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

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

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

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

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

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

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

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

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

While it is known that whole amygdala volume is lastingly reduced after premature birth, it is unknown whether different amygdala nuclei are distinctively affected by prematurity. This question is motivated by two points: First, the observation that developmental trajectories of superficial, centromedial and basolateral amygdala nuclei are different. And second, the expectation that these different developmental pathways are distinctively affected by prematurity. Furthermore, we stated the question whether alterations in amygdala nuclei are associated with increased adults’ anxiety traits after premature birth.We investigated 101 very premature-born adults (

Background: Previous multimodal neuroimaging studies analyzed each dataset independently in subjective cognitive decline (SCD) and mild cognitive impairment (MCI), missing the cross-information. Multi-modal fusion analysis can provide more integral and comprehensive information regarding the brain. There has been a paucity of research on fusion analysis of sMRI and DTI in SCD and MCI. Materials and Methods: In the present study, we conducted fusion analysis of structural MRI and DTI by applying multimodal canonical correlation analysis with joint independent component analysis (mCCA-jICA) to capture the cross-information of gray matter (GM) and white matter (WM) in 62 SCD patients, 99 MCI patients, and 70 healthy controls (HCs). We further analyzed correlations between the mixing coefficients of mCCA-jICA and neuropsychological scores among the three groups. Results: A set of joint-discriminative independent components of GM and fractional anisotropy (FA) exhibited significant links between SCD and HCs, as well as between MCI and HCs. The covariant abnormalities primarily involved the frontal lobe/middle temporal gyrus/calcarine sulcus-anterior thalamic radiation/superior longitudinal fasciculus in SCD, and middle temporal gyrus/ fusiform gyrus/caudate necleus-forceps minor/anterior thalamic radiation in MCI. There was no significant difference between SCD and MCI groups. Conclusions: The covariant GM-WM abnormalities in SCD and MCI were found in specific brain regions involved in cognitive processing, which confirms the simultaneous GM and WM changes underlying cognitive decline. These findings suggest that multimodal fusion analysis allows for a more comprehensive understanding of the association among different types of brain tissues and its crucial role in the neuropathological mechanism of SCD and MCI.

Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.

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.)

The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair — all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for tracking AD pathogenesis and diagnosis. However, existing methods tend to treat each time point equally without considering the temporal characteristics of longitudinal data. In this paper, we propose a weighted hypergraph convolution network (WHGCN) to use the internal correlations among different time points and leverage high-order relationships between subjects for AD detection. Specifically, we construct hypergraphs for sMRI data at each time point using the K-nearest neighbor (KNN) method to represent relationships between subjects, and then fuse the hypergraphs according to the importance of the data at each time point to obtain the final hypergraph. Subsequently, we use hypergraph convolution to learn high-order information between subjects while performing feature dimensionality reduction. Finally, we conduct experiments on 518 subjects selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and the results show that the WHGCN can get higher AD detection performance and has the potential to improve our understanding of the pathogenesis of AD., Competing Interests: Declaration of competing interest 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., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Disclosure statement The authors have no conflicts of interest to disclose., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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.

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., Competing Interests: Declaration of Competing Interest T.E. is a consultant to BrainWaveBank and Synovion and received speaker's honoraria from Lundbeck and Janssen Cilag. O.A.A. is a consultant to HealthLytix and received speaker's honoraria from Lundbeck. I.A. has received speaker's honoraria from Lundbeck. The other authors have no conflict of interest to report., (Copyright © 2023. Published by Elsevier B.V.)

Background: 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., Methods: 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., Results: 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., Conclusions: 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., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Exposures to fine particulate matter (PM 2.5 ) and nitrogen dioxide (NO 2 ) have been associated with the emergence of depressive symptoms in older adulthood, although most studies used cross-sectional outcome measures. Elucidating the brain structures mediating the adverse effects can strengthen the causal role between air pollution and increasing depressive symptoms. We evaluated whether smaller volumes of brain structures implicated in late-life depression mediate associations between ambient air pollution exposure and changes in depressive symptoms. This prospective study included 764 community-dwelling older women (aged 81.6 ± 3.6 in 2008-2010) from the Women's Health Initiative Memory Study (WHIMS) Magnetic Resonance Imaging study (WHIMS-MRI; 2005-06) and WHIMS-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO; 2008-16). Three-year average annual mean concentrations (scaled by interquartile range [IQR]) of ambient PM 2.5 (in μg/m 3 ; IQR = 3.14 μg/m 3 ) and NO 2 (in ppb; IQR = 7.80 ppb) before WHIMS-MRI were estimated at participants' addresses via spatiotemporal models. Mediators included structural brain MRI-derived grey matter volumes of the prefrontal cortex and structures of the limbic-cortical-striatal-pallidal-thalamic circuit. Depressive symptoms were assessed annually by the 15-item Geriatric Depression Scale. Structural equation models were constructed to estimate associations between exposure, structural brain volumes, and depressive symptoms. Increased exposures (by each IQR) were associated with greater annual increases in depressive symptoms (β PM2.5 = 0.022; 95% Confidence Interval (CI) = 0.003, 0.042; β NO2 = 0.019; 95% CI = 0.001, 0.037). The smaller volume of prefrontal cortex associated with exposures partially mediated the associations of increased depressive symptoms with NO 2 (8%) and PM 2.5 (13%), and smaller insula volume associated with NO 2 contributed modestly (13%) to the subsequent increase in depressive symptoms. We demonstrate the first evidence that the smaller volumes of the prefrontal cortex and insula may mediate the subsequent increases in depressive symptoms associated with late-life exposures to NO 2 and PM 2.5 ., Competing Interests: Declaration of competing interest 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., (Copyright © 2022. Published by Elsevier B.V.)

