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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.

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.

Objective This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer’s disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both. Methods For the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10–20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database. Results The ANN with stacked autoencoders and a deep leaning model representing both ADD and control participants showed classification accuracies in discriminating them of 80%, 85%, and 89% using rsEEG, sMRI, and rsEEG + sMRI features, respectively. The two ANNs with stacked autoencoders and a deep leaning model specialized for either ADD or control participants showed classification accuracies of 77%, 83%, and 86% using the same input features. Conclusions The two architectures of ANNs using stacked (autoencoding) hidden layers consistently reached moderate to high accuracy in the discrimination between ADD and healthy control participants as a function of the rsEEG and sMRI features employed. Significance The present results encourage future multi-centric, prospective and longitudinal cross-validation studies using high resolution EEG techniques and harmonized clinical procedures towards clinical applications of the present ANNs.

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.

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

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.

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.

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.

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.

Alzheimer’s disease (AD) is a heterogeneous disease with different subtypes. Studying AD subtypes from brain structure, neuropathology, and cognition are of great importance for AD heterogeneity research. Starting from the study of constructing AD subtypes based on the features of T1-weighted structural magnetic resonance imaging, this paper introduces the major connections between the subtype definition and analysis strategies, including brain region-based subtype definition, and their demographic, neuropathological, and neuropsychological characteristics. The advantages and existing problems are analyzed, and reasonable improvement schemes are prospected. Overall, this review offers a more comprehensive view in the field of atrophy subtype in AD, along with their advantages, challenges, and future prospects, and provide a basis for improving individualized AD diagnosis.

This study aimed to explore brain surface-based morphometry cortical thickness changes in patients with idiopathic tinnitus before and after 24 weeks of sound therapy. In this prospective observational study, we recruited 33 tinnitus patients who had undergone 24 weeks of sound therapy and 26 matched healthy controls. For the two groups of subjects, a 3D-BRAVO pulse sequence was acquired both at baseline and at the 24th week. Structural image data preprocessing was performed using the DPABISurf toolbox. The Tinnitus Handicap Inventory (THI) score was assessed to determine the severity of tinnitus before and after treatment. Two-way mixed-model analysis of variance (ANOVA) and Pearson’s correlation analysis were used in the statistical analysis. Student–Newman–Keuls (SNK) tests were used in the post hoc analysis. Significantly lower cortical thickness was found in the left somatosensory and motor cortex (SMC), left posterior cingulate cortex (PCC), and right orbital and polar frontal cortex (OPFC) of the participants in the tinnitus group at baseline than in the participants in the HC group at baseline and after 24 weeks; in the tinnitus group, significantly higher cortical thickness was found after the 24 weeks sound therapy in comparison to the baseline in the left SMC, bilateral superior parietal cortex (SPC), left inferior parietal cortex (IPC), left PCC, and right OPFC. In the HC group, no statistically significant difference in cortical thickness was found after the 24 weeks treatment in comparison to the baseline in the bilateral SMC, bilateral SPC, left IPC, left PCC, or right OPFC. The changes in cortical thickness before and after sound therapy can provide certain reference values for clinical tinnitus treatment. These brain regions could serve as potential targets for neuroimaging.

BackgroundSelf-esteem is the individual evaluation of oneself. People with high self-esteem grade have mental health and can bravely cope with the threats from the environment. With the development of neuroimaging techniques, researches on cognitive neural mechanisms of self-esteem are increased. Existing methods based on brain morphometry and single-layer brain network cannot characterize the subtle structural differences related to self-esteem.MethodTo solve this issue, we proposed a multiple anatomical brain network based on multi-resolution region of interest (ROI) template to study the brain structural connections of self-esteem. The multiple anatomical brain network consists of ROI features and hierarchal brain network features that are extracted from structural MRI. For each layer, we calculated the correlation relationship between pairs of ROIs. In order to solve the high-dimensional problem caused by the large amount of network features, feature selection methods (t-test, mRMR, and SVM-RFE) are adopted to reduce the number of features while retaining discriminative information to the maximum extent. Multi-kernel SVM is employed to integrate the various types of features by appropriate weight coefficient.ResultThe experimental results show that the proposed method can improve classification accuracy to 97.26% compared with single-layer brain network.ConclusionsThe proposed method provides a new perspective for the analysis of brain structural differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.

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.

