Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

Photodiagnosis and photodynamic recognition of cervical cancer with SEM and AFM images

So far, the number of patients who die from cancer is quite high. Continuation of early detection research is important to reduce the number of deaths due to cancer. At the time of the literature review, images of the same patients taken from Scanning Electron Microscope (SEM) and Atomic Force Microscope (AFM) for early diagnosis of cervix cancer have not been addressed to date. This article, Photodiagnosis and Photodynamics with SEM and AFM images are valuable in recognizing cervical cancer and starting treatment early. Simultaneous examination of the, Photodiagnosis and Photodynamics with SEM and AFM cervix images of patients will provide us with a far more powerful solution than a one-way solution. Daubechies (db2, db3, db4, and db5), Coiflet (coif5, coif4, coif3, and coif2), Symlet (sym5, sym4, sym3, and sym2), and Biorthogonal (bior1.3, bior2.8, bior1.5, and bior3.3) 16 discrete wavelet transformation families (DWTF) have been applied to AFM and SEM images. One approximate and three detail coefficients have been obtained for each one AFM and SEM cervix images. Homogeneity, contrast, angular second moment, entropy, mean, standard deviation, correlation, cluster prominence, dissimilarity, and cluster shade values have been calculated for each of these one approximate and three detail coefficients. The classification rate found by the averages of the results obtained from the DWTF_JSD, DWTF_HD and DWTF_TD algorithms for AFM and SEM cervix images are 98.29% and 97.10%, respectively. According to these results, it has been determined that SEM images have lower classification rate than AFM images. It has been also observed that the surface roughness of the mAFM images was larger than nAFM and bAFM images. But, it was observed that the volume of particles of the mAFM images has been smaller than nAFM and bAFM images.

The Neurodevelopmental Dynamics of Multilingual Experience During Childhood: A Longitudinal Behavioral, Structural, and Functional MRI Study

BACKGROUND/OBJECTIVES

A neurobiological framework of bi- or multilingual neurocognitive development must consider the following: (i) longitudinal behavioral and neural measures; (ii) brain developmental constraints across structure and function; and (iii) the development of global multilingual competence in a homogeneous social environment. In this study, we investigated whether multilingual competence yields early changes in executive attention control mechanisms and their underlying neural structures in the frontal-striatal system, such as the dorsal anterior cingulate cortex/pre-supplemental area and the left caudate.

METHODS

We employed longitudinal neuroimaging and functional connectivity methods in a small group of multilingual children over two years.

RESULTS

We found that the dACC/preSMA is functionally influenced by changes in multilingual competence but not yet structurally adapted, while the left caudate, in a developmental stage, is influenced, adapts, and specializes due to multilingual experience. Furthermore, increases in multilingual competence strengthen connections between the dACC/preSMA, left caudate, and other structures of the cognitive control network, such as the right inferior frontal gyrus and bilateral inferior parietal lobules.

CONCLUSIONS

These findings suggest that multilingual competence impacts brain "adaptation" and "specialization" during childhood. The results may provide insights and guide future research on experience-expectant and experience-dependent brain plasticity to explain the "interaction" between multilingualism and neurodevelopment.

Phonemic word fluency is related to temporal and striatal gray matter volume in healthy older adults

Word fluency (WF) tasks that tap verbal and executive function show deteriorating performance by advancing age. To address the scarcely studied age-related brain correlates of WF, we employed whole-brain voxel-based morphometry to examine gray matter (GM) correlates of semantic and phonemic WF in 46 healthy older adults. Lower phonemic WF score was related to smaller anterior medial temporal GM volume as well as smaller GM volume in the putamen bilaterally. A disproportionally weak score on phonemic WF in relation to semantic WF was associated with smaller GM volume in the left inferior frontal cortex, the right anterior medial temporal lobe, and the right striatum. There were no significant associations for semantic WF. The fact that our temporal and subcortical findings were bilateral and right-lateralized, may reflect age-related compensation by these brain areas.

Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study

BACKGROUND

Measures of cortical topology are believed to characterize large-scale cortical networks. Previous studies used region of interest (ROI)-based approaches with predefined templates that limit analyses to linear pair-wise interactions between regions. As cortical topology is inherently complex, a non-linear dynamic model that measures the brain complexity at the voxel level is suggested to characterize topological complexities of brain regions and cortical folding.

METHODS

T1-weighted brain images of 150 first-episode antipsychotic-naïve schizophrenia (FES) patients and 161 healthy comparison participants (HC) were examined. The Chaos analysis approach was applied to detect alterations in brain structural complexity using the largest Lyapunov exponent (Lambda) as the key measure. Then, the Lambda spatial series was mapped in the frequency domain using the correlation of the Morlet wavelet to reflect cortical folding complexity.

RESULTS

A widespread voxel-wise decrease in Lambda values in space and frequency domains was observed in FES, especially in frontal, parietal, temporal, limbic, basal ganglia, thalamic, and cerebellar regions. The widespread decrease indicates a general loss of brain topological complexity and cortical folding. An additional pattern of increased Lambda values in certain regions highlights the redistribution of complexity measures in schizophrenia at an early stage with potential progression as the illness advances. Strong correlations were found between the duration of untreated psychosis and Lambda values related to the cerebellum, temporal, and occipital gyri.

CONCLUSIONS

Our findings support the notion that defining brain complexity by non-linear dynamic analyses offers a novel approach for identifying structural brain alterations related to the early stages of schizophrenia.

aXonica: A support package for MRI based Neuroimaging

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian, BIDS Apps, Flywheel, QMENTA, Boutiques, Brainlife and Neurodesk. Overall, all these solutions complement each other. NeuroDebian covers neuroscience and has a wider scope, providing only 51 tools for MRI. Whereas, BIDS Apps is committed to the BIDS format, covering only 45 software related to MRI. Boutiques is more flexible, facilitating its pipelines to be easily installed as separate containers, validated, published, and executed. Whereas, both Flywheel and Qmenta are propriety, leaving four for users looking for 'free for use' tools, i.e., NeuroDebian, Brainlife, Neurodesk, and BIDS Apps. This paper presents an extensive survey of 317 tools published in MRI-based neuroimaging in the last ten years, along with 'aXonica,' an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than that of NeuroDebian (51), BIDS App (45), Flywheel (70), and Neurodesk (85). Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an important resource for researchers and teachers working in the field of MRI-based Neuroimaging to (a) develop software workstations, and/or (b) install newer tools in their existing workstations.

Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression

This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.

BrainAGE: Revisited and reframed machine learning workflow

Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.

