volume changes. [ABSTRACT FROM AUTHOR]

Introduction: The prefrontal–cingulate–thalamic areas are associated with ejaculation control. Functional abnormalities of these areas and decreased grey matter volume (GMV) in the subcortical areas have been confirmed in premature ejaculation (PE) patients. However, no study has explored the corresponding GMV changes in the prefrontal–cingulate–thalamic areas, which are considered as the important basis for functional abnormalities. This study aimed to investigated whether PE patients exhibited impaired GMV in the brain, especially the prefrontal–cingulate–thalamic areas, and whether these structural deficits were associated with declined ejaculatory control. Methods: T1‐weighted structural magnetic resonance imaging (MRI) data were acquired from 50 lifelong PE patients and 50 age‐, and education‐matched healthy controls (HCs). The PE diagnostic tool (PEDT) was applied to assess the subjective symptoms of PE. Based on the method of voxel‐based morphometry (VBM), GMV were measured and compared between groups. In addition, the correlations between GMV of brain regions showed differences between groups and PEDT scores were evaluated in the patient group. Results: PE patients showed decreased GMV in the right dorsolateral superior frontal gyrus (clusters = 13, peak T‐values = −4.30) and left thalamus (clusters = 47, T = −4.33), and increased GMV in the left middle cingulate gyrus (clusters = 12, T = 4.02) when compared with HCs. In the patient group, GMV of the left thalamus were negatively associated with PEDT scores (r = −0.35; P = 0.01). Receiver operating characteristic (ROC) analysis showed that GMV of the right dorsolateral superior frontal gyrus (AUC = 0.71, P < 0.01, sensitivity = 60%, specificity = 78%), left thalamus (AUC = 0.72, P < 0.01, sensitivity = 92%, specificity = 46%) and middle cingulate gyrus (AUC = 0.69, P < 0.01, sensitivity = 50%, specificity = 90%), and the combined model (AUC = 0.84, P < 0.01, sensitivity = 78%, specificity = 80%) all had the ability to distinguish PE patients from HCs. Conclusion: Disturbances in GMV were revealed in the prefrontal–cingulate–thalamic areas of PE patients. The findings implied that decreased GMV in the dorsolateral prefrontal cortex and thalamus might be associated with the central pathological neural mechanism underlying the declined ejaculatory control while increased GMV in the middle cingulate gyrus might be the compensatory mechanism underlying PE. [ABSTRACT FROM AUTHOR]

This study explored the interactions among prenatal stress, child sex, and polygenic risk scores (PGS) for attention‐deficit/hyperactivity disorder (ADHD) on structural developmental changes of brain regions implicated in ADHD. We used data from two population‐based birth cohorts: Growing Up in Singapore Towards healthy Outcomes (GUSTO) from Singapore (n = 113) and Generation R from Rotterdam, the Netherlands (n = 433). Prenatal stress was assessed using questionnaires. We obtained latent constructs of prenatal adversity and prenatal mood problems using confirmatory factor analyses. The participants were genotyped using genome‐wide single nucleotide polymorphism arrays, and ADHD PGSs were computed. Magnetic resonance imaging scans were acquired at 4.5 and 6 years (GUSTO), and at 10 and 14 years (Generation R). We estimated the age‐related rate of change for brain outcomes related to ADHD and performed (1) prenatal stress by sex interaction models, (2) prenatal stress by ADHD PGS interaction models, and (3) 3‐way interaction models, including prenatal stress, sex, and ADHD PGS. We observed an interaction between prenatal stress and ADHD PGS on mean cortical thickness annual rate of change in Generation R (i.e., in individuals with higher ADHD PGS, higher prenatal stress was associated with a lower rate of cortical thinning, whereas in individuals with lower ADHD PGS, higher prenatal stress was associated with a higher rate of cortical thinning). None of the other tested interactions were statistically significant. Higher prenatal stress may promote a slower brain developmental rate during adolescence in individuals with higher ADHD genetic vulnerability, whereas it may promote a faster brain developmental rate in individuals with lower ADHD genetic vulnerability. [ABSTRACT FROM AUTHOR]

Background: Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI. Methods: A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages. Results: The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone. Conclusion: Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis. [ABSTRACT FROM AUTHOR]

Background: Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder that not only causes intense pain but also affects the psychological health of patients. Since TN pain intensity and negative emotion may be grounded in our own pain experiences, they exhibit huge inter-individual differences. This study investigates the effect of inter-individual differences in pain intensity and negative emotion on brain structure in patients with TN and the possible pathophysiology mechanism underlying this disease. Methods: T1 weighted magnetic resonance imaging and diffusion tensor imaging scans were obtained in 46 patients with TN and 35 healthy controls. All patients with TN underwent pain-related and emotion-related questionnaires. Voxel-based morphometry and regional white matter diffusion property analysis were used to investigate whole brain grey and white matter quantitatively. Innovatively employing partial least squares correlation analysis to explore the relationship among pain intensity, negative emotion and brain microstructure in patients with TN. Results: Significant difference in white matter integrity were identified in patients with TN compared to the healthy controls group; The most correlation brain region in the partial least squares correlation analysis was the genus of the corpus callosum, which was negatively associated with both pain intensity and negative emotion. Conclusion: The genu of corpus callosum plays an important role in the cognition of pain perception, the generation and conduction of negative emotions in patients with TN. These findings may deepen our understanding of the pathophysiology of TN. [ABSTRACT FROM AUTHOR]