Background:Smoking addiction is a major public health issue which causes a series of chronic diseases and mortalities worldwide. We aimed to explore the most discriminative gray matter regions between heavy smokers and healthy controls with a data-driven multivoxel pattern analysis technique, and to explore the methodological differences between multivoxel pattern analysis and voxel-based morphometry.Methods:Traditional voxel-based morphometry has continuously contributed to finding smoking addiction-related regions on structural magnetic resonance imaging. However, voxel-based morphometry has its inherent limitations. In this study, a multivoxel pattern analysis using a searchlight algorithm and support vector machine was applied on structural magnetic resonance imaging to identify the spatial pattern of gray matter volume in heavy smokers.Results:Our proposed method yielded a voxel-wise accuracy of at least 81% for classifying heavy smokers from healthy controls. The identified regions were primarily located at the temporal cortex and prefrontal cortex, occipital cortex, thalamus (bilateral), insula (left), anterior and median cingulate gyri, and precuneus (left).Conclusions:Our results suggested that several regions, which were seldomly reported in voxel-based morphometry analysis, might be latently correlated with smoking addiction. Such findings might provide insights for understanding the mechanism of chronic smoking and the creation of effective cessation treatment. Multivoxel pattern analysis can be efficient in locating brain discriminative regions which were neglected by voxel-based morphometry.

The lifetime risk of developing amyotrophic lateral sclerosis (ALS) increases in the elderly, and greater age at symptom onset has been identified as a negative prognostic factor in the disease. However, the underlying neurobiological mechanisms are still poorly investigated. We hypothesized that older age at symptom onset would have been associated with greater extra-motor cortical damage contributing to worse prognosis, so we explored the relationship between age at symptom onset, cortical thinning (CT) distribution, and clinical markers of disease progression. We included 26 ALS patients and 29 healthy controls with T1-weighted magnetic resonance imaging (MRI). FreeSurfer 6.0 was used to identify regions of cortical atrophy (CA) in ALS, and to relate age at symptom onset to CT distribution. Linear regression analyses were then used to investigate whether MRI metrics of age-related damage were predictive of clinical progression. MRI results were corrected using the Monte Carlo simulation method, and regression analyses were further corrected for disease duration. ALS patients exhibited significant CA mainly encompassing motor regions, but also involving the cuneus bilaterally and the right superior parietal cortex (p < 0.05). Older age at symptom onset was selectively associated with greater extra-motor (frontotemporal) CT, including pars opercularis bilaterally, left middle temporal, and parahippocampal cortices (p < 0.05), and CT of these regions was predictive of shorter survival (p = 0.004, p = 0.03). More severe frontotemporal CT contributes to shorter survival in older ALS patients. These findings have the potential to unravel the neurobiological mechanisms linking older age at symptom onset to worse prognosis in ALS.

This paper investigates extensively Machine Learning (ML) algorithms for abnormal health conditions using two sessions of Structural Magnetic Resonance Imaging (sMRI) data. First abnormal health condition considered is classification of early MCI (EMCI) and Cognitively Normal (CN) subjects using benchmark OASIS-3 longitudinal neuroimaging dataset. The second abnormal health condition considered is classification of Autism Spectrum Disorders (ASDs) from Typical Development (TD) subjects using sMRI images from multisite ABIDE II longitudinal samples. Freesurfer longitudinal processing technique is used to process the longitudinal sMRI samples and extract the features. Our work is first of its kind and serves as baseline for future research in longitudinal analysis.

Asthma as a chronic inflammatory disease can be expected to affect central nervous system structures but little is known about subcortical structures in asthma and their potential association with illness-specific outcomes and anxiety. A total of 40 young adults (20 with asthma and 20 gender- and age-matched controls) underwent high-resolution T1-weighted MRI scan, viewed short distressing film clips, and filled in questionnaires about anxious and depressed mood, as well as asthma history, control, and catastrophizing thoughts about asthma, for those with asthma. The structural scans were processed in FSL's FIRST program to delineate subcortical structures of interest: amygdala, hippocampus, putamen, pallidum, caudate nucleus, nucleus accumbens, and thalamus. Findings showed no general reduction in subcortical gray matter volumes in asthma compared to controls. Asthma duration, asthma control, and catastrophizing of asthma and asthma attacks were negatively associated with volumes of putamen and pallidum, and to a weaker extent thalamus and amygdala, while controlling for gender, age, and corticosteroid inhaler use. In addition, stronger anxiety in response to distressing films was associated with lower volume of the pallidum, whereas general anxious and depressed mood was unrelated to subcortical structures. Thus, although there are no subcortical structural differences between young adults with asthma and healthy controls, longer asthma history, suboptimal management, and illness-related anxiety are reflected in lower gray matter volumes of subcortical structures, further emphasizing the importance of maintaining optimal asthma control. ispartof: Brain Imaging And Behavior vol:14 issue:6 pages:1-10 ispartof: location:United States status: Published online