Traumatic Brain Injury and Post-Traumatic Stress Disorder and Their Influence on Development and Pattern of Alzheimer's Disease Pathology in Later Life

Background

Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are potential risk factors for the development of dementia including Alzheimer's disease (AD) in later life. The findings of studies investigating this question are inconsistent though.

Objective

To investigate if these inconsistencies are caused by the existence of subgroups with different vulnerability for AD pathology and if these subgroups are characterized by atypical tau load/atrophy pattern.

Methods

The MRI and PET data of 89 subjects with or without previous TBI and/or PTSD from the DoD ADNI database were used to calculate an age-corrected gray matter tau mismatch metric (ageN-T mismatch-score and matrix) for each subject. This metric provides a measure to what degree regional tau accumulation drives regional gray matter atrophy (matrix) and can be used to calculate a summary score (score) reflecting the severity of AD pathology in an individual.

Results

The ageN-T mismatch summary score was positively correlated with whole brain beta-amyloid load and general cognitive function but not with PTSD or TBI severity. Hierarchical cluster analysis identified five different spatial patterns of tau-gray matter interactions. These clusters reflected the different stages of the typical AD tau progression pattern. None was exclusively associated with PTSD and/or TBI.

Conclusions

These findings suggest that a) although subsets of patients with PTSD and/or TBI develop AD-pathology, a history of TBI or PTSD alone or both is not associated with a significantly higher risk to develop AD pathology in later life. b) remote TBI or PTSD do not modify the typical AD pathology distribution pattern.

Low bone mineral density is associated with gray matter volume decrease in UK Biobank

Objectives

Previous research has found an association of low bone mineral density (BMD) and regional gray matter (GM) volume loss in Alzheimer's disease (AD). We were interested whether BMD is associated with GM volume decrease in brains of a healthy elderly population from the UK Biobank.

Materials and methods

T1-weighted images from 5,518 women (MAge = 70.20, SD = 3.54; age range: 65-82 years) and 7,595 men (MAge = 70.84, SD = 3.68; age range: 65-82 years) without neurological or psychiatric impairments were included in voxel-based morphometry (VBM) analysis in CAT12 with threshold-free-cluster-enhancement (TFCE) across the whole brain.

Results

We found a significant decrease of GM volume in women in the superior frontal gyri, middle temporal gyri, fusiform gyri, temporal poles, cingulate gyri, precunei, right parahippocampal gyrus and right hippocampus, right ventral diencephalon, and right pre- and postcentral gyrus. Only small effects were found in men in subcallosal area, left basal forebrain and entorhinal area.

Conclusion

BMD is associated with low GM volume in women but less in men in regions afflicted in the early-stages of AD even in a sample without neurodegenerative diseases.

Is musical engagement enough to keep the brain young?

Music-making and engagement in music-related activities have shown procognitive benefits for healthy and pathological populations, suggesting reductions in brain aging. A previous brain aging study, using Brain Age Gap Estimation (BrainAGE), showed that professional and amateur-musicians had younger appearing brains than non-musicians. Our study sought to replicate those findings and analyze if musical training or active musical engagement was necessary to produce an age-decelerating effect in a cohort of healthy individuals. We scanned 125 healthy controls and investigated if musician status, and if musical behaviors, namely active engagement (AE) and musical training (MT) [as measured using the Goldsmiths Musical Sophistication Index (Gold-MSI)], had effects on brain aging. Our findings suggest that musician status is not related to BrainAGE score, although involvement in current physical activity is. Although neither MT nor AE subscales of the Gold-MSI are predictive for BrainAGE scores, dispositional resilience, namely the ability to deal with challenge, is related to both musical behaviors and sensitivity to musical pleasure. While the study failed to replicate the findings in a previous brain aging study, musical training and active musical engagement are related to the resilience factor of challenge. This finding may reveal how such musical behaviors can potentially strengthen the brain's resilience to age, which may tap into a type of neurocognitive reserve.

Identifying and distinguishing of essential tremor and Parkinson's disease with grouped stability analysis based on searchlight-based MVPA

BACKGROUND

Since both essential tremor (ET) and Parkinson's disease (PD) are movement disorders and share similar clinical symptoms, it is very difficult to recognize the differences in the presentation, course, and treatment of ET and PD, which leads to misdiagnosed commonly.

PURPOSE

Although neuroimaging biomarker of ET and PD has been investigated based on statistical analysis, it is unable to assist the clinical diagnosis of ET and PD and ensure the efficiency of these biomarkers. The aim of the study was to identify the neuroimaging biomarkers of ET and PD based on structural magnetic resonance imaging (MRI). Moreover, the study also distinguished ET from PD via these biomarkers to validate their classification performance.

METHODS

This study has developed and implemented a three-level machine learning framework to identify and distinguish ET and PD. First of all, at the model-level assessment, the searchlight-based machine learning method has been used to identify the group differences of patients (ET/PD) with normal controls (NCs). And then, at the feature-level assessment, the stability of group differences has been tested based on structural brain atlas separately using the permutation test to identify the robust neuroimaging biomarkers. Furthermore, the identified biomarkers of ET and PD have been applied to classify ET from PD based on machine learning techniques. Finally, the identified biomarkers have been compared with the previous findings of the biology-level assessment.

RESULTS

According to the biomarkers identified by machine learning, this study has found widespread alterations of gray matter (GM) for ET and large overlap between ET and PD and achieved superior classification performance (PCA + SVM, accuracy = 100%).

CONCLUSIONS

This study has demonstrated the significance of a machine learning framework to identify and distinguish ET and PD. Future studies using a large data set are needed to confirm the potential clinical application of machine learning techniques to discern between PD and ET.

Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence

Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics texture features describe the interrelationship between voxel intensities across multiple spatial scales capturing the hidden information of underlying disease dynamics in addition to volumetric changes. Structural MR images were acquired from 77 first-episode psychosis (FEP) patients, 58 clinical high-risk subjects with no later transition to psychosis (CHR_NT), 15 clinical high-risk subjects with later transition (CHR_T), and 44 healthy controls (HC). Radiomics texture features were extracted from non-segmented images, and two-classification schemas were performed for the identification of FEP vs. HC and FEP vs. CHR_NT. The group of CHR_T was used as external validation in both schemas. The classification of a subject's clinical status was predicted by importing separately (a) the difference of entropy feature map and (b) the contrast feature map, resulting in classification balanced accuracy above 72% in both analyses. The proposed framework enhances the classification decision for FEP, CHR_NT, and HC subjects, verifies diagnosis-relevant features and may potentially contribute to identification of structural biomarkers for psychosis, beyond and above volumetric brain changes.

Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

Neuropsychiatric symptoms associated multimodal brain networks in Alzheimer's disease

Concomitant neuropsychiatric symptoms (NPS) are associated with accelerated Alzheimer's disease (AD) progression. Identifying multimodal brain imaging patterns associated with NPS may help understand pathophysiology correlates AD. Based on the AD continuum, a supervised learning strategy was used to guide four-way multimodal neuroimaging fusion (Amyloid, Tau, gray matter volume, brain function) by using NPS total score as the reference. Loadings of the identified multimodal patterns were compared across the AD continuum. Then, regression analyses were performed to investigate its predictability of longitudinal cognition performance. Furthermore, the fusion analysis was repeated in the four NPS subsyndromes. Here, an NPS-associated pathological-structural-functional covaried pattern was observed in the frontal-subcortical limbic circuit, occipital, and sensor-motor region. Loading of this multimodal pattern showed a progressive increase with the development of AD. The pattern significantly correlates with multiple cognitive domains and could also predict longitudinal cognitive decline. Notably, repeated fusion analysis using subsyndromes as references identified similar patterns with some unique variations associated with different syndromes. Conclusively, NPS was associated with a multimodal imaging pattern involving complex neuropathologies, which could effectively predict longitudinal cognitive decline. These results highlight the possible neural substrate of NPS in AD, which may provide guidance for clinical management.

Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection

Major depressive disorder (MDD) is one of the most common mental health disorders that can affect sleep, mood, appetite, and behavior of people. Multimodal neuroimaging data, such as functional and structural magnetic resonance imaging (MRI) scans, have been widely used in computer-aided detection of MDD. However, previous studies usually treat these two modalities separately, without considering their potentially complementary information. Even though a few studies propose integrating these two modalities, they usually suffer from significant inter-modality data heterogeneity. In this paper, we propose an adaptive multimodal neuroimage integration (AMNI) framework for automated MDD detection based on functional and structural MRIs. The AMNI framework consists of four major components: (1) a graph convolutional network to learn feature representations of functional connectivity networks derived from functional MRIs, (2) a convolutional neural network to learn features of T1-weighted structural MRIs, (3) a feature adaptation module to alleviate inter-modality difference, and (4) a feature fusion module to integrate feature representations extracted from two modalities for classification. To the best of our knowledge, this is among the first attempts to adaptively integrate functional and structural MRIs for neuroimaging-based MDD analysis by explicitly alleviating inter-modality heterogeneity. Extensive evaluations are performed on 533 subjects with resting-state functional MRI and T1-weighted MRI, with results suggesting the efficacy of the proposed method.

Cross-Sectional Investigation of Brain Volume in Dyslexia

The goal of the study was to determine whether dyslexia is associated with differences in local brain volume, and whether these local brain volume differences show cross-sectional age-effects. We investigated the local volume of gray and white brain matter with voxel-based morphometry (VBM) as well as reading performance in three age groups of dyslexic and neurotypical normal reading subjects (children, teenagers and adults). Performance data demonstrate a steady improvement of reading skills in both neurotypical as well as dyslexic readers. However, the pattern of gray matter volumes tell a different story: the children are the only group with significant differences between neurotypical and dyslexic readers in local gray matter brain volume. These differences are localized in brain areas associated with the reading network (angular, middle temporal and inferior temporal gyrus as well as the cerebellum). Yet the comparison of neurotypical and normal readers over the age groups shows that the steady increase in performance in neurotypical readers is accompanied by a steady decrease of gray matter volume, whereas the brain volumes of dyslexic readers do not show this linear correlation between brain volume and performance. This is further evidence that dyslexia is a disorder with a neuroanatomical basis in the form of a lower volume of gray matter in parts of the reading network in early dyslexic readers. The present data point out that network shaping processes in gray matter volume in the reading network does take place over age in dyslexia. Yet this neural foundation does not seem to be sufficient to allow normal reading performances even in adults with dyslexia. Thus dyslexia is a disorder with lifelong consequences, which is why consistent support for affected individuals in their educational and professional careers is of great importance. Longitudinal studies are needed to verify whether this holds as a valid pattern or whether there is evidence of greater interindividual variance in the neuroanatomy of dyslexia.

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large 'sample sizes'. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.

Interactive effects of polygenic risk and cognitive subtype on brain morphology in schizophrenia spectrum and bipolar disorders

Grey matter volume (GMV) may be associated with polygenic risk for schizophrenia (PRS-SZ) and severe cognitive deficits in people with schizophrenia, schizoaffective disorder (collectively SSD), and bipolar disorder (BD). This study examined the interactive effects of PRS-SZ and cognitive subtypes of SSD and BD in relation to GMV. Two-step cluster analysis was performed on 146 clinical cases (69 SSD and 77 BD) assessed on eight cognitive domains (verbal and visual memory, executive function, processing speed, visual processing, language ability, working memory, and planning). Among them, 55 BD, 51 SSD, and 58 healthy controls (HC), contributed to focal analyses of the relationships between cognitive subtypes, PRS-SZ and their interaction on GMV. Two distinct cognitive subtypes were evident among the combined sample of cases: a 'cognitive deficit' group (CD; N = 31, 20SSD/11BD) showed severe impairment across all cognitive indices, and a 'cognitively spared' (CS; N = 75; 31SSD/44BD) group showed intermediate cognitive performance that was significantly worse than the HC group but better than the CD subgroup. A cognitive subgroup-by-PRS-SZ interaction was significantly associated with GMV in the left precentral gyrus. Moderation analyses revealed a significant negative relationship between PRS-SZ and GMV in the CD group only. At low and average (but not high) PRS-SZ, larger precentral GMV was evident in the CD group compared to both CS and HC groups, and in the CS group compared to HCs. This study provides evidence for a relationship between regional GMV changes and PRS-SZ in psychosis spectrum cases with cognitive deficits, but not in cases cognitively spared.

Clinicoanatomical substrates of selfish behaviour in amyotrophic lateral sclerosis - An observational cohort study

OBJECTIVE

ALS primarily affects motor functions, but cognitive functions, including social understanding, may also be impaired. Von Economo neurons (VENs) are part of the neuronal substrate of social understanding and these cells are histopathologically altered in ALS. We investigated whether activity in areas including VENs is associated with an impairment of cognitive tasks that mirror social functioning.

