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Central nervous system involvement has been described in peripheral neuropathies, including different forms of Charcot-Marie-Tooth (CMT) disease. The aim of our study was to systematically investigate possible brain structural modifications in CMT1A patients, using volumetric MRI, and diffusion tensor imaging (DTI). In this prospective cross-sectional study, from May 2017 to May 2019, we acquired 3T MRI brain scans of genetically confirmed CMT1A patients and age- and sex-comparable healthy controls. Patients also underwent clinical and electrophysiological examinations assessing motor and sensory domains. Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) analyses were performed using a non-parametric approach based on permutations, including age and sex (and total intracranial volume for VBM) as nuisance covariates. When between-group differences emerged at VBM or TBSS analyses, the first eigenvariate was extracted from the cluster and its age- and sex-adjusted standardized residuals tested for correlation with clinical and electrophysiological variables. Twenty CMT1A patients (34.5 ± 11.1 years; M/F:11/9) were enrolled, along with 20 healthy controls (30.1 ± 10.2 years; M/F:11/9). The VBM analysis revealed clusters of significantly increased GM volume in CMT1A patients compared to healthy controls, encompassing the bilateral cerebellar lobules III-VI and the left hippocampus (all ps = 0.04), with no differences in terms of DTI metrics at the TBSS analysis. A negative correlation (r = −0.502, p = 0.03) emerged between ulnar compound motor action potential and the z-scores corresponding to the right cerebellar cluster of augmented GM volume. Our data show evidence of structural reorganization in the brain of CMT1A patients, possibly reflecting neural plasticity mechanisms in response to peripheral nerve pathology and modulating the effect of axonal degeneration on functional impairment.

Abstract Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end‐to‐end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross‐validation on in‐house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD.

Parkinson’s disease (PD) is a progressive, chronic, and neurodegenerative disorder that is primarily diagnosed by clinical examinations and magnetic resonance imaging (MRI). In this study, we proposed a machine learning based radiomics method to predict PD. Fifty healthy controls (HC) along with 70 PD patients underwent resting-state magnetic resonance imaging (rs-fMRI). For all subjects, we extracted five types of 6664 features, including mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), resting-state functional connectivity (RSFC), voxel-mirrored homotopic connectivity (VMHC) and gray matter (GM) volume. After conducting dimension reduction utilizing Least absolute shrinkage and selection operator (LASSO), fifty-three radiomic features including 46 RSFCs, 1 mALFF, 3 mReHos, 1 VMHC, 2 GM volumes and 1 clinical factor were retained. The selected features also indicated the most discriminative regions for PD. We further conducted model fitting procedure for classifying subjects in the training set employing random forest and support volume machine (SVM) to evaluate the performance of the two methods. After cross-validation, both methods achieved 100% accuracy and area under curve (AUC) for distinguishing between PD and HC in the training set. In the testing set, SVM performed better than random forest with the accuracy, true positive rate (TPR) and AUC being 85%, 1 and 0.97, respectively. These findings demonstrate the radiomics technique has the potential to support radiological diagnosis and to achieve high classification accuracy for clinical diagnostic systems for patients with PD.

Mild cognitive impairment (MCI) represents a transitional stage between normal aging and Alzheimer’s disease (AD), with a higher risk to convert to AD. The information of AD and normal control (NC) subjects can aid the classification between progressive MCI and stable MCI. In this paper, we develop an effective biomarker by combining the auxiliary information of AD and NC subjects with the relationship of brain regions of MCI subject, which makes best of auxiliary information and improves the prediction accuracy of MCI-to-AD conversion. Specifically, a projection vector is first obtained for each MCI subject via graph-guided information propagation. Next, the information of projection vector is integrated using a self-weighting grading method to acquire the novel biomarker. Finally, the self-weighting grading biomarkers derived from multiple morphological features are combined to provide more accurate prediction of MCI-to-AD conversion. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative database demonstrate the effectiveness of the proposed biomarkers for the prediction of MCI conversion.

