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Background: A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation. Methods: Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission. Results: Cognitive data significantly classified patients from controls (accuracies = 60–69%; p values = 0.0001–0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission. Conclusions: In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required. [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]

Purpose of Review: We summarize structural (s)MRI findings of gray matter (GM) atrophy related to cognitive impairment in Alzheimer's disease (AD) and Parkinson's disease (PD) in light of new analytical approaches and recent longitudinal studies results. Recent Findings: The hippocampus-to-cortex ratio seems to be the best sMRI biomarker to discriminate between various AD subtypes, following the spatial distribution of tau pathology, and predict rate of cognitive decline. PD is clinically far more variable than AD, with heterogeneous underlying brain pathology. Novel multivariate approaches have been used to describe patterns of early subcortical and cortical changes that relate to more malignant courses of PD. Summary: New emerging analytical approaches that combine structural MRI data with clinical and other biomarker outcomes hold promise for detecting specific GM changes in the early stages of PD and preclinical AD that may predict mild cognitive impairment and dementia conversion. [ABSTRACT FROM AUTHOR]

Extraversion is a fundamental personality dimension closely related to an individual's life outcomes and mental health. Although an increasing number of studies have attempted to identify the neurostructural markers of extraversion, the results have been highly inconsistent. The current study aimed to achieve a comprehensive understanding of brain gray matter (GM) correlates of extraversion with a systematic review and meta‐analysis approach. Our review showed relatively high interstudy heterogeneity among previous findings. Our meta‐analysis of whole‐brain voxel‐based morphometry studies revealed that extraversion was stably associated with six core brain regions. Additionally, meta‐regression analyses identified brain regions where the associations of extraversion with GM volume were modulated by gender and age. The relationships between extraversion and GM structures were discussed based on three extraversion‐related functional systems. Furthermore, we explained the gender and age effects. Overall, our study is the first to reveal a comprehensive picture of brain GM correlates of extraversion, and the findings may be useful for the selection of targeted brain areas for extraversion interventions. [ABSTRACT FROM AUTHOR]

A single nucleotide polymorphism (SNP) in the gene coding for brain-derived neurotrophic factor (BDNF) has previously been associated with a reduction in recognition memory performance. While previous findings have highlighted that this SNP contributes to recognition memory, little is known about its influence on subprocesses of recognition, familiarity and recollection. Previous research has reported reduced hippocampal volume and decreased fractional anisotropy in carriers of the Met allele across a range of white matter tracts, including those networks that may support recognition memory. Here, in a sample of 61 healthy young adults, we used a source memory task to measure accuracy on each recognition subprocess, in order to determine whether the Val66Met SNP (rs6265) influences these equally. Additionally, we compared grey matter volume between these groups for structures that underpin familiarity and recollection separately. Finally, we used probabilistic tractography to reconstruct tracts that subserve each of these two recognition systems. Behaviourally, we found group differences on the familiarity measure, but not on recollection. However, we did not find any group difference on grey- or white-matter structures. Together, these results suggest a functional influence of the Val66Met SNP that is independent of coarse structural changes, and nuance previous research highlighting the relationship between BDNF, brain structure, and behaviour. • This study examines the relationship between the Val66Met SNP and recognition memory. • Val/Vals outperformed Met allele carriers on familiarity-based recognition judgments. • There was no difference between genotype groups for recollection-based recognition. • There was no genotype differences in memory-related grey- or white-matter structures. • Val66Met genotype impacts recognition, despite a lack of coarse structural correlates. [ABSTRACT FROM AUTHOR]