METHODS

In this observational prospective study, ALS patients (N = 26) were tested for cognitive behavioural function, encompassing different aspects of empathetic understanding (interpersonal reactivity index, IRI), social behaviour (ultimatum game), recognition of faux-pas situations, and general cognitive functioning (Edinburgh Cognitive and Behavioural ALS Screen, ECAS). For in vivo pathological staging according to Braak, DTI-MRI was performed to determine those ALS patients with expected pathological involvement of VENs (B ALS stages 3 + 4) compared to those without (B ALS stages 1 + 2). Expected hypometabolism of cerebral areas was determined with 18F-FDG PET in N = 20 ALS patients and compared to N = 20 matched healthy controls. Volume of interest analysis was performed in the anterior cingulate cortex (ACC) and the anterior insular cortex (AIC), which contain high numbers of VENs.

RESULTS

Compared to those without expected pathological involvement of VENs (B/B ALS stages 1 + 2), ALS patients with anticipated pathological involvement of VENs (B/B ALS stages 3 + 4) presented with significantly reduced fantasy to understand the mindset of others (IRI) and, social behaviour was more selfish (ultimatum game) despite the fact that cognitive understanding of socially inappropriate behaviour of others (faux-pas) was unimpaired. 18F-FDG-PET showed hypometabolism in ACC and AIC in ALS patients with anticipated pathological involvement of VENs compared to those without and this was significantly correlated to cognitive-behavioral functions in certain tasks.

CONCLUSION

Here, we present evidence of altered social behaviour in ALS patients associated with regional 18FDG-PET hypometabolism in areas with a high density of VENs, thereby suggesting a possible causal association.

Schizotypy, childhood trauma and brain morphometry

BACKGROUND

Childhood trauma confers risk for psychosis and is associated with increased 'schizotypy' (a multi-dimensional construct reflecting risk for psychosis in the general population). Structural brain alterations are associated with both childhood trauma and schizotypy, but the potential role of trauma exposure in moderating associations between schizotypy and brain morphology has yet to be determined.

METHODS

Participants were 160 healthy individuals (mean age: 40.08 years, SD = 13.64, range 18-64; 52.5% female). Childhood trauma exposure was assessed using the Childhood Adversity Questionnaire, and schizotypy was assessed using the Schizotypal Personality Questionnaire. Univariate voxel-based morphometry and multivariate analyses of grey matter volume covariation (GMC; derived from independent component analysis) were performed to determine the main effects of schizotypy, trauma exposure and their interaction on these indices of grey matter volume. Moderation analyses were performed following significant interaction.

RESULTS

Levels of schizotypy, in particular the Cognitive-Perceptual and Interpersonal dimensions, were negatively associated with GMC in the striatum, the hippocampus/parahippocampal gyrus, thalamus and insulae. Trauma exposure was negatively associated with GMC of the middle frontal gyrus and parietal lobule, while negatively associated with GMC in the cerebellum. Levels of schizotypy (total scores, and the cognitive-perceptual dimension) were negatively associated with striatal GMC in individuals not exposed to trauma, but not in those exposed to trauma.

CONCLUSIONS

Schizotypy and childhood trauma were independently associated with changes of grey matter in brain regions critical for cognition and social cognition. In individuals not exposed to trauma, increased schizotypy was associated with decreased striatal and limbic grey matter.

Human time perspective and its structural associations with voxel-based morphometry and gyrification

Time perspective refers to humans' concept of integrating and evaluating temporal position and evaluation of memories, emotions, and experiences. We tested the hypothesis that different aspects of time perspective, as assessed with the Zimbardo Time Perspective Inventory (ZTPI) are related to variation of brain structure in non-clinical subjects. Analysing data from n = 177 psychiatrically healthy subjects using voxel-based morphometry with the CAT12 software package, we identified several significant (p < 0.05 FWE, cluster-level corrected) associations. The factors past negative, reflecting a negative attitude towards past events and present fatalistic, measuring a hopeless and fatalistic attitude towards future life, were both negatively associated with grey matter volumes of the anterior insula. The ZTPI factor future was negatively associated with precuneus grey matter. There was no association of ZTPI scores with gyrification using an absolute mean curvature method, a marker of early brain development. These findings provide a link between a general psychological construct of time perspective and brain structural variations in key areas related to time keeping (anterior insula) and the default mode network (precuneus), both of which overlap with variation in behavioral aspects and psychopathology.

Polygenic Hazard Score Associated Multimodal Brain Networks Along the Alzheimer's Disease Continuum

BACKGROUND

Late-onset Alzheimer's disease (AD) is a polygenic neurodegenerative disease. Identifying the neuroimaging phenotypes behind the genetic predisposition of AD is critical to the understanding of AD pathogenesis. Two major questions which previous studies have led to are: (1) should the general "polygenic hazard score" (PHS) be a good choice to identify the individual genetic risk for AD; and (2) should researchers also include inter-modality relationships in the analyses considering these may provide complementary information about the AD etiology.

METHODS

We collected 88 healthy controls, 77 patients with mild cognitive impairment (MCI), and 22 AD patients to simulate the AD continuum included from the ADNI database. PHS-guided multimodal fusion was used to investigate the impact of PHS on multimodal brain networks in AD-continuum by maximizing both inter-modality association and reference-modality correlation. Fractional amplitude of low frequency fluctuations, gray matter (GM) volume, and amyloid standard uptake value ratios were included as neuroimaging features. Eventually, the changes in neuroimaging features along AD continuum were investigated, and relationships between cognitive performance and identified PHS associated multimodal components were established.

RESULTS

We found that PHS was associated with multimodal brain networks, which showed different functional and structural impairments under increased amyloid deposits. Notably, along with AD progression, functional impairment occurred before GM atrophy, amyloid deposition started from the MCI stage and progressively increased throughout the disease continuum.

CONCLUSION

PHS is associated with multi-facets of brain impairments along the AD continuum, including cognitive dysfunction, pathological deposition, which might underpin the AD pathogenesis.

Narcissistic personality traits and prefrontal brain structure

Narcissistic traits have been linked to structural and functional brain networks, including the insular cortex, however, with inconsistent findings. In this study, we tested the hypothesis that subclinical narcissism is associated with variations in regional brain volumes in insular and prefrontal areas. We studied 103 clinically healthy subjects, who were assessed for narcissistic traits using the Narcissistic Personality Inventory (NPI, 40-item version) and received high-resolution structural magnetic resonance imaging. Voxel-based morphometry was used to analyse MRI scans and multiple regression models were used for statistical analysis, with threshold-free cluster enhancement (TFCE). We found significant (p < 0.05, family-wise error FWE corrected) positive correlations of NPI scores with grey matter in multiple prefrontal cortical areas (including the medial and ventromedial, anterior/rostral dorsolateral prefrontal and orbitofrontal cortices, subgenual and mid-anterior cingulate cortices, insula, and bilateral caudate nuclei). We did not observe reliable links to particular facets of NPI-narcissism. Our findings provide novel evidence for an association of narcissistic traits with variations in prefrontal and insular brain structure, which also overlap with previous functional studies of narcissism-related phenotypes including self-enhancement and social dominance. However, further studies are needed to clarify differential associations to entitlement vs. vulnerable facets of narcissism.

Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions

Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture measures such as entropy and contrast, capturing the neighboring variation of MRI voxel intensities, were computed and fed into deep learning technique for group classification. Layer-wise relevance was applied for the localization of the classification results. Texture feature map of non-segmented brain MRI scans were extracted from 141 SCZ, 103 MD and 238 HC. The gray level co-occurrence matrix (GLCM) was calculated on a voxel-by-voxel basis in a cube of voxels. Deep learning tested if texture feature map could predict diagnostic group membership of three classes under a binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method was applied in a repeated nested cross-validation scheme and cross-validated feature selection. The regions with the highest relevance (positive/negative) are presented. The method was applied on non-segmented images reducing the computation complexity and the error associated with segmentation process.

Neuroanatomical correlates of self-awareness of highly practiced visuomotor skills

Metacognition is the ability to introspect and control ongoing cognitive processes. Despite the extensive investigation of the brain architectures supporting metacognition for perception and memory, little is known about the neural basis of metacognitive capacity for motor function, a vital aspect of human behavior. Here, using functional and structural magnetic resonance imaging (MRI), we examined the brain substrates underlying self-awareness of handwriting, a highly practiced visuomotor skill. Results showed that experienced adult writers generally overestimated their handwriting quality, and such overestimation was more pronounced in men relative to women. Individual variations in self-awareness of handwriting quality were positively correlated with gray matter volume in the left fusiform gyrus, right middle frontal gyrus and right precuneus. The left fusiform gyrus and right middle frontal gyrus are thought to represent domain-specific brain mechanisms for handwriting self-awareness, while the right precuneus that has been reported in other domains likely represents a domain-general brain mechanism for metacognition. Furthermore, the activity of these structurally related regions in a handwriting task was not correlated with self-awareness of handwriting, suggesting the correlation with metacognition was independent of task performance. Together, this study reveals that metacognition for practiced motor skills relies on both domain-general and domain-specific brain systems, extending our understanding about the neural basis of human metacognition.

Development of sex differences in the human brain

Sex differences in brain anatomy have been described from early childhood through late adulthood, but without any clear consensus among studies. Here, we applied a machine learning approach to estimate 'Brain Sex' using a continuous (rather than binary) classifier in 162 boys and 185 girls aged between 5 and 18 years. Changes in the estimated sex differences over time at different age groups were subsequently calculated using a sliding window approach. We hypothesized that males and females would differ in brain structure already during childhood, but that these differences will become even more pronounced with increasing age, particularly during adolescence. Overall, the classifier achieved a good performance, with an accuracy of 80.4% and an AUC of 0.897 across all age groups. Assessing changes in the estimated sex with age revealed a growing difference between the sexes with increasing age. That is, the very large effect size of d = 1.2 which was already evident during childhood increased even further from age 11 onward, and eventually reached an effect size of d = 1.6 at age 17. Altogether these findings suggest a systematic sex difference in brain structure already during childhood, and a subsequent increase of this difference during adolescence.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder

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.

Metabolic and amyloid PET network reorganization in Alzheimer's disease: differential patterns and partial volume effects

Alzheimer’s disease (AD) is a neurodegenerative disorder, considered a disconnection syndrome with regional molecular pattern abnormalities quantifiable by the aid of PET imaging. Solutions for accurate quantification of network dysfunction are scarce. We evaluate the extent to which PET molecular markers reflect quantifiable network metrics derived through the graph theory framework and how partial volume effects (PVE)-correction (PVEc) affects these PET-derived metrics 75 AD patients and 126 cognitively normal older subjects (CN). Therefore our goal is twofold: 1) to evaluate the differential patterns of [¹⁸F]FDG- and [¹⁸F]AV45-PET data to depict AD pathology; and ii) to analyse the effects of PVEc on global uptake measures of [¹⁸F]FDG- and [¹⁸F]AV45-PET data and their derived covariance network reconstructions for differentiating between patients and normal older subjects. Network organization patterns were assessed using graph theory in terms of “degree”, “modularity”, and “efficiency”. PVEc evidenced effects on global uptake measures that are specific to either [¹⁸F]FDG- or [¹⁸F]AV45-PET, leading to increased statistical differences between the groups. PVEc was further shown to influence the topological characterization of PET-derived covariance brain networks, leading to an optimised characterization of network efficiency and modularisation. Partial-volume effects correction improves the interpretability of PET data in AD and leads to optimised characterization of network properties for organisation or disconnection.

Systemic inflammation and grey matter volume in schizophrenia and bipolar disorder: Moderation by childhood trauma severity

BACKGROUND

Elevated levels of systemic inflammation are consistently reported in both schizophrenia (SZ) and bipolar-I disorder (BD), and are associated with childhood trauma exposure. We tested whether childhood trauma exposure moderates associations between systemic inflammation and brain morphology in people with these diagnoses.

METHODS

Participants were 55 SZ cases, 52 BD cases and 59 healthy controls (HC) who underwent magnetic resonance imaging. Systemic inflammation was measured using a composite z-score derived from serum concentrations of interleukin 6, tumor necrosis factor alpha and C-reactive protein. Indices of grey matter volume covariation (GMC) were derived from independent component analysis. Childhood trauma was measured using the Childhood Trauma Questionnaire (CTQ Total score).

RESULTS

A series of moderated moderation analyses indicated that increased systemic inflammation were associated with increased GMC in the striatum and cerebellum among all participants. Severity of childhood trauma exposure moderated the relationship between systemic inflammation and GMC in one component, differently among the groups. Specifically, decreased GMC in the PCC/precuneus, parietal lobule and postcentral gyrus, and increased GMC in the left middle temporal gyrus was associated with increased systemic inflammation in HC individuals exposed to high (but not low or average) levels of trauma and in SZ cases exposed to low (but not average or high) levels of trauma, but not in BD cases.

CONCLUSIONS

Increased systemic inflammation is associated with grey matter changes in people with psychosis, and these relationships may be partially and differentially moderated by childhood trauma exposure according to diagnosis.