Brain structural abnormalities of schizophrenia subjects are often considered as the main neurobiological basis of this brain disease. Therefore, with the rapid development of artificial intelligence and medical imaging technologies, machine learning and structural magnetic resonance imaging have often been applied to computer-aided diagnosis of brain diseases such as schizophrenia, Alzheimer, glioma segmentation, etc. In this paper, statistical analysis of schizophrenic and normal subjects is initially made. Additionally, a slicing and weighted average method is proposed for gray matter images of the structural magnetic resonance imaging stored as three-dimensional volume data. Grey-level co-occurrence matrix texture features from the previously processed gray matter images of structural magnetic resonance imaging are then extracted and normalized. Finally, an eXtreme Gradient Boosting classifier is used for schizophrenia classification. Experiments employed 100 schizophrenic subjects and 100 normal controls. Results show the proposed method improves the respective classification accuracy of healthy controls and schizophrenic subjects by 8% and 10.6% of the area under the receiver operating characteristic. This suggests that the textural features of gray matter changes may be of diagnostic value in schizophrenia.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that causes brain regions to undergo structural changes. Shape descriptors are useful in reflecting the morphological alterations of brain structures in AD conditions compared to volume based measures. In this work, moment based shape descriptors are used to classify control, mild cognitive impairment (MCI) and AD subjects. Lattice Boltzmann criterion‐based hybrid level set method (LSM) is used to delineate lateral ventricles. Legendre and United moments are extracted from the segmented binary images and are statistically analysed using Statistical Package for Social Science (SPSS). The performance of significant moments in the shape analysis is validated using machine learning algorithm. Results demonstrate that, level set is able to delineate ventricles and found to have high similarity index with ground truth. The area under curve (AUC) values for Legendre moment is found to be 1.0, 0.75 and 1.0 for Control‐MCI, MCI‐AD and Control‐AD subjects. Rather, the AUC for United moment is found to be 0.98, 0.76 and 0.98 for Control‐MCI, MCI‐AD and Control‐AD subjects respectively. Further, machine learning algorithm could classify Control from AD subjects with high accuracy of 99.25% using Legendre moments and hence the study seems to be clinically significant.

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 < 0.001 and AUC = 0.88) as we reported before, while no significant difference was found between AD and FTD (p = 0.647). FTDI showed excellent accuracy in identifying FTD from AD (AUC = 0.90; SEN = 89%, SPE = 75% with threshold value = 1.08). The validation using independent data from ADNI and FTLDNI datasets also confirmed the efficacy of FTDI (AUC = 0.93; SEN = 96%, SPE = 70% with threshold value = 1.08). Conclusions: Brain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: Individual anatomical biomarkers have limited power for the classification of autism. The present study introduces a multivariate classification approach using structural magnetic resonance imaging data from individuals with and without autism. Methods: The classifier utilizes z‐normalization, parameter weighting, and interindividual comparison on brain segmentation data, for estimation of an individual summed total index (TI). The TI indicates whether the gross morphological pattern of each individual's brain is in the direction of cases or controls. Results: Morphometric analysis found significant differences within subcortical gray matter structures and limbic areas. There was no significant difference in total brain volume. A case‐control pilot‐study of TIs in normally intelligent individuals with autism (24) and without (21) yielded a maximal accuracy of 78.9% following cross‐validation. It showed a high accuracy compared with machine learning methods when tested on the same dataset. The TI correlated well with the autism quotient (R = 0.51) across groups. Conclusion: These results are on par with studies on autism using machine learning. The main contributions are its transparency and simplicity. The possibility of including additional neuroimaging data further increases the potential of the classifier as a diagnostic aid for neuropsychiatric disorders, as well as a research tool for neuroscientific investigations. [ABSTRACT FROM AUTHOR]

Cortical thickness (CTh) reflects cortical properties such as dendritic complexity and synaptic density, which are not only vulnerable to developmental disturbances caused by premature birth but also highly relevant for cognitive performance. We tested the hypotheses whether CTh in young adults is altered after premature birth and whether these aberrations are relevant for general cognitive abilities. We investigated CTh based on brain structural magnetic resonance imaging and surface‐based morphometry in a large and prospectively collected cohort of 101 very premature‐born adults (<32 weeks of gestation and/or birth weight [BW] below 1,500 g) and 111 full‐term controls at 26 years of age. Cognitive performance was assessed by full‐scale intelligence quotient (IQ) using the Wechsler Adult Intelligence Scale. CTh was reduced in frontal, parietal, and temporal associative cortices predominantly in the left hemisphere in premature‐born adults compared to controls. We found a significant positive association of CTh with both gestational age and BW, particularly in the left hemisphere, and a significant negative association between CTh and intensity of neonatal treatment within limited regions bilaterally. Full‐scale IQ and CTh in the left hemisphere were positively correlated. Furthermore, CTh in the left hemisphere acted as a mediator on the association between premature birth and full‐scale IQ. Results provide evidence that premature born adults have widespread reduced CTh that is relevant for their general cognitive performance. Data suggest lasting reductions in cortical microstructure subserving CTh after premature birth. [ABSTRACT FROM AUTHOR]

