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There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including Major Depressive Disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980), and UK Biobank (UKB, N = 8900); for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K-nearest neighbour graphs, and graph partitioning with Markov Stability to determine optimal clustering of participants. Resultant clusters were i) checked whether they were replicated in an independent cohort, and ii) tested for associations with depression status, and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case-control status in either cohort (GS subsample: pFDR = 0.2239 - 0.6585; UKB: pFDR = 0.2003 - 0.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets; for CSA, CV, subCV (GS subsample: d = 0.2529 - 0.3490, pFDR < 0.005; UKB: d = 0.0868 - 0.1070, pFDR < 0.005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case-control status.

Attention-deficit/hyperactivity disorder (ADHD) is presumed to be heterogeneous, but the best way to characterize this heterogeneity remains unclear. Although considerable evidence suggests that the 2 different types of ADHD, inattention and combined, have different cognitive and behavioral profiles, and underlying neurobiologies, we currently lack information on whether these subtypes reflect separated brain structure changes. Structural magnetic resonance imaging scans (N = 234), diagnostic, and demographic information were obtained from the ADHD-200 database. Of this sample, 138 were Typically Developing people, 37 were ADHD-Combined, and 59 were ADHD-Inattentive patients. Freesurfer segmentation methods were used to measure cortical thickness, area, and volume, subcortical volume and hipposubfield volume. ADHD-Inattentive patients showed milder clinical symptoms but more serious cognitive injury than ADHD-Combined patients. In addition, dissociable structural brain changes were found in different subtypes of ADHD, particularly in terms of decreased subcortical volume in ADHD-Combined patients compared with Typically Developing people. Clinical symptoms were predominantly related to smaller rh_caudalanteriorcingulate thickness and left-Pallidum volume, whereas verbal IQ injury was correlated strongly with smaller rh_insula area. These findings indicate that there are significant differences in clinical symptoms and gray matter damage between ADHD-Combined and -Inattentive patients. This supports the growing evidence of heterogeneity in the ADHD-Inattentive subtype and the evidence of brain structure differences.

BACKGROUND The main clinical manifestation of Alzheimer’s disease (AD) is memory loss, which can be accompanied by neuropsychiatric symptoms at different stages of the disease. Amygdala is closely related to emotion and memory. AIM To evaluate the diagnostic value of amygdala on structural magnetic resonance imaging (sMRI) for AD. METHODS In this study, 22 patients with AD and 26 controls were enrolled. Their amygdala volumes were measured by sMRI and analyzed using an automatic analysis software. RESULTS The bilateral amygdala volumes of AD patients were significantly lower than those of the controls and were positively correlated with the hippocampal volumes. Receiver operating characteristic curve analyses showed that the sensitivity of the left and right amygdala volumes in diagnosing AD was 80.8% and 88.5%, respectively. Subgroup analyses showed that amygdala atrophy was more serious in AD patients with neuropsychiatric symptoms, which mainly included irritability (22.73%), sleep difficulties (22.73%), apathy (18.18%), and hallucination (13.64%). CONCLUSION Amygdala volumes measured by sMRI can be used to diagnose AD, and amygdala atrophy is more serious in patients with neuropsychiatric symptoms.

Children’s behavioral problems have been associated with their family environments. Here, we investigate whether specific features of brain structures could relate to this link. Using structural magnetic resonance imaging of 8756 children aged 9-11 from the Adolescent Brain Cognitive Developmental study, we show that high family conflict and low parental monitoring scores are associated with children’s behavioral problems, as well as with smaller cortical areas of the orbitofrontal cortex, anterior cingulate cortex, and middle temporal gyrus. A longitudinal analysis indicates that psychiatric problems scores are associated with increased family conflict and decreased parental monitoring 1 year later, and mediate associations between the reduced cortical areas and family conflict, and parental monitoring scores. These results emphasize the relationships between the brain structure of children, their family environments, and their behavioral problems., Child behavior has been associated with parenting behavior. Here, the authors investigate associations between child behavior, parental behavior, and structural MRI using the Adolescent Brain Cognitive Developmental (ABCD) study dataset.

Background: Transsexuality is explained and defined as a gender-identity disorder, characterised by very strong conviction of belonging to the opposite sex and has been associated with a distinct neuroanatomical pattern. Materials and methods: We performed a structural analysis in search of possible differences in grey matter structures based on magnetic resonance imaging scans of the brains of 26 individuals between 19 and 38 years of age. The participants were divided into two groups of 15 controls and 11 transgender individuals. The segmentation of subcortical grey matter was performed using FIRST model a model-based segmentation/registration tool, from FSL software package. Results: The results showed that the volume of the brain region called nucleus accumbens on the left side was significantly smaller in the group of transgender individuals compared to the control. It was the most important parameter which was shown to make distinction between two examined groups. Conclusions: The results also showed decreased volumes of the left thalamus, right hippocampus and right caudate nucleus.

The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD‐Stream) methods, and multi‐fiber (UKF‐2T) and single‐fiber (UKF‐1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF‐2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF‐2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points., In this work, we investigate the performance of four tractography methods (SD‐Stream, iFOD1, UKF‐1T, and UKF‐2T) for reconstruction of the complete retinogeniculate visual pathway (RGVP). Anatomical measurement and expert rating assessments shows that the iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF‐2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy. However, it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers.

Individual‐based morphological brain networks built from T1‐weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio‐cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual‐based morphological networks were constructed by using high‐resolution structural MRI data from 40 young children with ASD (age range: 2–8 years) and 38 age‐, gender‐, and handedness‐matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small‐worldness (i.e., σ) of individual‐level morphological brain networks, increased morphological connectivity in cortico‐striatum‐thalamic‐cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico‐cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio‐cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically‐relevant biomarkers for ASD., This research shed light on the crucial role of abnormal morphological connectivity patterns in the socio‐cognitive deficits of young children with autism spectrum disorder. We found that the morphological brain network showed poor efficiency of segregated and distributed information processing, and further exhibited the increased morphological connectivity in cortico‐striatum‐thalamic‐cortical circuitry and the decreased morphological connectivity in the cortico‐cortical network, which are associated with social deficits in the autistic developmental brain.

