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Adverse childhood experiences (ACEs) have been associated with volume alterations of stress-related brain structures among aging and clinical populations, however, existing studies have predominantly assessed only one type of ACE, with small sample sizes, and it is less clear if these associations exist among a general population of young adults. The aims were to describe structural hippocampal volumetric differences by ACEs exposure and investigate the association between ACEs exposure and left and right hippocampal volume in a student sample of young adults. 959 young adult students (18–24 years old) completed an online questionnaire on ACEs, mental health conditions, and sociodemographic characteristics. Magnetic resonance imaging (MRI) was used to measure left and right hippocampal volume (mm3). We used linear regression to explore the differences of hippocampal volumes in university students with and without ACEs. Two thirds of students (65.9%) reported ACEs exposure. As ACEs exposure increased there were significant volumetric reductions in left (p < 0.0001) and right hippocampal volume (p = 0.001) and left (p = 0.0023) and right (p = 0.0013) amygdala volume. After adjusting for intracranial brain volume, sex, age, and depression diagnosis there was a negative association between ACEs exposure and left (β = −22.6, CI = −44.5, −0.7, p = 0.0412) but not right hippocampal volume (β = −18.3, CI = −39.2, 2.6, p = 0.0792). After adjusting for intracranial volume there were no associations between ACEs exposure and left (β = −9.2, CI = −26.2, 7.9 p = 0.2926) or right (β = −5.6, CI = −19.9,8.8 p = 0.4466) amygdala volume. Hippocampal volume varied by ACEs exposure in young adult students. ACEs appear to contribute to neuroanatomic differences in young adults from the general population. • Cumulative ACEs were negatively associated with left hippocampal volume among young adult students. • For each additional unit increase of ACEs exposure, there was a corresponding decrease in left hippocampal volume. • ACEs appear to contribute to neuroanatomic differences in young adults from the general population. [ABSTRACT FROM AUTHOR]

• The study identified cortical and subcortical structural changes in MCI patients. • Correlation analysis linked structural changes to cognitive performance in MCI. • SVM classification achieved 89% accuracy in distinguishing MCI from controls. • Findings provide insights into neural mechanisms behind MCI-related memory loss. Gray matter changes are thought to be closely related to cognitive decline in mild cognitive impairment (MCI) patients. The study aimed to explore cortical and subcortical structural alterations in MCI and their association with cognitive assessment. 24 MCI patients and 22 normal controls (NCs) were included. Voxel-based morphometry (VBM), vertex-based shape analysis and surface-based morphometry (SBM) analysis were applied to explore subcortical nuclei volume, shape and cortical morphology. Correlations between structural changes and cognition were explored using spearman correlation analysis. Support vector machine (SVM) classification evaluated MCI identification accuracy. MCI patients showed significant atrophy in the left thalamus, left hippocampus, left amygdala, right pallidum, right hippocampus, along with inward deformation in the left amygdala. SBM analysis revealed that MCI group exhibited shallower sulci depth in the left hemisphere and increased cortical gyrification index (GI) in the right frontal gyrus. Correlation analysis showed the positive correlation between right hippocampus volume and episodic memory, while negative correlation between the altered GI and memory performance in MCI group. SVM analysis demonstrated superior performance of sulci depth and GI derived from SBM in MCI identification. When combined with cortical and subcortical metrics, SVM achieved a peak accuracy of 89 % in distinguishing MCI from NC. The study reveals significant gray matter structural changes in MCI, suggesting their potential role in underlying functional differences and neural mechanisms behind memory impairment in MCI. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD. We conducted a comprehensive search (from database creation date to March 2024) for relevant English articles on sMRI or/and fMRI-based ML techniques for diagnosing ADHD. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the diagnostic value of sMRI or/and fMRI-based ML techniques. The I2 test was used to assess heterogeneity and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. Forty-three studies were included in the systematic review, 27 of which were included in our meta-analysis. The pooled sensitivity and specificity of sMRI or/and fMRI-based ML techniques for the diagnosis of ADHD were 0.74 (95 % CI 0.65–0.81) and 0.75 (95 % CI 0.67–0.81), respectively. SROC curve showed that AUC was 0.81 (95 % CI 0.77–0.84). Based on these findings, the sMRI or/and fMRI-based ML techniques have relatively good diagnostic value for ADHD. Our meta-analysis specifically focused on ML techniques based on sMRI or/and fMRI studies. Since EEG-based ML techniques are also used for diagnosing ADHD, further systematic analyses are necessary to explore ML methods based on multimodal medical data. sMRI or/and fMRI-based ML technique is a promising objective diagnostic method for ADHD. • sMRI/fMRI-based ML: Promising ADHD objective diagnosis. • Meta-analysis confirms sMRI/fMRI ML's good ADHD diagnostic value. • ML complements behavioral assessments; together, they enhance ADHD diagnosis and treatment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Catatonia is a complex psychomotor disorder characterized by motor, affective, and behavioral symptoms. Despite being known for almost 150 years, its pathomechanisms are still largely unknown.Methods: A systematic research on PubMed, Web of Science, and Scopus was conducted to identify neuroimaging studies conducted on group or single individuals with catatonia. Overall, 33 studies employing structural magnetic resonance imaging (sMRI, n = 11), functional magnetic resonance imaging (fMRI, n = 10), sMRI and fMRI (n = 2), functional near-infrared spectroscopy (fNIRS, n = 1), single positron emission computer tomography (SPECT, n = 4), positron emission tomography (PET, n = 4), and magnetic resonance spectroscopy (MRS, n = 1), and 171 case reports were retrieved.Results: Observational sMRI studies showed numerous brain changes in catatonia, including diffuse atrophy and signal hyperintensities, while case-control studies reported alterations in fronto-parietal and limbic regions, the thalamus, and the striatum. Task-based and resting-state fMRI studies found abnormalities located primarily in the orbitofrontal, medial prefrontal, motor cortices, cerebellum, and brainstem. Lastly, metabolic and perfusion changes were observed in the basal ganglia, prefrontal, and motor areas. Most of the case-report studies described widespread white matter lesions and frontal, temporal, or basal ganglia hypoperfusion.Conclusions: Catatonia is characterized by structural, functional, perfusion, and metabolic cortico-subcortical abnormalities. However, the majority of studies and case reports included in this systematic review are affected by considerable heterogeneity, both in terms of populations and neuroimaging techniques, which calls for a cautious interpretation. Further elucidation, through future neuroimaging research, could have great potential to improve the description of the neural motor and psychomotor mechanisms underlying catatonia. [ABSTRACT FROM AUTHOR]

In the present study we investigated whether hippocampal subfield (cornu ammonis 1–3, dentate gyrus, and subiculum) and anteroposterior hippocampal subregion (head,body, and tail) volumes can predict episodic memory function using high-field high resolution structural magnetic resonance imaging (MRI). We recruited 126 healthy participants (18–85 years). MRI datasets were collected on a 4.7 T system. Participants were administered the Wechsler Memory Scale (WMS–IV) to evaluate episodic memory function. Structural equation modeling was used to test the relationship between studied variables. We found that the volume of the dentate gyrus subfield and posterior hippocampus (body) showed a significant direct effect on visuospatial memory performance; additionally, an indirect effect of age on visuospatial memory mediated through these hippocampal subfield/subregion was significant. Logical and verbal memory were not significantly associated with hippocampal subfield or subregion volumes. [ABSTRACT FROM AUTHOR]