Asthma as a chronic inflammatory disease can be expected to affect central nervous system structures but little is known about subcortical structures in asthma and their potential association with illness-specific outcomes and anxiety. A total of 40 young adults (20 with asthma and 20 gender- and age-matched controls) underwent high-resolution T1-weighted MRI scan, viewed short distressing film clips, and filled in questionnaires about anxious and depressed mood, as well as asthma history, control, and catastrophizing thoughts about asthma, for those with asthma. The structural scans were processed in FSL's FIRST program to delineate subcortical structures of interest: amygdala, hippocampus, putamen, pallidum, caudate nucleus, nucleus accumbens, and thalamus. Findings showed no general reduction in subcortical gray matter volumes in asthma compared to controls. Asthma duration, asthma control, and catastrophizing of asthma and asthma attacks were negatively associated with volumes of putamen and pallidum, and to a weaker extent thalamus and amygdala, while controlling for gender, age, and corticosteroid inhaler use. In addition, stronger anxiety in response to distressing films was associated with lower volume of the pallidum, whereas general anxious and depressed mood was unrelated to subcortical structures. Thus, although there are no subcortical structural differences between young adults with asthma and healthy controls, longer asthma history, suboptimal management, and illness-related anxiety are reflected in lower gray matter volumes of subcortical structures, further emphasizing the importance of maintaining optimal asthma control. [ABSTRACT FROM AUTHOR]

Conduct disorder (CD) is a common child and adolescent psychiatric disorder with various representative symptoms, and may cause long-term burden to patients and society. Recently, an increasing number of studies have used deep learning-based approaches, such as convolutional neural network (CNN), to analyze neuroimaging data and to identify biomarkers. In this study, we applied an optimized 3D AlexNet CNN model to automatically extract multi-layer high dimensional features of structural magnetic resonance imaging (sMRI), and to classify CD from healthy controls (HCs). We acquired high-resolution sMRI from 60 CD and 60 age- and gender-matched HCs. All subjects were male, and the age (mean ± std. dev) of participants in the CD and HC groups was 15.3 ± 1.0 and 15.5 ± 0.7, respectively. Five-fold cross validation (CV) was used to train and test this model. The receiver operating characteristic (ROC) curve for this model and that for support vector machine (SVM) model were compared. Feature visualization was performed to obtain intuition about the sMRI features learned by our AlexNet model. Our proposed AlexNet model achieved high classification performance with accuracy of 0.85, specificity of 0.82 and sensitivity of 0.87. The area under the ROC curve (AUC) of AlexNet was 0.86, significantly higher than that of SVM (AUC = 0.78; p = 0.046). The saliency maps for each convolutional layer highlighted the different brain regions in sMRI of CD, mainly including the frontal lobe, superior temporal gyrus, parietal lobe and occipital lobe. The classification results indicated that deep learning-based method is able to explore the hidden features from the sMRI of CD and might assist clinicians in the diagnosis of CD. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) may cause secondary debilitating problems, such as post-traumatic epilepsy (PTE), which occurs with unprovoked recurrent seizures, months or even years after TBI. Currently, the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) has been enrolling moderate-severe TBI patients with the goal to identify biomarkers of epileptogenesis that may help to prevent seizure occurrence and better understand the mechanism underlying PTE. In this work, we used a novel complex network approach based on segmenting T1-weighted Magnetic Resonance Imaging (MRI) scans in patches of the same dimension (network nodes) and measured pairwise patch similarities using Pearson's correlation (network connections). This network model allowed us to obtain a series of single and multiplex network metrics to comprehensively analyze the different interactions between brain components and capture structural MRI alterations related to seizure development. We used these complex network features to train a Random Forest (RF) classifier and predict, with an accuracy of 70 and a 95% confidence interval of [67, 73%], which subjects from EpiBioS4Rx have had at least one seizure after a TBI. This complex network approach also allowed the identification of the most informative scales and brain areas for the discrimination between the two clinical groups: seizure-free and seizure-affected subjects, demonstrating to be a promising pilot study which, in the future, may serve to identify and validate biomarkers of PTE. [ABSTRACT FROM AUTHOR]

