Functional gradients reveal altered functional segregation in patients with amnestic mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease and amnestic mild cognitive impairment are associated with disrupted functional organization in brain networks, involved with alteration of functional segregation. Connectome gradients are a new tool representing brain functional topological organization to smoothly capture the human macroscale hierarchy. Here, we examined altered topological organization in amnestic mild cognitive impairment and Alzheimer's disease by connectome gradient mapping. We further quantified functional segregation by gradient dispersion. Then, we systematically compared the alterations observed in amnestic mild cognitive impairment and Alzheimer's disease patients with those in normal controls in a two-dimensional functional gradient space from both the whole-brain level and module level. Compared with normal controls, the first gradient, which described the neocortical hierarchy from unimodal to transmodal regions, showed a more distributed and significant suppression in Alzheimer's disease than amnestic mild cognitive impairment patients. Furthermore, gradient dispersion showed significant decreases in Alzheimer's disease at both the global level and module level, whereas this alteration was limited only to limbic areas in amnestic mild cognitive impairment. Notably, we demonstrated that suppressed gradient dispersion in amnestic mild cognitive impairment and Alzheimer's disease was associated with cognitive scores. These findings provide new evidence for altered brain hierarchy in amnestic mild cognitive impairment and Alzheimer's disease, which strengthens our understanding of the progressive mechanism of cognitive decline.

Heterogeneity in subjective cognitive decline in the Sino Longitudinal Study on Cognitive Decline(SILCODE): Empirically derived subtypes, structural and functional verification.

AIMS: We evaluated whether Subjective Cognitive Decline (SCD) subtypes could be empirically derived within the Sino Longitudinal Study on Cognitive Decline (SILCODE) SCD cohort and examined associated neuroimaging markers, biomarkers, and clinical outcomes. METHODS: A cluster analysis was performed on eight neuropsychological test scores from 124 SCD SILCODE participants and 57 normal control (NC) subjects. Structural and functional neuroimaging indices were used to evaluate the SCD subgroups. RESULTS: Four subtypes emerged: (1) dysexecutive/mixed SCD (n = 23), (2) neuropsychiatric SCD (n = 24), (3) amnestic SCD (n = 22), and (4) cluster-derived normal (n = 55) who exhibited normal performance in neuropsychological tests. Compared with the NC group, each subgroup showed distinct patterns in gray matter (GM) volume and the amplitude of low-frequency fluctuations (ALFF). Lower fractional anisotropy (FA) values were only found in the neuropsychiatric SCD group relative to NC. CONCLUSION: The identification of empirically derived SCD subtypes demonstrates the presence of heterogeneity in SCD neuropsychological profiles. The cluster-derived normal group may represent the majority of SCD individuals who do not show progressive cognitive decline; the dysexecutive/mixed SCD and amnestic SCD might represent high-risk groups with progressing cognitive decline; and finally, the neuropsychiatric SCD may represent a new topic in SCD research.

The inferior frontal gyrus spontaneous activity mediates the association of early life adversity with self-control ability in late adolescents.

Self-control, the ability to regulate prepotent desires or impulses in order to realize one's valued goal, has been found to be associated with early life adversity. However, the neural correlates underlying this relationship remain poorly understood. The present study employed resting-state functional magnetic resonance imaging (fMRI) to investigate this issue among late adolescents (N = 538). Results showed that family unpredictability rather than family harshness of early life adversity was negatively correlated with self-control ability. The whole brain analysis showed that self-control was associated with enhanced ALFF in the right middle and inferior frontal gyrus, the left anterior insula, and with decreased ALFF in the left precuneus. Moreover, the mediating analysis showed that ALFF in the inferior frontal gyrus could partially mediated the association of family unpredictability with self-control ability. These findings suggested that the brain regions implicating in executive control might be the neural correlates underlying the relationship between early life adversity and self-control ability, which advances the mechanistic understanding of how early family environment relates to the development of self-regulation in late adolescence.

Aberrant topological organization and age-related differences in the human connectome in subjective cognitive decline by using regional morphology from magnetic resonance imaging.

Subjective cognitive decline (SCD) is characterized by self-experienced deficits in cognitive capacity with normal performance in objective cognitive tests. Previous structural covariance studies showed specific insights into understanding the structural alterations of the brain in neurodegenerative diseases. Moreover, in subjects with neurodegenerative diseases, accelerated brain degeneration with aging was shown. However, the age-related variations in coordinated topological patterns of morphological networks in individuals with SCD remain poorly understood. In this study, 77 individual morphological networks were constructed, including 42 normal controls (NCs) and 35 SCD individuals, from structural magnetic resonance imaging (sMRI). A stepwise linear regression model and partial correlation analysis were constructed to evaluate the differences in age-related alterations of the network properties in individuals with SCD compared with NCs. Compared with NC, the properties of integration and segregation in individuals with SCD were lower, and the aberrant metrics were negatively correlated with age in SCD. The rich-club connections persevered, but the paralimbic system connections were disrupted in individuals with SCD compared with NCs. In addition, age-related differences in nodal global efficiency are distributed mainly in prefrontal cortex regions. In conclusion, the age-related disruption of topological organizations in individuals with SCD may indicate that the degeneration of brain efficiency with aging was accelerated in individuals with SCD.