Objective: Few studies have focused on brain structure in atypical anorexia nervosa (atypical AN). This study investigates differences in gray matter volume (GMV) between females with anorexia nervosa (AN) and atypical AN, and healthy controls (HC). Method: Structural magnetic resonance imaging data were acquired for 37 AN, 23 atypical AN, and 41 HC female participants. Freesurfer was used to extract GMV, cortical thickness, and surface area for six brain lobes and associated cortical regions of interest (ROI). Primary analyses employed linear mixed‐effects models to compare group differences in lobar GMV, followed by secondary analyses on ROIs within significant lobes. We also explored relationships between cortical gray matter and both body mass index (BMI) and symptom severity. Results: Our primary analyses revealed significant lower GMV in frontal, temporal and parietal areas (FDR <.05) in AN and atypical AN when compared to HC. Lobar GMV comparisons were non‐significant between atypical AN and AN. The parietal lobe exhibited the greatest proportion of affected cortical ROIs in both AN versus HC and atypical AN versus HC. BMI, but not symptom severity, was found to be associated with cortical GMV in the parietal, frontal, temporal, and cingulate lobes. No significant differences were observed in cortical thickness or surface area. Discussion: We observed lower GMV in frontal, temporal, and parietal areas, when compared to HC, but no differences between AN and atypical AN. This indicates potentially overlapping structural phenotypes between these disorders and evidence of brain changes among those who are not below the clinical underweight threshold. Public significance: Despite individuals with atypical anorexia nervosa presenting above the clinical weight threshold, lower cortical gray matter volume was observed in partial, temporal, and frontal cortices, compared to healthy individuals. No significant differences were found in cortical gray matter volume between anorexia nervosa and atypical anorexia nervosa. This underscores the importance of continuing to assess and target weight gain in clinical care, even for those who are presenting above the low‐weight clinical criteria. [ABSTRACT FROM AUTHOR]

Theory of Mind (ToM) is understanding others' minds. Empathy is an insight into emotions and feelings of others. Persons with multiple sclerosis (pwMS) may experience impairment in ToM and empathy. To investigate ToM, empathy, and their relationship with neuroimaging, neuropsychological, and neuropsychiatric data. 41 pwMS and 41 HC were assessed using RMET for ToM, EQ, BICAMS, HADS. Cortical and subcortical gray matter volumes were calculated with Freesurfer from 3T MRI scans. pwMS showed lower EQ scores (44.82 ± 11.9 vs 51.29 ± 9.18, p = 0.02) and worse RMET performance (22.37 ± 4.09 vs 24,47 ± 2.93, p = 0.011). Anxiety and depression were higher in pwMS. EQ correlated with subcortical (amygdala) and cortical (anterior cingulate) volumes. RMET correlated with cortical volumes (posterior cingulate, lingual). In regression analysis, amygdala volume was the single predictor of empathy performance (p = 0.041). There were no significant correlations between social cognitive tests and general cognition. A weak negative correlation was found between EQ and the level of anxiety (r = -0.342, p = 0.038) The present study indicates that pwMS have impairment on ToM and empathy. The performance of ToM and empathy in MS is linked to the volumes of critical brain areas involved in social cognition. [ABSTRACT FROM AUTHOR]

Background: Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD. Method s : This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M‐AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index. Results: QMTA distinguished M‐AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA. Conclusions: QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk. [ABSTRACT FROM AUTHOR]

The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person's brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual's brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy. [ABSTRACT FROM AUTHOR]

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain‐wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman‐rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103–0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043–0.068), while the proportional approach showed a decrease of −0.025 (95% CI: −0.035 to −0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations. [ABSTRACT FROM AUTHOR]

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real‐world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real‐time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta‐Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full‐text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross‐frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1‐weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI‐PAD combinations. These combinations enable in‐depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain–behavior models that extend beyond the scanner and into real‐world contexts. [ABSTRACT FROM AUTHOR]

