Alterations of Graphic Properties and Related Cognitive Functioning Changes in Mild Alzheimer's Disease Revealed by Individual Morphological Brain Network

Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment

We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.

Aberrant morphometric networks in Alzheimer's disease have hemispheric asymmetry and age dependence

Alzheimer's disease (AD) is associated with abnormal accumulations of hyperphosphorylated tau and amyloid-β proteins, resulting in unique patterns of atrophy in the brain. We aimed to elucidate some characteristics of the AD's morphometric networks constructed by associating different morphometric features among brain areas and evaluating their relationship to Mini-Mental State Examination total score and age. Three-dimensional T1-weighted (3DT1) image data scanned by the same 1.5T magnetic resonance imaging (MRI) were obtained from 62 AD patients and 41 healthy controls (HCs) and were analysed by using FreeSurfer. The associations of the extracted six morphometric features between regions were estimated by correlation coefficients. The global and local graph theoretical measures for this network were evaluated. Associations between graph theoretical measures and age, sex and cognition were evaluated by multiple regression analysis in each group. Global measures of integration: global efficiency and mean information centrality were significantly higher in AD patients. Local measures of integration: node global efficiency and information centrality were significantly higher in the entorhinal cortex, fusiform gyrus and posterior cingulate cortex of AD patients but only in the left hemisphere. All global measures were correlated with age in AD patients but not in HCs. The information centrality was associated with age in AD's broad brain regions. Our results showed that altered morphometric networks due to AD are left-hemisphere dominant, suggesting that AD pathogenesis has a left-right asymmetry. Ageing has a unique impact on the morphometric networks in AD patients. The information centrality is a sensitive graph theoretical measure to detect this association.

Distinguishing Patients with MRI-Negative Temporal Lobe Epilepsy from Normal Controls Based on Individual Morphological Brain Network

Temporal Lobe Epilepsy (TLE) is the most common subtype of focal epilepsy and the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable structural abnormalities. In other words, MRI-negative TLE has normal MRI scans on visual inspection. Thus, MRI-negative TLE is a diagnostic and therapeutic challenge. In this study, we investigate the cortical morphological brain network to identify MRI-negative TLE. The 210 cortical ROIs based on the Brainnetome atlas were used to define the network nodes. The least absolute shrinkage and selection operator (LASSO) algorithm and Pearson correlation methods were used to calculate the inter-regional morphometric features vector correlation respectively. As a result, two types of networks were constructed. The topological characteristics of networks were calculated by graph theory. Then after, a two-stage feature selection strategy, including a two-sample t-test and support vector machine-based recursive feature elimination (SVM-RFE), was performed in feature selection. Finally, classification with support vector machine (SVM) and leave-one-out cross-validation (LOOCV) was employed for the training and evaluation of the classifiers. The performance of two constructed brain networks was compared in MRI-negative TLE classification. The results indicated that the LASSO algorithm achieved better performance than the Pearson pairwise correlation method. The LASSO algorithm provides a robust method of individual morphological network construction for distinguishing patients with MRI-negative TLE from normal controls.

Identification of Alzheimer’s Disease Progression Stages Using Topological Measures of Resting-State Functional Connectivity Networks: A Comparative Study

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely employed to examine brain functional connectivity (FC) alterations in various neurological disorders. At present, various computational methods have been proposed to estimate connectivity strength between different brain regions, as the edge weight of FC networks. However, little is known about which model is more sensitive to Alzheimer's disease (AD) progression. This study comparatively characterized topological properties of rs-FC networks constructed with Pearson correlation (PC), dynamic time warping (DTW), and group information guided independent component analysis (GIG-ICA), aimed at investigating the sensitivity and effectivity of these methods in differentiating AD stages. A total of 54 subjects from Alzheimer's Disease Neuroimaging Initiative (ANDI) database, divided into healthy control (HC), mild cognition impairment (MCI), and AD groups, were included in this study. Network-level (global efficiency and characteristic path length) and nodal (clustering coefficient) metrics were used to capture groupwise difference across HC, MCI, and AD groups. The results showed that almost no significant differences were found according to global efficiency and characteristic path length. However, in terms of clustering coefficient, 52 brain parcels sensitive to AD progression were identified in rs-FC networks built with GIG-ICA, much more than PC (6 parcels) and DTW (3 parcels). This indicates that GIG-ICA is more sensitive to AD progression than PC and DTW. The findings also confirmed that the AD-linked FC alterations mostly appeared in temporal, cingulate, and angular areas, which might contribute to clinical diagnosis of AD. Overall, this study provides insights into the topological properties of rs-FC networks over AD progression, suggesting that FC strength estimation of FC networks cannot be neglected in AD-related graph analysis.

