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Abstract 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

Abstract 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

ABSTRACT Purpose The objective of this study is to examine the alterations in subcortical brain volume and cortical thickness among individuals diagnosed with Type 2 diabetes mellitus (T2DM) through the application of morphometry techniques and, additionally, to investigate the potential association between these modifications and insulin resistance (IR). Materials and methods The present cross‐sectional study comprised a total of 121 participants (n = 48 with healthy controls [HCs] and n = 73 with T2DM) who were recruited and underwent a battery of cognitive testing and structural magnetic resonance imaging (MRI). FreeSurfer was used to process the MRI data. Analysis of covariance compared discrepancies in cortical thickness and subcortical brain volume between T2DM and HCs, adjusting for the potential confounding effects of gender, age, education, and body mass index (BMI). Exploratory partial correlations investigated links between IR and brain structure in T2DM participants. Results Compared with HCs, individuals with T2DM demonstrated a cortical thickness decrease in the right caudal middle frontal gyrus, right pars opercularis, left precentral gyrus, and bilateral superior frontal gyrus. Furthermore, this study for T2DM found that the severity of IR was inversely related to the volume of the left putamen and left hippocampus, as well as the thickness of the left pars orbitalis, left pericalcarine, right entorhinal area, and right rostral anterior cingulate gyrus. Conclusion The evidence for structural brain changes in T2DM was observed, and alterations in cortical thickness were concentrated in the frontal lobes. Correlations between IR and frontal cortical thinning may serve as a potential neuroimaging marker of T2DM and lead to various diabetes‐related brain complications.

ObjectiveAutism 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.MethodsA 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.ResultsOut 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.ConclusionStructural 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.

IntroductionCognitive 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.MethodsForty-seven ESRD patients and 45 control subjects underwent whole-brain high-resolution 3D T1-weighted imaging scans and neuropsychological assessments. The voxel-based 3D brain TA was performed to examine inter-group 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.ResultsCompared 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

Lasting thalamus volume reduction after preterm birth is a prominent finding. However, whether thalamic nuclei volumes are affected differentially by preterm birth and whether nuclei aberrations are relevant for cognitive functioning remains unknown.Using T1-weighted MR-images of 83 adults born very preterm (≤ 32 weeks’ gestation; VP) and/or with very low body weight (≤ 1,500 g; VLBW) as well as of 92 full-term born (≥ 37 weeks’ gestation) controls, we compared thalamic nuclei volumes of six subregions (anterior, lateral, ventral, intralaminar, medial, and pulvinar) across groups at the age of 26 years. To characterize the functional relevance of volume aberrations, cognitive performance was assessed by full-scale intelligence quotient using the Wechsler Adult Intelligence Scale and linked to volume reductions using multiple linear regression analyses.Thalamic volumes were significantly lower across all examined nuclei in VP/VLBW adults compared to controls, suggesting an overall rather than focal impairment. Lower nuclei volumes were linked to higher intensity of neonatal treatment, indicating vulnerability to stress exposure after birth. Furthermore, we found that single results for lateral, medial, and pulvinar nuclei volumes were associated with full-scale intelligence quotient in preterm adults, albeit not surviving correction for multiple hypotheses testing.These findings provide evidence that lower thalamic volume in preterm adults is observable across all subregions rather than focused on single nuclei. Data suggest the same mechanisms of aberrant thalamus development across all nuclei after premature birth.

Abstract Background Alzheimer’s disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer’s disease. Methods Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer’s disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer’s disease was assessed by stepwise regression. Results Compared to cognitively normal participants, individuals with Alzheimer’s disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer’s disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer’s disease. Conclusions Our study suggested that individuals with Alzheimer’s disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer’s disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer’s disease.

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.

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

Abstract 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.

Objective: Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC). Materials and methods: Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback–Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline. Results: The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p

BackgroundVascular 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.MethodsA 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.ResultsThe 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.ConclusionPatients 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.

BackgroundTrigeminal 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.MethodsT1 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.ResultsSignificant 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.ConclusionThe 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.

