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Background: Modules in brain network represent groups of brain regions that are collectively involved in one or more cognitive domains. Exploring aging-related reorganization of the brain modular architecture using metabolic brain network could further our understanding about aging-related neuromechanism and neurodegenerations. Materials and Methods: In this study, 432 subjects who performed 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) were enrolled and divided into young and old adult groups, as well as female and male groups. The modular architecture was detected, and the connector and hub nodes were identified to explore the topological role of the brain regions based on the metabolic brain network. Results: This study revealed that human metabolic brain network was modular and could be clustered into three modules. The modular architecture was reorganized from young to old ages with regions related to sensorimotor function clustered into the same module; and the number of connector nodes was reduced and most connector nodes were localized in temporo-occipital areas related to visual and auditory functions in old ages. The major gender difference is that the metabolic brain network was delineated into four modules in old female group with the nodes related to sensorimotor function split into two modules. Discussion: Those findings suggest aging is associated with reorganized brain modular architecture. Clinical Trial Registration number: ChiCTR2000036842. Impact statement Distinguishing the basic biology underlying aging from that underlying disease is critical for the prevention, diagnosis, and treatment of the aging-related brain disorders. In this study, we tried to uncover aging-related brain modular reorganization by using metabolic brain network. We found the modular architecture was slightly reorganized from young to old ages with regions related to sensorimotor function more converged. The number of connector nodes was reduced and most connector nodes were localized into the temporo-occipital regions. The major gender difference was that metabolic brain network was delineated into four modules in the old female group with the sensorimotor functions split into two modules.

Objective: To explore the changes of entropy index and cerebral oxygen metabolism in the maintenance of remifentanil anesthesia and the predictive value of postoperative hyperalgesia., Methods: A total of 266 patients undergoing general anesthesia in our hospital from January 2020 to October 2021 were selected, and remifentanil was used to maintain anesthesia. The state entropy, reaction entropy, and cerebral oxygen metabolism indexes (cerebral oxygen uptake rate (CERO 2 ), arterial-venous blood oxygen difference (Da-jvO 2 )) of patients before induction of anesthesia, 15 minutes during the operation, and at the end of the operation were compared. The influencing factors of postoperative hyperalgesia were analyzed. The logistic regression model of postoperative hyperalgesia was established, and the value of entropy index and cerebral oxygen metabolism in predicting postoperative hyperalgesia was evaluated by drawing the receiver operating characteristic curve (ROC)., Results: The state entropy, response entropy, and CERO 2 at 30 min during the operation and at the end of the operation were lower than those before the induction of anesthesia, and Da-jvO 2 was higher than that before the induction of anesthesia ( P < 0.001). At the end of the operation, the state entropy, reaction entropy, and CERO 2 were higher than 30 minutes during the operation, and Da-jvO 2 was lower than 30 minutes during the operation ( P < 0.001). The dosage of remifentanil, reaction entropy, and CERO 2 at the end of the operation entered the logistic model. The AUC value of the reaction entropy and CERO 2 combined to predict postoperative hyperalgesia at the end of the operation was 0.851 greater than the reaction entropy at the end of the operation ( χ 2 = 3.847, P = 0.036), CERO 2 ( χ 2 = 2.589, P = 0.010) single index predictive value., Conclusion: The entropy index and cerebral oxygen metabolism in general anesthesia patients change with the progress and discontinuation of remifentanil maintenance anesthesia, and the combination of the two has a high predictive power in postoperative hyperalgesia risk assessment. When the reaction entropy > 54.23, CERO 2 > 34.14%, or the total dosage of remifentanil ≥ 30  μ g/kg at the end of the operation, we should be highly vigilant of the occurrence of postoperative hyperalgesia and postoperative analgesia management should be strengthened., Competing Interests: The authors declare no competing interests., (Copyright © 2022 Tianhao Zhang and Fang Ma.)