Recent research indicates that learning a second language (L2) results in both functional and structural brain changes. However, few studies have examined whether structural brain changes vary as a function of the context in which L2 learning takes place. The current study examines changes in cortical thickness (CT) and gray matter volume (GMV) in response to short-term L2 vocabulary learning. In particular, we compared structural changes for learning with paired picture-word (PW) association versus learning within virtual environments (VE) and non-trained controls. Both L2 training groups learned the same 90 Mandarin Chinese nouns across 7 training sessions over approximately 20 days. Our results show (a) CT and GMV increased in regions implicated in a language control network for both L2 training groups, and (b) participants in different learning contexts may rely on different structures within this language control network. In particular, CT in the right IFG was associated with L2 training performance for the PW group, whereas CT in the right IPL showed a positive correlation with L2 training performance for the VE group. Our findings indicate that short-term L2 training leads to changes in brain structure, which vary based on L2 learning contexts and individual differences in cognitive ability. [ABSTRACT FROM AUTHOR]

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

Structural magnetic resonance imaging (MRI) is of fundamental importance to the diagnosis and treatment of epilepsy, particularly when surgery is being considered. Despite previous recommendations and guidelines, practices for the use of MRI are variable worldwide and may not harness the full potential of recent technological advances for the benefit of people with epilepsy. The International League Against Epilepsy Diagnostic Methods Commission has thus charged the 2013‐2017 Neuroimaging Task Force to develop a set of recommendations addressing the following questions: (1) Who should have an MRI? (2) What are the minimum requirements for an MRI epilepsy protocol? (3) How should magnetic resonance (MR) images be evaluated? (4) How to optimize lesion detection? These recommendations target clinicians in established epilepsy centers and neurologists in general/district hospitals. They endorse routine structural imaging in new onset generalized and focal epilepsy alike and describe the range of situations when detailed assessment is indicated. The Neuroimaging Task Force identified a set of sequences, with three‐dimensional acquisitions at its core, the harmonized neuroimaging of epilepsy structural sequences—HARNESS‐MRI protocol. As these sequences are available on most MR scanners, the HARNESS‐MRI protocol is generalizable, regardless of the clinical setting and country. The Neuroimaging Task Force also endorses the use of computer‐aided image postprocessing methods to provide an objective account of an individual's brain anatomy and pathology. By discussing the breadth and depth of scope of MRI, this report emphasizes the unique role of this noninvasive investigation in the care of people with epilepsy. [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]

Objective: Widespread functional and structural alterations in the brain have been extensively reported in unaffected relatives (RELs) of patients with bipolar disorder (BD) who are at genetic risk for BD. A sufficiently powered meta‐analysis of structural (sMRI) and functional magnetic resonance imaging (fMRI) alterations in RELs is still lacking. Methods: Functional and structural magnetic resonance imaging studies investigating RELs and healthy controls (HCs) published by July 2017 were included in the meta‐analyses. Study procedures were conducted in accordance with the Meta‐analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. Random‐effects coordinate‐based meta‐analyses were performed across all the studies per imaging modality using Seed‐based d Mapping (SDM). For fMRI studies, meta‐analyses were calculated for each task type. For sMRI studies, regional volumetric changes‐analyses were estimated using R. Finally, multimodal meta‐analyses of structural and functional abnormalities were performed. Results: Sixty‐nine imaging studies (2195 RELs and 3169 HCs) were included in the meta‐analyses. RELs showed hyperactivation in the fronto‐striatal regions as well as parietal hypoactivation during cognition. Also, activation was increased in the amygdala during emotional processing and in the orbitofrontal cortex during reward, respectively. Frontal and superior temporal cortex were hypertrophic in RELs. The right inferior frontal gyrus (rIFG) showed both increased activation during cognitive tasks and greater volume in RELs. Conclusions: Our findings demonstrate that increased brain volume and activation are present in RELs and may represent intermediate phenotypes for the disorder. Furthermore, some neural changes including increased rIFG volume may be associated with the resilience to BD. [ABSTRACT FROM AUTHOR]