Degeneration of the nucleus basalis of Meynert (NBM) has been implicated in cognitive impairments in Parkinson's disease. The role of the NBM volumes in the cognitive function in isolated rapid eye movement (REM) sleep behavior disorder (iRBD) has not been explored. We investigated changes in NBM volumes and their associations with cognitive deficits in iRBD. Baseline NBM volumes were compared between 29 iRBD patients and 29 healthy controls by using structural MRI data from the Parkinson Progression Marker Initiative database. Partial correlation analyses were used to evaluate cross-sectional relationships between baseline NBM volumes and cognitive performance in iRBD. Linear mixed models were applied to assess between-group differences in longitudinal cognitive changes, and whether baseline NBM volumes could predict longitudinal changes of cognition in iRBD. Compared with controls, NBM volumes were significantly reduced in iRBD patients. In patients with iRBD, higher NBM volumes were significantly associated with greater performance in global cognition function. In the longitudinal analyses, iRBD patients showed more severe and rapid decline on tests of global cognition compared to healthy controls. Furthermore, greater baseline NBM volumes were significantly associated with greater follow-up Montreal Cognitive Assessment (MoCA) scores, thus predicting less longitudinal cognitive changes in iRBD. This study provides important in vivo evidence for an association between the NBM degeneration and cognitive impairments in iRBD. • iRBD patients demonstrated significantly reduced NBM volumes. • NBM volumes were positively correlated with scores of global cognition in iRBD. • iRBD patients showed significant decline in global cognition over time. • Baseline NBM volumes of iRBD predict longitudinal changes in global cognition. [ABSTRACT FROM AUTHOR]

Electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS) are effective neuromodulation therapies for treatment-resistant depression (TRD). While ECT is generally considered the most effective antidepressant, rTMS is less invasive, better tolerated and leads to more durable therapeutic benefits. Both interventions are established device antidepressants, but it remains unknown if they share a common mechanism of action. Here we aimed to compare the brain volumetric changes in patients with TRD after right unilateral (RUL) ECT versus left dorsolateral prefrontal cortex (lDLPFC) rTMS. We assessed 32 patients with TRD before the first treatment session and after treatment completion using structural magnetic resonance imaging. Fifteen patients were treated with RUL ECT and seventeen patients received lDLPFC rTMS. Patients receiving RUL ECT, in comparison with patients treated with lDLPFC rTMS, showed a greater volumetric increase in the right striatum, pallidum, medial temporal lobe, anterior insular cortex, anterior midbrain, and subgenual anterior cingulate cortex. However, ECT- or rTMS-induced brain volumetric changes were not associated with the clinical improvement. We evaluated a modest sample size with concurrent pharmacological treatment and without neuromodulation therapies randomization. Our findings suggest that despite comparable clinical outcomes, only RUL ECT is associated with structural change, while rTMS is not. We hypothesize that structural neuroplasticity and/or neuroinflammation may explain the larger structural changes observed after ECT, whereas neurophysiological plasticity may underlie the rTMS effects. More broadly, our results support the notion that there are multiple therapeutic strategies to move patients from depression to euthymia. • RUL 0.3-0.5 ms pulse width ECT and 10 Hz lDLPFC rTMS may have comparable clinical antidepressant efficacy. • ECT and rTMS induce different patterns of structural change: rTMS did not induce significant changes, while RUL ECT did. • Temporal, subcortical and cingulate volume increases are greater after ECT than rTMS. • Generally, results suggest there are multiple circuit-level antidepressant strategies. [ABSTRACT FROM AUTHOR]

Excessive exercise may also lead to exercise addiction (EXA), which is harmful to people's physical and mental health. Behavioral and neuroimaging studies have demonstrated that addictive disorders are essentially motivational problems. However, little is known about the neuropsychological mechanism of EXA and the effects of motivation on EXA. We investigated 130 regularly exercised participants with EXA symptoms to explore the neurobiological basis of EXA and its association with motivation. The correlation between EXA and gray matter volume (GMV) was evaluated by whole-brain regression analysis based on voxel-based morphometry. Then, regional brain function was extracted and the relationship between brain structure-function-EXA was analyzed. Finally, mediation analysis was performed to further detect the relationship between the brain, motivation, and EXA. Whole-brain correlation analyses showed that the GMV of the right orbitofrontal cortex (OFC) was negatively correlated with EXA. The function of the right OFC played an indirect role in EXA and affected EXA via the GMV of the OFC. Importantly, the GMV of the right OFC played a mediating role in the relationship between ability motivation and EXA. These results remain significant even when adjusting for sex, age, body mass index, family socioeconomic status, general intelligence, total intracranial volume, and head motion. The results should be interpreted carefully because only the people with EXA symptoms were included. This study provided evidence for the underlying neuropsychological mechanism of the important role of the right OFC in EXA and revealed that there may be a decrease in executive control function in EXA. • EXA was negatively correlated with the GMV of the right OFC. • Ability is a potential risk factor for EXD. • The function of the right OFC played an indirect role in EXA and affected EXA via the GMV of the OFC. • The GMV of the right OFC served as a mediator between ability and EXD. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been increasingly used in the computer-aided diagnosis of Alzheimer's Disease (AD). This study takes the advantage of the 2D-slice CNN fast computation and ensemble approaches to develop a Monte Carlo Ensemble Neural Network (MCENN) by introducing Monte Carlo sampling and an ensemble neural network in the integration with ResNet50. Our goals are to improve the 2D-slice CNN performance and to design the MCENN model insensitive to image resolution. Unlike traditional ensemble approaches with multiple base learners, our MCENN model incorporates one neural network learner and generates a large number of possible classification decisions via Monte Carlo sampling of feature importance within the combined slices. This can overcome the main weakness of the lack of 3D brain anatomical information in 2D-slice CNNs and develop a neural network to learn the 3D relevance of the features across multiple slices. Brain images from Alzheimer's Disease Neuroimaging Initiative (ADNI, 7199 scans), the Open Access Series of Imaging Studies-3 (OASIS-3, 1992 scans), and a clinical sample (239 scans) are used to evaluate the performance of the MCENN model for the classification of cognitively normal (CN), patients with mild cognitive impairment (MCI) and AD. Our MCENN with a small number of slices and minimal image processing (rigid transformation, intensity normalization, skull stripping) achieves the AD classification accuracy of 90%, better than existing 2D-slice CNNs (accuracy: 63 % ∼ 84 %) and 3D CNNs (accuracy: 74 % ∼ 88 %). Furthermore, the MCENN is robust to be trained in the ADNI dataset and applied to the OASIS-3 dataset and the clinical sample. Our experiments show that the AD classification accuracy of the MCENN model is comparable when using high- and low-resolution brain images, suggesting the insensitivity of the MCENN to image resolution. Hence, the MCENN does not require high-resolution 3D brain structural images and comprehensive image processing, which supports its potential use in a clinical setting. • Possible decisions are created by Monte Carlo sampling of features among 2D slices. • One base learner learns the 3D relevance of the brain anatomy. • The MCENN model performs better than 2D-slice and 3D CNNs in the AD classification. • The MCENN performance is not sensitive to the MRI image resolution. • The MCENN model for the AD classification is clinically applicable. [ABSTRACT FROM AUTHOR]

Background: Convergent studies have demonstrated morphological abnormalities in various brain regions in depression patients. However, the molecular underpinnings of the structural impairments remain largely unknown, despite a pressing need for treatment targets and mechanisms. Here, we investigated the gray matter volume (GMV) alteration in patients with depression and its underlying molecular architecture.Methods: We recruited 195 first-episode, treatment-naïve depression patients and 78 gender-, age-, and education level-matched healthy controls (HCs) who underwent high-resolution T1-weighted magnetic resonance scans. Voxel-based morphometry (VBM) was adopted to calculate the GMV differences between two groups. Then we analyzed the spatial correlation between depression-induced alteration in GMV and density maps of 10 receptors/transporters deriving from prior molecular imaging in healthy people.Results: Compared to HCs, the depression group had significantly increased GMV in the left ventral portions of the ventral medial prefrontal cortex, parahippocampal gyrus, amygdala, the right superior parietal lobule and precuneus while decreased GMV in the bilateral hippocampus extending to the thalamus and cerebellum. The GMV alteration introduced by depression was spatially correlated with serotonin receptors (5-HT1a, 5-HT1b, and 5-HT2a), dopamine receptors (D1 and D2) and GABAergic receptor (GABAa) densities.Limitations: The conclusions drawn in this study were obtained from a single dataset.Conclusions: This study reveals abnormal GMV alteration and provides a series of neurotransmitters receptors possibly related to GMV alteration in depression, which facilitates an integrative understanding of the molecular mechanism underlying the structural abnormalities in depression and may provide clues to new treatment strategies. [ABSTRACT FROM AUTHOR]