Neuroticism is major higher‐order personality trait and has been robustly associated with mental and physical health outcomes. Although a growing body of studies have identified neurostructural markers of neuroticism, the results remained highly inconsistent. To characterize robust associations between neuroticism and variations in gray matter (GM) structures, the present meta‐analysis investigated the concurrence across voxel‐based morphometry (VBM) studies using the anisotropic effect size signed differential mapping (AES‐SDM). A total of 13 studies comprising 2,278 healthy subjects (1,275 females, 29.20 ± 14.17 years old) were included. Our analysis revealed that neuroticism was consistently associated with the GM structure of a cluster spanning the bilateral dorsal anterior cingulate cortex and extending to the adjacent medial prefrontal cortex (dACC/mPFC). Meta‐regression analyses indicated that the neuroticism‐GM associations were not confounded by age and gender. Overall, our study is the first whole‐brain meta‐analysis exploring the brain structural correlates of neuroticism, and the findings may have implications for the intervention of high‐neuroticism individuals, who are at risk of mental disorders, by targeting the dACC/mPFC., In this meta‐analysis, we used the anisotropic effect size signed differential mapping (AES‐SDM) to investigate the association between neuroticism and gray matter (GM) structures based on previous whole‐brain voxel‐based morphometry (VBM) studies. Our analysis revealed that neuroticism was consistently associated with the GM structure of a cluster spanning the bilateral dorsal anterior cingulate cortex and extending to the adjacent medial prefrontal cortex (dACC/mPFC). Our findings may shed light on the elucidation of the neural mechanisms that underpin individual variations in neuroticism and the determination of promising intervention targets for neuroticism, which may reduce the risk of anxiety and mood disorders.

The progression of Alzheimer’s disease (AD) is a continuous process in cognitive and biomarker changes, with only a fraction of mild cognitive impairment (MCI) patients eventually advancing to AD. ...

Xiandi Pan,1,* Weina Ding,2,* Xia Sun,3,* Chenfeng Ji,3 Qian Zhou,3 Chao Yan,4 Yan Zhou,2 Yanli Luo3 1Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, People’s Republic of China; 2Department of Radiology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China; 3Department of Psychological Medicine, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China; 4Key Laboratory of Brain Functional Genomics (MOE&STCSM), Shanghai Changning-ECNU Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yanli LuoDepartment of Psychological Medicine, Renji Hospital, Shanghai Jiaotong University School of Medicine, Pujian Road 160, Shanghai, 200127, People’s Republic of ChinaTel +86-21-68382998Email luoluoyanli@163.comYan ZhouDepartment of Radiology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Pujian Road 160, Shanghai, 200127, People’s Republic of ChinaTel +86-21-68385105Email clare1475@hotmail.comObjective: Catastrophizing is commonly co-occurrence with anxiety in somatic symptom disorder (SSD). However, the quantitative relationship between catastrophizing and anxiety in SSD and its underlying neuropsychopathology remains unclear.Methods: To address the issue, twenty-eight SSD patients and twenty-nine healthy controls (HCs) completed the Hamilton anxiety scale and the catastrophizing subscale of the cognitive emotion regulation questionnaire. Then they underwent structural magnetic resonance imaging and voxel-based morphometry analysis was performed to obtain gray matter density (GMD) of the dorsomedial prefrontal cortex (dmPFC).Results: Independent samples t-tests showed no significance between SSD patients and HCs in the scores on the catastrophizing subscale and GMD of the dmPFC. However, correlation analysis found that catastrophizing was significantly positively associated with anxiety in SSD. Further, mediation analyses revealed that GMD of the dmPFC (bilateral medial Brodmann area 8) mediated the relationship between catastrophizing and anxiety in SSD.Conclusion: These findings support Kirmayer’s disease model of SSD that catastrophic interpretations of somatic symptoms resulted in increased anxiety and demonstrate that the dmPFC may be a potential neural site linking catastrophizing and anxiety in SSD.Keywords: somatic symptom disorder, dorsomedial prefrontal cortex, catastrophizing, anxiety, mediating effect

High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.

Background The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer’s disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD. Methods In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in “FTD dominant” structures over that obtained from “AD dominant” structures. The weights and the identification of “FTD/AD dominant” structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases. Results AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p p = 0.647). FTDI showed excellent accuracy in identifying FTD from AD (AUC = 0.90; SEN = 89%, SPE = 75% with threshold value = 1.08). The validation using independent data from ADNI and FTLDNI datasets also confirmed the efficacy of FTDI (AUC = 0.93; SEN = 96%, SPE = 70% with threshold value = 1.08). Conclusions Brain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice.

There is no definitive neural marker of suicidal thoughts and behaviors (STBs) or nonsuicidal self-injury (NSSI), and relative to adults, research in youth is more limited. This comprehensive review focuses on magnetic resonance imaging studies reporting structural and functional neural correlates of STBs and NSSI in youth to 1) elucidate shared and independent neural alternations, 2) clarify how developmental processes may interact with neural alterations to confer risk, and 3) provide recommendations based on convergence across studies. Forty-seven articles were reviewed (STBs = 27; NSSI = 20), and notably, 63% of STB articles and 45% of NSSI articles were published in the previous 3 years. Structural magnetic resonance imaging research suggests reduced volume in the ventral prefrontal and orbitofrontal cortices among youth reporting STBs, and there is reduced anterior cingulate cortex volume related to STBs and NSSI. With regard to functional alterations, blunted striatal activation may characterize STB and NSSI youth, and there is reduced frontolimbic task-based connectivity in suicide ideators and attempters. Resting-state functional connectivity findings highlight reduced positive connectivity between the default mode network and salience network in attempters and show that self-injurers exhibit frontolimbic alterations. Together, suicidal and nonsuicidal behaviors are related to top-down and bottom-up neural alterations, which may compromise approach, avoidance, and regulatory systems. Future longitudinal research with larger and well-characterized samples, especially those integrating ambulatory stress assessments, will be well positioned to identify novel targets that may improve early identification and treatment for youth with STBs and NSSI.