Divergent Connectivity Changes in Gray Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease.

Alzheimer's disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD.

Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method.

Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (-0.005±0.023), while individual-shared connectivity has high inter-subject similarity (0.822±0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.

Altered Neuroanatomical Asymmetries of Subcortical Structures in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease.

BACKGROUND: Individuals with subjective cognitive decline (SCD), defined by self-reported memory complaints but normal performance in objective neuropsychological tests, may be at higher risk of worsening or more frequent memory loss until conversion to Alzheimer's disease (AD) or related dementia. Asymmetry in two hemispheres is a cardinal character of human brain's structure and function, and altered brain asymmetry has also been connected with AD. OBJECTIVE: This study aimed to determine whether the asymmetry of subcortical structures in individuals with SCD and amnestic mild cognitive impairment (aMCI) and AD patients are altered compared with normal controls (NC). METHODS: We investigated neuroanatomical alterations in 35 SCD, 43 aMCI, and 41 AD subjects compared with 42 NC, focusing on asymmetrical changes in subcortical structures based on structural magnetic resonance images (sMRI). General linear model was conducted to test group differences, and partial correlation was used to model the interaction between asymmetry measurements and cognitive tests. RESULTS: Individuals with SCD (lateral ventricle and cerebellum-WM), aMCI patients (lateral ventricle, pallidum, hippocampus, amygdala, accumbens, and ventral DC), and AD patients (lateral-ventricle, cerebellum-cortical pallidum, thalamus, hippocampus, amygdala, accumbens, and ventral DC) exhibited significant altered neuroanatomical asymmetries of volume, surface area, and shape compared with NC. Significant associations between shape asymmetry and neuropsychological examinations were found in the hippocampus and accumbens. CONCLUSION: Altered neuroanatomical asymmetries of subcortical structures were significantly detected in SCD individuals and aMCI patients as well AD patients, and these specific asymmetry alterations are potential to be used as neuroimaging markers and for monitoring disease progression.

Structural Covariance Changes of Anterior and Posterior Hippocampus During Musical Training in Young Adults.

Musical training can induce the functional and structural changes of the hippocampus. The hippocampus is not a homogeneous structure which can be divided into anterior and posterior parts along its longitudinal axis, and the whole-brain structural covariances of anterior (aHC) and posterior hippocampus (pHC) show distinct patterns in young adults. However, little is known about whether the anterior and posterior hippocampal structural covariances change after long-term musical training. Here, we investigated the musical training-induced changes of the whole-brain structural covariances of bilateral aHC and pHC in a longitudinal designed experiment with two groups (training group and control group) across three time points [the beginning (TP1) and the end (TP2) of 24 weeks of training, and 12 weeks after training (TP3)]. Using seed partial least square, we identified two significant patterns of structural covariance of the aHC and pHC. The first showed common structural covariance of the aHC and pHC. The second pattern revealed distinct structural covariance of the two regions and reflected the changes of structural covariance of the left pHC in the training group across three time points: the left pHC showed significant structural covariance with bilateral hippocampus and parahippocampal gyrus, left calcarine sulcus only at TP1 and TP3. Furthermore, the integrity of distinct structural networks of aHC and pHC in the second pattern significantly increased in the training group. Our findings suggest that musical training could change the organization of structural whole-brain covariance for left pHC and enhance the degree of the structural covariance network differentiation of the aHC and pHC in young adults.

[A review on brain age prediction in brain ageing].

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.

Voluntary wheel running delays brain atrophy in aged mice.

BACKGROUND: Physical exercises have been shown to be a surprisingly effective strategy to take advantage of the brain's natural capacity for plasticity, and prevent brain degeneration in mouse histological studies. In vivo magnetic resonance microscopy (MRM) provides highly resolved anatomical images and allows quantitative assessment of brain atrophy in the aged mouse model. OBJECTIVE: The aim of the present study was to investigate, through the effects of 10 weeks voluntary wheel running, the mouse's brain atrophy. METHODS: Sixteen C57BL/6J mice, aged 21 months, were randomized to the exercise or sedentary group. Each mouse was scanned in a 7.0-T MRM scanner at two time points: 22 months old baseline and a follow-up three months later. Multi-atlas based brain segmentation approach was used to obtain volumes of 39 brain regions. RESULTS: The results showed that mice in the exercise group had less brain atrophy compared with the mice in the sedentary group. CONCLUSIONS: The results provide new insights into exercise induced brain plasticity in aged animals.

Small-world indices via network efficiency for brain networks from diffusion MRI.

