Neuroinflammation, functional connectivity and structural network integrity in the Alzheimer's spectrum.

Brain network dysfunction is increasingly recognised in Alzheimer’s disease (AD). However, the causes of brain connectivity disruption are still poorly understood. Recently, neuroinflammation has been identified as an important factor in AD pathogenesis. Microglia participate in the construction and maintenance of healthy neuronal networks, but pro-inflammatory microglia can also damage these circuits. We hypothesised that microglial activation is independently associated with brain connectivity disruption in AD. We performed a cross-sectional multimodal imaging study and interrogated the relationship between imaging biomarkers of neuroinflammation, Aβ deposition, brain connectivity and cognition. 42 participants (12 Aβ-positive MCI, 14 Aβ-positive AD and 16 Aβ-negative healthy controls) were recruited. Participants had 11C-PBR28 and 18F-flutemetamol PET to quantify Aβ deposition and microglial activation, T1-weighted, diffusion tensor and resting-state functional MRI to assess structural network and functional network. 11C-PBR28 uptake, structural network integrity and functional network orgnisation were compared across diagnostic groups and the relationship between neuroinflammation and brain network was tested in 26 Aβ-positive patients. Increased 11C-PBR28 uptake, decreased FA, network small-worldness and local efficiency were observed in AD patients. Cortical 11C-PBR28 uptake correlated negatively with structural integrity (standardised β = −0.375, p = 0.037) and network local efficiency (standardised β = −0.468, p < 0.001), independent of cortical thickness and Aβ deposition, while Aβ was not. Network structural integrity, small-worldness and local efficiency, and cortical thickness were positively associated with cognition. Our findings suggest cortical neuroinflammation coincide with structural and functional network disruption independent of Aβ and cortical atrophy. These findings link the brain connectivity change and pathological process in Alzheimer’s disease, and suggest a pathway from neuroinflammation to systemic brain dysfunction.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Research links gait impairment in Alzheimer’s disease (AD) to cognitive abnormalities, brain atrophy, or amyloid-β (Aβ) deposition, with the exact cause unclear. This study investigated the relationship between gait, neuroimaging biomarkers, and cognition across the AD spectrum. We recruited 48 AD dementia patients, 27 with prodromal AD, and 41 cognitively unimpaired individuals, analyzing associations among gait parameters, cognitive scores, Aβ deposition, and cortical atrophy. Path and receiver operating characteristic (ROC) analyses evaluated gait impairment’s interdependent interactions and diagnostic potential. Prodromal AD and AD dementia patients showed significantly slower gait pace than CU (p = 0.014 [velocity], p = 0.003 [step length]), linked to attention and executive functions, widespread Aβ deposition, and cortical atrophy, in the inferior parietal lobule, middle temporal gyrus, precuneus, and insula. Compared to CU, AD dementia patients exhibited greater gait variability and phase (p = 0.017 [step length standard deviation], p = 0.001 [double support percentage]), significantly correlated with cognition and Aβ deposition. Path analysis revealed a combined influence of Aβ deposition, cognitive impairment, and cortical atrophy on gait impairment, with > 80% observed gait impairments directly affected by Aβ deposition. ROC curves for diagnosing AD stages showed significant areas under the curve, suggesting gait characteristics as noninvasive biomarkers for early AD diagnosis and progression monitoring.

Despite potential implications for the early detection of impending Alzheimer’s disease (AD), very little is known about the differences of large-scale brain networks between amnestic mild cognitive impairment (aMCI) with high cerebral amyloid-beta protein (Aβ) deposition (i.e., aMCI+) and aMCI with no or very little Aβ deposition (i.e., aMCI−). We first aimed to extend the current literature on altering intrinsic functional connectivity (FC) of the default mode network (DMN) and salience network (SN) from cognitively normal (CN) to AD dementia. Second, we further examined the differences of the DMN and the SN between aMCI−, aMCI+, and CN. Forty-three older adult (12 CN, 10 aMCI+, 10 aMCI−, and 11 AD dementia) subjects were included. All participants received comprehensive clinical and neuropsychological assessment, resting-state functional magnetic resonance imaging, structural MRI, and Pittsburgh compound-B-PET scans. FC data were preprocessed using multivariate exploratory linear optimized decomposition into independent components of FMRIB’s Software Library. Group comparisons were carried out using the “dual-regression” approach. In addition, to verify presence of gray matter volume changes with intrinsic functional network alterations, voxel-based morphometry was performed on the acquired T1-weighted data. As expected, AD dementia participants exhibited decreased FC in the DMN compared to CN (particularly in the precuneus and cingulate gyrus). The degree of alteration in the DMN in aMCI+ compared to CN was intermediate to that of AD. In contrast, aMCI− exhibited increased FC in the DMN compared to CN (primarily in the precuneus) as well as aMCI+. In terms of the SN, aMCI− exhibited decreased FC compared to both CN and aMCI+ particularly in the inferior frontal gyrus. FC within the SN in aMCI+ and AD did not differ from CN. Compared to CN, aMCI− showed atrophy in bilateral superior temporal gyri whereas aMCI+ showed atrophy in right precuneus. The results indicate that despite the similarity in cross-sectional cognitive features, aMCI− has quite different functional brain connectivity compared to aMCI+.