Introduction: Predominant polarity (PP) has been proposed as a specifier of bipolar disorder (BD) due to its relationship with clinical and prognostic variables. It is possible that this is due to a different underlying neurobiology, in such a way that the changes found by structural nuclear magnetic resonance imaging (sMRI) in BD are different and specific., Objectives: To explore findings of structural neuroimaging in patients with BD type I (BD-I) according to PP., Methods: Cross-sectional study that evaluated 77 patients with BD-I using the DIGS interview. PP was established using the operative definition of two-thirds of all affective episodes throughout life to classify PP as manic (MPP), depressive (DPP) or indeterminate (IPP). MRI was performed during the euthymia phase to measure intracranial structures. The data obtained was analysed using a linear regression model adjusted for confounding variables (drug use, alcohol use, psychoactive substance use) and were compared between the three groups finding the standardised mean difference (SMD)., Results: Differences with adequate effect size were found in three brain structures after adjusting for confounding variables, specifically in the right fusiform gyrus and the left lingual gyrus, which were greater in the DPP group than in the MPP group (SMD = 0.92; 95% CI = 0.34-1.49 and SMD = 0.78; 95% CI = 0.21-1.35). Likewise, in the right thalamus, it was shown to be greater in the IPP group compared to MPP group (SMD 0.89, 95% CI = 0.31-1.46)., Conclusions: A reduction in the thickness of the right fusiform gyrus and the left lingual gyrus, as well as the right thalamic volume was observed in patients with BD-I with PPM, which supports the hypothesis that PP has a plausible neurobiological correlate and could have potential utility as a BD specifier., (Copyright © 2020 Asociación Colombiana de Psiquiatría. Published by Elsevier España, S.L.U. All rights reserved.)

Considerable data relate major depressive disorder (MDD) with aberrant immune system functioning. Pro-inflammatory cytokines facilitate metabolism of tryptophan along the kynurenine pathway (KP) putatively resulting in reduced neuroprotective and increased neurotoxic KP metabolites in MDD, in addition to modulating metabolic and immune function. This central nervous system hypothesis has, however, only been tested in the periphery. Here, we measured KP-metabolite levels in both plasma and cerebrospinal fluid (CSF) of depressed patients (n = 63/36 respectively) and healthy controls (n = 48/33). Further, we assessed the relation between KP abnormalities and brain-structure volumes, as well as body mass index (BMI), an index of metabolic disturbance associated with atypical depression. Plasma levels of picolinic acid (PIC), the kynurenic/quinolinic acid ratio (KYNA/QUIN), and PIC/QUIN were lower in MDD, but QUIN levels were increased. In the CSF, we found lower PIC in MDD. Confirming previous work, MDD patients had lower hippocampal, and amygdalar volumes. Hippocampal and amygdalar volumes were correlated positively with plasma KYNA/QUIN ratio in MDD patients. BMI was increased in the MDD group relative to the control group. Moreover, BMI was inversely correlated with plasma and CSF PIC and PIC/QUIN, and positively correlated with plasma QUIN levels in MDD. Our results partially confirm previous peripheral KP findings and extend them to the CSF in MDD. We present the novel finding that abnormalities in KP metabolites are related to metabolic disturbances in depression, but the relation between KP metabolites and depression-associated brain atrophy might not be as direct as previously hypothesized., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: 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., Methods: 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., Results: 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., Conclusions: 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., (Copyright © 2021 Society of Biological Psychiatry. All rights reserved.)