When your brain looks older than expected: combined lifestyle risk and BrainAGE

Lifestyle may be one source of unexplained variance in the great interindividual variability of the brain in age-related structural differences. While physical and social activity may protect against structural decline, other lifestyle behaviors may be accelerating factors. We examined whether riskier lifestyle correlates with accelerated brain aging using the BrainAGE score in 622 older adults from the 1000BRAINS cohort. Lifestyle was measured using a combined lifestyle risk score, composed of risk (smoking, alcohol intake) and protective variables (social integration and physical activity). We estimated individual BrainAGE from T1-weighted MRI data indicating accelerated brain atrophy by higher values. Then, the effect of combined lifestyle risk and individual lifestyle variables was regressed against BrainAGE. One unit increase in combined lifestyle risk predicted 5.04 months of additional BrainAGE. This prediction was driven by smoking (0.6 additional months of BrainAGE per pack-year) and physical activity (0.55 less months in BrainAGE per metabolic equivalent). Stratification by sex revealed a stronger association between physical activity and BrainAGE in males than females. Overall, our observations may be helpful with regard to lifestyle-related tailored prevention measures that slow changes in brain structure in older adults.

Longitudinal investigation of cognitive deficits in breast cancer patients and their gray matter correlates: impact of education level

Cognitive deficits are a major complaint in breast cancer patients, even before chemotherapy. Comprehension of the cerebral mechanisms related to cognitive impairment in breast cancer patients remains difficult due to the scarcity of studies investigating both cognitive and anatomical imaging changes. Furthermore, only some of the patients experienced cognitive decline following chemotherapy, yet few studies have identified risk factors for cognitive deficits in these patients. It has been shown that education level could impact cognitive abilities during the recovery phase following chemotherapy. Our main aim was to longitudinally evaluate cognitive and anatomical changes associated with cancer and chemotherapy in breast cancer patients. Our secondary aim was to assess the impact of education level on cognitive performances and gray matter (GM) atrophy in these patients. Twenty patients were included before chemotherapy (T1), 1 month (T2) and 1 year (T3) after chemotherapy. Twenty-seven controls without a history of cancer were assessed at T1 and T3 only. Cluster groups based on education level were defined for both groups and were further compared. Comparison between patients and controls revealed deficits in patients on verbal episodic memory retrieval at T1 and T3 and on executive functions at T3. After chemotherapy, breast cancer patients had GM atrophy that persisted or recovered 1 year after chemotherapy depending on the cortical areas. Increase in GM volumes from T1 to T3 were also found in both groups. At T2, patients with a higher level of education compared to lower level exhibited higher episodic memory retrieval and state anxiety scores, both correlating with cerebellar volume. This higher level of education group exhibited hippocampal atrophy. Our results suggest that, before chemotherapy, cancer-related processes impact cognitive functioning and that this impact seems exacerbated by the effect of chemotherapy on certain brain regions. Increase in GM volumes after chemotherapy were unexpected and warrant further investigations. Higher education level was associated, 1 month after the end of chemotherapy, with greater anxiety and hippocampal atrophy despite a lack of cognitive deficits. These results suggest, for the first time, the occurrence of compensation mechanisms that may be linked to cognitive reserve in relationship to state anxiety. This identification of factors, which may compensate cognitive impairment following chemotherapy, is critical for patient care and quality of life.

Subclinical Cognitive and Neuropsychiatric Correlates and Hippocampal Volume Features of Brain White Matter Hyperintensity in Healthy People

White matter hyperintensities (WMH) are associated with brain aging and behavioral symptoms as a possible consequence of disrupted white matter pathways. In this study, we investigated, in a cohort of asymptomatic subjects aged 50 to 80, the relationship between WMH, hippocampal atrophy, and subtle, preclinical cognitive and neuropsychiatric phenomenology. Thirty healthy subjects with WMH (WMH+) and thirty individuals without (WMH−) underwent comprehensive neuropsychological and neuropsychiatric evaluations and 3 Tesla Magnetic Resonance Imaging scan. The presence, degree of severity, and distribution of WMH were evaluated with a semi-automated algorithm. Volumetric analysis of hippocampal structure was performed through voxel-based morphometry. A multivariable logistic regression analysis indicated that phenomenology of subclinical apathy and anxiety was associated with the presence of WMH. ROI-based analyses showed a volume reduction in the right hippocampus of WMH+. In healthy individuals, WMH are associated with significant preclinical neuropsychiatric phenomenology, as well as hippocampal atrophy, which are considered as risk factors to develop cognitive impairment and dementia.

Shape and Volumetric Differences in the Corpus Callosum between Patients with Major Depressive Disorder and Healthy Controls

OBJECTIVE

This study aimed to investigate the morphometric differences in the corpus callosum between patients with major depressive disorder (MDD) and healthy controls and analyze their relationship to gray matter changes.

METHODS

Twenty female MDD patients and 21 healthy controls (HCs) were included in the study. To identify the difference in the regional gray matter concentration (GMC), VBM was performed with T1 magnetic resonance imaging. The shape analysis of the corpus callosum was processed. Diffusion tensor imaging (DTI) fiber-tracking was performed to identify the regional tract pathways in the damaged corpus callosal areas.

RESULTS

In the shape analysis, regional shape contractions in the rostrum and splenium were found in the MDD patients. VBM analysis showed a significantly lower white matter concentration in the genu and splenium, and a significantly lower GMC in the frontal, limbic, insular, and temporal regions of the MDD patients compared to the HCs. In DTI fiber-tracking, the fibers crossing the damaged areas of the genu, rostrum, and splenium were anatomically connected to the areas of lower GMC in MDD patients.

CONCLUSION

These findings support that major depressive disorder may be due to disturbances in multiple neuronal circuits, especially those associated with the corpus callosum.

Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.

Higher impulsivity and lower grey matter volume in the bilateral prefrontal cortex in long-term abstinent individuals with severe methamphetamine use disorder

BACKGROUND

Previous studies have shown that grey matter volume (GMV) might be lower in individuals with methamphetamine use disorder and that dynamic alterations in selected brain regions might appear in individuals after short-term abstinence. However, the GMV of brains in these individuals after long-term abstinence is poorly understood. Moreover, individuals with severe methamphetamine use disorder have been considered to have high levels of impulsivity, but the biological mechanism is still unclear.

METHODS

In this study, the impulsivity of all participants was assessed using the Barratt Impulsiveness Scale (BIS-11). Using voxel-based morphometry (VBM) in conjunction with statistical parametric mapping on structural magnetic resonance images, the GMVs of the whole brain were compared among 32 drug-naïve healthy controls (HC) and 40 individuals with severe methamphetamine use disorder who had been abstinent for at least 20 months (SMUD-A).

RESULTS

We observed significantly higher BIS-11 impulsivity scores and lower GMV in the bilateral superior frontal cortex of SMUD-A individuals than in those of control subjects. The impulsivity score was negatively correlated with GMV in the right superior frontal cortex.