Background: Non-suicidal self-injury (NSSI) involves repetitive self-harm without suicidal intent and is common among adolescents, often linked to major depressive disorder (MDD). NSSI can lead to physical harm, cognitive impairments, interpersonal issues, violent behavior, and increased risks of psychological disorders and suicide attempts later in life. Methods: Voxel-based morphometry (VBM) and surface-based morphometry (SBM) were performed on 44 NSSI patients and 44 healthy controls (HCs). Differences in GMV, CT, and cortical complexity were compared using the two-sample t-tests and correlated with neuropsychological scales. Results: NSSI patients exhibited significant GMV atrophy in multiple regions, including the left insula, left anterior cingulate cortex, left putamen, left middle frontal gyrus, and right superior frontal gyrus showing increased GMV in the cerebellum posterior lobe. NSSI patients had increased CT in multiple left hemisphere regions and decreased CT in the right middle frontal gyrus. Additionally, they exhibited reduced cortical complexity, including decreased SD in the right frontal gyrus, and lower GI in the left insula. There were no significant differences between the two groups in terms of fractal dimension (FD). NSSI patients showed negative correlation between the CT of the right middle frontal gyrus and the anger dimension of the BPAQ, as well as the SD of the right superior frontal gyrus and the hostility dimension of the BPAQ. Conclusion: NSSI patients have significant structural changes in the insular cortex, prefrontal cortex, precentral and postcentral gyrus, temporal lobe, putamen, and anterior cingulate cortex, offering a morphological perspective on the pathophysiology of NSSI in MDD.

Anxiety has been associated with a greater risk of Alzheimer's disease (AD). Existing research has identified structural differences in regional brain tissue in participants with anxiety, but results have been inconsistent. We sought to determine the association between anxiety and regional brain volumes, and the moderation effect of APOE ε4. Using data from participants in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set, with complete imaging (MRI) and biomarker data (n = 1533), multiple linear regression estimated the adjusted effect of anxiety on 30 structural MRI regions. The moderation effect of APOE ε4 on the relation between structural MRI regions and anxiety was assessed as was the moderation effect of cognitive status. False discovery rate was used to adjust for multiple comparisons. After controlling for intracranial volume, age, sex, years of education, race, Hispanic ethnicity, and cognitive status, seven MRI regions demonstrated lower volumes among participants with anxiety: total cerebrum gray matter volume, right hippocampus volume, hippocampal volume (total), right and left frontal lobe cortical gray matter volume, and right and total temporal lobe cortical gray matter volume. Findings suggest that anxiety is associated with significant atrophy in multiple brain regions, with corresponding ventricular enlargement. Future research should investigate if anxiety-related changes to brain morphology contribute to greater AD risk.

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.

Abstract 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.

Based on the mining of micro- and macro-relationships of genetic variation and brain imaging data, imaging genetics has been widely applied in the early diagnosis of Alzheimer's disease (AD). However, effective integration of prior knowledge remains a barrier to determining the biological mechanism of AD. This paper proposes a new connectivity-based orthogonal sparse joint non-negative matrix factorization (OSJNMF-C) method based on integrating the structural magnetic resonance image, single nucleotide polymorphism and gene expression data of AD patients; the correlation information, sparseness, orthogonal constraint and brain connectivity information between the brain image data and genetic data are designed as constraints in the proposed algorithm, which efficiently improved the accuracy and convergence through multiple iterative experiments. Compared with the competitive algorithm, OSJNMF-C has significantly smaller related errors and objective function values than the competitive algorithm, showing its good anti-noise performance. From the biological point of view, we have identified some biomarkers and statistically significant relationship pairs of AD/mild cognitive impairment (MCI), such as rs75277622 and BCL7A, which may affect the function and structure of multiple brain regions. These findings will promote the prediction of AD/MCI.

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%.

Background and objectivesIntermittent 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 iTBS-induced 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.MethodsA total of 24 PD patients will be assessed in a randomized, double-blind, 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.DiscussionThe 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.

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.

BackgroundThe 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.MethodsA 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).ResultsWe 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.ConclusionThese 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.

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.

ObjectiveTo 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.MethodsTwenty-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.ResultsWe 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.ConclusionThese 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.

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.