In this study, we aimed to understand the contributions of hippocampal anteroposterior subregions (head, body, tail) and subfields (cornu ammonis 1‐3 [CA1‐3], dentate gyrus [DG], and subiculum [Sub]) and encoding strategies to the age‐related verbal memory decline. Healthy participants were administered the California Verbal Learning Test‐II to evaluate verbal memory performance and encoding strategies and underwent 4.7 T magnetic resonance imaging brain scan with subsequent hippocampal subregions and subfields manual segmentation. While total hippocampal volume was not associated with verbal memory performance, we found the volumes of the posterior hippocampus (body) and Sub showed significant effects on verbal memory performance. Additionally, the age‐related volume decline in hippocampal body volume contributed to lower use of semantic clustering, resulting in lower verbal memory performance. The effect of Sub on verbal memory was statistically independent of encoding strategies. While total CA1‐3 and DG volumes did not show direct or indirect effects on verbal memory, exploratory analyses with DG and CA1‐3 volumes within the hippocampal body subregion suggested an indirect effect of age‐related volumetric reduction on verbal memory performance through semantic clustering. As semantic clustering is sensitive to age‐related hippocampal volumetric decline but not to the direct effect of age, further investigation of mechanisms supporting semantic clustering can have implications for early detection of cognitive impairments and decline. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study is to investigate differences in gray matter volume and cortical complexity between Parkinson's disease with depression (PDD) patients and Parkinson's disease without depression (PDND) patients. Methods: A total of 41 PDND patients, 36 PDD patients, and 38 healthy controls (HC) were recruited and analyzed by Voxel‐based morphometry (VBM) and surface‐based morphometry (SBM). Differences in gray matter volume and cortical complexity were compared using the one‐way analysis of variance (ANOVA) and correlated with the Hamilton Depression Scale‐17 (HAMD‐17) scores. Results: PDD patients exhibited significant cortical atrophy in various regions, including bilateral medial parietal–occipital–temporal lobes, right dorsolateral temporal lobes, bilateral parahippocampal gyrus, and bilateral hippocampus, compared to HC and PDND groups. A negative correlation between the GMV of left precuneus and HAMD‐17 scores in the PDD group tended to be significant (r = −0.318, p = 0.059). Decreased gyrification index was observed in the bilateral insular and dorsolateral temporal cortex. However, there were no significant differences found in fractal dimension and sulcal depth. Conclusion: Our research shows extensive cortical structural changes in the insular cortex, parietal–occipital–temporal lobes, and hippocampal regions in PDD. This provides a morphological perspective for understanding the pathophysiological mechanism underlying depression in Parkinson's disease. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) affects approximately 1.4% of the population and imposes significant social and economic burdens. Because its etiology is unknown, effective diagnosis is challenging. Advancements in structural magnetic resonance imaging (sMRI) allow for the objective assessment of ASD by examining structural brain changes. Recently, machine learning (ML)-based diagnostic systems have emerged to expedite and enhance the diagnostic process. However, the expected success in ASD was not yet achieved. This study evaluates and compares the performance of seven optimized ML models to identify sMRI-based biomarkers for early and accurate detection of ASD in children aged 5 to 10 years. The effect of using hyperparameter tuning and feature selection techniques are investigated using two public datasets from Autism Brain Imaging Data Exchange Initiative. Furthermore, these models are tested on a local Saudi dataset to verify their generalizability. The integration of the grey wolf optimizer with a support vector machine achieved the best performance with an average accuracy of 71% (with further improvement to 71% after adding personal features) using 10-fold Cross-validation. The optimized models identified relevant biomarkers for diagnosis, lending credence to their truly generalizable nature and advancing scientific understanding of neurological changes in ASD. [ABSTRACT FROM AUTHOR]

The pathological mechanism of autism spectrum disorder (ASD) remains unclear. Nowadays, surface-based morphometry (SBM) based on structural magnetic resonance imaging (sMRI) techniques have reported cortical thickness (CT) variations in ASD. However, the findings were inconsistent and heterogeneous. This current meta-analysis conducted a whole-brain vertex-wise coordinate‐based meta‐analysis (CBMA) on CT studies to explore the most noticeable and robust CT changes in ASD individuals by applying the seed-based d mapping (SDM) program. A total of 26 investigations comprised 27 datasets were included, containing 1,635 subjects with ASD and 1470 HC, along with 94 coordinates. Individuals with ASD exhibited significantly altered CT in several regions compared to HC, including four clusters with thicker CT in the right superior temporal gyrus (STG.R), the left middle temporal gyrus (MTG.L), the left anterior cingulate/paracingulate gyri, the right superior frontal gyrus (SFG.R, medial orbital parts), as well as three clusters with cortical thinning including the left parahippocampal gyrus (PHG.L), the right precentral gyrus (PCG.R) and the left middle frontal gyrus (MFG.L). Adults with ASD only demonstrated CT thinning in the right parahippocampal gyrus (PHG.R), revealed by subgroup meta-analyses. Meta-regression analyses found that CT in STG.R was positively correlated with age. Meanwhile, CT in MFG.L and PHG.L had negative correlations with the age of ASD individuals. These results suggested a complicated and atypical cortical development trajectory in ASD, and would provide a deeper understanding of the neural mechanism underlying the cortical morphology in ASD. [ABSTRACT FROM AUTHOR]

Aims: The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)‐based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI‐based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual‐level morphological networks. Background: There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual‐level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders. Conclusion: This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions. [ABSTRACT FROM AUTHOR]