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

Bipolar disorders (BDs) represent one of the leading causes of disability and morbidity globally. The use of functional magnetic resonance imaging (fMRI) is being increasingly studied as a tool to improve the diagnosis and treatment of BDs. While morphological biomarkers can be identified through the use of structural magnetic resonance imaging (sMRI), recent studies have demonstrated that varying degrees of both structural and functional impairments indicate differing bipolar subtypes. Within fMRI, resting-state fMRI has specifically drawn increased interest for its capability to detect different neuronal activation patterns compared to task-based fMRI. This study aims to review recently published literature regarding the use of fMRI to investigate structural-functional relationships in BD diagnosis and specifically resting-state fMRI to provide an opinion on fMRI's modern clinical application. All sources in this literature review were collected through searches on both PubMed and Google Scholar databases for terms such as 'resting-state fMRI' and 'functional neuroimaging biomarkers of bipolar disorder'. While there are promising results supporting the use of fMRI for improving differential accuracy and establishing clinically relevant biomarkers, additional evidence will be required before fMRI is considered a dependable component of the overall BD diagnostic process.

Magnetic resonance imaging (MRI) plays a central role in the management and evaluation of patients with epilepsy. It is important that structural MRI scans are optimally acquired and carefully reviewed by trained experts within the context of all available clinical data. The aim of this review is to discuss the essentials of MRI that will be useful to health care providers specialized in epilepsy, as outlined by the competencies and learning objectives of the recently developed ILAE curriculum. This review contains information on basic MRI principles, sequences, field strengths and safety, when to perform and repeat an MRI, epilepsy MRI protocol (HARNESS-MRI) and the basic reading guidelines, and common epileptic pathologies. More advanced topics such as MRI-negative epilepsy, functional MRI and diffusion-weighted imaging are also briefly discussed. Although the available resources can differ markedly across different centers, it is the hope that this review can provide general guidance in the everyday practice of using MRI for patients with epilepsy.

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

Randomized controlled trials have shown efficacy of trauma-focused psychotherapies in youth with posttraumatic stress disorder (PTSD), but little is known about the relationship between treatment response and alternations in brain structures associated with PTSD. In this study, we longitudinally examined the association between treatment response and pre-to posttreatment changes in structural magnetic resonance imaging (MRI) scans using a voxel-based morphometry approach. We analyzed MRI scans of 35 patients (ages 8–18 years, 21 female) with PTSD (80%) or partial PTSD (20%) before and after eight weekly sessions of trauma-focused psychotherapy. PTSD severity was assessed longitudinally using the Clinician-Administered PTSD scale for Children and Adolescents to divide participants into responders and non-responders. Group by time interaction analysis showed significant differences in grey-matter volume in the bilateral insula due to volume reductions over time in non-responders compared to responders. Despite the significant group by time interaction, there were no significant group differences at baseline or follow-up. As typical development is associated with insula volume increase, these longitudinal MRI findings suggest that treatment non-response is associated with atypical neurodevelopment of the insula, which may underlie persistence of PTSD in youth. The absence of structural MRI changes in treatment responders, while in need of replication, suggest that successful trauma-focused psychotherapy may not directly normalize brain abnormalities associated with PTSD.

The present study examined the relationship between subthreshold depressive symptoms and gray matter volume in subregions of the posterior cerebellum. Structural magnetic resonance imaging data from 38 adults aged 51 to 80 years were analyzed along with participants’ responses to the Center for Epidemiologic Studies Depression Scale. Subscale scores for depressed mood, somatic symptoms, and lack of positive affect were calculated, and multiple regression analyses were used to examine the relationship between symptom dimensions and cerebellar volumes. Greater total depressive symptoms and greater somatic symptoms of depression were significantly related to larger volumes of vermis VI, a region within the salience network, which is altered in depression. Exploratory analyses revealed that higher scores on the lack of positive affect subscale were related to larger vermis VIII volumes. These results support that depressive symptom profiles have unique relationships within the cerebellum that may be important as the field move towards targeted treatment approaches for depression.