Xiaoxin Zhao,1 Shuyi Zhu,1 Yang Cao,2 Peipei Cheng,1 Yuxiong Lin,1 Zhixin Sun,1 Wenqing Jiang,1 Yasong Du1 1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Suzhou Guangji Hospital, Suzhou, People’s Republic of ChinaCorrespondence: Yasong Du, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600 Wanping Nan Road, Shanghai, 200030, People’s Republic of China, Tel +86 18816935313, Email yasongdu2020@126.comBackground: Previous studies have indicated abnormal gray matter volume (GMV) in individuals with autism spectrum disorder (ASD); however, there is little consistency across the findings within these studies, partly due to small sample size and great heterogeneity among participants between studies. Additionally, few studies have explored the correlation between clinical symptoms and GMV abnormalities in individuals with ASD. Here, the current study examined GMV alterations in whole brain and their correlations with clinical symptoms in a relatively large and homogeneous sample of participants with ASD matched typically developing (TD) controls.Methods: Forty-eight adolescents with high-functioning ASD and 29 group-matched TD controls underwent structural magnetic resonance images. Voxel-based morphometry was applied to investigate regional GMV alterations. The participants with ASD were examined for the severity of clinical symptoms with Autism Behavior Checklist (ABC). The relationship between GMV abnormalities and clinical symptoms was explored in ASD group using voxel-wise correlation analysis within brain regions that showed significant GMV alterations in individuals with ASD compared with TD controls.Results: We found increased GMV in multiple brain regions, including the inferior frontal gyrus, medial frontal gyrus, superior frontal gyrus, superior temporal gyrus, occipital pole, anterior cingulate, cerebellum anterior lobe, cerebellum posterior lobe, and midbrain, as well as decreased GMV in cerebellum posterior lobe in individuals with ASD. The correlation analysis showed the GMV in the left fusiform was negatively associated with the scores of sensory factor, and the GMV in the right cerebellum anterior lobe was positively associated with the scores of social self-help factor.Conclusion: Our results indicated that widespread GMV abnormalities of brain regions occurred in individuals with ASD, suggesting a potential neural basis for the pathogenesis and symptomatology of ASD.Keywords: autism spectrum disorder, symptoms severity, Autism Behavior Checklist, structural magnetic resonance imaging, gray matter volume

Abstract Deep learning (DL) on brain magnetic resonance imaging (MRI) data has shown excellent performance in differentiating individuals with Alzheimer's disease (AD). However, the value of DL in detecting progressive structural MRI (sMRI) abnormalities linked to AD pathology has yet to be established. In this study, an interpretable DL algorithm named the Ensemble of 3‐dimensional convolutional neural network (Ensemble 3DCNN) with enhanced parsing techniques is proposed to investigate the longitudinal trajectories of whole‐brain sMRI changes denoting AD onset and progression. A set of 2369 T1‐weighted images from the multi‐centre Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies cohorts are applied to model derivation, validation, testing, and pattern analysis. An Ensemble‐3DCNN‐based P‐score is generated, based on which multiple brain regions, including amygdala, insular, parahippocampal, and temporal gyrus, exhibit early and connected progressive neurodegeneration. Complex individual variability in the sMRI is also observed. This study combining non‐invasive sMRI and interpretable DL in detecting patterned sMRI changes confirmed AD pathological progression, shedding new light on predicting AD progression using whole‐brain sMRI.

BackgroundDepression in Parkinson’s disease (PD) is identified and diagnosed with behavioral observations and neuropsychological measurements. Due to the large overlaps of depression and PD symptoms in clinical manifestations, it is challenging for neurologists to distinguish and diagnose depression in PD (DPD) in the early clinical stage of PD. The advancement in magnetic resonance imaging (MRI) technology provides potential clinical utility in the diagnosis of DPD. This study aimed to explore the alterations of functional and structural MRI in DPD to produce neuroimaging markers in discriminating DPD from non-depressed PD (NDPD) and healthy controls (HC).MethodsWe recruited 20 DPD, 37 NDPD, and 41 HC matched in age, gender, and education years. The patients’ diagnosis with PD was de novo. The differences in regional homogeneity (ReHo), voxel-wise degree centrality (DC), cortical thickness, cortical gray matter (GM) volumes, and subcortical GM volumes among these groups were detected, and the relationship between altered indicators and depression was analyzed. Moreover, the receiver operating characteristic (ROC) analysis was performed to assess the diagnostic efficacy of altered indicators for DPD.ResultsCompared to NDPD and HC, DPD showed significantly increased ReHo in left dorsolateral superior frontal gyrus (DSFG) and DC in left inferior temporal gyrus (ITG), and decreased GM volumes in left temporal lobe and right Amygdala. Among these altered indicators, ReHo value in left DSFG and DC values in left ITG and left DSFG were significantly correlated with the severity of depression in PD patients. Comparing DPD and NDPD, the ROC analysis revealed a better area under the curve value for the combination of ReHo value in left DSFG and DC value in left ITG, followed by each independent indicator. However, the difference is not statistically significant.ConclusionThis study demonstrates that both functional and structural impairments are present in DPD. Among them, ReHo value of left DSFG and DC value of left ITG are equally well suited for the diagnosis and differential diagnosis of DPD, with a combination of them being slightly preferable. The multimodal MRI technique represents a promising approach for the classification of subjects with PD.