This study used structural magnetic resonance imaging to analyze changes in the gray matter volume (GMV) of preterm‐born (PTB) and term‐born (TB) children to help elucidate the influence of magnesium sulfate treatment on the nervous system development. A total of 51 subjects were recruited, including 28 PTB and 23 TB children. The intelligence scale and MRI scan were completed at the corrected age of 10 to 16 years. A whole‐brain voxel‐wise analysis tested the main effect of the status (PTB without magnesium, PTB with magnesium, and TB) using a factorial design in SPM8. The mean volumes of the regions that showed significant group effects on the GMV after the FDR correction were extracted in the common space for each subject. Verbal and full‐scale intelligence quotient scores were significantly lower for PTB children without magnesium than for TB children; however, the scores of PTB children with magnesium and TB children were almost identical. Compared with TB children, PTB children had significantly reduced left straight gyrus and left inferior frontal gyrus GMVs; however, the volumes of PTB children with magnesium were closer to those of TB children. Changes in the GMV of the left inferior frontal gyrus were significantly correlated with full‐scale and verbal intelligence quotient scores, whereas the lower gestational age at the time of mgsou4 treatment led to a larger GMV of the left inferior frontal gyrus. Brain structural abnormalities could exist in PTB children. The GMVs of the left straight gyrus and left inferior frontal gyrus were significantly reduced in these children. The influence of magnesium sulfate treatment was not significant, but the cognitive levels of these children were significantly increased and almost identical to those of TB children. Initiation of magnesium sulfate treatment during gestation is negatively correlated with the left inferior frontal gyrus GMV. [ABSTRACT FROM AUTHOR]

Aims: To investigate cortical organization in brain magnetic resonance imaging (MRI) of preterm‐born adults using percent contrast of gray‐to‐white matter signal intensities (GWPC), which is an in vivo proxy measure for cortical microstructure. Methods: Using structural MRI, we analyzed GWPC at different percentile fractions across the cortex (0%, 10%, 20%, 30%, 40%, 50%, and 60%) in a large and prospectively collected cohort of 86 very preterm‐born (<32 weeks of gestation and/or birth weight <1500 g, VP/VLBW) adults and 103 full‐term controls at 26 years of age. Cognitive performance was assessed by full‐scale intelligence quotient (IQ) using the Wechsler Adult Intelligence Scale. Results: GWPC was significantly decreased in VP/VLBW adults in frontal, parietal, and temporal associative cortices, predominantly in the right hemisphere. Differences were pronounced at 20%, 30%, and 40%, hence, in middle cortical layers. GWPC was significantly increased in right paracentral lobule in VP/VLBW adults. GWPC in frontal and temporal cortices was positively correlated with birth weight, and negatively with duration of ventilation (p < 0.05). Furthermore, GWPC in right paracentral lobule was negatively correlated with IQ (p < 0.05). Conclusions: Widespread aberrant gray‐to‐white matter contrast suggests lastingly altered cortical microstructure after preterm birth, mainly in middle cortical layers, with differential effects on associative and primary cortices. [ABSTRACT FROM AUTHOR]

Background: The success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods. Methods: Fifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set. Results: SVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set. Conclusion: SVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model. [ABSTRACT FROM AUTHOR]

Background and objectives: Intermittent theta-burst stimulation (iTBS) is a patterned form of excitatory transcranial magnetic stimulation that has yielded encouraging results as an adjunctive therapeutic option to alleviate the emergence of clinical deficits in Parkinson’s disease (PD) patients. Although it has been demonstrated that iTBS influences dopamine-dependent corticostriatal plasticity, little research has examined the neurobiological mechanisms underlying iTBSinduced clinical enhancement. Here, our primary goal is to verify whether iTBS bilaterally delivered over the primary motor cortex (M1) is effective as an add-on treatment at reducing scores for both motor functional impairment and nonmotor symptoms in PD. We hypothesize that these clinical improvements following bilateral M1-iTBS could be driven by endogenous dopamine release, which may rebalance cortical excitability and restore compensatory striatal volume changes, resulting in increased striato-cortico-cerebellar functional connectivity and positively impacting neuroglia and neuroplasticity. Methods: A total of 24 PD patients will be assessed in a randomized, doubleblind, sham-controlled crossover study involving the application of iTBS over the bilateral M1 (M1 iTBS). Patients on medication will be randomly assigned to receive real iTBS or control (sham) stimulation and will undergo 5 consecutive sessions (5  days) of iTBS over the bilateral M1 separated by a 3-month washout period. Motor evaluation will be performed at different follow-up visits along with a comprehensive neurocognitive assessment; evaluation of M1 excitability; combined structural magnetic resonance imaging (MRI), resting-state electroencephalography and functional MRI; and serum biomarker quantification of neuroaxonal damage, astrocytic reactivity, and neural plasticity prior to and after iTBS. Discussion: The findings of this study will help to clarify the efficiency of M1 iTBS for the treatment of PD and further provide specific neurobiological insights into improvements in motor and nonmotor symptoms in these patients. This novel project aims to yield more detailed structural and functional brain evaluations than previous studies while using a noninvasive approach, with the potential to identify prognostic neuroprotective biomarkers and elucidate the structural and functional mechanisms of M1 iTBS-induced plasticity in the cortico-basal ganglia circuitry. Our approach may significantly optimize neuromodulation paradigms to ensure state-of-the-art and scalable rehabilitative treatment to alleviate motor and nonmotor symptoms of PD. [ABSTRACT FROM AUTHOR]