Mesial temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structural brain alterations. Machine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controls. However, either functional or structural neuroimaging data are mostly used separately as input, and the opportunity to combine both has not been exploited yet. We conducted a multimodal ML study based on functional and structural neuroimaging measures. We enrolled 37 patients with left mTLE, 37 patients with right mTLE, and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures. For each single measure, we obtained a mean accuracy of 74% and 69% for discriminating left mTLE and right mTLE from controls, respectively, and 64% when all patients were combined. We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data for left mTLE, and the highest accuracy of 84% was obtained when all functional and structural measures were combined. These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE. [ABSTRACT FROM AUTHOR]

Central nervous system involvement has been described in peripheral neuropathies, including different forms of Charcot-Marie-Tooth (CMT) disease. The aim of our study was to systematically investigate possible brain structural modifications in CMT1A patients, using volumetric MRI, and diffusion tensor imaging (DTI). In this prospective cross-sectional study, from May 2017 to May 2019, we acquired 3T MRI brain scans of genetically confirmed CMT1A patients and age- and sex-comparable healthy controls. Patients also underwent clinical and electrophysiological examinations assessing motor and sensory domains. Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) analyses were performed using a non-parametric approach based on permutations, including age and sex (and total intracranial volume for VBM) as nuisance covariates. When between-group differences emerged at VBM or TBSS analyses, the first eigenvariate was extracted from the cluster and its age- and sex-adjusted standardized residuals tested for correlation with clinical and electrophysiological variables. Twenty CMT1A patients (34.5 ± 11.1 years; M/F:11/9) were enrolled, along with 20 healthy controls (30.1 ± 10.2 years; M/F:11/9). The VBM analysis revealed clusters of significantly increased GM volume in CMT1A patients compared to healthy controls, encompassing the bilateral cerebellar lobules III-VI and the left hippocampus (all p s = 0.04), with no differences in terms of DTI metrics at the TBSS analysis. A negative correlation (r = −0.502, p = 0.03) emerged between ulnar compound motor action potential and the z -scores corresponding to the right cerebellar cluster of augmented GM volume. Our data show evidence of structural reorganization in the brain of CMT1A patients, possibly reflecting neural plasticity mechanisms in response to peripheral nerve pathology and modulating the effect of axonal degeneration on functional impairment. [ABSTRACT FROM AUTHOR]

Several modeling approaches have been developed to quantify differences in multiple sclerosis lesion evolution on magnetic resonance imaging to identify the effect of treatment on disease progression. These studies have limited clinical applicability due to onerous scan frequency and lengthy study duration. Efficient methods are needed to reduce the required sample size, study duration, and sampling frequency in longitudinal magnetic resonance imaging studies. We develop a data-driven approach to identify parameters of study design for evaluation of longitudinal magnetic resonance imaging biomarkers of multiple sclerosis lesion evolution. Our design strategies are considerably shorter than those described in previous studies, thus having the potential to lower costs of clinical trials. From a dataset of 36 multiple sclerosis patients with at least six monthly magnetic resonance imagings, we extracted new lesions and performed principal component analysis to estimate a biomarker that recapitulated lesion recovery. We tested the effect of multiple sclerosis disease modifying therapy on the lesion evolution index in three experimental designs and calculated sample sizes needed to appropriately power studies. Our proposed methods can be used to calculate required sample size and scan frequency in observational studies of multiple sclerosis disease progression as well as in designing clinical trials to find effects of treatment on multiple sclerosis lesion evolution. [ABSTRACT FROM AUTHOR]