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

Author(s): Harrison, Theresa M; Du, Richard; Klencklen, Giuliana; Baker, Suzanne L; Jagust, William J | Abstract: IntroductionPublished reports of associations between β-amyloid (Aβ) and cortical integrity conflict. Tau biomarkers may help elucidate the complex relationship between pathology and neurodegeneration in aging.MethodsWe measured cortical thickness using magnetic resonance imaging, Aβ using Pittsburgh compound B positron emission tomography (PiB-PET), and tau using flortaucipir (FTP)-PET in 125 cognitively normal older adults. We examined relationships among PET measures, cortical thickness, and cognition.ResultsCortical thickness was reduced in PiB+/FTP+ participants compared to the PiB+/FTP- and PiB-/FTP- groups. Continuous PiB associations with cortical thickness were weak but positive in FTP- participants and negative in FTP+. FTP strongly negatively predicted thickness regardless of PiB status. FTP was associated with memory and cortical thickness, and mediated the association of PiB with memory.DiscussionPast findings linking Aβ and cortical thickness are likely weak due to opposing effects of Aβ on cortical thickness relative to tau burden. Tau, in contrast to Aβ, is strongly related to cortical thickness and memory.

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2–7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P

Attention-deficit hyperactivity disorder (ADHD) has a high prevalence of co-occurring with emotion dysregulation (ED). Youths with ADHD and ED are more likely to have increased functional impairment. There is accumulating research on defining the features, behavioral, and physiological manifestations of ED, but there are currently few studies elucidating neuroanatomical correlations of ED in ADHD. Structural magnetic resonance imaging data from 118 children (aged 7–18 years) with ADHD (50 ADHD+high ED, 68 ADHD+low ED), and 104 typically developing controls (TDC) were processed using voxel-based morphometry. We used both dichotomous and continuous indices of ED to examine the possible correspondence between ED and ADHD. Relative to ADHD+high ED, ADHD+low ED had greater gray matter (GM) volumes over the left anterior prefrontal cortex (PFC). ADHD+low ED and ADHD+high ED shared a negative association of ED levels with the left middle temporal pole GM volume. TDC and ADHD+low ED also shared negative relationships of ED levels with the right temporal volume, and positive relationships with the left dorsolateral PFC volume. Besides, ED-by-group interactions were also noted. Specifically, medial PFC GM volumes increased and decreased with ED severity in ADHD+low ED and ADHD+high ED, respectively; and left cerebellum Crus GM volumes decreased and increased with ED severity in ADHD+low ED and ADHD+high ED, respectively. Our findings add to the evidence that some specific neural correlates are underpinning ED across ADHD and TDC. These findings suggest the importance of incorporating ED problems when considering heterogeneity in studies of ADHD.

Background Numerous studies have suggested that structural changes in the cerebellum are implicated in the pathophysiology of bipolar disorder (BD). We aimed to investigate differences in the volume and cortical thickness of the cerebellar subregions between patients with BD and healthy controls (HCs). Methods Ninety patients with BD and one hundred sixty-six HCs participated in this study and underwent T1-weighted structural magnetic resonance imaging. We analyzed the volume and cortical thickness of each cerebellar hemisphere divided into 12 subregions using T1-weighted images of participants. One-way analysis of covariance was used to evaluate differences between the groups, with age, sex, medication, and total intracranial cavity volume used as covariates. Results The BD group had significantly increased cortical thickness of the cerebellum in all cerebellar subregions compared to the HC group. The cortical thicknesses of the whole cerebellum and each hemisphere were also significantly thicker in the BD group than in the HC group. The volume of the left lobule IX was significantly lower in patients with BD than in HCs, whereas no significant differences in the volumes were observed in the other subregions. Limitations Our cross-sectional design cannot provide a causal relationship between the increased cortical thickness of the cerebellum and the risk of BD. Conclusions We observed widespread and significant cortical thickening in all the cerebellar subregions. Our results provide evidence for the involvement of the cerebellum in BD. Further studies are required to integrate neurobiological evidence and structural brain imaging to elucidate the pathophysiology of BD.

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

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

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

BackgroundEvidence suggests a link between smaller hippocampal volume (HV) and post-traumatic stress disorder (PTSD). However, there has been little prospective research testing this question directly and it remains unclear whether smaller HV confers risk or is a consequence of traumatization and PTSD.MethodsU.S. soldiers (N = 107) completed a battery of clinical assessments, including structural magnetic resonance imaging pre-deployment. Once deployed they completed monthly assessments of traumatic-stressors and symptoms. We hypothesized that smaller HV would potentiate the effects of traumatic stressors on PTSD symptoms in theater. Analyses evaluated whether total HV, lateral (right v. left) HV, or HV asymmetry (right – left) moderated the effects of stressor-exposure during deployment on PTSD symptoms.ResultsFindings revealed no interaction between total HV and average monthly traumatic-stressors on PTSD symptoms b = −0.028, p = 0.681 [95% confidence interval (CI) −0.167 to 0.100]. However, in the context of greater exposure to average monthly traumatic stressors, greater right HV was associated with fewer PTSD symptoms b = −0.467, p = 0.023 (95% CI −0.786 to −0.013), whereas greater left HV was unexpectedly associated with greater PTSD symptoms b = 0.435, p = 0.024 (95% CI 0.028–0.715).ConclusionsOur findings highlight the importance of considering the complex role of HV, in particular HV asymmetry, in predicting the emergence of PTSD symptoms in response to war-zone trauma.