Recent neuroanatomical pattern recognition studies have shown some promises for developing an objective neuroimaging-based classification related to schizophrenia. This study explored the feasibility of reliably identifying schizophrenia using single and multimodal multivariate neuroimaging features. Multiple brain measures including regional gray matter (GM) volume, cortical thickness, gyrification, fractional anisotropy (FA), and mean diffusivity (MD) were extracted using fully automated procedures. We used Gradient Boosting Decision Tree to identify the most frequently selected features of each set of neuroanatomical metric and fused multimodal measures. The current classification model was trained and validated based on 98 patients with first-episode schizophrenia (FES) and 106 matched healthy controls (HCs). The classification model was trained and tested in an independent dataset of 54 patients with FES and 48 HCs using imaging data acquired on a different magnetic resonance imaging scanner. Using the most frequently selected features from fused structural and diffusion tensor imaging metrics, a classification accuracy of 75.05% was achieved, which was higher than accuracy derived from a single imaging metric. Most prominent discriminative features included cortical thickness of left transverse temporal gyrus and right parahippocampal gyrus, the FA of left corticospinal tract and right external capsule. In the independent cohort, average accuracy was 76.54%, derived from combined features selected from cortical thickness, gyrification, FA, and MD. These features characterized by GM abnormalities and white matter disruptions have discriminative power with respect to the underlying pathological changes in the brain of individuals having schizophrenia. Our results further highlight the potential advantage of multimodal data fusion for identifying schizophrenia. [ABSTRACT FROM AUTHOR]

Abstract Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures using FreeSurfer-processed scans. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans acquired in three different waves in a longitudinal study. Different supervised-learning models were then compared to predict manual quality ratings using FreeSurfer segmented output data. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of two novel unseen datasets (total N = 872). Finally, analyses of age effects showed that younger participants were more likely to have lower scan quality, underlining that scan quality might confound findings attributed to age effects. These outcomes indicate that this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies. Graphical abstract Image 1 Highlights • Variability of manual quality control procedures reduces comparability among studies. • We introduce Qoala-T to automatically assess quality of FreeSurfer-processed scans. • Scan quality of novel datasets was adequately predicted using the Qoala-T tool. • In three different datasets younger age was associated with lower scan quality. • Qoala-T is publicly available and easy-to-use as an add-on to manual QC. [ABSTRACT FROM AUTHOR]

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

Alzheimer’s disease (AD) is a disease of a heterogeneous nature, which can be disentangled by exploring the characteristics of each AD subtype in the brain structure, neuropathology, and cognition. In this study, a total of 192 AD and 228 cognitively normal (CN) subjects were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Based on the cortical thickness patterns, the mixture of experts method (MOE) was applied to the implicit model spectrum of transforms lined with each AD subtype, then their neuropsychological and neuropathological characteristics were analyzed. Furthermore, the piecewise linear classifiers composed of each AD subtype and CN were resolved, and each subtype was comprehensively explained. The following four distinct AD subtypes were discovered: bilateral parietal, frontal, and temporal atrophy AD subtype (occipital sparing AD subtype (OSAD), 29.2%), left temporal dominant atrophy AD subtype (LTAD, 22.4%), minimal atrophy AD subtype (MAD, 16.1%), and diffuse atrophy AD subtype (DAD, 32.3%). These four subtypes display their own characteristics in atrophy pattern, cognition, and neuropathology. Compared with the previous studies, our study found that some AD subjects showed obvious asymmetrical atrophy in left lateral temporal-parietal cortex, OSAD presented the worst cerebrospinal fluid levels, and MAD had the highest proportions of APOE ε4 and APOE ε2. The subtype characteristics were further revealed from the aspect of the model, making it easier for clinicians to understand. The results offer an effective support for individual diagnosis and prognosis.