• Sleep disturbance was associated with greater volumes in the lateral ventricles and greater total white matter hyperintensities. • Sleep disturbance was associated with lower mean volumes in 12 hippocampal, frontal, parietal, and temporal lobe regions. • Males, Hispanic participants, and those with less formal education were more likely to report sleep disruption. • A dementia diagnosis moderated the relationship between sleep disturbance and right and left lateral ventricular volumes. This study aimed to 1) determine the association between sleep disturbance and brain structure volumes, 2) the moderation effect of apolipoprotein ε4 genotype on sleep disturbance and brain structures, and 3) the moderation effect of sleep disturbance on cognitive status and regional brain volumes. Using the National Alzheimer's Coordinating Center Uniform Data Set (n = 1,533), multiple linear regressions were used to evaluate the association between sleep disturbance and brain volumes. Sleep disturbance was measured using one question from the NPI-Q. After controlling for intracranial volume, age, sex, years of education, race, ethnicity, and applying the FDR correction, total cerebrospinal fluid volume, left lateral ventricle volume, total lateral ventricle volume, and total third ventricle volume demonstrated significantly higher means for those with sleep disturbance. Total brain volume, total white and gray matter volume, total cerebrum brain volume (including gray but not white matter), left hippocampus volume, total hippocampal volume, the left, right, and total frontal lobe cortical gray matter volume, and the left, right, and total temporal lobe cortical gray matter volume demonstrated significantly lower mean volumes for those with sleep disturbance. Sleep disturbance moderated the association between cognitive status and lateral ventricular volumes. These findings suggest that disrupted sleep is associated with atrophy across multiple brain regions and ventricular hydrocephalus ex vacuo. [ABSTRACT FROM AUTHOR]

Dysfunction in cortico-striatal circuitry represents a core component of the pathophysiology in schizophrenia (SZ) but its potential as a candidate endophenotype of the illness is often confounded by neuroleptic medication. Accordingly, 26 adolescent and young adult participants at genetic high-risk for schizophrenia, but who were asymptomatic and neuroleptic naïve, and 28 age-matched controls underwent 1.5T structural magnetic resonance imaging of the striatum, manually parcellated into limbic (LST), associative (AST), and sensorimotor (SMST) functional subregions. In relation to their age peers, participants at genetic high-risk for schizophrenia showed overall lower striatal gray matter volume with their most pronounced loss, bilaterally in the AST, but not the LST or SMST. Neuropsychological testing revealed reduced executive functioning for genetically at-risk participants, although the groups did not differ significantly in overall intelligence or oral reading. For controls but not for at-genetic high-risk participants, stronger executive functioning correlated with increased bilateral AST volume. Reduced bilateral AST volume in genetic high-risk adolescents and young adults, accompanied by heritable loss of higher cognitive brain-behavior relationships, might serve as a useful endophenotype of SZ. [ABSTRACT FROM AUTHOR]

Clinical features and genetics overlap in schizophrenia (SCZ) and bipolar disorder (BD). Identifying brain alterations associated with genetic vulnerability for SCZ and BD could help to discover intermediate phenotypes, quantifiable biological traits with greater prevalence in unaffected relatives (RELs), and early recognition biomarkers in ultrahigh risk populations. However, a comprehensive meta-analysis of structural and functional magnetic resonance imaging (MRI) studies examining relatives of patients with SCZ and BD has not been performed yet. We systematically searched PubMed, Scopus, and Web of Science for structural and functional MRI studies investigating relatives and healthy control subjects. A total of 230 eligible neuroimaging studies (6274 SCZ-RELs, 1900 BD-RELs, 10,789 healthy control subjects) were identified. We conducted coordinate-based activation likelihood estimation meta-analyses on 26 structural MRI and 81 functional MRI investigations, including stratification by task type. We also meta-analyzed regional and global volumetric changes. Finally, we performed a meta-analysis of all MRI studies combined. Reduced thalamic volume was present in both SCZ and BD RELs. Moreover, SCZ-RELs showed alterations in corticostriatal-thalamic networks, spanning the dorsolateral prefrontal cortex and temporal regions, while BD-RELs showed altered thalamocortical and limbic regions, including the ventrolateral prefrontal, superior parietal, and medial temporal cortices, with frontoparietal alterations in RELs of BD type I. Familiarity for SCZ and BD is associated with alterations in the thalamocortical circuits, which may be the expression of the shared genetic mechanism underlying both disorders. Furthermore, the involvement of different prefrontocortical and temporal nodes may be associated with a differential symptom expression in the two disorders. [ABSTRACT FROM AUTHOR]

• Propose a novel method called IRBCFA to improve the precision of regional FD. • IRBCFA method can calculate regional 3D fractal dimensionality in fine brain regions. • Regional FD in gray matter is effective in identifying individuals with SCD. • The constructed identification model is validated on an independent testing dataset. Accurate identification of individuals with subjective cognitive decline (SCD) is crucial for early intervention and prevention of neurodegenerative diseases. Fractal dimensionality (FD) has emerged as a robust and replicable measure, surpassing traditional geometric metrics, in characterizing the intricate fractal geometrical properties of brain structure. Nevertheless, the effectiveness of FD in identifying individuals with SCD remains largely unclear. A 3D regional FD method can be suggested to characterize and quantify the spatial complexity of the precise gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. This study introduces a novel integer ratio based 3D box-counting fractal analysis (IRBCFA) to quantify regional fractal dimensions (FDs) in structural magnetic resonance imaging (MRI) data. The innovative method overcomes limitations of conventional box-counting techniques by accommodating arbitrary box sizes, thereby enhancing the precision of FD estimation in small, yet neurologically significant, brain regions. The application of IRBCFA to two publicly available datasets, OASIS-3 and ADNI, consisting of 520 and 180 subjects, respectively. The method identified discriminative regions of interest (ROIs) predominantly within the limbic system, fronto-parietal region, occipito-temporal region, and basal ganglia-thalamus region. These ROIs exhibited significant correlations with cognitive functions, including executive functioning, memory, social cognition, and sensory perception, suggesting their potential as neuroimaging markers for SCD. The identification model trained on these ROIs demonstrated exceptional performance achieving over 93 % accuracy on the discovery dataset and exceeding 87 % on the independent testing dataset. Furthermore, an exchange experiment between datasets revealed a substantial overlap in discriminative ROIs, highlighting the robustness of our method across diverse populations. Our findings indicate that IRBCFA can serve as a valuable tool for quantifying the spatial complexity of gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. The demonstrated generalizability and robustness of this method position it as a promising tool for neurodegenerative disease research and offer potential for clinical applications. [ABSTRACT FROM AUTHOR]