SUMMARYBackgroundSuicidal behavior is highly heterogeneous and complex. A better understanding of its biological substrates and mechanisms could inform the design of more effective suicide prevention and intervention strategies. Neuroimaging studies of suicidality have so far been conducted in small samples, prone to biases and false-positive associations, yielding inconsistent results. The ENIGMA-MDD working group aims to address the issues of poor replicability and comparability by coordinating harmonized analyses across neuroimaging studies of major depressive disorder and related phenotypes, including suicidal behavior.MethodsHere, we pool data from eighteen international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy controls and 6,448 people with depression, of whom 694 had attempted suicide). We compare regional cortical thickness and surface area, and measures of subcortical, lateral ventricular and intracranial volumes between suicide attempters, clinical controls (non-attempters with depression) and healthy controls.FindingsWe identified 25 regions of interest with statistically significant (FDRPost-hoc examinations identified neuroimaging markers associated with suicide attempt over and above the effects of depression, including smaller volumes of the left and right thalamus and the right pallidum, and lower surface area of the left inferior parietal lobe.InterpretationThis study addresses the lack of replicability and consistency in several previously published neuroimaging studies of suicide attempt, and further demonstrates the need for well-powered samples and collaborative efforts to avoid reaching biased or misleading conclusions. Our results highlight the potential involvement of the thalamus, a structure viewed historically as a passive gateway in the brain, and the pallidum, a region linked to reward response and positive affect. Future functional and connectivity studies of suicidality may focus on understanding how these regions relate to the neurobiological mechanisms of suicide attempt risk.

Pattern separation (PS) describes the process by which the brain discriminates similar stimuli from previously encoded stimuli. This fundamental process requires the intact processing by specific subfields in the hippocampus and can be examined using mnemonic discrimination tasks. Previous studies reported different patterns for younger and older individuals between mnemonic discrimination performance and hippocampal subfield activation. Here, we investigated the relationship between the lure discrimination index (LDI) and hippocampal subfield volume and activity across the adult lifespan (20–70 years old). Using ultra-high field functional and structural magnetic resonance imaging at 7 T, we found that lower DG volume and higher CA3 activation was associated with worse LDI performance in individuals (>60 years), suggesting that this higher activation may be an indication of aberrant neurodegenerative-related processes. In fact, higher activation in the CA1 and DG was associated with lower volumes in these subfields. For individuals around 40–50 years old, we observed that greater left and right DG volume, and greater activity in the CA3 was associated with lower LDI performance. Taken together, these results suggest that the relationship between memory and hippocampal subfield structure or function varies nonlinearly and possibly reciprocally with age, with midlife being a critically vulnerable period in life.

We investigated whether the gray matter volume of primary auditory cortex (Heschl’s gyrus [HG]) was associated with abnormal patterns of auditory γ activity in schizophrenia, namely impaired γ synchronization in the 40-Hz auditory steady-state response (ASSR) and increased spontaneous broadband γ power. (The γ data were previously reported in Hirano et al, JAMA Psychiatry, 2015;72:813-821). Participants were 24 healthy controls (HC) and 23 individuals with chronic schizophrenia (SZ). The ASSR was obtained from the electroencephalogram to click train stimulation at 20, 30, and 40 Hz rates. Dipole source localization of the ASSR was used to provide a spatial filter of auditory cortex activity, from which ASSR evoked power and phase locking factor (PLF), and induced γ power were computed. HG gray matter volume was derived from structural magnetic resonance imaging at 3 T with manually traced regions of interest. As expected, HG gray matter volume was reduced in SZ compared with HC. In SZ, left hemisphere ASSR PLF and induced γ power during the 40-Hz stimulation condition were positively and negatively correlated with left HG gray matter volume, respectively. These results provide evidence that cortical gray matter structure, possibly resulting from reduced synaptic connectivity at the microcircuit level, is related to impaired γ synchronization and increased spontaneous γ activity in schizophrenia.

Background The detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data. Methods This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. Results The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. Conclusion The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.

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

Autism spectrum disorder (ASD) is a developmental disorder underdiagnosed in adults. To date, no consistent evidence of alterations in brain structure has been reported in adults with ASD and few studies were conducted at that age. We analyzed structural magnetic resonance imaging data from 167 high functioning adults with ASD and 195 controls. We ran our analyses on a discovery (n = 301) and a replication sample (n = 61). The right caudal anterior cingulate cortical thickness was significantly thinner in adults with ASD compared to controls in both the discovery and the replication sample. Our work underlines the relevance of studying the brain anatomy of an adult ASD population.