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

In this cross-sectional study, we sought to describe cognitive and neuroimaging profiles of Memory clinic patients with Mild Cognitive Impairment (MCI). 51 MCI patients and 51 controls, matched on age, sex, and socio-economic status (SES), were assessed with an extensive neuropsychological test battery that included a measure of intelligence (General Ability Index, “GAI,” from WAIS-IV), and structural magnetic resonance imaging (MRI). MCI subtypes were determined after inclusion, and z-scores normalized to our control group were generated for each cognitive domain in each MCI participant. MR-images were scored by visual rating scales. MCI patients performed significantly worse than controls on 23 of 31 cognitive measures (Bonferroni corrected p = 0.001), and on 8 of 31 measures after covarying for intelligence (GAI). Compared to nonamnestic MCI patients, amnestic MCI patients had lower test results in 13 of 31 measures, and 5 of 31 measures after co-varying for GAI. Compared to controls, the MCI patients had greater atrophy on Schelten's Medial temporal lobe atrophy score (MTA), especially in those with amnestic MCI. The only structure-function correlation that remained significant after correction for multiple comparisons was the MTA—long delay recall domain. Intelligence operationalized as GAI appears to be an important moderator of the neuropsychological outcomes. Atrophy of the medial temporal lobe, based on MTA scores, may be a sensitive biomarker for the functional episodic memory deficits associated with MCI.

Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal “affective” circuit, the dorsolateral, prefronto-striatal “executive” circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.

The current insight into the neurobiological pathogenesis underlying social anxiety disorder (SAD) is still rather limited. We implemented a meta-analysis to explore the neuroanatomical basis of SAD. We undertook a systematic search of studies comparing gray matter volume (GMV) differences between SAD patients and healthy controls (HC) using a whole-brain voxel-based morphometry (VBM) approach. The anisotropic effect size version of seed-based d mapping (AES-SDM) meta-analysis was conducted to explore the GMV differences of SAD patients compared with HC. We included eleven studies with 470 SAD patients and 522 HC in the current meta-analysis. In the main meta-analysis, relative to HC, SAD patients showed larger GMVs in the left precuneus, right middle occipital gyrus (MOG) and supplementary motor area (SMA), as well as smaller GMV in the left putamen. In the subgroup analyses, compared with controls, adult patients (age ≥ 18 years) with SAD exhibited larger GMVs in the left precuneus, right superior frontal gyrus (SFG), angular gyrus, middle temporal gyrus (MTG), MOG and SMA, as well as a smaller GMV in the left thalamus; SAD patients without comorbid depressive disorder exhibited larger GMVs in the left superior parietal gyrus and precuneus, right inferior temporal gyrus, fusiform gyrus, MTG and superior temporal gyrus (STG), as well as a smaller GMV in the bilateral thalami; and currently drug-free patients with SAD exhibited a smaller GMV in the left thalamus compared with HC while no larger GMVs were found. For SAD patients with different clinical features, our study revealed directionally consistent larger cortical GMVs and smaller subcortical GMVs, including locationally consistent larger precuneus and thalamic deficits in the left brain. Age, comorbid depressive disorder and concomitant medication use of the patients might be potential confounders of SAD at the neuroanatomical level.

Patients with prolonged disorders of consciousness (DoC) after severe brain injury may present residual behavioral and cognitive functions. Yet the bedside assessment of these functions is compromised by patients' multiple impairments. Standardized behavioral scales such as the Coma Recovery Scale-Revised (CRS-R) have been developed to diagnose DoC, but there is also a need for neuropsychological measurement in these patients. The Cognitive Assessment by Visual Election (CAVE) was therefore recently created. In this study, we describe five patients in minimally conscious state (MCS) or emerging from the MCS (EMCS). Their cognitive profiles, derived from the CRS-R and CAVE, are presented alongside their neuroimaging results using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). Scores on the CAVE decreased along with the CRS-R total score, establishing a consistent behavioral/cognitive profile for each patient. Out of these five cases, the one with highest CRS-R and CAVE performance had the least extended cerebral hypometabolism. All patients showed structural and functional brain impairments that were consistent with their behavioral/cognitive profile as based on previous literature. For instance, the presence of visual and motor residual functions was respectively associated with a relative preservation of occipital and motor cortex/cerebellum metabolism. Moreover, residual language comprehension skills were found in the presence of preserved temporal and angular cortex metabolism. Some patients also presented structural impairment of hippocampus, suggesting the presence of memory impairments. Our results suggest that brain-behavior relationships might be observed even in severely brain-injured patients and they highlight the importance of developing new tools to assess residual cognition and language in MCS and EMCS patients. Indeed, a better characterization of their cognitive profile will be helpful in preparation of rehabilitation programs and daily routines.