Purpose: The human brain develops rapidly from infant to adolescent. Establishment of the brain developmental trajectory is important to understand cognition, behavior, and emotions, as well to evaluate the risk of neuropsychiatric disorders. 18 F-FDG PET has been widely used to study brain glucose metabolism, but functional brain segregation and integration based on 18 F-FDG PET remains largely unknown., Methods: Two hundred one Chinese child patients with extracranial malignancy were retrospectively enrolled as surrogates to healthy children. All images were spatially normalized into MNI space using pediatric brain template, and the 18F-FDG uptakes were calculated for 90 regions using AAL atlas. The group-level metabolic brain network was constructed by measuring Pearson correlation coefficients between each pair of brain regions in an inter-subject manner for infant (1 to 4 years), childhood (5 to 8 years), early adolescent (9 to 12 years), and adolescent (13 to 18 years) group, respectively. Global efficiency of each group was calculated, and the modular architectures were detected by a greedy algorithm., Results: Both metabolic brain network connectivity and global efficiency increased with aging. Brain network was grouped into 4, 6, 4, and 4 modules from infant to adolescent, respectively. The modular architecture dramatically reorganized from childhood to early adolescent. The hubs spatiotemporally rewired. The ratio of the connector hub to the provincial hub increased from infant to early adolescent, but decreased during the adolescent period., Conclusions: The topological properties and modular reorganization of human brain network dramatically changed with age, especially from childhood to early adolescence. These findings would help understand the Chinese developmental trajectory of human brain functional integration and segregation.

Objective: Motor network plays an important role in people's daily lives. However, until now, the energy consumption mechanism of motor network remains unclear. In this study, we aimed to investigate the energy consumption of motor network., Materials and Methods: Fluorine-18-fluorodeoxyglucose PET ([F]FDG PET) data of 81 healthy male Sprague-Dawley rats were included in this study. Metabolic motor network was constructed on the basis of group independent component analysis. Properties of motor network such as degree and nodal efficiency were investigated using graph theory-based analysis. Furthermore, the relationships between [F]FDG standardized uptake value ratio and these properties of each node were investigated., Results: A motor network comprising of the following 11 regions were found: left primary motor cortex, right primary motor cortex, left secondary motor cortex, right secondary motor cortex, left primary somatosensory cortex, right primary somatosensory cortex, left secondary somatosensory cortex, right secondary somatosensory cortex, left insular cortex, right insular cortex, and left orbital cortex. Graph theory-based analysis indicated that right primary somatosensory cortex and left secondary somatosensory cortex were the hubs of motor network, and the nodal efficiency and nodal degree share the same order. Further investigation found a significantly negative correlation between nodal efficiency and [F]FDG standardized uptake value ratios., Conclusion: This study investigated the energy consumption of motor network and found a relationship between energy consumption and communication efficiency. These results may provide insights into the understanding of energy consumption mechanism underlying motor network.Video abstract: http://links.lww.com/NMC/A142.