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.

Functional connectome of arousal and motor brainstem nuclei in living humans by 7 Tesla resting-state fMRI

Brainstem nuclei play a pivotal role in many functions, such as arousal and motor control. Nevertheless, the connectivity of arousal and motor brainstem nuclei is understudied in living humans due to the limited sensitivity and spatial resolution of conventional imaging, and to the lack of atlases of these deep tiny regions of the brain. For a holistic comprehension of sleep, arousal and associated motor processes, we investigated in 20 healthy subjects the resting-state functional connectivity of 18 arousal and motor brainstem nuclei in living humans. To do so, we used high spatial-resolution 7 Tesla resting-state fMRI, as well as a recently developed in-vivo probabilistic atlas of these nuclei in stereotactic space. Further, we verified the translatability of our brainstem connectome approach to conventional (e.g. 3 Tesla) fMRI. Arousal brainstem nuclei displayed high interconnectivity, as well as connectivity to the thalamus, hypothalamus, basal forebrain and frontal cortex, in line with animal studies and as expected for arousal regions. Motor brainstem nuclei showed expected connectivity to the cerebellum, basal ganglia and motor cortex, as well as high interconnectivity. Comparison of 3 Tesla to 7 Tesla connectivity results indicated good translatability of our brainstem connectome approach to conventional fMRI, especially for cortical and subcortical (non-brainstem) targets and to a lesser extent for brainstem targets. The functional connectome of 18 arousal and motor brainstem nuclei with the rest of the brain might provide a better understanding of arousal, sleep and accompanying motor function in living humans in health and disease.

Brain Structural Network Compensation Is Associated With Cognitive Impairment and Alzheimer's Disease Pathology

BACKGROUND

Structural network alterations in Alzheimer's disease (AD) are related to worse cognitive impairment. The aim of this study was to quantify the alterations in gray matter associated with impaired cognition and their pathological biomarkers in AD-spectrum patients.

METHODS

We extracted gray matter networks from 3D-T1 magnetic resonance imaging scans, and a graph theory analysis was used to explore alterations in the network metrics in 34 healthy controls, 70 mild cognitive impairment (MCI) patients, and 40 AD patients. Spearman correlation analysis was computed to investigate the relationships among network properties, neuropsychological performance, and cerebrospinal fluid pathological biomarkers (i.e., Aβ, t-tau, and p-tau) in these subjects.

RESULTS

AD-spectrum individuals demonstrated higher nodal properties and edge properties associated with impaired memory function, and lower amyloid-β or higher tau levels than the controls. Furthermore, these compensations at the brain regional level in AD-spectrum patients were mainly in the medial temporal lobe; however, the compensation at the whole-brain network level gradually extended from the frontal lobe to become widely distributed throughout the cortex with the progression of AD.

CONCLUSION

The findings provide insight into the alterations in the gray matter network related to impaired cognition and pathological biomarkers in the progression of AD. The possibility of compensation was detected in the structural networks in AD-spectrum patients; the compensatory patterns at regional and whole-brain levels were different and the clinical significance was highlighted.

Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on Alzheimer's Disease

While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer's disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD.