CONCLUSIONS

These findings offer novel evidence with respect to the impulsivity trait and brain GMV feature in long-term abstinent individuals with severe methamphetamine use disorder. Moreover, our findings suggest that lower GMV in the right superior frontal cortex might reflect a trait marker of higher impulsivity in this population.

Speaking of aging: Changes in gray matter asymmetry in Broca's area in later adulthood

Several theories suggest a change in the brain's asymmetry as we get older. However, it is currently unresolved whether Broca's area, consisting of left Brodmann Areas (BA) 45 and 44, undergoes age-related changes. To address this question, we mapped associations between chronological age and gray matter asymmetry of BA45 and BA44 in a large sample (n = 485) of adults ranging between 42 and 97 years of age. Hemisphere-specific gray matter volumes and asymmetry indices were obtained by integrating cytoarchitectonic probabilities with MRI-based signal intensities. For BA44, we did not observe any significant correlation between age and gray matter asymmetry. In contrast, for BA45, the analysis revealed a significant correlation, which indicates a decreasing asymmetry from rightward to less rightward with increasing age. A subsequent characterization of hemisphere-specific volume loss revealed significant negative associations between age and gray matter volume for left and right BA45, but with weaker effects in the left hemisphere compared to the right. These findings seem to support the assumption of reduced structural asymmetries later in life, at least for BA45, which seem to be driven by a stronger tissue loss in the right hemisphere than the left hemisphere.

Segmentation of MR Brain Images Through Hidden Markov Random Field and Hybrid Metaheuristic Algorithm

Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.

The impact of aging on the subregions of the fusiform gyrus in healthy older adults

The fusiform gyrus is known to decrease in size with increasing age. However, reported findings are inconsistent and existing studies differ in terms of the cohorts examined and/or the methods applied. Here, we analyzed age-related links in four distinct subregions of the fusiform gyrus through integrating imaging-based intensity information with microscopically defined cytoarchitectonic probabilities. In addition to age effects we investigated sex effects as well as age-by-sex interactions in a relatively large sample of 468 healthy subjects (272 females/196 males) covering a broad age range (42-97 years). We observed significant negative correlations between age and all four subregions of the fusiform gyrus indicating volume decreases over time, albeit with subregion-specific trajectories. Additionally, we observed significant negative quadratic associations with age for some subregions, suggesting an accelerating volume loss over time. These findings may serve as a frame of reference for future cross-sectional as well as longitudinal studies, not only for normative samples but also potentially for clinical conditions that present with abnormal atrophy of the fusiform gyrus. We did not detect any significant sex differences or sex-by-age interactions, suggesting that the size of the fusiform gyrus is similar in male and female brains and that age-related atrophy follows a similar trajectory in both men and women.

Bariatric Surgery and Brain Health-A Longitudinal Observational Study Investigating the Effect of Surgery on Cognitive Function and Gray Matter Volume

Dietary modifications leading to weight loss have been suggested as a means to improve brain health. In morbid obesity, bariatric surgery (BARS)-including different procedures, such as vertical sleeve gastrectomy (VSG), gastric banding (GB), or Roux-en-Y gastric bypass (RYGB) surgery-is performed to induce rapid weight loss. Combining reduced food intake and malabsorption of nutrients, RYGB might be most effective, but requires life-long follow-up treatment. Here, we tested 40 patients before and six months after surgery (BARS group) using a neuropsychological test battery and compared them with a waiting list control group. Subsamples of both groups underwent structural MRI and were examined for differences between surgical procedures. No substantial differences between BARS and control group emerged with regard to cognition. However, larger gray matter volume in fronto-temporal brain areas accompanied by smaller volume in the ventral striatum was seen in the BARS group compared to controls. RYGB patients compared to patients with restrictive treatment alone (VSG/GB) had higher weight loss, but did not benefit more in cognitive outcomes. In sum, the data of our study suggest that BARS might lead to brain structure reorganization at long-term follow-up, while the type of surgical procedure does not differentially modulate cognitive performance.

Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?

With the aging population, prevalence of neurodegenerative diseases is increasing, thus placing a growing burden on individuals and the whole society. However, individual rates of aging are shaped by a great variety of and the interactions between environmental, genetic, and epigenetic factors. Establishing biomarkers of the neuroanatomical aging processes exemplifies a new trend in neuroscience in order to provide risk-assessments and predictions for age-associated neurodegenerative and neuropsychiatric diseases at a single-subject level. The "Brain Age Gap Estimation (BrainAGE)" method constitutes the first and actually most widely applied concept for predicting and evaluating individual brain age based on structural MRI. This review summarizes all studies published within the last 10 years that have established and utilized the BrainAGE method to evaluate the effects of interaction of genes, environment, life burden, diseases, or life time on individual neuroanatomical aging. In future, BrainAGE and other brain age prediction approaches based on structural or functional markers may improve the assessment of individual risks for neurological, neuropsychiatric and neurodegenerative diseases as well as aid in developing personalized neuroprotective treatments and interventions.

Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation

Magnetic resonance imaging (MRI) is extensively applied in clinical practice. Segmentation of the MRI brain image is significant to the detection of brain abnormalities. However, owing to the coexistence of intensity inhomogeneity and noise, dividing the MRI brain image into different clusters precisely has become an arduous task. In this paper, an improved possibilistic fuzzy c -means (FCM) method based on a similarity measure is proposed to improve the segmentation performance for MRI brain images. By introducing the new similarity measure, the proposed method is more effective for clustering the data with nonspherical distribution. Besides that, the new similarity measure could alleviate the "cluster-size sensitivity" problem that most FCM-based methods suffer from. Simultaneously, the proposed method could preserve image details as well as suppress image noises via the use of local label information. Experiments conducted on both synthetic and clinical images show that the proposed method is very effective, providing mitigation to the cluster-size sensitivity problem, resistance to noisy images, and applicability to data with more complex distribution.

Partial volume mapping using magnetic resonance fingerprinting

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction. The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error. Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient. PV-MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T₁ resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi-automatically constructed in vivo using k-means clustering of MRF-mapped relaxation times. Dictionary-based PV-MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV-MRF could also estimate partial volumes of solid tumor and peritumoral edema. We conclude that PV-MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.

Previous glucocorticoid treatment in childhood and adolescence is associated with long-term differences in subcortical grey matter volume and microstructure

BACKGROUND

Glucocorticoids are widely used in the treatment of several pediatric diseases with undisputed disease-related benefits. Perinatal exposure to high levels of glucocorticoids can have long-term adverse cerebral effects. In adults, glucocorticoid treatment has been associated with smaller volumes of subcortical grey matter structures. Recently, we observed smaller total brain volumes in children and adolescents treated with glucocorticoid during childhood compared to healthy controls. The possible long-term effects of glucocorticoid treatment during childhood on subcortical brain volume and microstructure remain unknown.