BackgroundWhether alteration in regional brain volumes can be detected in Type A alcoholics both at baseline and after a long follow-up remains to be confirmed. Therefore, we examined volume alterations at baseline, and longitudinal changes in a small follow-up subsample.MethodsIn total of 26 patients and 24 healthy controls were assessed at baseline using magnetic resonance imaging and voxel-based morphometry, among which 17 patients and 6 controls were re-evaluated 7 years later. At baseline, regional cerebral volumes of patients were compared to controls. At follow-up, three groups were compared: abstainers (n = 11, more than 2 years of abstinence), relapsers (n = 6,

ObjectiveThis study explored the structural imaging changes in patients with subcortical ischemic vascular disease (SIVD)-vascular cognitive impairment no dementia (VCIND) and the correlation between the changes in gray matter volume and the field of cognitive impairment to provide new targets for early diagnosis and treatment.MethodsOur study included 15 patients with SIVD-normal cognitive impairment (SIVD-NCI), 63 with SIVD-VCIND, 26 with SIVD-vascular dementia (SIVD-VD), and 14 normal controls (NC). T1-weighted images of all participants were collected, and DPABI and SPM12 software were used to process the gray matter of the four groups based on voxels. Fisher’s exact test, one-way ANOVA and Kruskal-Wallis H test were used to evaluate all clinical and demographic data and compare the characteristics of diencephalic gray matter atrophy in each group. Finally, the region of interest (ROI) of the SIVD-VCIND was extracted, and Pearson correlation analysis was performed between the ROI and the results of the neuropsychological scale.ResultsCompared to the NC, changes in gray matter atrophy were observed in the bilateral orbitofrontal gyrus, right middle temporal gyrus, superior temporal gyrus, and precuneus in the SIVD-VCIND. Gray matter atrophy was observed in the left cerebellar region 6, cerebellar crural region 1, bilateral thalamus, right precuneus, and calcarine in the SIVD-VD. Compared with the SIVD-VCIND, gray matter atrophy changes were observed in the bilateral thalamus in the SIVD-VD (p

IntroductionSubjective Cognitive Decline (SCD) can progress to mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia and thus may represent a preclinical stage of the AD continuum. However, evidence about structural changes observed in the brain during SCD remains inconsistent.Materials and methodsThis cross-sectional study aimed to evaluate, in subjects recruited from the CompAS project, neurocognitive and neurostructural differences between a group of forty-nine control subjects and forty-nine individuals who met the diagnostic criteria for SCD and exhibited high levels of subjective cognitive complaints (SCCs). Structural magnetic resonance imaging was used to compare neuroanatomical differences in brain volume and cortical thickness between both groups.ResultsRelative to the control group, the SCD group displayed structural changes involving frontal, parietal, and medial temporal lobe regions of critical importance in AD etiology and functionally related to several cognitive domains, including executive control, attention, memory, and language.ConclusionDespite the absence of clinical deficits, SCD may constitute a preclinical entity with a similar (although subtle) pattern of neuroanatomical changes to that observed in individuals with amnestic MCI or AD dementia.

Major depression disorder is one of the diseases with the highest rate of disability and morbidity and is associated with numerous structural and functional differences in neural systems. However, it is difficult to analyze digital medical imaging data without computational intervention. A voxel-wise densely connected convolutional neural network, Three-dimensional Densenet (3D-DenseNet), is proposed to mine the feature differences. In addition, a novel transfer learning method, called Alzheimer’s Disease Neuroimaging Initiative Transfer (ADNI-Transfer), is designed and combined with the proposed 3D-DenseNet. The experimental results on a database that contains 174 subjects, including 99 patients with major depression disorder and 75 healthy controls, show that large changes in brain structures between major depressive disorder patients and healthy controls mainly are located in the regions including superior frontal gyrus, dorsolateral, middle temporal gyrus, middle frontal gyrus, postcentral gyrus, inferior temporal gyrus. In addition, the proposed deep learning network can better extract different features of brain structures between major depressive disorder patients and healthy controls and achieve excellent classification results of major depressive disorder. At the same time, the designed transfer learning method can further improve classification performance. These results verify that our proposed method is feasible and valid for diagnosing and analyzing major depression disorder.

IntroductionPediatric 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.MethodsFull 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.ConclusionMultiple 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.

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.