Neuroimaging studies of suicidal behavior have so far been conducted in small samples, prone to biases and false-positive associations, yielding inconsistent results. The ENIGMA-MDD Working Group aims to address the issues of poor replicability and comparability by coordinating harmonized analyses across neuroimaging studies of major depressive disorder and related phenotypes, including suicidal behavior. Here, we pooled data from 18 international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy control subjects and 6448 people with depression, of whom 694 had attempted suicide). We compared regional cortical thickness and surface area and measures of subcortical, lateral ventricular, and intracranial volumes between suicide attempters, clinical control subjects (nonattempters with depression), and healthy control subjects. We identified 25 regions of interest with statistically significant (false discovery rate <.05) differences between groups. Post hoc examinations identified neuroimaging markers associated with suicide attempt including smaller volumes of the left and right thalamus and the right pallidum and lower surface area of the left inferior parietal lobe. This study addresses the lack of replicability and consistency in several previously published neuroimaging studies of suicide attempt and further demonstrates the need for well-powered samples and collaborative efforts. Our results highlight the potential involvement of the thalamus, a structure viewed historically as a passive gateway in the brain, and the pallidum, a region linked to reward response and positive affect. Future functional and connectivity studies of suicidal behaviors may focus on understanding how these regions relate to the neurobiological mechanisms of suicide attempt risk. [ABSTRACT FROM AUTHOR]

Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models. 3D-T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence-based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics. 16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three-classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics. This study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs. • The findings showed that the ML algorithms achieved good classification performance in classification tasks, the most powerful 17 discriminative features mainly involved the cerebello-thalamo-cortical circuits and basal ganglia area. • RF classifier to perform the classification tasks had achieved good results, especially in identifying DT from ET and HCs. • A significant proportion of discriminative features (15/17, 88.2 %) consistently showed morphological relations between frontal lobe and other brain regions were deemed as the most vital in distinguishing ET, DT, and HCs. • Using SHAP analysis and bounding by a feature importance greater than 0.05, the involvement of the middle frontal gyrus, anterior cingulate and paracingulate gyri, and lingual gyrus were characteristic alterations of ET; the involvement of the SMA, vermis_4_5, and putamen were characteristic alterations of DT. [ABSTRACT FROM AUTHOR]

Introduction: Cognitive deficits occur in Parkinson's disease (PD). Cardiorespiratory fitness (CRF) is associated with better cognitive performance in aging especially in executive function (EF) and memory. The association between CRF and cognitive performance is understudied in people with PD. Brain structures underlying associations also remains unknown. This cross-sectional study examined the associations between CRF and cognitive performance in PD. We also examined associations between CRF and brain structures impacted in PD. Mediation analysis were conducted to examine whether brain structures impacted in PD mediate putative associations between CRF and cognitive performance.Methods: Individuals with PD (N = 33) underwent magnetic resonance imaging (MRI), CRF evaluation (estimated VO2max), and neuropsychological assessment. Composite cognitive scores of episodic memory, EF, attention, language, and visuospatial functioning were generated. Structural equation models were constructed to examine whether MRI volume estimates (thalamus and pallidum) mediated associations between CRF and cognitive performance (adjusting for age, education, PD disease duration, sex, MDS-UPDRS motor score, and total intracranial volume).Results: Higher CRF was associated with better episodic memory (Standardized β = 0.391; p = 0.008), EF (Standardized β = 0.324; p = 0.025), and visuospatial performance (Standardized β = 0.570; p = 0.005). Higher CRF was associated with larger thalamic (Standardized β = 0.722; p = 0.004) and pallidum (Standardized β = 0.635; p = 0.004) volumes. Thalamic volume mediated the association between higher CRF and better EF (Indirect effect = 0.309) and episodic memory (Indirect effect = 0.209) performance (p < 0.05). The pallidum did not significantly mediate associations between CRF and cognitive outcomes.Conclusion: The thalamus plays an important role in the association between CRF and both EF and episodic memory in PD. [ABSTRACT FROM AUTHOR]

Schizophrenia patients show signs of accelerated aging in cognitive and physiological domains. Both schizophrenia and accelerated aging, as measured by MRI brain images and epigenetic clocks, are correlated with increased mortality. However, the association between these aging measures have not yet been studied in schizophrenia patients. In schizophrenia patients and healthy subjects, accelerated aging was assessed in brain tissue using a longitudinal MRI (N = 715 scans; mean scan interval 3.4 year) and in blood using two epigenetic age clocks (N = 172). Differences ('gaps') between estimated ages and chronological ages were calculated, as well as the acceleration rate of brain aging. The correlations between these aging measures as well as with polygenic risk scores for schizophrenia (PRS; N = 394) were investigated. Brain aging and epigenetic aging were not significantly correlated. Polygenic risk for schizophrenia was significantly correlated with brain age gap, brain age acceleration rate, and negatively correlated with DNAmAge gap, but not with PhenoAge gap. However, after controlling for disease status and multiple comparisons correction, these effects were no longer significant. Our results imply that the (accelerated) aging observed in the brain and blood reflect distinct biological processes. Our findings will require replication in a larger cohort. [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]

In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets. [ABSTRACT FROM AUTHOR]

Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validation techniques. Here, we perform a critical appraisal of the accuracy of machine learning methodologies used in SZ/HC classifications studies by comparing three machine learning algorithms (logistic regression [LR], support vector machines [SVMs], and linear discriminant analysis [LDA]) on three independent datasets (435 subjects total) using two tissue density estimates and cortical thickness (CT). Performance is assessed using 10-fold cross-validation, as well as a held-out validation set. Classification using CT outperformed tissue densities, but there was no clear effect of dataset. LR, SVMs, and LDA each yielded the highest accuracies for a different feature set and validation paradigm, but most accuracies were between 55 and 70%, well below previously reported values. The highest accuracy achieved was 73.5% using CT data and an SVM. Taken together, these results illustrate some of the obstacles to constructing effective disease classifiers, and suggest that tissue densities and CT may not be sufficiently sensitive for SZ/HC classification given current available methodologies and sample sizes. [ABSTRACT FROM AUTHOR]

Performance of instrumental activities of daily living (IADLs) can become compromised in older adults with mild cognitive impairment (MCI). Patients' level of insight into their everyday functioning varies both amongst individuals and across domains assessed, with some individuals exhibiting complete unawareness of deficits. The current cross-sectional study examined the neuroanatomical substrates of self-awareness in order to help explain the variability in this phenomenon in older adults across a continuum of cognitive impairment. Eighty-five participants (ages 54–88, mean age = 73 years, 57% female, 89% Caucasian) diagnosed with MCI or mild probable dementia underwent structural magnetic resonance imaging. Level of self-awareness was assessed by calculating the discrepancy between objective and subjective performance across six IADLs (Financial Management, Driving, Grocery Shopping, Nutrition Evaluation, Medication Management, and Telephone Use). Over-estimation of current abilities occurred in 13–31% of the sample depending on which IADL was evaluated. Poor awareness was significantly related to reduced volume in the bilateral medial prefrontal cortex, middle and posterior cingulate cortex, right insular cortex, and cerebellum. No associations were found with total white matter lesion load. These findings were broadly consistent across all functional domains assessed, supporting the theory that cortical midline and cerebellar structures are involved in self-referential processing across a variety of different cognitive and behavioral skills. Longitudinal studies are needed to confirm this association. • Some older adults with mild cognitive impairment overestimate functional abilities. • Unawareness of deficit related to reduced volume in cortical midline and cerebellum. • No relationship between unawareness of deficit and white matter lesions. • Imaging findings consistent across different instrumental activities of daily living. [ABSTRACT FROM AUTHOR]

