Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer's disease

Functional Connectivity Changes on Resting-State fMRI after Mild Traumatic Brain Injury: A Systematic Review

BACKGROUND

Mild traumatic brain injury is theorized to cause widespread functional changes to the brain. Resting-state fMRI may be able to measure functional connectivity changes after traumatic brain injury, but resting-state fMRI studies are heterogeneous, using numerous techniques to study ROIs across various resting-state networks.

PURPOSE

We systematically reviewed the literature to ascertain whether adult patients who have experienced mild traumatic brain injury show consistent functional connectivity changes on resting-state -fMRI, compared with healthy patients.

DATA SOURCES

We used 5 databases (PubMed, EMBASE, Cochrane Central, Scopus, Web of Science).

STUDY SELECTION

Five databases (PubMed, EMBASE, Cochrane Central, Scopus, and Web of Science) were searched for research published since 2010. Search strategies used keywords of "functional MR imaging" and "mild traumatic brain injury" as well as related terms. All results were screened at the abstract and title levels by 4 reviewers according to predefined inclusion and exclusion criteria. For full-text inclusion, each study was evaluated independently by 2 reviewers, with discordant screening settled by consensus.

DATA ANALYSIS

Data regarding article characteristics, cohort demographics, fMRI scan parameters, data analysis processing software, atlas used, data characteristics, and statistical analysis information were extracted.

DATA SYNTHESIS

Across 66 studies, 80 areas were analyzed 239 times for at least 1 time point, most commonly using independent component analysis. The most analyzed areas and networks were the whole brain, the default mode network, and the salience network. Reported functional connectivity changes varied, though there may be a slight trend toward decreased whole-brain functional connectivity within 1 month of traumatic brain injury and there may be differences based on the time since injury.

LIMITATIONS

Studies of military, sports-related traumatic brain injury, and pediatric patients were excluded. Due to the high number of relevant studies and data heterogeneity, we could not be as granular in the analysis as we would have liked.

CONCLUSIONS

Reported functional connectivity changes varied, even within the same region and network, at least partially reflecting differences in technical parameters, preprocessing software, and analysis methods as well as probable differences in individual injury. There is a need for novel rs-fMRI techniques that better capture subject-specific functional connectivity changes.

Brain functional specialization and cooperation in Alzheimer's disease

BACKGROUND

Cerebral specialization and interhemispheric cooperation are two vital features of the human brain. Their dysfunction may be associated with disease progression in patients with Alzheimer's disease (AD), which is featured as progressive cognitive degeneration and asymmetric neuropathology.

OBJECTIVE

This study aimed to examine and define two inherent properties of hemispheric function in patients with AD by utilizing resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Sixty-four clinically diagnosed AD patients and 52 age- and sex-matched cognitively normal subjects were recruited and underwent MRI and clinical evaluation. We calculated and compared brain specialization (autonomy index, AI) and interhemispheric cooperation (connectivity between functionally homotopic voxels, CFH).

RESULTS

In comparison to healthy controls, patients with AD exhibited enhanced AI in the left middle occipital gyrus. This increase in specialization can be attributed to reduced functional connectivity in the contralateral region, such as the right temporal lobe. The CFH of the bilateral precuneus and prefrontal areas was significantly decreased in AD patients compared to controls. Imaging-cognitive correlation analysis indicated that the CFH of the right prefrontal cortex was marginally positively related to the Montreal Cognitive Assessment score in patients and the Auditory Verbal Learning Test score. Moreover, taking abnormal AI and CFH values as features, support vector machine-based classification achieved good accuracy, sensitivity, specificity, and area under the curve by leave-one-out cross-validation.

CONCLUSION

This study suggests that individuals with AD have abnormal cerebral specialization and interhemispheric cooperation. This provides new insights for further elucidation of the pathological mechanisms of AD.