Background: Walking speed during fast-paced walking task has been associated with cognitive function. It is unclear what underlying brain structures are related to fast-paced walking. We investigated the association of gray matter (GM) density with fast-paced walking speed and usual-paced walking speed.Methods: We collected data from 284 older adults from a subset of the Health, Aging, and Body composition study (mean age = 83 [SD = 2.8], 58% women, 41% black). Voxel-wise analyses on magnetic resonance imaging data identified regions of the brain where GM density was associated with fast-paced walking speed. We then extracted GM density for all identified regions and modeled the association with fast-paced walking speed after adjusting for demographic factors, clinical factors, and cognitive function. Analyses were repeated for usual-paced walking. Regions with beta coefficients ≥0.3 m/s were considered to be meaningfully correlated.Results: GM density of clusters from cortical regions in the right middle and superior frontal gyrus, right postcentral gyrus, and left superior temporal gyrus were positively correlated with fast-paced walking speed in adjusted models. Adjustment for cognitive function had little impact on the findings. Caudate was correlated with usual paced walking speed at coefficient ≥0.3 m/s after adjustment of demographic factors and clinical factors, but not after further adjustment of cognitive function.Conclusions: Fast-paced walking speed was correlated with GM density of right middle and superior frontal gyrus, right postcentral gyrus, and left superior temporal gyrus, and could potentially provide evidence about subclinical structural change of brain related to aging. [ABSTRACT FROM AUTHOR]

Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end‐to‐end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross‐validation on in‐house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive, chronic, and neurodegenerative disorder that is primarily diagnosed by clinical examinations and magnetic resonance imaging (MRI). In this study, we proposed a machine learning based radiomics method to predict PD. Fifty healthy controls (HC) along with 70 PD patients underwent resting-state magnetic resonance imaging (rs-fMRI). For all subjects, we extracted five types of 6664 features, including mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), resting-state functional connectivity (RSFC), voxel-mirrored homotopic connectivity (VMHC) and gray matter (GM) volume. After conducting dimension reduction utilizing Least absolute shrinkage and selection operator (LASSO), fifty-three radiomic features including 46 RSFCs, 1 mALFF, 3 mReHos, 1 VMHC, 2 GM volumes and 1 clinical factor were retained. The selected features also indicated the most discriminative regions for PD. We further conducted model fitting procedure for classifying subjects in the training set employing random forest and support volume machine (SVM) to evaluate the performance of the two methods. After cross-validation, both methods achieved 100% accuracy and area under curve (AUC) for distinguishing between PD and HC in the training set. In the testing set, SVM performed better than random forest with the accuracy, true positive rate (TPR) and AUC being 85%, 1 and 0.97, respectively. These findings demonstrate the radiomics technique has the potential to support radiological diagnosis and to achieve high classification accuracy for clinical diagnostic systems for patients with PD. [ABSTRACT FROM AUTHOR]

Background: Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria (HD). However, the brain morphological changes associated with HD have not been investigated. Moreover, no reliable model for predicting the severity of HD based on neuroimaging has yet been developed. Methods: A total of 134 PD patients were included in this study and divided into a training set and a test set. All participants underwent a structural magnetic resonance imaging (MRI) scan and neuropsychological evaluation. Individual cortical thickness, subcortical structure, and white matter volume were extracted, and their association with HD severity was analyzed. After feature selection, a machine‐learning model was established using a support vector machine in the training set. The severity of HD was then predicted in the test set. Results: Atrophy of the right precentral cortex and the right fusiform gyrus was significantly associated with HD. No association was found between HD and volume of white matter or subcortical structures. Favorable and optimal performance of machine learning on HD severity prediction was achieved using feature selection, giving a correlation coefficient (r) of.7516 and a coefficient of determination (R2) of.5649 (P <.001). Conclusion: The brain morphological changes were associated with HD. Excellent prediction of the severity of HD was achieved using machine learning based on neuroimaging. [ABSTRACT FROM AUTHOR]