Bipolar disorder is associated with cognitive deficits and cortical changes for which the developmental dynamics are not well understood. The dopamine D2 receptor (DRD2) gene has been associated with both psychiatric disorders and cognitive variability. Here we examined the mediating role of brain structure in the relationship between DRD2 genomic variation and cognitive performance, with target cortical regions selected based on evidence of association with DRD2, bipolar disorder and/or cognition from prior literature. Participants (n = 143) were aged 12-30 years and comprised 62 first-degree relatives of bipolar patients (deemed 'at-risk'), 55 controls, and 26 patients with established bipolar disorder; all were unrelated Caucasian individuals with complete data across the three required modalities (structural magnetic resonance imaging, neuropsychological and genetic data). A DRD2 haplotype was derived from three functional polymorphisms (rs1800497, rs1076560, rs2283265) associated with alternative splicing (i.e., D2-short/-long isoforms). Moderated mediation analyses explored group differences in relationships between this DRD2 haplotype, three structural brain networks which subsume the identified cortical regions of interest (frontoparietal, dorsal-attention, and ventral-attention), and three cognitive indices (intelligence, attention, and immediate memory). Controls who were homozygous for the DRD2 major haplotype demonstrated greater cognitive performance as a result of dorsal-attention network mediation. However, this association was absent in the 'at-risk' group. This study provides the first evidence of a functional DRD2-brain-cognition pathway. The absence of typical brain-cognition relationships in young 'at-risk' individuals may reflect biological differences that precede illness onset. Further insight into early pathogenic processes may facilitate targeted early interventions., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Introduction: Predominant polarity (PP) has been proposed as a specifier of bipolar disorder (BD) due to its relationship with clinical and prognostic variables. It is possible that this is due to a different underlying neurobiology, in such a way that the changes found by structural nuclear magnetic resonance imaging (sMRI) in BD are different and specific., Objectives: To explore findings of structural neuroimaging in patients with BD type I (BD-I) according to PP., Methods: Cross-sectional study that evaluated 77 patients with BD-I using the DIGS interview. PP was established using the operative definition of two-thirds of all affective episodes throughout life to classify PP as manic (MPP), depressive (DPP) or indeterminate (IPP). MRIwas performed during the euthymia phase to measure intracranial structures. The data obtained was analysed using a linear regression model adjusted for confounding variables (drug use, alcohol use, psychoactive substance use) and were compared between the three groups finding the standardised mean difference (SMD)., Results: Differences with adequate effect size were found in three brain structures after adjusting for confounding variables, specifically in the right fusiform gyrus and the left lingual gyrus, which were greater in the DPP group than in the MPP group (SMD = 0.92; 95% CI = 0.34 to 1.49 and SMD = 0.78; 95% CI = 0.21 to 1.35). Likewise, in the right thalamus, it was shown to be greater in the IPP group compared to MPP group (SMD 0.89, 95% CI = 0.31 to 1.46)., Conclusions: A reduction in the thickness of the right fusiform gyrus and the left lingual gyrus, as well as the right thalamic volume was observed in patients with BD-I with PPM, which supports the hypothesis that PP has a plausible neurobiological correlate and could have potential utility as a BD specifier., (Copyright © 2020 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España, S.L.U. All rights reserved.)

Background: Diffuse traumatic brain injury (TBI) is known to lead to microstructural changes within both white and grey matter detected in vivo with diffusion tensor imaging (DTI). Numerous studies have shown alterations in fractional anisotropy (FA) and mean diffusivity (MD) within prominent white matter tracts, but few have linked these to changes within the grey matter with confirmation via histological assessment. This is especially important as alterations in the grey matter may be predictive of long-term functional deficits., Methods: A total of 33 male Sprague Dawley rats underwent severe closed-head TBI. Eight animals underwent tensor-based morphometry (TBM) and DTI at baseline (pre-TBI), 24 hours (24 h), 7, 14, and 30 days post-TBI. Immunohistochemical analysis for the detection of ionised calcium-binding adaptor molecule 1 (IBA1) to assess microglia number and percentage of activated cells, β-amyloid precursor protein (APP) as a marker of axonal injury, and myelin basic protein (MBP) to investigate myelination was performed at each time-point., Results: DTI showed significant alterations in FA and RD in numerous white matter tracts including the corpus callosum, internal and external capsule, and optic tract and in the grey-matter in the cortex, thalamus, and hippocampus, with the most significant effects observed at 14 D post-TBI. TBM confirmed volumetric changes within the hippocampus and thalamus. Changes in DTI were in line with significant axonal injury noted at 24 h post-injury via immunohistochemical analysis of APP, with widespread microglial activation seen within prominent white matter tracts and the grey matter, which persisted to 30 D within the hippocampus and thalamus. Microstructural alterations in MBP+ve fibres were also noted within the hippocampus and thalamus, as well as the cortex., Conclusion: This study confirms the widespread effects of diffuse TBI on white matter tracts which could be detected via DTI and extends these findings to key grey matter regions, with a comprehensive investigation of the whole brain. In particular, the hippocampus and thalamus appear to be vulnerable to ongoing pathology post-TBI, with DTI able to detect these alterations supporting the clinical utility in evaluating these regions post-TBI., Competing Interests: Declaration of Competing Interest The authors report no competing interests., (Copyright © 2019. Published by Elsevier Inc.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Few studies have examined the time course of second language (L2) induced neuroplasticity or how individual differences may be associated with brain changes. The current longitudinal structural magnetic resonance imaging study examined changes in cortical thickness (CT) and gray matter volume (GMV) across two semesters of L2 Spanish classroom learning. Learners' lexical processing was assessed via a language decision task containing English and Spanish words. Our findings indicated that (1) CT increased in the left anterior cingulate cortex (ACC) and right middle temporal gyrus (MTG) after L2 learning, (2) CT in the right MTG increased in individuals who were better able to discriminate between native language and L2 words, and (3) CT in the left ACC was correlated with functional connectivity between the ACC and MTG. These findings indicate that L2 lexical development is associated with functional and structural changes in brain regions important for cognitive control and semantic processing., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2018. Published by Elsevier B.V.)