Structural changes in the brain due to Alzheimer’s disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area. In this study, we developed a 2-dimensional convolutional neural network-based classification model by adopting several methods, such as using instance normalization layer, Mixup, and sharpness aware minimization. To train the model, MRI images from 2,765 cognitively normal individuals and 1,192 patients with ADD from the Samsung medical center cohort were exploited. To assess the reliability of our classification model, we designed external validation in multiple scenarios: (1) multi-cohort validation using four additional cohort datasets including more than 30 different centers in multiple countries, (2) multi-vendor validation using three different MRI vendor subgroups, (3) LRMRI image validation, and finally, (4) head-to-head validation using ten pairs of MRI images from ten individual subjects scanned in two different centers. For multi-cohort validation, we used the MRI images from 739 subjects from the Alzheimer’s Disease Neuroimaging Initiative cohort, 125 subjects from the Dementia Platform of Korea cohort, 234 subjects from the Premier cohort, and 139 subjects from the Gachon University Gil Medical Center. We further assessed classification performance across different vendors and protocols for each dataset. We achieved a mean AUC and classification accuracy of 0.9868 and 0.9482 in 5-fold cross-validation. In external validation, we obtained a comparable AUC of 0.9396 and classification accuracy of 0.8757 to other cross-validation studies in the ADNI cohorts. Furthermore, we observed the possibility of broad clinical application through LRMRI image validation by achieving a mean AUC and classification accuracy of 0.9404 and 0.8765 at cross-validation and AUC and classification accuracy of 0.8749 and 0.8281 at the ADNI cohort external validation.

Subjective cognitive decline (SCD) is considered the first stage of Alzheimer’s disease (AD). Accurate diagnosis and the exploration of the pathological mechanism of SCD are extremely valuable for targeted AD prevention. However, there is little knowledge of the specific altered morphological network patterns in SCD individuals. In this present study, 36 SCD cases and 34 paired-matched normal controls (NCs) were recruited. The Jensen-Shannon distance-based similarity (JSS) method was implemented to construct and derive the attributes of multiple brain connectomes (i.e., morphological brain connections and global and nodal graph metrics) of individual morphological brain networks. A t-test was used to discriminate between the selected nodal graph metrics, while the leave-one-out cross-validation (LOOCV) was used to obtain consensus connections. Comparisons were performed to explore the altered patterns of connectome features. Further, the multiple kernel support vector machine (MK-SVM) was used for combining brain connectomes and differentiating SCD from NCs. We showed that the consensus connections and nodal graph metrics with the most discriminative ability were mostly found in the frontal, limbic, and parietal lobes, corresponding to the default mode network (DMN) and frontoparietal task control (FTC) network. Altered pattern analysis demonstrated that SCD cases had a tendency for modularity and local efficiency enhancement. Additionally, using the MK-SVM to combine the features of multiple brain connectomes was associated with optimal classification performance [area under the curve (AUC): 0.9510, sensitivity: 97.22%, specificity: 85.29%, and accuracy: 91.43%]. Therefore, our study highlighted the combination of multiple connectome attributes based on morphological brain networks and offered a valuable method for distinguishing SCD individuals from NCs. Moreover, the altered patterns of multidimensional connectome attributes provided a promising insight into the neuroimaging mechanism and early intervention in SCD subjects.

Background: The frontal lobe is affected by Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, we still lack sufficient understanding of subregion atrophy in the frontal cortex, and the relationship between subregions volume and cognitive decline in AD or MCI remains unclear. Methods: This study enrolled 434 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including 150 cognitively normals (CN), 187 subjects with MCI, and 97 patients with AD. The gray matter of frontal regions and subregions was divided based on the BNA-246 atlas and its volume was measured by voxel-based morphometry (VBM). Analysis of covariance was performed to compare the differences in frontal regions and subregions volume. Then, receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to analyze the discriminative ability of subregion volume to distinguish the three groups. In addition, we investigated the association of subregion volume with Mini-Mental State Examination (MMSE) score and Alzheimer’s Disease Assessment Scale-Cognitive Behavior section (ADAS-cog) scores with age, gender, education, and the estimated total intracranial volume (eTIV) as covariates. Results: In addition to the regions of frontal lobe atrophy found in previous studies, atrophy of the precentral gyrus (PrG) and some of its subregions were found in MCI. The volume of the right dorsal area 9/46 (MFG_7_1) was the best index to differentiate AD from CN, with an AUC value of 0.7. Moreover, we found that some subregions are associated with cognition in patients with MCI and AD. Conclusions: Frontal lobe atrophy in MCI is more extensive than we assumed. In addition, the volume of right MFG_7_1 has the potential to distinguish AD from CN.