Vascular cognitive impairment (VCI) is the initial stage of vascular dementia (VaD). Early diagnosis and treatment of VCI are crucial to prevent the progression of VaD. In order to gain a better understanding of VCI, this study aimed to investigate the use of advanced imaging techniques such as structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). These techniques allow researchers to observe the structural and functional changes in the brain that are associated with VCI. Functional magnetic resonance imaging (fMRI) and sMRI techniques have been widely used in studies focusing on gray matter, brain networks, and functional abnormalities during rest. By searching and summarizing recent literature, this study has provided valuable evidence on the use of advanced imaging techniques in understanding and treating VCI. The findings from this study can aid in the development of early intervention strategies for patients with VCI, potentially slowing down or even halting the progression of VCI to full-blown VaD. [ABSTRACT FROM AUTHOR]

Objective: The structural covariance network (SCN) alterations in patients with temporal lobe epilepsy and comorbid sleep disorder (PWSD) remain poorly understood. This study aimed to investigate changes in SCNs using structural magnetic resonance imaging. Methods: Thirty-four PWSD patients, thirty-three patients with temporal lobe epilepsy without sleep disorder (PWoSD), and seventeen healthy controls underwent high-resolution structural MRI imaging. Subsequently, SCNs were constructed based on gray matter volume and analyzed via graph-theoretical approaches. Results: PWSD exhibited significantly increased clustering coefficients, shortest path lengths, transitivity, and local efficiency. In addition, various distributions and numbers of SCN hubs were identified in PWSD. Furthermore, PWSD networks were less robust to random and target attacks than those of healthy controls and PWoSD patients. Conclusion: This study identifies aberrant SCN changes in PWSD that may be related to the susceptibility of patients with epilepsy to sleep disorders. [ABSTRACT FROM AUTHOR]

Background: The locus coeruleus (LC) produces catecholamines (norepinephrine and dopamine) and is implicated in a broad range of cognitive functions including attention and executive function. Recent advancements in magnetic resonance imaging (MRI) approaches allow for the visualization and quantification of LC structure. Human research focused on the LC has since exploded given the LC's role in cognition and relevance to current models of psychopathology and neurodegenerative disease. However, it is unclear to what extent LC structure reflects underlying catecholamine function, and how LC structure and neurochemical function are collectively associated with cognitive performance. Methods: A partial least squares correlation (PLSC) analysis was applied to 19 participants' LC structural MRI measures and catecholamine synthesis capacity measures assessed using [18F]Fluoro-m-tyrosine ([18F]FMT) positron emission tomography (PET). Results: We found no direct association between LC-MRI and LC-[18F]FMT measures for rostral, middle, or caudal portions of the LC. We found significant associations between LC neuroimaging measures and neuropsychological performance that were driven by rostral and middle portions of the LC, which is in line with LC cortical projection patterns. Specifically, associations with executive function and processing speed arose from contributions of both LC structure and interactions between LC structure and catecholamine synthesis capacity. Conclusion: These findings leave open the possibility that LC MRI and PET measures contribute unique information and suggest that their conjoint use may increase sensitivity to brain-behavior associations in small samples. [ABSTRACT FROM AUTHOR]

Despite the reported lack of structural alterations in the amygdala of individuals with attention deficit/hyperactivity disorder (ADHD) in previous meta‐analyses, subsequent observational studies produced conflicting results. Through incorporating the updated data from observational studies on structural features of the amygdala in ADHD, the primary goal of this study was to examine the anatomical differences in amygdala between subjects with ADHD and their neurotypical controls. Using the appropriate keyword strings, we searched the PubMed, Embase, and Web of Science databases for English articles from inception to February 2022. Eligibility criteria included observational studies comparing the structure of the amygdala between ADHD subjects and their comparators using magnetic resonance imaging (MRI). Subgroup analyses were conducted focusing on the amygdala side, as well as the use of different scanners and approach to segmentation. The effects of other continuous variables, such as age, intelligence quotient, and male percentage, on amygdala size were also investigated. Of the 5703 participants in 16 eligible studies, 2928 were diagnosed with ADHD. Compared with neurotypical controls, subjects with ADHD had a smaller amygdala surface area (particularly in the left hemisphere) but without a significant difference in volume between the two groups. Subgroup analysis of MRI scanners and different approaches to segmentation showed no statistically significant difference. There was no significant correlation between continuous variables and amygdala size. Our results showed consistent surface morphological alterations of the amygdala, in particular on the left side, in subjects with ADHD. However, the preliminary findings based on the limited data available for analysis warrant future studies for verification. This meta‐analytic review finds a difference in amygdala size between individuals with attention‐deficit/hyperactivity disorder (ADHD) and neurotypical controls, by using different image analyses. Although volumetric image analysis revealed no significant differences in amygdala anatomy, we used its surface area to identify the structural alterations, particularly on the left side, in ADHD, shedding light on psychotherapy designed to improve their emotion dysregulation. [ABSTRACT FROM AUTHOR]