Background: Aggression in psychoses is of high clinical importance, and volumetric MRI techniques have been used to explore its structural brain correlates.Methods: We conducted a systematic review searching EMBASE, ScienceDirect, and PsycINFO through September 2017 using thesauri representing aggression, psychosis, and brain imaging. We calculated effect sizes for each study and mean Hedge's g for whole brain (WB) volume. Methodological quality was established using the PRISMA checklist (PROSPERO: CRD42014014461).Results: Our sample consisted of 12 studies with 470 patients and 155 healthy controls (HC). After subtracting subjects due to cohort overlaps, 314 patients and 96 HC remained. Qualitative analyses showed lower volumes of WB, prefrontal regions, temporal lobe, hippocampus, thalamus and cerebellum, and higher volumes of lateral ventricles, amygdala, and putamen in violent vs. non-violent people with schizophrenia. In quantitative analyses, violent persons with schizophrenia exhibited a significantly lower WB volume than HC (p = 0.004), and also lower than non-violent persons with schizophrenia (p = 0.007).Conclusions: We reviewed evidence for differences in brain volume correlates of aggression in persons with schizophrenia. Our results point toward a reduced whole brain volume in violent as opposed to non-violent persons with schizophrenia. However, considerable sample overlap in the literature, lack of reporting of potential confounding variables, and missing research on affective psychoses limit our explanatory power. To permit stronger conclusions, further studies evaluating structural correlates of aggression in psychotic disorders are needed.

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

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., Competing Interests: The authors declare that there is no conflict of interest., (© 2024 The Author(s).)

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The pathological mechanism of autism spectrum disorder (ASD) remains unclear. Nowadays, surface-based morphometry (SBM) based on structural magnetic resonance imaging (sMRI) techniques have reported cortical thickness (CT) variations in ASD. However, the findings were inconsistent and heterogeneous. This current meta-analysis conducted a whole-brain vertex-wise coordinate-based meta-analysis (CBMA) on CT studies to explore the most noticeable and robust CT changes in ASD individuals by applying the seed-based d mapping (SDM) program. A total of 26 investigations comprised 27 datasets were included, containing 1,635 subjects with ASD and 1470 HC, along with 94 coordinates. Individuals with ASD exhibited significantly altered CT in several regions compared to HC, including four clusters with thicker CT in the right superior temporal gyrus (STG.R), the left middle temporal gyrus (MTG.L), the left anterior cingulate/paracingulate gyri, the right superior frontal gyrus (SFG.R, medial orbital parts), as well as three clusters with cortical thinning including the left parahippocampal gyrus (PHG.L), the right precentral gyrus (PCG.R) and the left middle frontal gyrus (MFG.L). Adults with ASD only demonstrated CT thinning in the right parahippocampal gyrus (PHG.R), revealed by subgroup meta-analyses. Meta-regression analyses found that CT in STG.R was positively correlated with age. Meanwhile, CT in MFG.L and PHG.L had negative correlations with the age of ASD individuals. These results suggested a complicated and atypical cortical development trajectory in ASD, and would provide a deeper understanding of the neural mechanism underlying the cortical morphology in ASD., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

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., Competing Interests: Declaration of competing interest No funding source supported this project. None of the authors report biomedical financial interests or potential conflicts of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

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.)

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., 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 © 2024. Published by Elsevier Inc.)

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.