Abstract Background Self-esteem is the individual evaluation of oneself. People with high self-esteem grade have mental health and can bravely cope with the threats from the environment. With the development of neuroimaging techniques, researches on cognitive neural mechanisms of self-esteem are increased. Existing methods based on brain morphometry and single-layer brain network cannot characterize the subtle structural differences related to self-esteem. Method To solve this issue, we proposed a multiple anatomical brain network based on multi-resolution region of interest (ROI) template to study the brain structural connections of self-esteem. The multiple anatomical brain network consists of ROI features and hierarchal brain network features that are extracted from structural MRI. For each layer, we calculated the correlation relationship between pairs of ROIs. In order to solve the high-dimensional problem caused by the large amount of network features, feature selection methods (t-test, mRMR, and SVM-RFE) are adopted to reduce the number of features while retaining discriminative information to the maximum extent. Multi-kernel SVM is employed to integrate the various types of features by appropriate weight coefficient. Result The experimental results show that the proposed method can improve classification accuracy to 97.26% compared with single-layer brain network. Conclusions The proposed method provides a new perspective for the analysis of brain structural differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.

Background and PurposeAccording to reports, type 2 diabetes (T2D) is a progressive disease. However, no known research has examined the progressive brain structural changes associated with T2D. The purpose of this study was to determine whether T2D patients exhibit progressive brain structural alterations and, if so, how the alterations progress.Materials and MethodsStructural magnetic resonance imaging scans were collected for 81 T2D patients and 48 sex-and age-matched healthy controls (HCs). Voxel-based morphometry (VBM) and causal structural covariance network (CaSCN) analyses were applied to investigate gray matter volume (GMV) alterations and the likely chronological processes underlying them in T2D. Two sample t-tests were performed to compare group differences, and the differences were corrected using Gaussian random field (GRF) correction (voxel-level p < 0.001, cluster-level p < 0.01).ResultsOur findings demonstrated that GMV alterations progressed in T2D patients as disease duration increased. In the early stages of the disease, the right temporal pole of T2D patients had GMV atrophy. As the diseases duration prolonged, the limbic system, cerebellum, subcortical structures, parietal cortex, frontal cortex, and occipital cortex progressively exhibited GMV alterations. The patients also exhibited a GMV alterations sequence exerting from the right temporal pole to the limbic-cerebellum-striatal-cortical network areas.ConclusionOur results indicate that the progressive GMV alterations of T2D patients manifested a limbic-cerebellum-striatal-cortical sequence. These findings may contribute to a better understanding of the progression and an improvement of current diagnosis and intervention strategies for T2D.

Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people’s quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients’ brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.

Background and objectives: Type 2 diabetes mellitus (T2DM) is an important risk factors for mild cognitive impairment (MCI). Structural magnetic resonance imaging (sMRI) is an effective and widely used method to investigate brain pathomorphological injury in neural diseases. In present study, we aimed to determine the brain regional alterations that correlated to the incidence of MCI in T2DM patients. Materials and methods: Eighteen T2DM patients with and without MCI (DMCI/T2DM) respectively, and eighteen age/gender-matched healthy controls (HC) were recruited. Brain MRI imagines of all the individuals were subjected to automatic quantified brain sub-structure volume segmentation and measurement by Dr. brain ™ software. The relative volume of total gray matter (TGM), total white matter (TWM), and 68 pairs (left and right) of brain sub-structures were compared between the three groups. Cognitive function correlation analysis and receiver operating characteristic (ROC) curve analysis were conducted in the MCI-related brain regions in T2DM patients, and we utilized a machine learning method to classify the three group of subjects. Results: 10 and 27 brain sub-structures with significant relative volumetric alterations were observed in T2DM patients without MCI and T2DM patients with MCI, respectively (p < 0.05). Compared with T2DM patients without MCI, eight critical regions include right anterior orbital gyrus, right calcarine and cerebrum, left cuneus, left entorhinal area, left frontal operculum, right medial orbital gyrus, right occipital pole, left temporal pole had significant lower volumetric ratio in T2DM patients with MCI (p < 0.05). Among them, the decrease of volumetric ratio in several regions had a positive correlation with Montreal Cognitive Assessment (MoCA) scores and Mini-Mental State Examination (MMSE) scores. The classification results conducted based on these regions as features by random forest algorithm yielded good accuracies of T2DM/HC 69.4%, DMCI/HC 72.2% and T2DM/DMCI 69.4%. Conclusions: Certain brain regional structural lesions occurred in patients with T2DM, and this condition was more serious in T2DM patients combined with MCI. A systematic way of segmenting and measuring the whole brain has a potential clinical value for predicting the presence of MCI for T2DM patients.