METHOD

We examined 30 children and adolescents, who had previously been treated with glucocorticoids for nephrotic syndrome or rheumatic disease, and 30 healthy volunteers. Patients and healthy control groups were matched by age, gender, and level of parent education. Participants underwent 3 T magnetic resonance (MR) brain imaging. T1-weighted and diffusion-weighted images were acquired. Volume and mean diffusivity (MD) measures were extracted for hippocampus, amygdala, nucleus accumbens, caudate nucleus and putamen. Multiple linear regression analyses were used to assess differences between patients and controls, and to evaluate possible dose-response relationships. A priori, we expected patients to display lower hippocampal and amygdala volumes.

RESULTS

While children previously treated with glucocorticoids displayed smaller right hippocampal volumes than controls, this difference did not survive correction for multiple comparisons. Furthermore, patients as compared to controls showed lower right hippocampal MD, which remained when correcting for global changes in MD. The longer the time between the glucocorticoid treatment termination and MR-scan, the more right hippocampal MD values resembled that of healthy controls. Patient and controls did not differ in amygdala volume or MD. Analyses of the nucleus accumbens, caudate nucleus and putamen only revealed smaller putamen volumes in patients compared to controls, which remained significant when controlling for total brain volume.

CONCLUSION

The results suggest that extra-cerebral diseases during childhood treated with glucocorticoids may be associated with reduced subcortical grey matter volumes and lower right hippocampal mean diffusivity later in life. Our findings warrant replication and elaboration in larger, preferably prospective and longitudinal studies. Such studies may also allow disentangling disease-specific effects from possible glucocorticoid treatment effects.

Functional and structural alterations in the migraine cerebellum

The cerebellum plays an important role in pain processing but its function in headache and specifically in migraine is not known. We therefore compared 54 migraineurs with pairwise matched healthy controls in a magnetic resonance imaging study on neuronal cerebellar activity in response to nociceptive trigeminal sensation and also investigated possible structural alterations. Headache frequency, disease duration, and the proximity to a migraine attack were used as co-factors. Migraine patients showed functional and structural alterations in the posterior part of the cerebellum, namely crus I and crus II. Gray matter volume changes were seen on the right side whereas functional changes were ipsilateral to the stimulation, on the left side. Neuronal activity in the crus in response to trigeminal pain was modulated by migraine severity and the migraine phase. As the crus is strongly interconnected to higher cognitive areas in the temporal, frontal, and parietal part of the cortex our results suggest an specific cerebellar involvement in migraine. This is further supported by our finding of decreased connectivity from the crus to the thalamus and higher cortical areas in the patients. We therefore suggest an abnormally decreased inhibitory involvement of the migraine cerebellum on gating and nociceptive evaluation.

Altered grey matter volume, perfusion and white matter integrity in very low birthweight adults

This study examined the long-term effects of being born very-low-birth-weight (VLBW, <1500 g) on adult cerebral structural development using a multi-method neuroimaging approach. The New Zealand VLBW study cohort comprised 413 individuals born VLBW in 1986. Of the 338 who survived to discharge, 229 were assessed at age 27–29 years. Of these, 150 had a 3 T MRI scan alongside 50 healthy term-born controls. The VLBW group included 53/57 participants born <28 weeks gestation. MRI analyses included: a) structural MRI to assess grey matter (GM) volume and cortical thickness; b) arterial spin labelling (ASL) to quantify GM perfusion; and c) diffusion tensor imaging (DTI) to measure white matter (WM) integrity. Compared to controls, VLBW adults had smaller GM volumes within frontal, temporal, parietal and occipital cortices, bilateral cingulate gyri and left caudate, as well as greater GM volumes in frontal, temporal and occipital areas. Thinner cortex was observed within frontal, temporal and parietal cortices. VLBW adults also had less GM perfusion within limited temporal areas, bilateral hippocampi and thalami. Finally, lower fractional anisotropy (FA) and axial diffusivity (AD) within principal WM tracts was observed in VLBW subjects. Within the VLBW group, birthweight was positively correlated with GM volume and perfusion in cortical and subcortical regions, as well as FA and AD across numerous principal WM tracts. Between group differences within temporal cortices were evident across all imaging modalities, suggesting that the temporal lobe may be particularly susceptible to disruption in development following preterm birth. Overall, findings reveal enduring and pervasive effects of preterm birth on brain structural development, with individuals born at lower birthweights having greater long-term neuropathology.

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Very-low-birth-weight adults had smaller GM volumes and thinner cortex than controls.

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VLBW adults also showed regions of larger grey matter volumes and thicker cortex.

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Several small regions showed lower cerebral perfusion in VLBW adults than in controls.

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Diffusion tensor MRI suggested poorer WM integrity in VLBW adults than in controls.

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Within VLBW adults, all MRI measures showed positive associations with birthweight.

Correlation of proinflammatory cytokines levels and reduced gray matter volumes between patients with bipolar disorder and unipolar depression

BACKGROUND

Gray matter volume reduction in specific brain regions, such as the prefrontal cortex, was found in patients with bipolar disorder and those with unipolar depression. However, few studies have directly compared gray matter volumes between bipolar disorder and unipolar depression. In addition, it is unknown whether proinflammatory cytokines play a role in the gray matter volume difference between bipolar disorder and unipolar depression.

METHODS

Twenty-three patients with bipolar disorder and 23 with unipolar depression in a mildly ill state (Clinical Global Impression-Severity ≤ 3) were enrolled in our study. Each participant underwent structural magnetic resonance imaging and proinflammatory cytokines examination. Voxel-based morphometry was performed to investigate the gray matter volume difference between bipolar disorder and unipolar depression. Correlations of the proinflammatory cytokines and the gray matter volume difference were analyzed.

RESULTS

Several brain regions, including the orbitofrontal cortex, lingual gyrus, inferior frontal cortex, middle frontal cortex, and planum polare, were significantly smaller in patients with bipolar disorder than in those with unipolar depression. These gray matter volume differences between bipolar disorder and unipolar depression were negatively correlated with soluble IL-6 receptor levels.

DISCUSSION

Proinflammatory cytokines, especially IL-6, were associated with the gray matter volumes in bipolar disorder and unipolar depression. However, the complicated associations between proinflammatory cytokines, neurocognitive function, and gray matter volumes require further investigation.