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

Mitigating the loss of brain tissue due to age is a major problem for an ageing population. Improving cardiorespiratory fitness has been suggested as a possible strategy, but the influenceon brain morphology has not been fully characterized. To investigate the dependent shifts in brain tissue distribution as a function of cardiorespiratory fitness, we used a 3D transport-based morphometry approach. In this study of 172 inactive older adults aged 58–81 (66.5 ± 5.7) years, cardiorespiratory fitness was determined by VO2 peak (ml/kg/min) during graded exercise and brain morphology was assessed through structural magnetic resonance imaging. After correcting for covariates including age (in the fitness model), gender and level of education, we compared dependent tissue shifts with age to those due to VO2 peak. We found a significant association between cardiorespiratory fitness and brain tissue distribution (white matter, r = 0.30, P = 0.003; grey matter, r = 0.40, P < 0.001) facilitated by direct visualization of the brain tissue shifts due to cardiorespiratory fitness through inverse transformation—a key capability of 3D transport-based morphometry. A strong statistical correlation was found between brain tissue changes related to ageing and those associated with lower cardiorespiratory fitness (white matter, r = 0.62, P < 0.001; grey matter, r = 0.74, P < 0.001). In both cases, frontotemporal regions shifted the most while basal ganglia shifted the least. Our results highlight the importance of cardiorespiratory fitness in maintaining brain health later in life. Furthermore, this work demonstrates 3D transport-based morphometry as a novel neuroinformatic technology that may aid assessment of therapeutic approaches for brain ageing and neurodegenerative diseases., Cardiorespiratory fitness is a potential strategy to reduce brain tissue loss caused by ageing. Kundu et al. report a statistically important benefit of cardiorespiratory fitness on brain morphology using a novel automated discovery and visualization technique called 3D transport-based morphometry—prompting new questions about cardiorespiratory fitness in brain ageing and dementia., Graphical Abstract Graphical Abstract

Highlights • Covariant abnormalities of GM and WM occurred in SCD and MCI. • GM-WM covariant abnormalities were correlated with cognitive performance in SCD. • Multimodal fusion highlighted the interaction of sMRI and DTI in SCD and MCI., 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.

Background Ischemic cerebrovascular stroke is defined as an acute neurological dysfunction caused by focal cerebral infarction after decrease in the blood supply of the brain either by stenosis or occlusion leading to gross physical impairment or disability lasting more than 24 hours. Objective To assess correlation between site of infarction and upper extremity ( UE) function in chronic stroke patients. Methodology A cross sectional study with (30) Patients with chronic ischemic stroke ( >3 months) suffering from persistent UE motor function impairment, MRI brain done to assess the site of infarction. UE motor function assessment using the Fugl Meyer Assessment (FMA-UE) Scale. Results The current study found that no significant correlation between the site of infarction and UE motor function. Conclusion We concluded that the site of infarction not correlated with UE motor function in chronic stroke patients.

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, p < 0.001) and 3D (MAE = 1.114, p < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, p < 0.001) and 3D (1.241 weeks, p < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps.

Background Autism spectrum disorder (ASD) is characterized by a spectrum of social and communication impairments and rigid and stereotyped behaviors that have a neurodevelopmental origin. Although many imaging studies have reported structural and functional alterations in multiple brain regions, clinically useful diagnostic imaging biomarkers for ASD remain unavailable. Methods In this study, we applied machine learning (ML) models to regional volumetric and cortical thickness data from the largest structural magnetic resonance imaging (sMRI) dataset available from the Enhancing Neuro Imaging Genetics Through Meta‐Analysis (ENIGMA) consortium (1833 subjects with ASD and 1838 without ASD; age range: 1.5–64; average age: 15.6; male/female ratio: 4.2:1). Results The highest classification accuracy on a hold‐out test set was achieved using a stacked Extra Tree Classifier. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.62 (95% confidence interval [CI]: 0.57, 0.68) and the area under the precision‐recall curve was 0.58. Learning curve analysis showed the good fit of the model and suggests that more training examples will not likely benefit model performance. Conclusions Our results suggest that sMRI volumetric and cortical thickness data alone may not provide clinically sufficient useful diagnostic biomarkers for ASD. Developing clinically useful imaging classifiers for ASD will benefit from combining other data modalities or feature types, such as functional MRI data and raw images that can leverage other machine learning (ML) techniques such as convolutional neural networks.

Bipolar disorder (BD) is associated with a 20–30-fold increased suicide risk compared to the general population. First-degree relatives of BD patients show inflated rates of psychopathology including suicidal behaviors. As reliable biomarkers of suicide attempts (SA) are lacking, we examined associations between suicide-related polygenic risk scores (PRSs)—a quantitative index of genomic risk—and variability in brain structures implicated in SA. Participants (n = 206; aged 12–30 years) were unrelated individuals of European ancestry and comprised three groups: 41 BD cases, 96 BD relatives (“high risk”), and 69 controls. Genotyping employed PsychArray, followed by imputation. Three PRSs were computed using genome-wide association data for SA in BD (SA-in-BD), SA in major depressive disorder (SA-in-MDD) (Mullins et al., 2019, The American Journal of Psychiatry, 176(8), 651–660), and risky behavior (Karlsson Linnér et al., 2019, Nature Genetics, 51(2), 245–257). Structural magnetic resonance imaging processing employed FreeSurfer v5.3.0. General linear models were constructed using 32 regions-of-interest identified from suicide neuroimaging literature, with false-discovery-rate correction. SA-in-MDD and SA-in-BD PRSs negatively predicted parahippocampal thickness, with the latter association modified by group membership. SA-in-BD and Risky Behavior PRSs inversely predicted rostral and caudal anterior cingulate structure, respectively, with the latter effect driven by the “high risk” group. SA-in-MDD and SA-in-BD PRSs positively predicted cuneus structure, irrespective of group. This study demonstrated associations between PRSs for suicide-related phenotypes and structural variability in brain regions implicated in SA. Future exploration of extended PRSs, in conjunction with a range of biological, phenotypic, environmental, and experiential data in high risk populations, may inform predictive models for suicidal behaviors., Australian National Health and Medical Research Council (NHMRC); Lansdowne Foundation, Paul and Jenny Reid, Good Talk, and the Keith Pettigrew Family Bequest. DNA extraction was undertaken by Genetic Repositories Australia (GRA; www.neura.edu.au/GRA), Claudio Toma is a recipient of a “Ramón y Cajal” fellowship (RYC2018-024106-I) from the Spanish MINECO. This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government