Purpose: Manganese-enhanced magnetic resonance imaging (MEMRI) can help us trace the active neurons and neuronal pathway in transgenic mouse AD model. 5XFAD has been widespread accepted as a valuable model system for studying brain dysfunction progresses in the courses of AD. To further understand the development of AD at early stages, an effective and objective data analysis platform for MEMRI studies should be constructed., Materials and Methods: A set of stereotaxic templates of mouse brain in Paxinos and Franklin space, "the Institute of High Energy Physics Mouse Template", or IMT for short, was constructed by iteratively registration and averaging. An atlas image was reconstructed from the Paxinos and Franklin atlas figures and each sub-anatomical segmentation was assigning a unique integer. An analysis SPM plug-in toolbox was further created, that automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and statistical analysis for MEMRI scans., Results: The IMT comprised a T2WI template image, a MEMRI template image, intracranial tissue segmentations, and accompany with a digital mouse brain atlas image, in which 707 sub-anatomical brain regions are delineated. Data analyses were performed on groups of developing 5XFAD mice to demonstrate the usage of IMT, and the results shows that abnormal neuronal activity occurs at early stage in 5XFAD mice., Conclusion: We have constructed a stereotaxic template set of mouse brain named IMT with fine delineations of sub-anatomical structures, which is compatible with SPM. It will give a widely range of researchers a standardized coordinate system for localization of any mouse brain related data., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Metabolic brain network, which is based on functional correlation patterns of 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images, has been widely applied in both basic and clinical neuroscience. Exploring the properties of the metabolic brain network can provide valuable insight to the physiologic and pathologic processes of the brain. Based on the network theory, modular architecture has the ability to limit the spread of local perturbation impact and therefore modular networks are more robust against external damage. However, whether the metabolic brain network has modular architecture remains unknown., Methods: 77 rats performed 18 F-FDG PET brain imaging. The metabolic brain network was then constructed by measuring interregional metabolic correlation in inter-subject manner. Afterwards, modular architecture of the network was detected by a greedy algorithm. Further, we perturbed the metabolic brain network by inducing focal photothrombotic ischemia in the bilateral motor cortex and then measured the glucose metabolic change of each brain region using FDG-PET., Results: A significant modular architecture was found in the metabolic brain network. The network could be divided into four modules which corresponding approximately to executive, learning/memory, visual/auditory and sensorimotor processing functional domains. After inducing the focal ischemia on the bilateral motor cortex, most of the significantly changed brain regions (13 of 17) belong to the sensorimotor module., Conclusion: Our results revealed an inherent modular architecture in the metabolic brain network and gave an experimental evidence that the modularity of the metabolism brain network could limit the spread of local perturbation impact., (Copyright © 2018. Published by Elsevier Inc.)

Stroke at the acute stage is a major cause of disability in adults, and is associated with dysfunction of brain networks. However, the mechanisms underlying changes in brain connectivity in stroke are far from fully elucidated. In the present study, we investigated brain metabolism and metabolic connectivity in a rat ischemic stroke model of middle cerebral artery occlusion (MCAO) at the acute stage using 18 F-fluorodeoxyglucose positron emission tomography. Voxel-wise analysis showed decreased metabolism mainly in the ipsilesional hemisphere, and increased metabolism mainly in the contralesional cerebellum. We used further metabolic connectivity analysis to explore the brain metabolic network in MCAO. Compared to sham controls, rats with MCAO showed most significantly reduced nodal and local efficiency in the ipsilesional striatum. In addition, the MCAO group showed decreased metabolic central connection of the ipsilesional striatum with the ipsilesional cerebellum, ipsilesional hippocampus, and bilateral hypothalamus. Taken together, the present study demonstrated abnormal metabolic connectivity in rats at the acute stage of ischemic stroke, which might provide insight into clinical research.

With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity.

Background: Tree shrews are proposed as an alternative animal model to nonhuman primates due to their close affinity to primates. Neuroimaging techniques are widely used to study brain functions and structures of humans and animals. However, tree shrews are rarely applied in neuroimaging field partly due to the lack of available species specific analysis methods., New Method: In this study, 10 PET/CT and 10 MRI images of tree shrew brain were used to construct PET and MRI templates; based on histological atlas we reconstructed a three-dimensional digital atlas with 628 structures delineated; then the digital atlas and templates were aligned into a stereotaxic space. Finally, we integrated the digital atlas and templates into a toolbox for tree shrew brain spatial normalization, statistical analysis and results localization., Results: We validated the feasibility of the toolbox by simulated data with lesions in laterodorsal thalamic nucleus (LD). The lesion volumes of simulated PET and MRI images were (12.97±3.91)mm 3 and (7.04±0.84)mm 3 . Statistical results at p<0.005 showed the lesion volumes of PET and MRI were 13.18mm 3 and 8.06mm 3 in LD., Comparison With Existing Method(s): To our knowledge, we report the first PET template and digital atlas of tree shrew brain. Compared to the existing MRI templates, our MRI template was aligned into stereotaxic space. And the toolbox is the first software dedicated for tree shrew brain analysis., Conclusions: The templates and digital atlas of tree shrew brain, as well as the toolbox, facilitate the use of tree shrews in neuroimaging field., (Copyright © 2017 Elsevier B.V. All rights reserved.)