Introduction: Poor sleep quality is associated with cognitive outcomes in Alzheimer's disease (AD). We analyzed the associations between self-reported sleep quality and brain structure and function in cognitively unimpaired (CU) individuals., Methods: CU adults (N = 339) underwent structural magnetic resonance imaging, lumbar puncture, and the Pittsburgh Sleep Quality Index (PSQI) questionnaire. A subset (N = 295) performed [18F] fluorodeoxyglucose positron emission tomography scans. Voxel-wise associations with gray matter volumes (GMv) and cerebral glucose metabolism (CMRGlu) were performed including interactions with cerebrospinal fluid (CSF) AD biomarkers status., Results: Poorer sleep quality was associated with lower GMv and CMRGlu in the orbitofrontal and cingulate cortices independently of AD pathology. Self-reported sleep quality interacted with altered core AD CSF biomarkers in brain areas known to be affected in preclinical AD stages., Discussion: Poor sleep quality may impact brain structure and function independently from AD pathology. Alternatively, AD-related neurodegeneration in areas involved in sleep-wake regulation may induce or worsen sleep disturbances. Highlights Poor sleep impacts brain structure and function independent of Alzheimer's disease (AD) pathology. Poor sleep exacerbates brain changes observed in preclinical AD. Sleep is an appealing therapeutic strategy for preventing AD., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Aims: The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)-based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI-based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual-level morphological networks., Background: There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual-level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders., Conclusion: This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions., (© 2023 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Aims: To investigate cortical organization in brain magnetic resonance imaging (MRI) of preterm-born adults using percent contrast of gray-to-white matter signal intensities (GWPC), which is an in vivo proxy measure for cortical microstructure., Methods: Using structural MRI, we analyzed GWPC at different percentile fractions across the cortex (0%, 10%, 20%, 30%, 40%, 50%, and 60%) in a large and prospectively collected cohort of 86 very preterm-born (<32 weeks of gestation and/or birth weight <1500 g, VP/VLBW) adults and 103 full-term controls at 26 years of age. Cognitive performance was assessed by full-scale intelligence quotient (IQ) using the Wechsler Adult Intelligence Scale., Results: GWPC was significantly decreased in VP/VLBW adults in frontal, parietal, and temporal associative cortices, predominantly in the right hemisphere. Differences were pronounced at 20%, 30%, and 40%, hence, in middle cortical layers. GWPC was significantly increased in right paracentral lobule in VP/VLBW adults. GWPC in frontal and temporal cortices was positively correlated with birth weight, and negatively with duration of ventilation (p < 0.05). Furthermore, GWPC in right paracentral lobule was negatively correlated with IQ (p < 0.05)., Conclusions: Widespread aberrant gray-to-white matter contrast suggests lastingly altered cortical microstructure after preterm birth, mainly in middle cortical layers, with differential effects on associative and primary cortices., (© 2023 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

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Graphical abstract Highlights • Reviews 123 studies investigating structural brain changes associated with ASD. • Identifies biomarkers of brain structure for ASD diagnosis and treatment selection. • Outlines directions for aggregating image data to elucidate inconsistent findings. • Provides a discussion on the advantages and utility of machine learning methods. Abstract Autism Spectrum Disorder (ASD) affects approximately 1% of the population and leads to impairments in social interaction, communication and restricted, repetitive behaviours. Establishing robust neuroimaging biomarkers of ASD using structural magnetic resonance imaging (MRI) is an important step for diagnosing and tailoring treatment, particularly early in life when interventions can have the greatest effect. However currently, there is mixed findings on the structural brain changes associated with autism. Therefore in this systematic review, recent (post-2007), high-resolution (3 T) MRI studies investigating brain morphology associated with ASD have been collated to identify robust neuroimaging biomarkers of ASD. A systematic search was conducted on three databases; PubMed, Web of Science and Scopus, resulting in 123 reviewed articles. Patients with ASD were observed to have increased whole brain volume, particularly under 6 years of age. Other consistent changes observed in ASD patients include increased volume in the frontal and temporal lobes, increased cortical thickness in the frontal lobe, increased surface area and cortical gyrification, and increased cerebrospinal fluid volume, as well as reduced cerebellum volume and reduced corpus callosum volume, compared to typically developing controls. Findings were inconsistent regarding the developmental trajectory of brain volume and cortical thinning with age in ASD, as well as potential volume differences in the white matter, hippocampus, amygdala, thalamus and basal ganglia. To elucidate these inconsistencies, future studies should look towards aggregating MRI data from multiple sites or available repositories to avoid underpowered studies, as well as utilising methods which quantify larger-scale image features to reduce the number of statistical tests performed, and hence risk of false positive findings. Additionally, studies should look to perform a thorough validation strategy, to ensure generalisability of study findings, as well as look to leverage the improved image resolution of 3 T scanning to identify subtle brain changes related to ASD. [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]