Connectome analysis of brain functional network alterations in breast cancer survivors with and without chemotherapy

Purpose

Treatment modalities for breast cancer, the leading cause of cancer-related deaths in women worldwide, include surgery, radiotherapy, adjuvant chemotherapy, targeted therapy, and hormonal therapy. The advancement in medical technology has facilitated substantial reduction in breast cancer mortality. However, patients may experience cognitive impairment after chemotherapy. This phenomenon called chemotherapy-induced cognitive impairment (i.e., “chemobrain”) is common among breast cancer survivors. However, cognitive function deficits may exist before chemotherapy initiation. This study examined the functional network alterations in breast survivors by using resting-state functional magnetic resonance imaging (fMRI).

Methods

We recruited 172 female participants and separated them into three groups: C+ (57 breast cancer survivors who had finished 3–12-month-long chemotherapy), C- (45 breast cancer survivors who had not undergone chemotherapy), and HC (70 participants with no breast cancer history). We analyzed mean fractional amplitudes of low-frequency fluctuation and graph theoretical topologies from resting-state fMRI and applied network-based analysis to portray functional changes among the three groups.

Results

Among the three groups, the C- group demonstrated hyperactivity in the prefrontal cortex, bilateral middle temporal gyrus, right inferior temporal gyrus and right angular gyrus. Only the left caudate demonstrated significantly more hypoactivity in the C- group than in the C+ group. Graph theoretical analysis demonstrated that the brains of the C+ group became inclined toward regular networks and the brains of the C- group became inclined toward random networks.

Conclusion

Subtle alterations were noted in the brain activity and networks of our cancer survivors. Moreover, functional network disruptions occurred regardless of chemotherapeutic agent administration.

Jidong cognitive impairment cohort study: objectives, design, and baseline screening

The risk of dementia increases in patients with cognitive impairment. However, it is not clear what factors contribute to the onset of dementia in those with cognitive impairment. In this prospective cohort study, we will investigate the every-five-year incidence of cognitive impairment and prognostic factors for cognitive impairment. The Jidong cognitive impairment cohort was established from April 2012 to August 2015, during which we recruited 5854 healthy participants (55.1% male) older than 45 years (mean, 57 years). Participants received a health examination in the Staff Hospital, Jidong Oilfield Branch, China National Petroleum Corporation. Baseline data and blood samples were collected. Cognitive impairment was evaluated using the Mini-Mental State Examination, and was defined as a Mini-Mental State Examination score of less than 24. Dementia was assessed using the criteria of Diagnostic and Statistical Manual of Mental Disorders (Fourth edition), the International Working Group criteria, and the Mini-Mental State Examination score. The follow-up will continue until December 2024, during which a prognostic model will be constructed. The primary outcome is the presence/absence of dementia and the secondary outcome is quality of life. Baseline screening results showed the following: (1) Cognitive impairment was apparent in 320 participants (5.5%). These participants will be excluded from the Jidong cohort study, and the remaining participants will be followed up. (2) Of the 320 participants with cognitive impairment, there was a significantly higher prevalence of illiteracy than other education levels (35.9%, P < 0.05). Age, arterial hypertension, alcohol consumption, and passive smoking differed significantly between the cognitive impairment and healthy groups (P < 0.05). Multivariate logistic regression models showed that age (odds ratio [OR] = 1.059, 95% confidence interval [CI]: 1.044–1.074) and arterial hypertension (OR = 1.665, 95% CI: 1.143–2.427) were risk factors for mild cognitive impairment. With the increase of educational level (illiteracy, primary school, junior high school, high school, university, and above), cognitive impairment gradually decreased (OR < 1, P < 0.05). (3) This cohort study has initially screened for several risk factors for cognitive impairment at baseline, and subsequent prospective data will further describe, validate, and evaluate the effects of these risk factors on cognitive impairment and dementia. These results can provide clinical evidence for the early prevention of cognitive impairment and dementia. The study was approved by the Ethics Committee of Kailuan General Hospital of Tangshan City and the Medical Ethics Committee, Staff Hospital, Jidong Oilfield Branch, China National Petroleum Corporation on July 12, 2013 (approval No. 2013 YILUNZI 1).