Mental health problems often emerge in adolescence and are associated with reduced gray matter thickness or volume in the prefrontal cortex (PFC) and limbic system and reduced fractional anisotropy (FA) and increased mean diffusivity (MD) of white matter linking these regions. However, few studies have investigated whether internalizing and externalizing behavior are associated with brain structure in children and adolescents without mental health disorders, which is important for understanding the progression of symptoms. 67 T1-weighted and diffusion tensor imaging datasets were obtained from 48 typically developing participants aged 6–16 years (37M/30F; 19 participants had two visits). Volume was calculated in the prefrontal and limbic structures, and diffusion parameters were assessed in limbic white matter. Linear mixed effects models were used to compute associations between brain structure and internalizing and externalizing behavior, assessed using the Behavioral Assessment System for Children (BASC-2) Parent Rating Scale. Internalizing behavior was positively associated with MD of the bilateral cingulum. Gender interactions were found in the cingulum, with stronger positive relationships between MD and internalizing behavior in females. Externalizing behavior was negatively associated with FA of the left cingulum, and the left uncinate fasciculus showed an age–behavior interaction. No relationships between behavior and brain volumes survived multiple comparison correction. These results show altered limbic white matter FA and MD related to sub-clinical internalizing and externalizing behavior and further our understanding of neurological markers that may underlie risk for future mental health disorders. [ABSTRACT FROM AUTHOR]

There has been increasing interest in identifying factors to predict subjective well-being in the emerging field of positive psychology over the past two decades. Dispositional hope, which reflects one's goal-directed tendencies, including both pathway thinking (planning to meet goals) and agency thinking (goal-directed determination), has emerged as a stable predictor for subjective well-being. However, the neurobiological substrates of dispositional hope and the brain-hope mechanism for predicting subjective well-being remain unclear. Here, we examined these issues in 231 high school graduates within the same grade by estimating cortical gray matter volume (GMV) utilizing a voxel-based morphometry method based on structural magnetic resonance imaging. Whole-brain regression analyses and prediction analyses showed that higher dispositional hope was stably associated with greater GMV in the left supplementary motor area (SMA). Furthermore, mediation analyses revealed that dispositional hope mediated the relation between left SMA volume and subjective well-being. Critically, our results were obtained after adjusting for age, sex, family socioeconomic status and total GMV. Altogether, our study presents novel evidence for the neuroanatomical basis of dispositional hope and suggests an underlying indirect effect of dispositional hope on the link between brain gray matter structure and subjective well-being. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases. Structural magnetic resonance imaging (MRI) would provide abundant information on the anatomical structure of human organs. Fluorodeoxy‐glucose positron emission tomography (PET) obtains the metabolic activity of the brain. Previous studies have demonstrated that multi‐modality images could contribute to improve diagnosis of AD. However, these methods need to extract the handcrafted features that demand domain specific knowledge and image processing stage is time consuming. In order to tackle these problems, in this study, the authors propose a novel framework that ensembles three state‐of‐the‐art deep convolutional neural networks (DCNNs) with multi‐modality images for AD classification. In detail, they extract some slices from each subject of each modality, and every DCNN generates a probabilistic score for the input slices. Furthermore, a 'dropout' mechanism is introduced to discard low discrimination slices of the category probabilities. Then average reserved slices of each subject are acquired as a new feature. Finally, they train the Adaboost ensemble classifier based on single decision tree classifier with the MRI and PET probabilistic scores of each DCNN. Evaluations on Alzheimer's Disease Neuroimaging Initiative database show that the proposed algorithm has better performance compared to existing method, the algorithm proposed in this study significantly improved the classification accuracy. [ABSTRACT FROM AUTHOR]

• Studies on twins may increase our knowledge of how gene and environment interaction. • Brain characteristics are highly heritable from early childhood to young adulthood. • Environment has a key role in mediating brain network differentiation. Neurodevelopment represents a period of increased opportunity and vulnerability, during which a complex confluence of genetic and environmental factors influences brain growth trajectories, cognitive and mental health outcomes. Recently, magnetic resonance imaging (MRI) studies on twins have increased our knowledge of the extent to which genes, the environment and their interactions shape inter-individual brain variability. The present review draws from highly salient MRI studies in young twin samples to provide a robust assessment of the heritability of structural and functional brain changes during development. The available studies suggest that (as with many other traits), global brain morphology and network organization are highly heritable from early childhood to young adulthood. Conversely, genetic correlations among brain regions exhibit heterogeneous trajectories, and this heterogeneity reflects the progressive, experience-related increase in brain network complexity. Studies also support the key role of environment in mediating brain network differentiation via changes of genetic expression and hormonal levels. Thus, rest- and task-related functional brain circuits seem to result from a contextual and dynamic expression of heritability. [ABSTRACT FROM AUTHOR]