Objectives: To elucidate the relationship between the course of bipolar disorder (BD) and structural brain changes across the life span, we conducted a systematic review of longitudinal imaging studies in adolescent and adult BD patients. Methods: Eleven studies with 329 BD patients and 277 controls met our PICOS criteria (participants, intervention, comparison, outcome and study design): BD diagnosis based on DSM criteria, natural course of disease, comparison of grey matter changes in BD individuals over ≥1‐year interval between scans. Results: The selected studies yielded heterogeneous findings, partly due to varying patient characteristics, data acquisition and statistical models. Mood episodes were associated with greater grey matter loss in frontal brain regions over time. Brain volume decreased or remained stable in adolescent patients, whereas it increased in healthy adolescents. Adult BD patients showed increased cortical thinning and brain structural decline. In particular, disease onset in adolescence was associated with amygdala volume reduction, which was not reported in adult BD. Conclusions: The evidence collected suggests that the progression of BD impairs adolescent brain development and accelerates structural brain decline across the lifespan. Age‐specific changes in amygdala volume in adolescent BD suggest that reduced amygdala volume is a correlate of early onset BD. Clarifying the role of BD in brain development across the lifespan promises a deeper understanding of the progression of BD patients through different developmental episodes. [ABSTRACT FROM AUTHOR]

Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex illness of unknown aetiology. Emerging theories suggest ME/CFS may reflect a progressive, aberrant state of homeostasis caused by disturbances within the hypothalamus, yet few studies have investigated this using magnetic resonance imaging in adolescents with ME/CFS. We conducted a volumetric analysis to investigate whether whole and regional hypothalamus volumes in adolescents with ME/CFS differed compared to healthy controls, and whether these volumes were associated with fatigue severity and illness duration. 48 adolescents (25 ME/CFS, 23 controls) were recruited. Lateralised whole and regional hypothalamus volumes, including the anterior–superior, superior tubular, posterior, anterior-inferior and inferior tubular subregions, were calculated from T1-weighted images. When controlling for age, sex and intracranial volume, Bayesian linear regression models revealed no evidence for differences in hypothalamus volumes between groups. However, in the ME/CFS group, a weak linear relationship between increased right anterior–superior volumes and fatigue severity was identified, which was absent in controls. In addition, Bayesian quantile regression revealed a likely-positive association between illness duration and right superior tubular volumes in the ME/CFS group. While these findings suggest overall comparability in regional and whole hypothalamus volumes between adolescents with ME/CFS and controls, preliminary evidence was identified to suggest greater fatigue severity and longer illness duration were associated with greater right anterior–superior and superior-tubular volumes, respectively. These regions contain the anterior and superior divisions of the paraventricular nucleus, involved in the neuroendocrine response to stress, suggesting involvement in ME/CFS pathophysiology. However, replication in a larger, longitudinal cohort is required. [ABSTRACT FROM AUTHOR]

Background: Behavioral research has shown that children with autism spectrum disorder (ASD) have a higher empathizing–systemizing difference (D score) than normal children. However, there is no research about the neuroanatomical mechanisms of the empathizing–systemizing difference in children with ASD. Methods: Participants comprised 41 children with ASD and 39 typically developing (TD) children aged 6‒12 years. Empathizing–systemizing difference was estimated using the D score from the Chinese version of Children's Empathy Quotient and Systemizing Quotient. We quantified brain morphometry, including global and regional brain volumes and surface-based cortical measures (cortical thickness, surface area, and gyrification) via structural magnetic resonance imaging. Results: We found that the D score was significantly negatively associated with amygdala gray matter volume [β = −0.16; 95% confidence interval (CI): −0.30, −0.02; P value = 0.030] in children with ASD. There was a significantly negative association between D score and gyrification in the left lateral occipital cortex (LOC) in children with ASD (B = −0.10; SE = 0.03; cluster-wise P value = 0.006) and a significantly positive association between D score and gyrification in the right fusiform in TD children (B = 0.10; SE = 0.03; cluster-wise P value = 0.022). Moderation analyses demonstrated significant interactions between D score and diagnosed group in amygdala gray matter volume (β = 0.19; 95% CI 0.04, 0.35; P value = 0.013) and left LOC gyrification (β = 0.11; 95% CI 0.05, 0.17; P value = 0.001) but not in right fusiform gyrification (β = 0.08; 95% CI −0.02, 0.17; P value = 0.105). Conclusions: Neuroanatomical variation in amygdala volume and gyrification of LOC could be potential biomarkers for the empathizing–systemizing difference in children with ASD but not in TD children. Large-scale neuroimaging studies are necessary to test the replicability of our findings. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning have led to increased adoption of convolutional neural networks (CNN) for structural magnetic resonance imaging (sMRI)-based Alzheimer’s disease (AD) detection. AD results in widespread damage to neurons in different brain regions and destroys their connections. However, current CNN-based methods struggle to relate spatially distant information effectively. To solve this problem, we propose a graph reasoning module (GRM), which can be directly incorporated into CNN-based AD detection models to simulate the underlying relationship between different brain regions and boost AD diagnosis performance. Specifically, in GRM, an adaptive graph Transformer (AGT) block is designed to adaptively construct a graph representation based on the feature map given by CNN, a graph convolutional network (GCN) block is adopted to update the graph representation, and a feature map reconstruction (FMR) block is built to convert the learned graph representation to a feature map. Experimental results demonstrate that the insertion of the GRM in the existing AD classification model can increase its balanced accuracy by more than 4.3%. The GRM-embedded model achieves state-of-the-art performance compared with current deep learning-based AD diagnosis methods, with a balanced accuracy of 86.2%. [ABSTRACT FROM AUTHOR]