Highlights • Androgren replacement therapy was associated with larger hippocampi in boys with Klinefelter Syndrome (KS). • A significant positive association was observed between hippocampus volume and performance on a spatial memory task. • These data suggest androgen replacement therapy influences brain structure and associated behavior in boys with KS. Abstract Klinefelter syndrome (KS, 47,XXY) is the most common sex chromosome aneuploidy in males. A variety of complex clinical needs is associated with KS, including physical, cognitive and psychosocial impairments. Standard treatment for KS consists of androgen replacement therapy in adolescence to offset testosterone deficiency. Such treatment has a beneficial effect on the physical and behavioral manifestations of this syndrome. Whether androgen supplementation has a significant influence on the brain, however, is unknown. In the current study, we examined regional gray matter volume in boys with KS to assess whether treatment with oxandrolone, a synthetic hormone analog of testosterone, was associated with structural changes in the brain. Specifically, we focused our investigation on the hippocampus, given (1) its involvement in KS, and (2) the high concentration of androgen receptors found in this region. Structural magnetic resonance imaging data was acquired from a subsample of boys who completed a 2-year double-blind clinical trial in which patients were randomized to treatment with oxandrolone or to placebo, as well as from a sample of typically developing (TD) boys. Group differences in hippocampal volume were examined. A significant main effect of group was observed. Pairwise comparisons indicated smaller hippocampal volume in the placebo group relative to the oxandrolone group, as well as smaller volume in the placebo group relative to the TD control group. No difference in volume was observed between the treatment and TD groups. Moreover, across KS subgroups, a significant positive association was observed between hippocampus volume and performance on a spatial memory task, indicating treatment-based changes in brain structure may underlie cognitive change. These findings confirm prior reports implicating a role of the hippocampus in KS and are important in extending previous research by demonstrating a significant effect of androgens on brain structure. [ABSTRACT FROM AUTHOR]

• This study is the first to perform a graph theory analysis of individual GM morphological networks in a relatively large sample of adolescent MDD patients. • Structural connectome analyses revealed disrupted small-world organization in adolescent MDD patients. • Adolescent MDD patients showed abnormal nodal centralities in the prefronal-subcortical-limbic areas and visual cortex regions. • Altered nodal centralities in the hippocampus were associated with illness duration and symptom severity. Although previous studies have demonstrated regional gray matter (GM) structural abnormalities in adolescents with major depressive disorder (MDD), how the topological organization of GM networks is affected in these patients is still unclear. Structural magnetic resonance imaging data were acquired from 100 first-episode drug-naïve adolescent MDD patients and 80 healthy controls (HCs). Whole-brain GM structural network was constructed for each subject, and a graph theory analysis was used to calculate the topological metrics of GM networks. Adolescent MDD patients showed significantly lower cluster coefficient and local efficiency compared to HCs. Compared to controls, adolescent MDD patients showed higher nodal centralities in the bilateral cuneus, left lingual gyrus, and right middle occipital gyrus and lower nodal centralities in the bilateral dorsolateral superior frontal gyrus, bilateral middle frontal gyrus, right anterior cingulate and paracingulate gyri, bilateral hippocampus, bilateral amygdala, bilateral caudate nucleus, and bilateral thalamus. Nodal centralities of the hippocampus were negatively associated with symptom severity and illness duration. Our findings suggest disrupted topological organization of GM structural networks in adolescent MDD patients. Impaired local segregation and abnormal nodal centralities in the prefrontal-subcortical-limbic areas and visual cortex regions may play important roles in the neurobiology of adolescent-onset MDD. [ABSTRACT FROM AUTHOR]

Highlights • We investigated brain structural alteration in un-medicated AN patients. • Relatively larger caudate volume and reduced cortical thickness at the left precuneus was found. • Cortical thinning was more prominent in the left hemisphere in AN patients. • Above structural brain abnormalities in patients with AN may shed light on the psychopathological processes underlying the disrupted eating behavior. Abstract Anorexia nervosa (AN) is a mental disorder characterized by a preoccupation with thinness and may be associated with brain structural alteration. The aim of the study was to examine the brain structural alteration in AN patients, including subcortical structure volume and cortical thickness. Thirty-five un-medicated AN patients and 20 matched healthy controls underwent structural magnetic resonance imaging brain scans. High resolution structural images were acquired on a SIEMENS 3T scanner and preprocessed using FreeSurfer software. Subcortical structure volume and cortical thickness were compared between the two groups. We found larger percentage of caudate volume relative to total grey matter (GM) volume in the AN group. Reduced cortical thickness at the left precuneus was also observed in AN patients. Moreover, an interaction between group and hemisphere was found, suggesting that cortical thinning was more prominent in the left hemisphere in AN patients. These findings provide further evidence for structural brain abnormalities in patients with AN. [ABSTRACT FROM AUTHOR]

Psychogenic non-epileptic seizures (PNES) are often viewed as manifestations of altered motor and sensory function resulting from psychological responses to adverse experiences. Yet many patients and non-expert healthcare professionals find it difficult to understand how severe disturbances in normal neurological functioning can solely result from underlying psychological mechanisms to the exclusion of other physical causes. Perhaps importantly, recent advances using neuroimaging techniques point to possible structural and functional correlates in PNES. In an attempt to further our understanding of the neurobiological correlates of this condition, we compared the brain scans of 20 patients with PNES (14 females, mean age 41.05, range 19–62) and 20 age- and gender-matched healthy controls (14 females, mean age 40.65, range 21–61) to investigate group differences for cortical thickness and gyrification patterns using FreeSurfer. Compared to controls, patients with PNES showed cortical thickness increases in motor, sensory and occipital areas as well as cortical thickness decreases in temporal and frontal brain regions. In addition, we observed age-related changes in cortical thickness in the right lateral occipital area in PNES. However, contrary to our prediction that atypical gyrification may be present, we did not find any evidence of abnormalities on a measure thought to reflect prenatal and early childhood cortical development and organization. Nor did we find significant correlations between cortical thickness results and clinical features. These findings partly corroborate, but also differ from previous morphometric studies in PNES. These inconsistencies likely reflect the aetiology and phenomenological heterogeneity of PNES. [ABSTRACT FROM AUTHOR]

There is increasing evidence showing that brain atrophy varies between patients with Alzheimer's disease (AD), suggesting that different anatomical patterns might exist within the same disorder. We investigated AD heterogeneity based on cortical and subcortical atrophy patterns in 299 AD subjects from 2 multicenter cohorts. Clusters of patients and important discriminative features were determined using random forest pairwise similarity, multidimensional scaling, and distance-based hierarchical clustering. We discovered 2 typical (72.2%) and 3 atypical (28.8%) subtypes with significantly different demographic, clinical, and cognitive characteristics, and different rates of cognitive decline. In contrast to previous studies, our unsupervised random forest approach based on cortical and subcortical volume measures and their linear and nonlinear interactions revealed more typical AD subtypes with important anatomically discriminative features, while the prevalence of atypical cases was lower. The hippocampal-sparing and typical AD subtypes exhibited worse clinical progression in visuospatial, memory, and executive cognitive functions. Our findings suggest there is substantial heterogeneity in AD that has an impact on how patients function and progress over time. [ABSTRACT FROM AUTHOR]