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia., (© The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objective: Understanding the effect of diabetes as well as of alternative treatment strategies on cerebral structure is critical for the development of targeted interventions against accelerated neurodegeneration in type 2 diabetes. We investigated whether diabetes characteristics were associated with spatially specific patterns of brain changes and whether those patterns were affected by intensive versus standard glycemic treatment., Research Design and Methods: Using baseline MRIs of 488 participants with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes (ACCORD-MIND) study, we applied a new voxel-based analysis methodology to identify spatially specific patterns of gray matter and white matter volume loss related to diabetes duration and HbA1c. The longitudinal analysis used 40-month follow-up data to evaluate differences in progression of volume loss between intensive and standard glycemic treatment arms., Results: Participants with longer diabetes duration had significantly lower gray matter volumes, primarily in certain regions in the frontal and temporal lobes. The longitudinal analysis of treatment effects revealed a heterogeneous pattern of decelerated loss of gray matter volume associated with intensive glycemic treatment. Intensive treatment decelerated volume loss, particularly in regions adjacent to those cross-sectionally associated with diabetes duration. No significant relationship between low versus high baseline HbA1c levels and brain changes was found. Finally, regions in which cognitive change was associated with longitudinal volume loss had only small overlap with regions related to diabetes duration and to treatment effects., Conclusions: Applying advanced quantitative image pattern analysis methods on longitudinal MRI data of a large sample of patients with type 2 diabetes, we demonstrate that there are spatially specific patterns of brain changes that vary by diabetes characteristics and that the progression of gray matter volume loss is slowed by intensive glycemic treatment, particularly in regions adjacent to areas affected by diabetes., (© 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Optimally-Discriminative Voxel-Based Analysis (ODVBA) (Zhang and Davatzikos, 2011) is a recently-developed and validated framework of voxel-based group analysis, which transcends limitations of traditional Gaussian smoothing in the forms of analysis such as the General Linear Model (GLM). ODVBA estimates the optimal non-stationary and anisotropic filtering of the data prior to statistical analyses to maximize the ability to detect group differences. In this paper, we extensively evaluate ODVBA to three sets of previously published data from studies in schizophrenia, mild cognitive impairment, and Alzheimer's disease, and evaluate the regions of structural difference identified by ODVBA versus standard Gaussian smoothing and other related methods. The experimental results suggest that ODVBA is considerably more sensitive in detecting group differences, presumably because of its ability to adapt the regional filtering to the underlying extent and shape of a group difference, thereby maximizing the ability to detect such difference. Although there is no gold standard in these clinical studies, ODVBA demonstrated highest significance in group differences within the identified voxels. In terms of spatial extent of detected area, agreement of anatomical boundary, and classification, it performed better than other tested voxel-based methods and competitively with the cluster enhancing methods., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Background: Subthreshold depression (StD) is a prevalent condition that may increase the risk of incident major depressive disorder (MDD). However, the relationship between StD and MDD remains unclear.Methods: A total of 153 adult subjects, including 53 drug-naive MDD, 50 StD and 50 healthy control (HC) subjects, underwent a T1-weighted magnetic resonance imaging scan, and the gray matter volume (GMV) alterations among the three groups were quantitatively analyzed using voxel-based morphometry (VBM). Then, to capture the whole-brain connectivity characteristics, we constructed morphological brain networks (MBN) based on the similarity among brain regions of individual VBM images and compared the network connection strengths among the three groups.Results: The StD and MDD subjects had similar patterns of GMV reductions in the orbitofrontal cortex and left temporal gyrus, although the magnitude of the reductions was smaller in StD subjects. Moreover, a total of 21 morphological connections were significantly different among the three groups. For the majority of the different connections (15/21), the connection strength of the StD group took an intermediate position between that of the MDD and HC groups.Limitations: There is still a lack of a consistent definition of StD, and the age range of the subjects in this study was wide. Meanwhile the mechanisms and biological significance of the MBN remains to be clarified.Conclusions: These results may support the hypothesis that depression is better expressed as a spectrum and that StD exists on a spectrum with MDD. [ABSTRACT FROM AUTHOR]