Differences in patterns of functional and structural connectivity alterations of hippocampal subregions in patients on the Alzheimer's disease spectrum.

In vivo imaging of β-amyloid (Αβ) has transformed the assessment of Αβ pathology and its changes over time, extending our insight into Aβ deposition by providing highly accurate, reliable and reproducible quantitative statements of regional or global Aβ burden in the brain, essential for therapeutic trial recruitment and for the evaluation of anti-Αβ treatments. Although cross-sectional evaluation of Αβ burden does not strongly correlate with cognitive impairment in AD, it does correlate with memory impairment and a higher risk for cognitive decline in the aging population and MCI subjects. This correlation with memory impairment, one of the earliest symptoms of AD, suggests that Αβ deposition is not part of normal aging, supporting the hypothesis that Αβ deposition occurs well before the onset of symptoms. Longitudinal observations, coupled with different disease-specific biomarkers to assess potential downstream effects of Aβ, are required to confirm this hypothesis and further elucidate the role of Αβ deposition in the course of Alzheimer’s disease.

Seeding plaques in Alzheimer's disease.

Predicted sequence of cortical tau and amyloid-β deposition in Alzheimer disease spectrum.

ObjectivesThis study aimed to investigate local and remote functional connectivity in mild Alzheimer’s disease patients with sleep disturbances (ADSD) and those without sleep disturbances (ADNSD).MethodsThirty eight mild AD patients with sleep disturbances and 21 mild AD patients without sleep disturbances participated in this study. All subjects underwent neuropsychological assessments and 3.0 Tesla magnetic resonance scanning. Static and dynamic regional homogeneity (ReHo) were used to represent the local functional connectivity. Seed-based whole-brain functional connectivity was used to represent the remote functional connectivity. The seed was chosen based on the results of ReHo.ResultsCompared to ADNSD, ADSD showed decreased static ReHo in the left posterior central gyrus and the right cuneus and increased dynamic ReHo in the left posterior central gyrus. As for the remote functional connectivity, comparing ADSD to ADNSD, it was found that there was a decreased functional connection between the left posterior central gyrus and the left cuneus as well as the left calcarine.ConclusionThe current study demonstrated that, compared with ADNSD, ADSD is impaired in both local and remote functional connectivity, manifested as reduced functional connectivity involving the primary sensory network and the primary visual network. The abnormality of the above functional connectivity is one of the reasons why sleep disorders promote cognitive impairment in AD. Moreover, sleep disorders change the temporal sequence of AD pathological damage to brain functional networks, but more evidence is needed to support this conclusion.

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly popular technique. This technique can assess several features of brain connectivity, such as inter-regional temporal correlation (functional connectivity), from which graph measures of network organization can be derived. However, these measures are prone to a certain degree of variability depending on the analytical steps during preprocessing. Many studies have investigated the effect of different preprocessing steps on functional connectivity measures; however, no study investigated whether different structural reconstructions lead to different functional connectivity metrics. Here, we evaluated the impact of different structural segmentation strategies on functional connectivity outcomes. To this aim, we compared different metrics computed after two different registration strategies. The first strategy used structural information from the 3D T1-weighted image (unimodal), while the second strategy implemented a multimodal approach, where an additional registration step used the information from the T2-weighted image. The impact of these different approaches was evaluated on a sample of 58 healthy adults. As expected, different approaches led to significant differences in structural measures (i.e., cortical thickness, volume, and gyrification index), with the maximum impact on the insula cortex. However, these differences were only slightly translated to functional metrics. We reported no differences in graph measures and seed-based functional connectivity maps, but slight differences in the insula when we compared the mean functional strength for each parcel. Overall, these results suggested that functional metrics are only slightly different when using a unimodal compared to a multimodal approach, while the structural output can be significantly affected.