Objective: This review aims to the existing structural neuroimaging literature in Parkinson disease presenting with freezing of gait. The summary of this article provides an opportunity for a better understanding of the structural findings of freezing of gait in Parkinson disease based on MRI. Methods: This systematic review of literature follows the procedures as described by the guideline of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Initial searches yielded 545 documents. After exclusions, 11 articles were included into our study. Current findings of structural MRI on freezing of gait in Parkinson disease are associated with structural damage between sensorimotor-related cortical grey matter structures and thalamus, but not cerebellum and smaller systems, as well as extensive injuries on white matter connecting between those structures. Conclusion: Current findings of structural MRI on freezing of gait in Parkinson disease are associated with structural damage between sensorimotorrelated cortical grey matter structures and thalamus, but not cerebellum and smaller systems, as well as extensive injuries on white matter connecting between those structures. [ABSTRACT FROM AUTHOR]

Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging. Currently, most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features, which cannot fully utilize the gray-scale values containing important intrinsic information about brain structure. In this study, we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly. Normalized network structure entropy (NNSE) is then introduced to quantify the overall heterogeneities of these graphs. The results demonstrate a decrease in the NNSEs of gray matter with age. Compared with the middle-aged and the elderly, the larger values of the NNSE in the younger group may indicate more homogeneous network structures, smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion. In addition, the hub nodes of different adult age groups are primarily located in the precuneus, cingulate gyrus, superior temporal gyrus, inferior temporal gyrus, parahippocampal gyrus, insula, precentral gyrus and postcentral gyrus. Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging. [ABSTRACT FROM AUTHOR]

In this study, we automated the diagnostic procedure of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI data was preprocessed using the FreeSurfer toolbox. Further, the brain regions were segmented into 148 regions of interest using the Destrieux atlas. Features such as volume, thickness, surface area, and mean curvature were extracted for each brain region, and the morphological connectivity was computed using Pearson correlation. These morphological connections were fed to XGBoost for feature reduction and to build the diagnostic model. The results showed an average accuracy of 94.16% for the top 18 features. The frontal and limbic regions contributed more features to the classification model. Our proposed method is thus effective for the classification of ASD and can also be useful for the screening of other similar neurological disorders. [ABSTRACT FROM AUTHOR]

Objective: To assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests. Methods: Twenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests. Results: We found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups. Conclusion: These results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE. [ABSTRACT FROM AUTHOR]

Objectives: Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the "top‐down" attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early‐stage AD. Methods: Resting‐state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low‐frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver‐operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI. Results: The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages. Conclusions: Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early‐stage AD for their high diagnostic value. [ABSTRACT FROM AUTHOR]

Introduction: Pediatric growth hormone deficiency (GHD) is a disease resulting from impaired growth hormone/insulin-like growth factor-1 (IGF-1) axis but the effects of GHD on children’s cognitive function, brain structure and brain function were not yet fully illustrated. Methods: Full Wechsler Intelligence Scales for Children, structural imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging were assessed in 11 children with GHD and 10 matched healthy controls. Results: (1) The GHD group showed moderate cognitive impairment, and a positive correlation existed between IGF-1 levels and cognitive indices. (2) Mean diffusivity was significantly increased in both corticospinal tracts in GHD group. (3) There were significant positive correlations between IGF-1 levels and volume metrics of left thalamus, left pallidum and right putamen but a negative correlation between IGF-1 levels and cortical thickness of the occipital lobe. And IGF-1 levels negatively correlated with fractional anisotropy in the superior longitudinal fasciculus and right corticospinal tract. (4) Regional homogeneity (ReHo) in the left hippocampus/parahippocampal gyrus was negatively correlated with IGF-1 levels; the amplitude of low-frequency fluctuation (ALFF) and ReHo in the paracentral lobe, postcentral gyrus and precentral gyrus were also negatively correlated with IGF-1 levels, in which region ALFF fully mediates the effect of IGF-1 on working memory index. Conclusion: Multiple subcortical, cortical structures, and regional neural activities might be influenced by serum IGF-1 levels. Thereinto, ALFF in the paracentral lobe, postcentral gyrus and precentral gyrus fully mediates the effect of IGF-1 on the working memory index. [ABSTRACT FROM AUTHOR]