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia. [ABSTRACT FROM AUTHOR]

Backgrounds: The Women's Health Initiative Memory Study Magnetic Resonance Imaging (WHIMS-MRI) provides an opportunity to evaluate how menopausal hormone therapy (HT) affects the structure of older women’s brains. Our earlier work based on region of interest (ROI) analysis demonstrated potential structural changes underlying adverse effects of HT on cognition. However, the ROI-based analysis is limited in statistical power and precision, and cannot provide fine-grained mapping of whole-brain changes. Methods: We aimed to identify local structural differences between HT and placebo groups from WHIMS-MRI in a whole-brain refined level, by using a novel method, named Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a recently proposed imaging pattern analysis approach for group comparisons utilizing a spatially adaptive analysis scheme to accurately locate areas of group differences, thereby providing superior sensitivity and specificity to detect the structural brain changes over conventional methods. Results: Women assigned to HT treatments had significant Gray Matter (GM) losses compared to the placebo groups in the anterior cingulate and the adjacent medial frontal gyrus, and the orbitofrontal cortex, which persisted after multiple comparison corrections. There were no regions where HT was significantly associated with larger volumes compared to placebo, although a trend of marginal significance was found in the posterior cingulate cortical area. The CEE-Alone and CEE+MPA groups, although compared with different placebo controls, demonstrated similar effects according to the spatial patterns of structural changes. Conclusions: HT had adverse effects on GM volumes and risk for cognitive impairment and dementia in older women. These findings advanced our understanding of the neurobiological underpinnings of HT effects. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia is a multifaceted mental disorder characterized by cognitive, perceptual, and affective symptom dimensions. This heterogeneity at the phenomenological level may be subserved by complex and heterogeneous patterns of structural abnormalities. Thus, delineating such patterns may improve the insight into the variability of disease and facilitate future magnetic resonance imaging-based diagnosis. Methods: We aimed to identify structurally complex signatures that directly differentiate patients with predominantly negative (pNEG), positive (pPOS), and disorganized (pDIS) symptoms using Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a new analytical framework for group analysis, which showed to have superior sensitivity and specificity over conventional voxel-based morphometric approaches, thus facilitating the identification of subtle neuroanatomical signatures delineating different subgroups. Results: pPOS were characterized by pronounced gray matter (GM) volume reductions in the ventromedial prefrontal cortex (vmPFC), which herein is defined to include the orbitofrontal cortex, and in occipitotemporal GM and parts of the lingual gyrus. pNEG was found to have vmPFC reduction but to a lesser degree than pPOS and with a relative sparing of the more medial vmPFC regions, compared to pDIS; it also had significantly less cerebellar GM. pDIS showed relatively highest GM volume preservation among three subtypes. Conclusions: Although a common prefronto-perisylvian GM reduction pattern was present at the whole-group level, marked morphometric differences emerged between the three subgroups, including reduced cerebellar GM in pNEG and reduced vmPFC and occipitotemporal GM in pPOS. Besides deepening our insight into the neurobiological underpinnings of clinical heterogeneity, these results also identify important imaging biomarkers that may aid patient stratification. [ABSTRACT FROM PUBLISHER]

Retrosplenial cortex (RSC) plays a key role in various cognitive functions. The fiber connectivity of RSC had been reported in rodent and primate studies by tracer injection methods To explore structural and functional connectivity of two sub-regions of RSC, Brodmann area (BA)29 and BA30, we constructed fiber connectivity networks of two sub-regions by diffusion tensor imaging (DTI) tractography based on diffusion magnetic resonance imaging (MRI) and functional connectivity networks by resting-state functional MRI. The BA29 network showed fiber connections with auditory cortex and functional connections with BA21. Both fiber and functional connections show that BA30 network includes visual cortex. Furthermore, BA30 had fiber connections with hippocampus, thalamus, and prefrontal cortex. We suggest that (1) structural connectivity reflects functional connectivity in some degree. (2) BA29 mainly received and processed sound information from auditory cortex, while BA30 mainly received and processed scene information from visual cortex. [ABSTRACT FROM AUTHOR]