In this cross-sectional study, we sought to describe cognitive and neuroimaging profiles of Memory clinic patients with Mild Cognitive Impairment (MCI). 51 MCI patients and 51 controls, matched on age, sex, and socio-economic status (SES), were assessed with an extensive neuropsychological test battery that included a measure of intelligence (General Ability Index, "GAI," from WAIS-IV), and structural magnetic resonance imaging (MRI). MCI subtypes were determined after inclusion, and z-scores normalized to our control group were generated for each cognitive domain in each MCI participant. MR-images were scored by visual rating scales. MCI patients performed significantly worse than controls on 23 of 31 cognitive measures (Bonferroni corrected p = 0.001), and on 8 of 31 measures after covarying for intelligence (GAI). Compared to nonamnestic MCI patients, amnestic MCI patients had lower test results in 13 of 31 measures, and 5 of 31 measures after co-varying for GAI. Compared to controls, the MCI patients had greater atrophy on Schelten's Medial temporal lobe atrophy score (MTA), especially in those with amnestic MCI. The only structure-function correlation that remained significant after correction for multiple comparisons was the MTA—long delay recall domain. Intelligence operationalized as GAI appears to be an important moderator of the neuropsychological outcomes. Atrophy of the medial temporal lobe, based on MTA scores, may be a sensitive biomarker for the functional episodic memory deficits associated with MCI. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal "affective" circuit, the dorsolateral, prefronto-striatal "executive" circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD. [ABSTRACT FROM AUTHOR]

Patients with prolonged disorders of consciousness (DoC) after severe brain injury may present residual behavioral and cognitive functions. Yet the bedside assessment of these functions is compromised by patients' multiple impairments. Standardized behavioral scales such as the Coma Recovery Scale-Revised (CRS-R) have been developed to diagnose DoC, but there is also a need for neuropsychological measurement in these patients. The Cognitive Assessment by Visual Election (CAVE) was therefore recently created. In this study, we describe five patients in minimally conscious state (MCS) or emerging from the MCS (EMCS). Their cognitive profiles, derived from the CRS-R and CAVE, are presented alongside their neuroimaging results using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). Scores on the CAVE decreased along with the CRS-R total score, establishing a consistent behavioral/cognitive profile for each patient. Out of these five cases, the one with highest CRS-R and CAVE performance had the least extended cerebral hypometabolism. All patients showed structural and functional brain impairments that were consistent with their behavioral/cognitive profile as based on previous literature. For instance, the presence of visual and motor residual functions was respectively associated with a relative preservation of occipital and motor cortex/cerebellum metabolism. Moreover, residual language comprehension skills were found in the presence of preserved temporal and angular cortex metabolism. Some patients also presented structural impairment of hippocampus, suggesting the presence of memory impairments. Our results suggest that brain-behavior relationships might be observed even in severely brain-injured patients and they highlight the importance of developing new tools to assess residual cognition and language in MCS and EMCS patients. Indeed, a better characterization of their cognitive profile will be helpful in preparation of rehabilitation programs and daily routines. [ABSTRACT FROM AUTHOR]