The small-world architecture has gained considerable attention in anatomical brain connectivity studies. However, how to adequately quantify small-worldness in diffusion networks has remained a problem. We addressed the limits of small-world measures and defined new metric indices: the small-world efficiency (SWE) and the small-world angle (SWA), both based on the tradeoff between high global and local efficiency. To confirm the validity of the new indices, we examined the behavior of SWE and SWA of networks based on the Watts-Strogatz model as well as the diffusion tensor imaging (DTI) data from 75 healthy old subjects (aged 50-70). We found that SWE could classify the subjects into different age groups, and was correlated with individual performance on the WAIS-IV test. Moreover, to evaluate the sensitivity of the proposed measures to network, two network attack strategies were applied. Our results indicate that the new indices outperform their predecessors in the analysis of DTI data.

Evaluation of five diffeomorphic image registration algorithms for mouse brain magnetic resonance microscopy.

The development of genetically engineered mouse models for neuronal diseases and behavioural disorders have generated a growing need for small animal imaging. High-resolution magnetic resonance microscopy (MRM) provides powerful capabilities for noninvasive studies of mouse brains, while avoiding some limits associated with the histological procedures. Quantitative comparison of structural images is a critical step in brain imaging analysis, which highly relies on the performance of image registration techniques. Nowadays, there is a mushrooming growth of human brain registration algorithms, while fine-tuning of those algorithms for mouse brain MRMs is rarely addressed. Because of their topology preservation property and outstanding performance in human studies, diffeomorphic transformations have become popular in computational anatomy. In this study, we specially tuned five diffeomorphic image registration algorithms [DARTEL, geodesic shooting, diffeo-demons, SyN (Greedy-SyN and geodesic-SyN)] for mouse brain MRMs and evaluated their performance using three measures [volume overlap percentage (VOP), residual intensity error (RIE) and surface concordance ratio (SCR)]. Geodesic-SyN performed significantly better than the other methods according to all three different measures. These findings are important for the studies on structural brain changes that may occur in wild-type and transgenic mouse brains.

Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks.

Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.

Mouse brain magnetic resonance microscopy: Applications in Alzheimer disease.

Over the past two decades, various Alzheimer's disease (AD) trangenetic mice models harboring genes with mutation known to cause familial AD have been created. Today, high-resolution magnetic resonance microscopy (MRM) technology is being widely used in the study of AD mouse models. It has greatly facilitated and advanced our knowledge of AD. In this review, most of the attention is paid to fundamental of MRM, the construction of standard mouse MRM brain template and atlas, the detection of amyloid plaques, following up on brain atrophy and the future applications of MRM in transgenic AD mice. It is believed that future testing of potential drugs in mouse models with MRM will greatly improve the predictability of drug effect in preclinical trials.

[Relationship between variation of coxsackievirus B3 VP1 sequence from cerebrospinal fluid of children and severity of damage to central nervous system].

OBJECTIVE: To investigate the possible relationship between variation of coxsackievirus B3 (CoxB3) VP1 sequence from cerebrospinal fluid of children with severe and mild central nervous system (CNS) infection and damage to CNS in children from Shandong province. METHODS: The enteroviruses were detected using VP1 typing and sequencing primer for enteroviruses from 73 enterovirus-infected cases confirmed by detection of cerebrospinal fluid by enteroviruses common primer. VP1 sequences (450 nucleotides) were determined and analyzed for 21 CoxB3 enteroviruses strains isolated in Qingdao and Binzhou, and were compared with that of BLAST search procedures from GeneBank in NCBI. The variation of VP1 gene and amino acids sequence of CoxB3 enteroviruses was analyzed for severe and mild CNS infection. RESULTS: The nucleotide homogeneity of these CoxB3 appeared to be 97% - 99%, however, the homogeneity among different genotypes were 83% - 76%. Replacement of glutamine by histidine at amino acid locus 856 of VP1 CoxB3 was found in 4 cases with severe encephalitis. There were different variation in VP1 nucleotide sequence of CoxB3 in 3 cases with mild encephalitis and 14 cases with meningitis, but amino acids sequences had no regular variation. The modified Glasgow's coma score was below 7 in all the 4 cases with severe encephalitis. Of these 4 cases, 3 had consciousness disturbance for less than 3 days. Lethargy, restlessness and psychiatric symptoms were major manifestations, of whom 3 also had dysphagia, 1 had encephalatrophy obviously, Glasgow's coma score was 3, deep coma lasted for 9 days, and had concomitant fatal epileptic attacks. Of these 4 cases, 2 completely recovered, 1 had high muscle tone, 1 remained under anti-epileptic drug treatment at follow-up 6 months later. CONCLUSION: There were a small epidemic of CoxB3 CNS infection in children in 2005 in this area. The amino acid variation of CoxB3 VP1 possibly caused increased viral virulence and caused damage to CNS.