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Gray matter (GM) density and cortical thickness (CT) obtained from structural magnetic resonance imaging are representative GM morphological measures that have been commonly used in Alzheimer’s disease (AD) subtype research. However, how the two measures affect the definition of AD subtypes remains unclear. Methods: A total of 180 AD patients from the ADNI database were used to identify AD subgroups. The subtypes were identified via a data-driven strategy based on the density features and CT features, respectively. Then, the similarity between the two features in AD subtype definition was analyzed. Results: Four distinct subtypes were discovered by both density and CT features: diffuse atrophy AD, minimal atrophy AD (MAD), left temporal dominant atrophy AD (LTAD), and occipital sparing AD. The matched subtypes exhibited relatively high similarity in atrophy patterns and neuropsychological and neuropathological characteristics. They differed only in MAD and LTAD regarding the carrying of apolipoprotein E ε2. Conclusions: The results verified that different representative morphological GM measurement methods could produce similar AD subtypes. Meanwhile, the influences of apolipoprotein E genotype, asymmetric disease progression, and their interactions should be considered and included in the AD subtype definition. This study provides a valuable reference for selecting features in future studies of AD subtypes.

MRI studies have consistently identified atrophy patterns in Alzheimer’s disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.

AbstractBackground.Studying offspring of schizophrenia (SZo) and bipolar disorder patients (BDo) provides important information on the putative neurodevelopmental trajectories underlying development toward severe mental illnesses. We compared intracranial volume (ICV), as a marker for neurodevelopment, and global and local brain measures between SZo or BDo and control offspring (Co) in relation to IQ and psychopathology.Methods.T1-weighted magnetic resonance imaging (MRI) brain scans were obtained from 146 participants (8–19 years; 40 SZo, 66 BDo, 40 Co). Linear mixed models were applied to compare ICV, global, and local brain measures between groups. To investigate the effect of ICV, IQ (four subtests Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale-III) or presence of psychopathology these variables were each added to the model.Results.SZo and BDo had significantly lower IQ and more often met criteria for a lifetime psychiatric disorder than Co. ICV was significantly smaller in SZo than in BDo (d = −0.56) and Co (d = −0.59), which was largely independent of IQ (respectively, d = −0.54 and d = −0.35). After ICV correction, the cortex was significantly thinner in SZo than in BDo (d = −0.42) and Co (d = −0.75) and lateral ventricles were larger in BDo than in Co (d = 0.55). Correction for IQ or lifetime psychiatric diagnosis did not change these findings.Conclusions.Despite sharing a lower IQ and a higher prevalence of psychiatric disorders, brain abnormalities in BDo appear less pronounced (but are not absent) than in SZo. Lower ICV in SZo implies that familial risk for schizophrenia has a stronger association with stunted early brain development than familial risk for bipolar disorder.

Aging is a known non-modifiable risk factor for stroke. Usually, this refers to chronological rather than biological age. Biological brain age can be estimated based on cortical and subcortical brain measures. For stroke patients, it could serve as a more sensitive marker of brain health than chronological age. In this study, we investigated whether there is a difference in brain age between stroke survivors and control participants matched on chronological age. We estimated brain age at 3 months after stroke, and then followed the longitudinal trajectory over three time-points: within 6 weeks (baseline), at 3 and at 12 months following their clinical event. We found that brain age in stroke participants was higher compared to controls, with the mean difference between the groups varying between 3.9 and 8.7 years depending on the brain measure used for prediction. This difference in brain age was observed at 6 weeks after stroke and maintained at 3 and 12 months after stroke. The presence of group differences already at baseline suggests that stroke might be an ultimate manifestation of gradual cerebrovascular burden accumulation and brain degeneration. Brain age prediction, therefore, has the potential to be a useful biomarker for quantifying stroke risk.

Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.