Abstract: The BrainMap database is a community resource that curates peer‐reviewed, coordinate‐based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta‐data, BrainMap facilitates coordinate‐based meta‐analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task‐activation literature, BrainMap is now expanding to include voxel‐based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole‐brain, voxel‐wise method that measures significant structural differences between or within groups which are reported as standardized, peak x–y–z coordinates. Here we describe BrainMap VBM, including the meta‐data structure, current data volume, and automated reverse inference functions (region‐to‐disease profile) of this new community resource. CBMA offers a robust methodology for retaining true‐positive and excluding false‐positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use‐case scenario for BrainMap VBM, we illustrate a trans‐diagnostic data‐mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data‐redundancy effects inherent to any database, we demonstrate two data‐filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network‐ and disease‐specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches. [ABSTRACT FROM AUTHOR]

The caudate nucleus has shown a high relevance with substance craving and addiction in the pathophysiology of alcohol dependence. In this study, caudate volumetric deficits in patients with alcohol dependence and their associations with carbohydrate-deficient transferrin (CDT), a biomarker of chronic alcohol consumption, were evaluated in 21 patients with alcohol dependence and age- and sex-matched 21 healthy comparison subjects. Between-group difference in caudate volumes and correlations between CDT and caudate volumes in the patient group were examined after adjusting for age, sex, and intracranial volume. Volumetric analysis results show prominent abnormalities in the left caudate among patients with alcohol dependence (z = − 2.70, p = 0.007). CDT levels significantly predicted volumetric deficits in the left caudate (β = − 1.23, p = 0.003). These findings suggest that the caudate nucleus may be one of the important brain regions related to alcohol-induced chronic brain damages. [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]

Background: Aggression in psychoses is of high clinical importance, and volumetric MRI techniques have been used to explore its structural brain correlates. Methods: We conducted a systematic review searching EMBASE, ScienceDirect, and PsycINFO through September 2017 using thesauri representing aggression, psychosis, and brain imaging. We calculated effect sizes for each study and mean Hedge's g for whole brain (WB) volume. Methodological quality was established using the PRISMA checklist (PROSPERO: CRD42014014461). Results: Our sample consisted of 12 studies with 470 patients and 155 healthy controls (HC). After subtracting subjects due to cohort overlaps, 314 patients and 96 HC remained. Qualitative analyses showed lower volumes of WB, prefrontal regions, temporal lobe, hippocampus, thalamus and cerebellum, and higher volumes of lateral ventricles, amygdala, and putamen in violent vs. non-violent people with schizophrenia. In quantitative analyses, violent persons with schizophrenia exhibited a significantly lower WB volume than HC (p = 0.004), and also lower than non-violent persons with schizophrenia (p = 0.007). Conclusions: We reviewed evidence for differences in brain volume correlates of aggression in persons with schizophrenia. Our results point toward a reduced whole brain volume in violent as opposed to non-violent persons with schizophrenia. However, considerable sample overlap in the literature, lack of reporting of potential confounding variables, and missing research on affective psychoses limit our explanatory power. To permit stronger conclusions, further studies evaluating structural correlates of aggression in psychotic disorders are needed. [ABSTRACT FROM AUTHOR]

As an important region of emotion regulation in the brain, functional abnormality of amygdala associates with emotional cognitive and processing impairment. This paper reviews the researches about structural and functional magnetic resonance imaging of amygdala, analyzes the correlation between alterations of them and symptoms in schizophrenia and further discusses the relationship between amygdalocortical network and abnormality of emotional cognitive in schizophrenia. Future research could analyze the dynamic changes of structural and functional magnetic resonance imaging of amygdala based on unified experimental paradigm before and after treatment in schizophrenic patients, this would help to search for imaging indicators related to clinical diagnosis and treatment and judgment of prognosis. [ABSTRACT FROM AUTHOR]