We aimed to investigate associations between regional cortical thickness and rate of decline over time in 4 cognitive domains in patients with subjective cognitive decline (SCD). We included 233 SCD patients with the total number of 654 neuropsychological assessments (median = 3, range = 2–8) and available baseline magnetic resonance imaging from the Amsterdam Dementia Cohort (125 males, age: 63 ± 9, Mini–Mental State Examination score: 28 ± 2). We assessed longitudinal cognitive functioning at baseline and follow-up in 4 cognitive domains (composite Z-scores): memory, attention, executive function, and language. Thickness (millimeter) was estimated using FreeSurfer for frontal, temporal, parietal, cingulate, and occipital cortices. We used linear mixed models to estimate effects of cortical thickness on cognitive performance (dependent variables). There were no associations between cortical thickness and baseline cognition, but a faster subsequent rate of memory loss was associated with thinner cortex of the frontal [β (SE) = 0.20 (0.07)], temporal [β (SE) = 0.18 (0.07)], and occipital [β (SE) = 0.22 (0.09)] cortices (all p < 0.05 FDR ). These findings illustrate that early cortical changes, particularly in the temporal cortex, herald incipient cognitive decline related to neurodegenerative diseases, most prominently Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Existing studies suggest that dehydroepiandrosterone (DHEA) may be important for human brain development and cognition. For example, molecular studies have hinted at the critical role of DHEA in enhancing brain plasticity. Studies of human brain development also support the notion that DHEA is involved in preserving cortical plasticity. Further, some, though not all, studies show that DHEA administration may lead to improvements in working memory in adults. Yet these findings remain limited by an incomplete understanding of the specific neuroanatomical mechanisms through which DHEA may impact the CNS during development. Here we examined associations between DHEA, cortico-hippocampal structural covariance, and working memory (216 participants [female=123], age range 6–22 years old, mean age: 13.6 +/−3.6 years, each followed for a maximum of 3 visits over the course of 4 years). In addition to administering performance-based, spatial working memory tests to these children, we also collected ecological, parent ratings of working memory in everyday situations. We found that increasingly higher DHEA levels were associated with a shift toward positive insular-hippocampal and occipito-hippocampal structural covariance. In turn, DHEA-related insular-hippocampal covariance was associated with lower spatial working memory but higher overall working memory as measured by the ecological parent ratings. Taken together with previous research, these results support the hypothesis that DHEA may optimize cortical functions related to general attentional and working memory processes, but impair the development of bottom-up, hippocampal-to-cortical connections, resulting in impaired encoding of spatial cues. [ABSTRACT FROM AUTHOR]

Offspring from multiplex, alcohol-dependent families are at heightened risk for substance use disorders (SUDs) in adolescence and young adulthood. These high-risk offspring have also been shown to have atypical structure and function of brain regions implicated in emotion regulation, social cognition, and reward processing. This study assessed the relationship between amygdala and orbitofrontal cortex (OFC) volumes obtained in adolescence and SUD outcomes in young adulthood among high-risk offspring and low-risk controls. A total of 78 participants (40 high-risk; 38 low-risk) from a longitudinal family study, ages 8–19, underwent magnetic resonance imaging; volumes of the amygdala and OFC were obtained with manual tracing. SUD outcomes were assessed at approximately yearly intervals. Cox regression survival analyses were used to assess the effect of regional brain volumes on SUD outcomes. The ratio of OFC to amygdala volume significantly predicted SUD survival time across the sample; reduction in survival time was seen in those with smaller ratios for both high-risk and low-risk groups. Morphology of prefrontal relative to limbic regions in adolescence prospectively predicts age of onset for substance use disorders. [ABSTRACT FROM AUTHOR]

Serotonin modulates neuroplasticity, especially during early life, and dysfunctions in both systems likewise contribute to pathophysiology of depression. Recent findings demonstrate that serotonin reuptake inhibitors trigger reactivation of juvenile-like neuroplasticity. How these findings translate to clinical antidepressant treatment in major depressive disorder remains unclear. With this review, we link preclinical with clinical work on serotonin and neuroplasticity to bring two pathophysiologic models in clinical depression closer together. Dysfunctional developmental plasticity impacts on later-life cognitive and emotional functions, changes of synaptic serotonin levels and receptor levels are coupled with altered synaptic plasticity and neurogenesis. Structural magnetic resonance imaging in patients reveals disease-state-specific reductions of gray matter, a marker of neuroplasticity, and reversibility upon selective serotonin reuptake inhibitor treatment. Translational evidence from magnetic resonance imaging in animals support that reduced densities and sizes of neurons and reduced hippocampal volumes in depressive patients could be attributable to changes of serotonergic neuroplasticity. Since ketamine, physical exercise or learning enhance neuroplasticity, combinatory paradigms with selective serotonin reuptake inhibitors could enhance clinical treatment of depression. [ABSTRACT FROM AUTHOR]

· Positive relationship between P100 amplitude and V1 surface area in healthy individuals. · No significant relationship between P100 amplitude and V1 thickness in healthy individuals. · Larger patient samples are needed to understand the function-structure relationship in the visual cortex in patients with severe mental disorders. · Combining event related potential techniques and structural magnetic resonance imaging techniques may help us to gain more insight into the function-structure relationship in the human cortex. Patients with schizophrenia spectrum disorders (SCZ spect) and bipolar disorders (BD) show impaired function in the primary visual cortex (V1), indicated by altered visual evoked potential (VEP). While the neural substrate for altered VEP in these patients remains elusive, altered V1 structure may play a role. One previous study found a positive relationship between the amplitude of the P100 component of the VEP and V1 surface area, but not V1 thickness, in a small sample of healthy individuals. Here, we aimed to replicate these findings in a larger healthy control (HC) sample (n = 307) and to examine the same relationship in patients with SCZ spect (n = 30) or BD (n = 45). We also compared the mean P100 amplitude, V1 surface area and V1 thickness between controls and patients and found no significant group differences. In HC only, we found a significant positive P100-V1 surface area association, while there were no significant P100-V1 thickness relationships in HC, SCZ spect or BD. Together, our results confirm previous findings of a positive P100-V1 surface area association in HC, whereas larger patient samples are needed to further clarify the function-structure relationship in V1 in SCZ spect and BD. [ABSTRACT FROM AUTHOR]

Structural magnetic resonance imaging (sMRI) is widely applied in Alzheimer's disease (AD) diagnosis tasks by reflecting structural anomalies of the brain. Currently, most existing methods solely focus on pathological changes in disease-affected brain regions and ignore their potential associations and interactions, which provide valuable information for brain investigation. Meanwhile, how to construct effective structural brain graphs composed of nodes and edges remains appealing. To tackle these issues, in this paper, we propose a novel multi-relation reasoning network (MRN) to learn multi-relation-aware representations of brain regions in sMRI data for AD diagnosis, including spatial correlations and topological information. We frame distinguishing different disease statuses as the graph classification problem. Each scan is regarded as a graph, where nodes represent brain regions with abnormal changes selected by group-wise comparison, and edges denote semantic or spatial relations between them. Specifically, the dilated convolution module learns informative features to provide discriminative node representations for constructing brain graphs. Multi-type inter-region relations are then captured by the local reasoning module based on the graph convolutional network to provide a reliable basis for AD diagnosis, including geometric correlations and semantic interactions. Moreover, global reasoning is employed on the learned graph structure to achieve information aggregation and gradually generate the subject-level representation for AD diagnosis. We evaluate the effectiveness of our proposed method on the ADNI dataset, and extensive experiments demonstrate that our MRN achieves competitive performance for multiple AD-related classification tasks, compared to several state-of-the-art methods. • A novel multi-relation reasoning network for sMRI-based AD diagnosis is proposed. • Brain connectivity graphs are constructed based on disease-related brain regions. • Potential interactions between discriminative brain regions in sMRI are learned. • The diagnosis results achieve promising performance for AD diagnosis tasks. [ABSTRACT FROM AUTHOR]

Delinquent youth have substantial trauma exposure, with life-course persistent delinquents [LCPD] demonstrating notably elevated cross-diagnostic psychopathology and cognitive deficits. Because adolescents remain in the midst of brain and neurocognitive development, tailored interventions are key to improving functional outcomes. This structural magnetic resonance imaging study compared neuroanatomical profiles of 23 LCPD and 20 matched control adolescent boys. LCPD youth had smaller overall gray matter, and left hippocampal, volumes alongside less cortical surface area and folding within the left pars opercularis and supramarginal cortex. LCPD youth had more adversity-related exposures, and their higher Cumulative Trauma, Adversity and Grief [C-TAG] symptoms were associated with less surface area and folding in the pars opercularis and lingual gyrus. Neuroanatomical differences between LCPD and control youth overlap with data from both maltreatment and antisocial literatures. The affected left frontal regions also share connections to language- and executive-related functions, aligning well with LCPD youths' cognitive and behavioral difficulties. These data also dovetail with research suggesting the possibility of neurodevelopmental delays or disruptions related to cumulative adversity burden. Thus, concurrent treatment of LCPD youths' C-TAG symptoms and, cognitive deficits with overlapping neuroanatomical bases, may be most effective in improving outcomes and optimizing neurodevelopmental trajectories. [ABSTRACT FROM AUTHOR]