Abstract Background The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer’s disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD. Methods In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in “FTD dominant” structures over that obtained from “AD dominant” structures. The weights and the identification of “FTD/AD dominant” structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases. Results AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p

Neurological abnormalities identified via neuroimaging are common in patients with Alzheimer’s disease. However, it is not yet possible to easily detect these abnormalities using head computed tomography in the early stages of the disease. In this review, we evaluated the ways in which modern imaging techniques such as positron emission computed tomography, single photon emission tomography, magnetic resonance spectrum imaging, structural magnetic resonance imaging, magnetic resonance diffusion tensor imaging, magnetic resonance perfusion weighted imaging, magnetic resonance sensitive weighted imaging, and functional magnetic resonance imaging have revealed specific changes not only in brain structure, but also in brain function in Alzheimer’s disease patients. The reviewed literature indicated that decreased fluorodeoxyglucose metabolism in the temporal and parietal lobes of Alzheimer’s disease patients is frequently observed via positron emission computed tomography. Furthermore, patients with Alzheimer’s disease often show a decreased N-acetylaspartic acid/creatine ratio and an increased myoinositol/creatine ratio revealed via magnetic resonance imaging. Atrophy of the entorhinal cortex, hippocampus, and posterior cingulate gyrus can be detected early using structural magnetic resonance imaging. Magnetic resonance sensitive weighted imaging can show small bleeds and abnormal iron metabolism. Task-related functional magnetic resonance imaging can display brain function activity through cerebral blood oxygenation. Resting functional magnetic resonance imaging can display the functional connection between brain neural networks. These are helpful for the differential diagnosis and experimental study of Alzheimer’s disease, and are valuable for exploring the pathogenesis of Alzheimer’s disease.

PurposeThis study aimed to develop machine learning models for the diagnosis of Parkinson’s disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.MethodsBrain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson’s Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson’s correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.ResultsAfter feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong’s test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.ConclusionThe combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

Background: Stop, Now And Plan (SNAP) is a cognitive behavioral-based psychosocial intervention that has a strong evidence base for treating youth with high aggression and externalizing behaviors, many of whom have disruptive behavior disorders. In a pre-post design, we tested whether SNAP could improve externalizing behaviors, assessed by the parent-rated Child Behavior Checklist (CBCL) and also improve behavioral measures of impulsivity in children with high aggression and impulsivity. We then investigated whether any improvement in externalizing behavior or impulsivity was associated with gray matter volume (GMV) changes assessed using structural magnetic resonance imaging (sMRI). We also recruited typically developing youth who were assessed twice without undergoing the SNAP intervention.Methods: Ten children who were participating in SNAP treatment completed the entire study protocol. CBCL measures, behavioral measures of impulsivity, and sMRI scanning was conducted pre-SNAP and then 13 weeks later post-SNAP. Twelve healthy controls also completed the study; they were rated on the CBCL, performed the same behavioral measure of impulsivity, and underwent sMRI twice, separated by 13 weeks. They did not receive the SNAP intervention.Result: At baseline, SNAP participants had higher CBCL scores and performed worse on the impulsivity task compared with the healthy controls. At the second visit, SNAP participants still had higher scores on the CBCL compared with normally-developing controls, but their performance on the impulsivity task had improved to the point where their results were indistinguishable from the healthy controls. Structural magnetic resonance imaging in the SNAP participants further revealed that improvements in impulsivity were associated with GMV changes in the frontotemporal region.Conclusion: These results suggest that SNAP led to improvement in behavioral measures of impulsivity in a cohort of boys with high externalizing behavior. Improvement in impulsivity was also associated with increased GMV changes. The mechanism behind these brain changes is unknown but could relate to cognitive behavioral therapy and contingency management interventions, important components of SNAP, that target frontotemporal brain regions. Clinically, this study offers new evidence for the potential targeting of brain regions by non-invasive modalities, such as repetitive transcranial magnetic stimulation, to improve externalizing behavior and impulsivity.

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.