The recovery of patients with disorders of consciousness is a real challenge, especially at the chronic stage. After a severe brain injury, patients can regain some slight signs of consciousness, while not being able to functionally communicate. This entity is called the minimally conscious state (MCS), which has been divided into MCS- and MCS+, respectively based on the absence or presence of language-related signs of consciousness. In this series of cases we aimed to describe retrospectively the longitudinal recovery of specific language-related behaviors using neuroimaging measurement in severely brain-injured patients. Among 209 chronic MCS patients admitted to our center from 2008 to 2018, 19 were assessed at two time points by means of behavioral and neuroimaging assessments. Three of them met our inclusion criteria and were diagnosed as MCS- during their first stay and had recovered command-following when they were reassessed (i.e., MCS+). As compared to their first assessments, when the three patients were in a MCS+, they showed less hypometabolism and/or higher gray matter volume in brain regions such as the precuneus and thalamus, as well as the left caudate and temporal/angular cortices known to be involved in various aspects of semantics. According to these preliminary results, the reappearance of language-related behaviors was concomitant with the recovery of metabolism and gray matter in neural regions that have been associated with self-consciousness and language processing. Prospective studies should be conducted to deepen our understanding of the neural correlates of the recovery of language-related behaviors in chronic MCS.

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., (© 2024. The Author(s).)

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Wu, Yin, Wang, Wang, Chen and Zhao.)

Background: Duchenne Muscular Dystrophy (DMD) is a genetic disease in which lack of the dystrophin protein causes progressive muscular weakness, cardiomyopathy and respiratory insufficiency. DMD is often associated with other cognitive and behavioral impairments, however the correlation of abnormal dystrophin expression in the central nervous system with brain structure and functioning remains still unclear., Objective: To investigate brain involvement in patients with DMD through a multimodal and multivariate approach accounting for potential comorbidities., Methods: We acquired T1-weighted and Diffusion Tensor Imaging data from 18 patients with DMD and 18 age- and sex-matched controls with similar cognitive and behavioral profiles. Cortical thickness, structure volume, fractional anisotropy and mean diffusivity measures were used in a multivariate analysis performed using a Support Vector Machine classifier accounting for potential comorbidities in patients and controls., Results: the classification experiment significantly discriminates between the two populations (97.2% accuracy) and the forward model weights showed that DMD mostly affects the microstructural integrity of long fiber bundles, in particular in the cerebellar peduncles (bilaterally), in the posterior thalamic radiation (bilaterally), in the fornix and in the medial lemniscus (bilaterally). We also reported a reduced cortical thickness, mainly in the motor cortex, cingulate cortex, hippocampal area and insula., Conclusions: Our study identified a small pattern of alterations in the CNS likely associated with the DMD diagnosis.

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.

Alzheimer's disease (AD) is a progressive neurodegenerative disease. Early detection and intervention are crucial in preventing the progression of AD. To achieve efficient and scalable AD auto-detection based on structural Magnetic Resonance Imaging (sMRI), a lightweight neural network using multi-slice sMRI is proposed in this paper. The backbone for feature extraction is based on ShuffleNet V1 architecture, which is effective for overcoming the limitations posed by limited sMRI data and resource-restricted devices. In addition, we incorporate Efficient Channel Attention (ECA) to capture cross-channel interaction information, enabling us to effectively enhance features of disease associated brain regions. To optimize the model, we employ both cross entropy loss and triplet loss functions to constrain the predicted probabilities to the ground-truth labels, and to ensure appropriate representation of distances between different classes in the learned features. Experimental results show that the classification accuracies of our method for AD vs. CN, AD vs. MCI, and MCI vs. CN classification tasks are 95.00%, 87.50%, and 85.62% respectively. Our method utilizes only 3.42 M parameters and 6.08G FLOPs, while maintaining a comparable level of performance compared to the other 5 latest lightweight methods. This model design is computationally efficient, allowing it to process large amounts of data quickly and accurately in a timely manner. Additionally, it has the potential to advance the intelligent detection of Alzheimer's disease on devices with limited computing capabilities., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., (© 2024 Wiley Periodicals LLC.)

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., Methods: 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 (AUC NC  = 0.976, AUC MCI  = 0.836, AUC MCIs  = 0.894, AUC MCIc  = 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., (© 2023 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

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 < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001)., Conclusion: The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sun, Zhang, Huang, Bhetuwal, Yang, Jing, Li, Lu, Zhang and Yang.)

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., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

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., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)