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Emerging research indicates that individuals with asthma have an increased risk of cognitive impairment, yet the associations of asthma with neural correlates of memory remain relatively unknown. The hippocampus is the predominant neural structure involved in memory, and alterations in the hippocampal metabolic profile are observed in individuals with mild cognitive impairment. We therefore hypothesized that individuals with asthma may have altered hippocampal metabolites compared to healthy controls. Structural magnetic resonance imaging (sMRI) and proton magnetic resonance spectroscopy ( 1 H-MRS) were used to compare hippocampal volume and metabolites of otherwise healthy adults with and without asthma (N = 40), and to study the association of these measures with cognitive function and asthma-related variables. Participants underwent 3-Tesla sMRI and 1 H-MRS, with the volume of interest placed in the left hippocampus to measure levels of N -acetylaspartate (NAA), glutamate (Glu), creatine (Cr), and myo-inositol (MI), as indicators of neuronal viability, cellular activity, cellular energy reserve, as well as glial activation. Individuals with asthma had lower hippocampal NAA compared to healthy controls. For all participants, poorer cognitive function was associated with reduced NAA and Glu. For individuals with asthma, poorer cognitive function was associated with reduced disease control. Additionally, short-acting rescue bronchodilator use was associated with significantly lower NAA, and Glu, whereas inhaled corticosteroid use was related to significantly higher Cr and in tendency higher NAA and Glu. All findings controlled for left hippocampal volume, which was not different between groups. These findings highlight that asthma and/or its treatment may affect hippocampal chemistry. It is possible that the observed reductions in hippocampal metabolites in younger individuals with asthma may precede cognitive and hippocampal structural deficits observed in older individuals with asthma.

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

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., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective: Most evidence for structural brain abnormalities associated with adolescent depression is based on cross-sectional study designs that do not take into account the dynamic course of depressive symptoms and brain maturation across adolescence. In this study, a longitudinal design was used to investigate the association between different trajectories of depressive symptoms and longitudinal changes in brain structure throughout adolescence., Method: One hundred forty-nine adolescents were assessed on depressive symptoms and underwent structural magnetic resonance imaging at 12 years of age and were followed up multiple times until 19 years. Three depressive symptom trajectories (low-stable [n = 97], early-decreasing [n = 33], late-increasing [n = 19]) were identified, and effects of group and group by time on hippocampus and amygdala volume and prefrontal cortical thickness and surface area were evaluated., Results: The early-decreasing symptoms group exhibited differences in cortical surface area compared to the low-stable and late-increasing symptoms groups, moderated by sex. Specifically, females in the early-decreasing symptoms group showed lower anterior cingulate and orbitofrontal cortex surface areas across adolescence compared to females in the other groups. Males in the early-decreasing symptoms group showed lower right orbitofrontal cortex surface area expansion over time compared to males in the low-stable and late-increasing symptoms groups. No effects were found for cortical thickness or for hippocampus and amygdala volume., Conclusion: Alterations in cortical surface area were specifically observed in young people experiencing depressive symptoms in early adolescence. These findings suggest that early adolescence is a particularly sensitive period for cortical surface area abnormalities associated with depressive symptoms and could provide a critical window for treatment of (subthreshold) depressive symptoms., (Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

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., (Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.)