Parkinson’s disease (PD) is a common neurodegenerative disease with progressive gait, cognition, and overall functional decline. Surface area changes are frequently seen with aging. In neurodegenerative diseases, the changes can be evident with disease progression. The current study aimed to study the regional microstructural alterations using surface-based morphometry to correlate with gait measures of the pace and rhythm domains in PD patients. We hypothesize that specific regional surface changes can be associated with PD gait impairments. Surface analysis might provide a useful tool for assessing PD for functional status and specific motor domains, such as gait in PD, and potentially could serve as an imaging marker in conjunction with other imaging markers. Twenty-seven PD patients and 37 healthy controls were included. The clinical assessment included Mini-Mental State Exanimation, PD motor assessment, clinical gait testing, and objective/quantitative gait assessment. For patients with PD, all motor and gait testing were performed during both OFF and ON medication states. Three Tesla MRI and high-resolution 3D structural images were acquired with an MP-RAGE pulse sequence. Structural image data preprocessing was performed using the DPABISurf toolbox. Clinical characteristics between PD and control group were compared, and correlation between the surface area and behavioral data were analyzed. At the left lateral temporal cortex (LTC) and right inferior parietal cortex (IPC), PD patients have significantly larger surface areas when compared to controls (P < 0.05) using surface-based morphometry. The surface area changes of the left LTC and right IPC were associated with the worse performance of gait assessed by Berg Balance Scale and Timed Up and Go during OFF (P < 0.01). The left LTC area changes significantly correlated with the number of steps, velocity, and the stride length of the pace domain in the ON state. Our findings suggest that PD is associated with a characteristic regional pattern of larger surface area in the left LTC and right IPC. These regional changes were associated with the pace domain of the gait in the ON state. Overall, surface-based analyses might provide a useful tool for assessing PD for functional status and specific motor domains, such as gait in PD, and potentially could serve as an imaging marker.

This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer’s Disease Neuroimaging Initiative dataset (ADNI, n = 1559) and validated on the other half of the ADNI dataset and the Open Access Series of Imaging Studies-3 (OASIS-3, n = 930). Our findings demonstrated that the graph-CNN-RNN can reliably and robustly diagnose AD at the accuracy rate of 85% and above across all the time points for both datasets. The graph-CNN-RNN predicted the AD conversion from 0 to 4 years before the AD onset at ∼80% of accuracy. The AD probabilistic risk was associated with clinical traits, cognition, and amyloid burden assessed using [18F]-Florbetapir (AV45) positron emission tomography (PET) across all the time points. The graph-CNN-RNN provided the quantitative trajectory of brain morphology from prognosis to overt stages of AD. Such a deep learning tool and the AD probabilistic risk have great potential in clinical applications for AD prognosis.

The developing brain grows exponentially in the first few years of life. There is a need to have age-appropriate brain atlases that coherently characterize the geometry of the cerebral cortex, white matter tracts, and functional organization. This study employed multi-modal brain images of an Asian cohort and constructed brain structural and functional atlases for 6-month-old infants, 4.5-, 6-, and 7.5-year-old children. We exploited large deformation diffeomorphic metric mapping and probabilistic atlas generation approaches to integrate structural MRI and diffusion weighted images (DWIs) and to create the atlas where white matter tracts well fit into the cortical folding pattern. Based on this structural atlas, we then employed spectral clustering to parcellate the brain into functional networks from resting-state fMRI (rs-fMRI). Our results provided the atlas that characterizes the cortical folding geometry, subcortical regions, deep white matter tracts, as well as functional networks in a stereotaxic coordinate space for the four different age groups. The functional networks consisting of the primary cortex were well established in infancy and remained stable to childhood, while specific higher-order functional networks showed specific patterns of hemispherical, subcortical-cerebellar, and cortical-cortical integration and segregation from infancy to childhood. Our multi-modal fusion analysis demonstrated the use of the integrated structural and functional atlas for understanding coherent patterns of brain anatomical and functional development during childhood. Hence, our atlases can be potentially used to study coherent patterns of brain anatomical and functional development.

The complex structure of the brain supports high-order cognition, which is crucial for mastering chess. Surface-based measures, including the fractional dimension (FD) and gyrification index (GI), may be more sensitive in detecting cortical changes relative to volumetric indexes. For this reason, structural magnetic resonance imaging data from 29 chess experts and 29 novice participants were analyzed using the CAT12 toolbox. FD and GI for each brain region were compared between the groups. A multivariate model was used to identify surface-based brain measures that can predict chess expertise. In chess experts, FD is increased in the left frontal operculum (p < 0.01), and this change correlates with the starting age of chess practice (ρ = −0.54, p < 0.01). FD is decreased in the right superior parietal lobule (p < 0.01). Chess expertise is predicted by the FD in a network of fronto-parieto-temporal regions and is associated with GI changes in the middle cingulate gyrus (p < 0.01) and the superior temporal sulcus (p < 0.01). Our findings add to the evidence that chess expertise is based on the complex properties of the brain surface of a network of transmodal association areas important for flexible high-level cognitive functions. Interestingly, these changes are associated with long-lasting practice, suggesting that neuroplastic effects develop over time.