Smoking accelerates the ageing of multiple organs. However, few studies have quantified the association between smoking, especially smoking cessation, and brain ageing. Using structural magnetic resonance imaging data from the UK Biobank (n = 33,293), a brain age predictor was trained using a machine learning technique in the non-smoker group (n = 14,667) and then tested in the smoker group (n = 18,626) to determine the relationships between BrainAge Gap (predicted age – true age) and smoking parameters. Further, we examined whether smoking was associated with poorer cognition and whether this relationship was mediated by brain age. The predictor achieved an appreciable performance in training data (r = 0.712, mean-absolute-error [MAE] = 4.220) and test data (r = 0.725, MAE = 4.160). On average, smokers showed a larger BrainAge Gap (+0.304 years, Cohens'd = 0.083) than controls, more explicitly, the extents vary depending on their smoking characteristic that active regular smokers had the largest BrainAge Gap (+1.190 years, Cohens'd = 0.321), and light smokers had a moderate BrainAge Gap (+0.478, Cohens'd = 0.129). The increased smoking amount was associated with a larger BrainAge Gap (β = 0.035, p = 1.72 × 10−20) while a longer duration of quitting smoking in ex-smokers was associated with a smaller BrainAge Gap (β = −0.015, p = 2.14 × 10−05). Furthermore, smoking was associated with poorer cognition, and this relationship was partially mediated by BrainAge Gap. The study provides insight into the association between smoking, brain ageing, and cognition, which provide more publicly acceptable propaganda against smoking.

The Erasmus Glioma Database (EGD) contains structural magnetic resonance imaging (MRI) scans, genetic and histological features (specifying the WHO 2016 subtype), and whole tumor segmentations of patients with glioma. Pre-operative MRI data of 774 patients with glioma (281 female, 492 male, 1 unknown, age range 19–86 years) treated at the Erasmus MC between 2008 and 2018 is available. For all patients a pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR scan are available, made on a variety of scanners from four different vendors. All scans are registered to a common atlas and defaced. Genetic and histological data consists of the IDH mutation status (available for 467 patients), 1p/19q co-deletion status (available for 259 patients), and grade (available for 716 patients). The full WHO 2016 subtype is available for 415 patients. Manual segmentations are available for 374 patients and automatically generated segmentations are available for 400 patients. The dataset can be used to relate the visual appearance of the tumor on the scan with the genetic and histological features, and to develop automatic segmentation methods.

The organization of the Alzheimer's disease (AD) connectome has been studied using single neuroimaging modalities. However, different neuroimaging modalities are not independent and often interact with each other in the course of AD. Here, we integrate the networks obtained from T1-weighted and 18F-Florbetapir PET to build a multiplex connectome using BRAPH 2.0 and assess how it changes across different AD stages. We assessed the overlapping strength, multilayer communities, overlapping connections, and the multiplex participation and clustering coefficients. There was a reorganization of the communities across the four groups and we found significant changes reflecting a loss of multiplex hubs and overlapping connections in medial frontal and occipital areas in the patients’ groups. These findings indicate that multiplex network changes can be useful to understand the relationship between amyloid pathology and gray matter atrophy occurring over the course of AD.

Objectives: Patients with subcortical ischemic vascular disease (SIVD) often have prominent frontal dysfunction. However, it remains unclear how SIVD affects prospective memory (PM), which strongly relies on the frontoparietal network. The present study aimed to investigate PM performance in patients with early stage SIVD as compared to those with Alzheimer's disease (AD) and to older adults with normal cognition, and to explore the neural correlates of PM deficits.Method: Patients with very-mild to mild dementia due to SIVD or AD and normal controls (NC) aged above 60 years were recruited. Seventy-three participants (20 SIVD, 22 AD, and 31 NC) underwent structural magnetic resonance imaging (MRI), cognitive screening tests, and a computerized PM test. Sixty-five of these participants (19 SIVD, 20 AD, and 26 NC) also received resting-state functional MRI.Results: The group with SIVD had significantly fewer PM hits than the control group on both time-based and non-focal event-based PM tasks. Among patients in the very early stage, only those with SIVD but not AD performed significantly worse than the controls. Correlational analyses showed that non-focal event-based PM in SIVD was positively correlated with regional homogeneity in bilateral superior and middle frontal gyri, while time-based PM was not significantly associated with regional homogeneity in any of the regions of interest within the dorsal frontoparietal regions.Conclusions: The findings of this study highlight the vulnerability of non-focal event-based PM to the disruption of regional functional connectivity in bilateral superior and middle frontal gyri in patients with SIVD.

Background Findings on structural brain volume associated with pediatric posttraumatic stress disorder (PTSD) have been variable, and it is unclear whether any structural differences are specific to pediatric PTSD in comparison with adult PTSD or other co-occurring pediatric psychiatric conditions. Methods We tested volumetric brain differences between pediatric groups with and without PTSD in a region-of-interest meta-analysis. We conducted meta-regressions to test the effects of age and sex on heterogeneous study findings. To assess specificity, we compared pediatric PTSD with the following: adult PTSD, pediatric trauma exposure without PTSD, pediatric depression, and pediatric anxiety. Results In 15 studies examined, pediatric PTSD was associated with smaller total gray matter and cerebral, temporal lobe (total, right, and left), total cerebellar vermis, and hippocampal (total, right, and left) volumes, compared to peers without PTSD. In the pediatric PTSD group, but not the comparison group, we found a trend toward smaller total, right, and left amygdalar volumes. In an external comparison, smaller hippocampal volume was not significantly different between adult and pediatric PTSD groups. Qualitative comparisons with a pediatric trauma exposure without PTSD group, a pediatric depression group, and a pediatric anxiety group revealed differences that may be unique to pediatric PTSD, and others that may be convergent with these related clinical conditions in youth. Conclusions Pediatric PTSD is associated with structural differences that parallel those associated with adult PTSD. Furthermore, pediatric PTSD appears to be distinct from other related pediatric conditions at the structural level. Future studies employing longitudinal, dimensional, and multimodal neuroimaging approaches will further elucidate the nature of neurobiological differences in pediatric PTSD.