Impaired mobility, such as falls, may be an early biomarker of subsequent cognitive decline and is associated with subclinical alterations in both brain structure and function. In this 12-month prospective study, we examined whether there are volumetric differences in gray matter and subcortical regions, as well as cerebral white matter, between older fallers and non-fallers. In addition, we assessed whether these baseline volumetric differences are associated with changes in cognitive function over 12 months. A total of 66 community-dwelling older adults were recruited and categorized by their falls status. Magnetic resonance imaging occurred at baseline and participants' physical and cognitive performances were assessed at baseline and 12-months. At baseline, fallers showed significantly lower volumes in gray matter, subcortical regions, and cerebral white matter compared with non-fallers. Notably, fallers had significantly lower left lateral orbitofrontal white matter volume. Moreover, lower left lateral orbitofrontal white matter volume at baseline was associated with greater decline in set-shifting performance over 12 months. Our data suggest that falls may indicate subclinical alterations in regional brain volume that are associated with subsequent decline in executive functions. [ABSTRACT FROM AUTHOR]

One of the most striking characteristics of early life maltreatment (ELM) is the risk of transmission across generations, which could be linked to differences in maternal behavior. Maternal sensitivity includes appropriate and positive affective exchanges between mother and child. Mothers with a history of ELM have been found to show a lower sensitivity representing a significant risk factor for maltreating their own children. 25 mothers with and 28 mothers without sexual and/or physical childhood maltreatment (as assessed with the Childhood Experience of Care and Abuse interview) and their children participated in a standardized mother–child interaction task. Videotaped interactions were rated by two independent trained raters based on the Emotional Availability Scales. In addition, empathic capabilities were assessed with the Interpersonal Reactivity Index. High resolution structural magnetic resonance brain images of the mothers were analyzed with unbiased voxel-based morphometry and correlated with maternal sensitivity. Results indicate that mothers with ELM were less sensitive in the standardized interaction with their own child. In non-maltreated control mothers, maternal sensitivity was positively related to anterior insular grey matter volume, a region which is crucially involved in emotional empathy, while there was a positive association between maternal sensitivity and grey matter volume in parts of the cognitive empathy network such as the superior temporal sulcus and temporal pole region in mothers with ELM. These results implicate that neurostructural alterations associated with poor maternal sensitivity might be a sequelae of ELM and that mothers with ELM may try to compensate deficits in emotional empathy by recruiting brain regions involved in cognitive empathy when interacting with their child. Thus, findings suggest possible coping strategies of mother with ELM to prevent an intergenerational transmission of abuse. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) studies have revealed brain structural abnormalities in obsessive–compulsive disorder (OCD) patients, involving both gray matter (GM) and white matter (WM). However, the results of previous publications were based on average differences between groups, which limited their usages in clinical practice. Therefore, the aim of this study was to examine whether the application of multivariate pattern analysis (MVPA) to high-dimensional structural images would allow accurate discrimination between OCD patients and healthy control subjects (HCS). High-resolution T1-weighted images were acquired from 33 OCD patients and 33 demographically matched HCS in a 3.0 T scanner. Differences in GM and WM volume between OCD and HCS were examined using two types of well-established MVPA techniques: support vector machine (SVM) and Gaussian process classifier (GPC). We also drew a receiver operating characteristic (ROC) curve to evaluate the performance of each classifier. The classification accuracies for both classifiers using GM and WM anatomy were all above 75%. The highest classification accuracy (81.82%, P <0.001) was achieved with the SVM classifier using WM information. Regional brain anomalies with high discriminative power were based on three distributed networks including the fronto-striatal circuit, the temporo-parieto-occipital junction and the cerebellum. Our study illustrated that both GM and WM anatomical features may be useful in differentiating OCD patients from HCS. WM volume using the SVM approach showed the highest accuracy in our population for revealing group differences, which suggested its potential diagnostic role in detecting highly enriched OCD patients at the level of the individual. [ABSTRACT FROM AUTHOR]

Increasing efforts have been made to investigate the underlying pathophysiology of generalized anxiety disorder (GAD), but only limited consistent information is available on gray (GM) and white matter (WM) volume changes in affected adults. Additionally, few studies employed dimensional approaches to GAD pathology. This study compares structural brain imaging data from n =19 GAD subjects and n =24 healthy comparison (HC) subjects, all medication-free and matched on age, sex and education. Separate categorical and dimensional models were employed using voxel-based morphometry for GM and WM. Significantly higher GM volumes were found in GAD subjects mainly in basal ganglia structures and less consistently in the superior temporal pole. For WM, GAD subjects showed significantly lower volumes in the dlPFC. Largely consistent findings in dimensional and categorical models point toward these structural alterations being reliable and of importance for GAD. While lower volume in the dlPFC could reflect impaired emotional processing and control over worry in GAD, basal ganglia alterations may be linked to disturbed gain and loss anticipation as implicated in previous functional GAD studies. As perturbations in anticipation processes are central to GAD, these areas may warrant greater attention in future studies. [ABSTRACT FROM AUTHOR]

Background Psychopathy is associated with dysfunction in regions that compose the paralimbic system, such as the orbitofrontal cortex (OFC), insular cortex (IC), temporal pole (TP), parahippocampal gyrus (PHG) and cingulate cortex (CC). However, findings of structural alterations in these regions are inconsistent in schizophrenia, and correlations between paralimbic system measures and symptomatology and cognitive function have not been investigated. Method 93 patients with schizophrenia and 99 healthy controls received structural magnetic resonance imaging and clinical and cognitive assessment. We compared gray matter volume (GMV) between the two groups using voxel-based morphometry, and evaluated correlations between abnormal GMVs and clinical variables, symptomatology and cognitive function. The assessment of cognition included measures of processing speed, verbal fluency and memory. Results Patients with schizophrenia demonstrated significant GMV decreases in the paralimbic system, including bilateral OFC, IC and TP ( p < 0.05, FWE corrected). GMV decreases were also observed in bilateral superior temporal gyri (STG). The GMVs in bilateral OFC, left IC, left TP and bilateral STG were positively correlated with processing speed, and the GMVs in bilateral OFC were positively correlated with memory function in all participants. In our patient group, the GMV deficits were also associated with earlier age of onset, longer duration of illness, greater number of hospitalizations and more severe positive symptoms. Conclusions GMVs in the paralimbic system were significantly reduced in schizophrenia, and these abnormalities were correlated with clinical variables, symptomatology and cognitive function. These results suggest the paralimbic system plays an important role in the pathophysiology of schizophrenia. [ABSTRACT FROM AUTHOR]