Background: New daily persistent headache (NDPH) is a rare but debilitating primary headache disorder that poses a significant burden on individuals and society. Despite its clinical importance, the underlying pathophysiological mechanisms of NDPH remain unclear. In this study, we aimed to investigate the brain structural changes and neural activity patterns in patients with NDPH using multimodal brain imaging analysis of structural magnetic resonance imaging (sMRI) combined with magnetoencephalography (MEG). Methods: Twenty-eight patients with NDPH and 37 healthy controls (HCs) were recruited for this study, and their structural and resting-state data were collected by 3.0 Tesla MRI and MEG. We analyzed the brain morphology using voxel-based morphometry and source-based morphometry. In each brain region, MEG sensor signals from 1 to 200 Hz were analyzed using an adapted version of Welch's method. MEG source localization was conducted using the dynamic statistical parametric mapping, and the difference of source distribution between patients with NDPH and HCs was examined. Results: Our results revealed significant differences in the regional grey matter volume, cortical thickness, and cortical surface area between the two groups. Specifically, compared with HCs, patients with NDPH showed a significant decrease in cortical thickness of the left rostral cortex in the middle frontal gyrus, decreased cortical surface area of the left fusiform gyrus, decreased grey matter volume of the left superior frontal gyrus and the left middle frontal gyrus, and increased grey matter volume of the left calcarine. Furthermore, the power of the whole brain, bilateral frontal lobes, and right temporal lobe in the NDPH group were higher than that in HCs in the ripple frequency band (80-200 Hz). Functional and structural analysis suggested that there were structural changes and abnormal high frequency cortical activity in both frontal and temporal lobes in patients with NDPH. Conclusion: Our findings indicated that patients with NDPH have abnormalities in brain morphology, such as cortical area, cortical thickness, and grey matter volume, accompanied by abnormal cortical neural activity. Brain structural changes in the frontotemporal cortex and abnormalities in cortical ripple activity may be involved in the pathogenesis of NDPH. [ABSTRACT FROM AUTHOR]

Several lines of evidence point towards the involvement of the cerebellum in reactive aggression. In addition to the posterior cerebellar hemisphere, the vermis has been suggested to play a prominent role in impulse regulation. In the present study, we set out to further examine the relationships between cerebellar grey matter volumes, aggression, and impulsivity in 201 healthy volunteers. 3 T structural magnetic resonance imaging scans were acquired to investigate grey matter volumes of the cerebellar vermis and the anterior and posterior lobules. Aggression was assessed with the Buss–Perry Aggression Questionnaire and impulsivity was measured with the Barratt Impulsiveness Scale-11. Results showed that impulsivity was positively associated with grey matter volumes of the cerebellar vermis and inversely correlated with grey matter volumes of the right posterior lobule. In addition, smaller volumes of the right posterior lobules were associated with higher physical aggression. Exploratory analyses indicated that for the right hemisphere, this association was driven by grey matter volumes of lobules VIIb and VIIIa. Our findings provide correlational evidence in healthy volunteers for the involvement of the cerebellar vermis and posterior lobules in a cortico-limbic-cerebellar circuit of aggression. [ABSTRACT FROM AUTHOR]

Purpose: Structural Magnetic Resonance Imaging (MRI) of the brain is an effective way to study its internal structure. Identifying and classifying brain malignancies is a difficult and onerous task commonly handled by radiologists. Digital image processing processes, such as preprocessing, segmentation, and classification, can help clinical specialists diagnose certain types of brain cancers in addition to detecting the precise location of tumors and studying minute alterations. The state of brain tumor identification using MRI scans is discussed in this survey using numerous state-of-the-art machine learning and deep learning approaches. Methods: This review highlights brain tumor image segmentation techniques, publicly available datasets, deep learning techniques, and deep learning architectures used by various researchers in the process of brain tumor detection. Additionally, the study presents a comprehensive review of the performance of existing deep learning algorithms, challenges, and future research directions. Results: Various methods proposed so far have been compared based on their accuracy. Many studies have attained an accuracy of more than 98% using SVM in the segmentation and analysis of brain tumor detection using MR images. The ANN outperformed all the other algorithms with 99% accuracy in brain tumor detection using deep learning. Conclusion: This review's objective is to increase scholars' interest in this difficult field and familiarize them with current advancements in it. To create CAD systems aimed at brain tumor identification using MR images, digital image processing approaches, such as preprocessing, segmentation, and classification, are applied. The classic machine learning and deep learning approaches for brain tumor identification are deliberated in this work. This paper provides a summary of commonly used MR image datasets. For classification, various machine learning and deep learning algorithms have been used. This survey examines current methodologies and can be used in the future to develop effective diagnostic plans for other brain disorders such as dementia, Alzheimer's disease, stroke, and Parkinson's disease using various Magnetic Resonance imaging modalities. [ABSTRACT FROM AUTHOR]