Aims The aim of our study was to assess the yield of routine brain magnetic resonance imaging (MRI) performed at our hospital as part of the diagnostic procedures focused on autism. Methods Our retrospective study involved children who had attended a diagnostic examination focused on autism and underwent brain MRIs between 1998-2015. The International Classification of Diseases, 10th edition was used to make clinical diagnoses. In 489 children (404 boys, 85 girls; mean age 8.0±4.2 years), a diagnosis of a pervasive developmental disorder was confirmed. Forty-five children, where the autism diagnosis was ruled out (but other psychiatric diagnoses found), served as a control group (36 boys, 9 girls; mean age 7.0±2.4 years). We can assume that in such a control group, brain abnormalities might occur at a higher frequency than in truly healthy children which would have the effect of reducing the difference between the groups. Results MRI pathologies were more common in the autistic (45.4 %) compared to the control group (31.8%) but the difference was significant only at the trend level (P=0.085). Hypoplasia of the corpus callosum (CC) was significantly more common in the autistic vs. the control group (13.7 vs. 0%; P=0.009). In contrast, nonmyelinated areas of white matter were significantly more common in controls (31.8 vs.17.3%; P=0.018). Differences in other parameters were not significant. Conclusion The occurrence of CC hypoplasia on routine MRI scans could represent a "red flag" for suspicion of autism.

Objective:Frascati international research criteria for HIV-associated neurocognitive disorders (HAND) are controversial; some investigators have argued that Frascati criteria are too liberal, resulting in a high false positive rate. Meyer et al. recommended more conservative revisions to HAND criteria, including exploring other commonly used methodologies for neurocognitive impairment (NCI) in HIV including the global deficit score (GDS). This study compares NCI classifications by Frascati, Meyer, and GDS methods, in relation to neuroimaging markers of brain integrity in HIV.Method:Two hundred forty-one people living with HIV (PLWH) without current substance use disorder or severe (confounding) comorbid conditions underwent comprehensive neurocognitive testing and brain structural magnetic resonance imaging and magnetic resonance spectroscopy. Participants were classified using Frascati criteria versus Meyer criteria: concordant unimpaired [Frascati(Un)/Meyer(Un)], concordant impaired [Frascati(Imp)/Meyer(Imp)], or discordant [Frascati(Imp)/Meyer(Un)] which were impaired via Frascati criteria but unimpaired via Meyer criteria. To investigate the GDS versus Meyer criteria, the same groupings were utilized using GDS criteria instead of Frascati criteria.Results:When examining Frascati versus Meyer criteria, discordant Frascati(Imp)/Meyer(Un) individuals had less cortical gray matter, greater sulcal cerebrospinal fluid volume, and greater evidence of neuroinflammation (i.e., choline) than concordant Frascati(Un)/Meyer(Un) individuals. GDS versus Meyer comparisons indicated that discordant GDS(Imp)/Meyer(Un) individuals had less cortical gray matter and lower levels of energy metabolism (i.e., creatine) than concordant GDS(Un)/Meyer(Un) individuals. In both sets of analyses, the discordant group did not differ from the concordant impaired group on any neuroimaging measure.Conclusions:The Meyer criteria failed to capture a substantial portion of PLWH with brain abnormalities. These findings support continued use of Frascati or GDS criteria to detect HIV-associated CNS dysfunction.

Measuring brain morphology with non-invasive structural magnetic resonance imaging is common practice, and can be used to investigate neuroplasticity. Brain morphology changes have been reported over the course of weeks, days, and hours in both animals and humans. If such short-term changes occur even faster, rapid morphological changes while being scanned could have important implications. In a randomized within-subject study on 47 healthy individuals, two high-resolution T1-weighted anatomical images were acquired (á 263 s) per individual. The images were acquired during passive viewing of pictures or a fixation cross. Two common pipelines for analyzing brain images were used: voxel-based morphometry on gray matter (GM) volume and surface-based cortical thickness. We found that the measures of both GM volume and cortical thickness showed increases in the visual cortex while viewing pictures relative to a fixation cross. The increase was distributed across the two hemispheres and significant at a corrected level. Thus, brain morphology enlargements were detected in less than 263 s. Neuroplasticity is a far more dynamic process than previously shown, suggesting that individuals’ current mental state affects indices of brain morphology. This needs to be taken into account in future morphology studies and in everyday clinical practice.

It has been a privilege to serve the Neuroimage community as a Handling Editor, with primary focus on machine learning, over the past six years. As I step down, I was asked to provide my thoughts on the evolution of the field over the past 10–15 years, as well as its potential future challenges and directions. It is exciting to reflect back on the spectacular growth of this field over the past decade (Fig. 1), and to ponder on some of the challenges faced currently by the field, and which are likely to become central points of investigation in the upcoming decade. Open in a separate window Fig. 1. Publications obtained from Pubmed with the following search query: ("MRI” OR “Magnetic Resonance Imaging” OR "Structural Magnetic Resonance Imaging" OR "Functional Magnetic Resonance Imaging" OR "Diffusion Tensor Imaging" OR "FDG-PET” OR "Amyloid-PET” OR “multimodal" OR "neuroimaging") AND (“brain”) AND (“Machine learning” OR “pattern classification”), on September 5, 2018. This plot reflects the exponential growth of adoption of these methods in neuroimaging.

We analyzed structural magnetic resonance imaging data from 58 cognitively normal and 101 mild cognitive impairment subjects. We used a general linear regression model to study the association between cognitive performance with hippocampal atrophy and ventricular enlargement using the radial distance method.Bilateral hippocampal atrophy was associated with baseline and longitudinal memory performance. Left hippocampal atrophy predicted longitudinal decline in visuospatial function. The multidomain ventricular analysis did not reveal any significant predictors.