Electroacupuncture at ST 36 ameliorates cognitive impairment and beta-amyloid pathology by inhibiting NLRP3 inflammasome activation in an Alzheimer’s disease animal model

The whole-brain structural and functional connectome in Alzheimer's disease spectrum: A multimodal Bayesian meta-analysis of graph theoretical characteristics.

BackgroundSynaptic loss is identified as a strong correlate of cognitive impairment in Alzheimer’s disease (AD). Pathological tau can induce direct toxicity to synapse and spread trans‐synaptically across connected neurons. Recent neuroimaging evidence revealed that tau pathology propagates along interconnected brain regions throughout macroscale brain networks. However, whether synaptic loss propagates in AD follows a similar vein is unclear.MethodSeventy‐six amyloid‐positive (Aβ+) subjects across AD spectrum and 48 cognitively normal (CN) Aβ‐ controls characterized by cross‐sectional [18F]florbetapir amyloid‐PET were included in the current study. The density of synaptic vesicle glycoprotein 2A (SV2A) was measured by [18F]SynVesT‐1 PET. A normative template of functional connectome distance across 200 neocortical regions of interest (ROIs) was generated using resting‐state fMRI data from 1000 subjects of the Human Connectome Project. We assessed the association between synaptic loss and functional connectivity by correlating the cross‐subject inter‐regional covariance with the normative functional connectivity template. In a next step, we tested whether the functional distance to synaptic loss epicenter (i.e., top 10% ROIs with greatest synaptic loss) is associated with synaptic loss in connected regions at both group‐ and subject‐level.ResultAmong Aβ+ subjects, inter‐regional covariance of SV2A‐PET‐assessed synaptic loss was associated with inter‐regional functional connectivity (Figure 1), suggesting that higher functional connectivity may facilitate the propagation of synaptic loss. At both group‐ and subject‐level, we found that shorter functional distance to synaptic loss epicenters is associated with greater levels of synaptic loss in connected regions (Figure 2).ConclusionWe found that inter‐regional functional connectivity is associated with greater synaptic loss in AD, suggesting that synaptic loss may systematically propagate along brain connections.

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

Functional magnetic resonance imaging (fMRI) studies on mental training techniques such as meditation have reported benefits like increased attention and concentration, better emotional regulation, as well as reduced stress and anxiety. Although several studies have examined functional activation and connectivity in long-term as well as short-term meditators from different meditation traditions, it is unclear if long-term meditation practice brings about distinct changes in network properties of brain functional connectivity that persist during task performance. Indeed, task-based functional connectivity studies of meditators are rare. This study aimed to differentiate between long-term and short-term Rajayoga meditators based on functional connectivity between regions of interest in the brain. Task-based fMRI was captured as the meditators performed an engaging task. The graph theoretical-based functional connectivity measures of task-based fMRI were calculated using CONN toolbox and were used as features to classify the two groups using Machine Learning models. In this study, we recruited two age and sex-matched groups of Rajayoga meditators from the Brahma Kumaris tradition that differed in the duration of their meditation experience: Long-term practitioners (n = 12, mean 13,596 h) and short-term practitioners (n = 10, mean 1095 h). fMRI data were acquired as they performed an engaging task and functional connectivity metrics were calculated from this data. These metrics were used as features in training machine learning algorithms. Specifically, we used adjacency matrices generated from graph measures, global efficiency, and local efficiency, as features. We computed functional connectivity with 132 ROIs as well as 32 network ROIs. Five machine learning models, such as logistic regression, SVM, decision tree, random forest, and gradient boosted tree, were trained to classify the two groups. Accuracy, precision, sensitivity, selectivity, area under the curve receiver operating characteristics curve were used as performance measures. The graph measures were effective features, and tree-based algorithms such as decision tree, random forest, and gradient boosted tree yielded the best performance (test accuracy >84% with 132 ROIs) in classifying the two groups of meditators. Our results support the hypothesis that long-term meditative practices alter brain functional connectivity networks even in nonmeditative contexts. Further, the use of adjacency matrices from graph theoretical measures of high-dimensional fMRI data yields a promising feature set for machine learning classifiers.