Borderline personality disorder (BPD) may be associated with smaller hippocampi in comparison to hippocampal size in controls. However, specific pathology in hippocampal substructures (i.e., head, body and tail) has not been sufficiently investigated. To address hippocampal structure in greater detail, we studied 39 psychiatric inpatients and outpatients with a DSM-IV diagnosis of BPD and 39 healthy controls. The hippocampus and its substructures were segmented manually on magnetic resonance imaging scans. The volumes of hippocampal substructures (and total hippocampal volume) did not differ between BPD patients and controls. Exploratory analysis suggests that patients with a lifetime history of posttraumatic stress disorder (PTSD) may have a significantly smaller hippocampus – affecting both the hippocampal head and body – in comparison to BPD patients without comorbid PTSD (difference in total hippocampal volume: −10.5%, 95%CI −2.6 to −18.5, significant). Also, patients fulfilling seven or more DSM-IV BPD criteria showed a hippocampal volume reduction, limited to the hippocampal head (difference in volume of the hippocampal head: −16.5%, 95%CI −6.1 to −26.8, significant). Disease heterogeneity in respect to, for example, symptom severity and psychiatric comorbidities may limit direct comparability between studies; the results presented here may reflect hippocampal volumes in patients who are “less” affected or they may simply be a chance finding. However, there is also the possibility that global effects of BPD on the hippocampus may have previously been overestimated. [ABSTRACT FROM AUTHOR]

Neuroimaging studies demonstrate considerable changes in white matter volume and microstructure during adolescence. Most studies have focused on age-related effects, whilst puberty-related changes are not well understood. Using diffusion tensor imaging and tract-based spatial statistics, we investigated the effects of pubertal status on white matter mean diffusivity (MD) and fractional anisotropy (FA) in 61 males aged 12.7–16.0 years. Participants were grouped into early-mid puberty (≤Tanner Stage 3 in pubic hair and gonadal development; n = 22) and late-post puberty (≥Tanner Stage 4 in pubic hair or gonadal development; n = 39). Salivary levels of pubertal hormones (testosterone, DHEA and oestradiol) were also measured. Pubertal stage was significantly related to MD in diverse white matter regions. No relationship was observed between pubertal status and FA. Regression modelling of MD in the significant regions demonstrated that an interaction model incorporating puberty, age and puberty × age best explained our findings. In addition, testosterone was correlated with MD in these pubertally significant regions. No relationship was observed between oestradiol or DHEA and MD. In conclusion, pubertal status was significantly related to MD, but not FA, and this relationship cannot be explained by changes in chronological age alone. [ABSTRACT FROM AUTHOR]

Summary Background Inflammation may reduce hippocampal volume by blocking neurogenesis and promoting neurodegeneration. Posttraumatic stress disorder (PTSD) has been linked with both elevated inflammation and reduced hippocampal volume. However, few studies have examined associations between inflammatory markers and hippocampal volume, and none have examined these associations in the context of PTSD. Methods We measured levels of the inflammatory markers interleukin-6 (IL-6) and soluble receptor II for tumor necrosis factor (sTNF-RII) as well as hippocampal volume in 246 Gulf War veterans with and without current and past PTSD as assessed with the Clinician Administered PTSD Scale (CAPS). Enzyme-linked immunosorbent assays were used to measure inflammatory markers, and 1.5 Tesla magnetic resonance imaging (MRI) and Freesurfer version 4.5 were used to quantify hippocampal volume. Hierarchical linear regression and analysis of covariance models were used to examine if hippocampal volume and PTSD status would be associated with elevated levels of IL-6 and sTNF-RII. Results Increased sTNF-RII, but not IL-6, was significantly associated with reduced hippocampal volume ( β = −0.14, p = 0.01). The relationship between sTNF-RII and hippocampal volume was independent of potential confounds and covariates, including PTSD status. Although we observed no PTSD diagnosis-related differences in either IL-6 or sTNF-RII, higher PTSD severity was associated with significantly increased sTNF-RII ( β = 0.24, p = 0.04) and reduced IL-6 levels ( β = −0.24, p = 0.04). Conclusions Our results indicate that specific inflammatory proteins may be associated with brain structure and function as indexed by hippocampal volume and PTSD symptoms. [ABSTRACT FROM AUTHOR]

Evidence is accumulating that various mental disorders are related to neural abnormalities in the parietal cortices that are associated with the default mode network (DMN). Participants comprised 67 persons with social anxiety disorder (SAD) and 64 matched healthy controls who underwent structural magnetic resonance imaging (MRI) and a comprehensive clinical assessment. Voxel-based morphometry (VBM) across the entire brain and manual volumetry of the parietal cortices were performed. The results indicate abnormal manually segmented volumes or gray matter (GM) volumes within the precuneus, postcentral gyrus and inferior parietal cortex, as well as in the premotor cortices including the supplementary motor cortex. Significant negative correlations were obtained between parietal, especially precuneus, abnormalities and social avoidance severity, indicating stronger avoidance in SAD participants with smaller volumes or less GM. We conclude that pathological avoidance behaviors in SAD are associated with structural deficits of parietal regions that are associated with the DMN, which has been shown to mediate introspection and reflection upon one׳s own mental state in healthy humans. [ABSTRACT FROM AUTHOR]

The game of chess can be seen as a typical example for an expertise task requiring domain-specific training and experience. Despite intensive behavioural studies the neural underpinnings of chess performance and expertise are not entirely understood. A few functional neuroimaging studies have shown that expert chess players recruit different psychological functions and activate different brain areas while they are engaged in chess-related activities. Based on this functional literature, we predicted to find morphological differences in a network comprised by parietal and frontal areas and especially the occipito-temporal junction (OTJ), fusiform gyrus, and caudate nucleus. Twenty expert chess players and 20 control subjects were investigated using voxel-based and surface-based morphometry as well as diffusion tensor imaging. Grey matter volume and cortical thickness were reduced in chess players compared with those of control men in the OTJ and precunei. The volumes of both caudate nuclei were not different between groups, but correlated inversely with the years of chess playing experience. Mean diffusivity was increased in chess players compared with that of controls in the left superior longitudinal fasciculus and the Elo score (a chess tournament ranking) was inversely related to mean diffusivity within the right superior longitudinal fasciculus. To the best of our knowledge we showed for the first time that there are specific differences in grey and white matter morphology between chess players and control subjects in brain regions associated with cognitive functions important for playing chess. Whether these anatomical alterations are the cause or consequence of the intensive and long-term chess training and practice remains to be shown in future studies. [ABSTRACT FROM AUTHOR]

Abstract: Numerous imaging studies have reported smaller hippocampal volumes in patients with PTSD. To investigate whether decreased hippocampal volume is associated with PTSD chronicity, independent of age, we used hierarchical linear regression to examine the relationship between PTSD duration (estimated from the amount of time that had elapsed since the traumatic event; mean=17 years; range=6–36 years) and hippocampal volume, adjusting for age and other factors. Freesurfer version 4.5 was used to quantify the volumes of the hippocampus and the caudate nucleus, which served as a “control” region, from the 1.5T Magnetic Resonance Images (MRI) of 55 combat veterans (mean age 45±9 years) with chronic and current PTSD. PTSD duration was significantly associated with right hippocampal volume (β=−0.34, t=−2.40, P=0.02) after accounting for intracranial volume, age, gender (entered in the first step) and comorbidities (e.g., early life trauma, current major depression, history of substance abuse/dependence, psychotropic medication use, entered in the second step). This finding provides support for the potential neurotoxic effects of PTSD on hippocampal volume. [Copyright &y& Elsevier]

Highlights: [•] Corpus callosum (CC) and inter-hemispheric connectivity are compromised early in the course of MS. [•] Structural MRI and EEG can be used to assess such an early imbalance. [•] Higher coherence in α band at rest involved sensorimotor sections of corpus callosum. [•] Inter-hemispheric connectivity displayed a hand-dominance dependence only in α band. [Copyright &y& Elsevier]

Abstract: Violent offenders with psychopathy present a lifelong pattern of callousness and aggression and fail to benefit from rehabilitation programs. This study presents the first, albeit preliminary, evidence suggesting that some of the structural brain anomalies distinguishing violent offenders with psychopathy may result from physical abuse in childhood. [Copyright &y& Elsevier]