Univariate analyses of structural neuroimaging data have produced heterogeneous results regarding anatomical sex- and gender-related differences. The current study aimed at delineating and cross-validating brain volumetric surrogates of sex and gender by comparing the structural magnetic resonance imaging data of cis- and transgender subjects using multivariate pattern analysis. Gray matter (GM) tissue maps of 29 transgender men, 23 transgender women, 35 cisgender women, and 34 cisgender men were created using voxel-based morphometry and analyzed using support vector classification. Generalizability of the models was estimated using repeated nested cross-validation. For external validation, significant models were applied to hormone-treated transgender subjects (n = 32) and individuals diagnosed with depression (n = 27). Sex was identified with a balanced accuracy (BAC) of 82.6% (false discovery rate [pFDR] 0.05). The neuroanatomical signature of sex in cisgender did not interact with depressive features (BAC = 74.7%) but was affected by hormone therapy when applied in transgender women (P

Background. Subcortical infarcts can result in verbal memory impairment, but the potential underlying mechanisms remain unknown. Objective. We investigated the spatiotemporal deterioration patterns of brain structures in patients with subcortical infarction and identified the regions that contributed to verbal memory impairment. Methods. Cognitive assessment and structural magnetic resonance imaging were performed 1, 4, and 12 weeks after stroke onset in 28 left-hemisphere and 22 right-hemisphere stroke patients with subcortical infarction. Whole-brain volumetric analysis combined with a further-refined shape analysis was conducted to analyze longitudinal morphometric changes in brain structures and their relationship to verbal memory performance. Results. Between weeks 1 and 12, significant volume decreases in the ipsilesional basal ganglia, inferior white matter, and thalamus were found in the left-hemisphere stroke group. Among those 3 structures, only the change rate of the thalamus volume was significantly correlated with that in immediate recall. For the right-hemisphere stroke group, only the ipsilesional basal ganglia survived the week 1 to week 12 group comparison, but its change rate was not significantly correlated with the verbal memory change rate. Shape analysis of the thalamus revealed atrophies of the ipsilesional thalamic subregions connected to the prefrontal, temporal, and premotor cortices in the left-hemisphere stroke group and positive correlations between the rates of those atrophies and the change rate in immediate recall. Conclusions. Secondary damage to the thalamus, especially to the left subregions connected to specific cortices, may be associated with early verbal memory impairment following an acute subcortical infarct.

Subjective cognitive decline (SCD) is frequently reported in diabetic patients. Diabetes mellitus (DM) is associated with changes in the microstructure of the brain arise in diabetic patients, including changes in gray matter volume (GMV). However, the underlying mechanisms of changes in GMV in DM patients with cognitive impairment remain uncertain. Here, we present an overview of amyloid-β-dependent cognitive impairment in DM patients with SCD. Moreover, we review the evolving insights from studies on the GMV changes in GMV and cognitive dysfunction to which provide the mechanisms of cognitive impairment in T2DM. Ultimately, the novel structural magnetic resonance imaging (MRI) protocol was used for detecting neuroimaging biomarkers that can predict the clinical outcomes in diabetic patients with SCD. A reliable MRI protocol would be helpful to detect neurobiomarkers, and to understand the pathological mechanisms of preclinical cognitive impairment in diabetic patients.

Background Prior studies of pediatric posttraumatic stress disorder (PTSD) have reported cross-sectional and age-related structural and functional brain abnormalities in networks associated with cognitive, affective, and self-referential processing. However, no reported studies have comprehensively examined longitudinal gray matter development and its intrinsic functional correlates in pediatric PTSD. Methods Twenty-seven youths with PTSD and 21 nontraumatized typically developing (TD) youths were assessed at baseline and 1-year follow-up. At each visit, youths underwent structural magnetic resonance imaging and resting-state functional magnetic resonance imaging. Regions with volumetric abnormalities in whole-brain structural analyses were identified and used as seeds in exploratory intrinsic connectivity analyses. Results Youths with PTSD exhibited sustained reductions in gray matter volume (GMV) in right ventromedial prefrontal cortex (PFC) and bilateral ventrolateral PFC. Group-by-time analyses revealed aberrant longitudinal development in dorsolateral PFC, where typically developing youths exhibited normative decreases in GMV between baseline and follow-up, and youths with PTSD showed increases in GMV. Using these regions as seeds, patients with PTSD exhibited atypical longitudinal decreases in intrinsic PFC–amygdala and PFC–hippocampus connectivity, in contrast to increases in typically developing youths. Specifically, youths with PTSD showed decreasing ventromedial PFC–amygdala connectivity as well as decreasing ventrolateral PFC–hippocampus connectivity over time. Notably, volumetric abnormalities in ventromedial PFC and ventrolateral PFC were predictive of symptom severity. Conclusions These findings represent novel longitudinal volumetric and connectivity changes in pediatric PTSD. Atypical prefrontal GMV and prefrontal-amygdala/hippocampus development may underlie persistence of PTSD in youths and could serve as future therapeutic targets.

IMPORTANCE The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological markers of psychosis risk.OBJECTIVE To investigate baseline structural neuroimaging differences between individuals at CHR and healthy controls as well as between participants at CHR who later developed a psychotic disorder (CHR-PS+) and those who did not (CHR-PS-).DESIGN, SETTING, AND PARTICIPANTS In this case-control study, baseline T1-weighted magnetic resonance imaging (MRI) data were pooled from 31 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. CHR status was assessed using the Comprehensive Assessment of At-Risk Mental States or Structured Interview for Prodromal Syndromes. MRI scans were processed using harmonized protocols and analyzed within a mega-analysis and meta-analysis framework from January to October 2020.MAIN OUTCOMES AND MEASURES Measures of regional cortical thickness (CT), surface area, and subcortical volumes were extracted from T1-weighted MRI scans. Independent variables were group (CHR group vs control group) and conversion status (CHR-PS+ group vs CHR-PS- group vs control group).RESULTS Of the 3169 included participants, 1428 (45.1%) were female, and the mean (SD; range) age was 21.1 (4.9; 9.5-39.9) years. This study included 1792 individuals at CHR and 1377 healthy controls. Using longitudinal clinical information, 253 in the CHR-PS+ group, 1234 in the CHR-PS- group, and 305 at CHR without follow-up data were identified. Compared with healthy controls, individuals at CHR exhibited widespread lower CT measures (mean [range] Cohen d = -0.13 [-0.17 to -0.09]), but not surface area or subcortical volume. Lower CT measures in the fusiform, superior temporal, and paracentral regions were associated with psychosis conversion (mean Cohen d = -0.22; 95% CI, -0.35 to 0.10). Among healthy controls, compared with those in the CHR-PS+ group, age showed a stronger negative association with left fusiform CT measures (F = 9.8; P < .001; q < .001) and left paracentral CT measures (F = 5.9; P = .005; q = .02). Effect sizes representing lower CT associated with psychosis conversion resembled patterns of CT differences observed in ENIGMA studies of schizophrenia (rho = 0.35; 95% CI, 0.12 to 0.55; P = .004) and individuals with 22q11.2 microdeletion syndrome and a psychotic disorder diagnosis (rho = 0.43; 95% CI, 0.20 to 0.61; P = .001).CONCLUSIONS AND RELEVANCE This study provides evidence for widespread subtle, lower CT measures in individuals at CHR. The pattern of CT measure differences in those in the CHR-PS+ group was similar to those reported in other large-scale investigations of psychosis. Additionally, a subset of these regions displayed abnormal age associations. Widespread disruptions in CT coupled with abnormal age associations in those at CHR may point to disruptions in postnatal brain developmental processes.Question How are brain morphometric features associated with later psychosis conversion in individuals at clinical high risk (CHR) for developing psychosis?Findings In this case-control study including 3169 participants, lower cortical thickness, but not cortical surface area or subcortical volume, was more pronounced in individuals at CHR in a manner highly consistent with thinner cortex in individuals with established psychosis. Regions that displayed lower cortical thickness in individuals at CHR who later developed a psychotic disorder additionally displayed abnormal associations with age.Meaning In this study, CHR status and later transition to psychosis was robustly associated with lower cortical thickness; abnormal age associations and specificity to cortical thickness may point to aberrant postnatal brain development in individuals at CHR, including pruning and myelination.This case-control study investigates baseline structural magnetic resonance imaging (MRI) differences between individuals at clinical high risk and healthy controls as well as between participants at clinical high risk who later developed a psychotic disorder and those who did not.

Converging evidence from structural and functional magnetic resonance imaging (MRI) studies points to amygdala alteration as crucial in the development of paediatric bipolar disorder (pBP). The high number of recent studies prompted us to comprehensively evaluate findings. We aimed to systematically review structural and functional MRI studies investigating the amygdala in patients with pBP and in youth at high-risk (HR) for developing pBP. We searched PubMed from any time to 25 September 2020 using: ���amygdala AND (MRI OR magnetic resonance imaging) AND bipolar AND (pediatr* OR child OR children OR childhood OR adolescent OR adolescents OR adolescence OR young OR familial OR at-risk OR sibling* OR offspring OR high risk)���. In this review, we adhered to the PRISMA statement. Amygdala hyperactivity to emotional stimuli is the most commonly reported finding in youth with pBP and HR compared to healthy peers (HC), whereas findings from structural MRI studies are inconsistent. Hyperactivation of the amygdala might be an endophenotype of pBP.

Alzheimer's Disease (AD) is the most prevailing form of dementia, killing more people than prostate and breast cancers combined. Structural Magnetic Resonance Imaging (sMRI) is widely used for the analysis of progressive brain aggravation and its clinical utility in discriminating AD is well established. Even if an effective cure does not exist yet, early detection is fundamental for slowing down the worsening of symptoms. Thus, the aim of the present work is to propose an end-to-end 3D Convolutional Long Short-Term Memory (ConvLSTM)-based framework for early diagnosis of AD from full-resolution whole-brain sMRI scans. The proposed framework was applied to 427 full-resolution whole-brain sMRI scans belonging to both OASIS and ADNI databases in order to provide a less dataset-specific approach. Results show that our framework is performing well in discriminating AD from Cognitively Normal (CN) patients, reaching a classification accuracy of 86%, sensitivity of 96%, f1-score of 88% and AUC of 93% on the test data. The tests were performed on a scalable GPU cloud service and are publicly available to guarantee reproducibility. Since the proposed framework performs well without domain-specific knowledge from AD as well as computationally-costly processes such as segmentation, it can be applied to other mental disorders using whole-brain sMRI scans as input data.

Questionable research practices are a well-recognized problem in psychology. Coding bias, or the tendency of review studies to disproportionately cite positive findings from original research, has received comparatively little attention. Coding bias is more likely to occur when original research, such as neuroimaging, includes large numbers of effects, and is most concerning in applied contexts. We evaluated coding bias in reviews of structural magnetic resonance imaging (sMRI) studies of PCL-R psychopathy. We used PRISMA guidelines to locate all relevant original sMRI studies and reviews. The proportion of null-findings cited in reviews was significantly lower than those reported in original research, indicating coding bias. Coding bias was not affected by publication date or review design. Reviews recommending forensic applications—such as treatment amenability or reduced criminal responsibility—were no more accurate than purely theoretical reviews. Coding bias may have contributed to a perception that structural brain abnormalities in psychopaths are more consistent than they actually are, and by extension that sMRI findings are suitable for forensic application. We discuss possible sources for the pervasive coding bias we observed, and we provide recommendations to counteract this bias in review studies. Until coding bias is addressed, we argue that this literature should not inform conclusions about psychopaths' neurobiology, especially in forensic contexts.