Blood-based biomarkers of cerebral small vessel disease

Age-associated cerebral small vessel disease (CSVD) represents a clinically heterogenous condition, arising from diverse microvascular mechanisms. These lead to chronic cerebrovascular dysfunction and carry a substantial risk of subsequent stroke and vascular cognitive impairment in aging populations. Owing to advances in neuroimaging, in vivo visualization of cerebral vasculature abnormities and detection of CSVD, including lacunes, microinfarcts, microbleeds and white matter lesions, is now possible, but remains a resource-, skills- and time-intensive approach. As a result, there has been a recent proliferation of blood-based biomarker studies for CSVD aimed at developing accessible screening tools for early detection and risk stratification. However, a good understanding of the pathophysiological processes underpinning CSVD is needed to identify and assess clinically useful biomarkers. Here, we provide an overview of processes associated with CSVD pathogenesis, including endothelial injury and dysfunction, neuroinflammation, oxidative stress, perivascular neuronal damage as well as cardiovascular dysfunction. Then, we review clinical studies of the key biomolecules involved in the aforementioned processes. Lastly, we outline future trends and directions for CSVD biomarker discovery and clinical validation.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization outcomes. We propose two DNN-based harmonization approaches that explicitly account for covariate distribution differences across datasets: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three different continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Therefore, future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat.

Plasma Neurofilament Light Relates to Divergent Default and Salience Network Connectivity in Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia

Background

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) show differential vulnerability to large-scale brain functional networks. Plasma neurofilament light (NfL), a promising biomarker of neurodegeneration, has been linked in AD patients to glucose metabolism changes in AD-related regions. However, it is unknown whether plasma NfL would be similarly associated with disease-specific functional connectivity changes in AD and bvFTD.

Objective

Our study examined the associations between plasma NfL and functional connectivity of the default mode and salience networks in patients with AD and bvFTD.

Methods

Plasma NfL and neuroimaging data from patients with bvFTD (n = 16) and AD or mild cognitive impairment (n = 38; AD + MCI) were analyzed. Seed-based functional connectivity maps of key regions within the default mode and salience networks were obtained and associated with plasma NfL in these patients.

RESULTS

We demonstrated divergent associations between NfL and functional connectivity in AD + MCI and bvFTD patients. Specifically, AD + MCI patients showed lower default mode network functional connectivity with higher plasma NfL, while bvFTD patients showed lower salience network functional connectivity with higher plasma NfL. Further, lower NfL-related default mode network connectivity in AD + MCI patients was associated with lower Montreal Cognitive Assessment scores and higher Clinical Dementia Rating sum-of-boxes scores, although NfL-related salience network connectivity in bvFTD patients was not associated with Neuropsychiatric Inventory Questionnaire scores.

CONCLUSIONS

Our findings indicate that plasma NfL is differentially associated with brain functional connectivity changes in AD and bvFTD.

Whole-brain structure-function coupling abnormalities in mild cognitive impairment: a study combining amplitude of low-frequency fluctuations and voxel-based morphometry

Alzheimer's disease (AD), one of the leading diseases of the nervous system, is accompanied by symptoms such as loss of memory, thinking and language skills. Both mild cognitive impairment (MCI) and very mild cognitive impairment (VMCI) are the transitional pathological stages between normal aging and AD. While the changes in whole-brain structural and functional information have been extensively investigated in AD, The impaired structure-function coupling remains unknown. The current study employed the OASIS-3 dataset, which includes 53 MCI, 90 VMCI, and 100 Age-, gender-, and education-matched normal controls (NC). Several structural and functional parameters, such as the amplitude of low-frequency fluctuations (ALFF), voxel-based morphometry (VBM), and The ALFF/VBM ratio, were used To estimate The whole-brain neuroimaging changes In MCI, VMCI, and NC. As disease symptoms became more severe, these regions, distributed in the frontal-inf-orb, putamen, and paracentral lobule in the white matter (WM), exhibited progressively increasing ALFF (ALFFNC < ALFFVMCI < ALFFMCI), which was similar to the tendency for The cerebellum and putamen in the gray matter (GM). Additionally, as symptoms worsened in AD, the cuneus/frontal lobe in the WM and the parahippocampal gyrus/hippocampus in the GM showed progressively decreasing structure-function coupling. As the typical focal areas in AD, The parahippocampal gyrus and hippocampus showed significant positive correlations with the severity of cognitive impairment, suggesting the important applications of the ALFF/VBM ratio in brain disorders. On the other hand, these findings from WM functional signals provided a novel perspective for understanding the pathophysiological mechanisms involved In cognitive decline in AD.

Longitudinal associations between β-amyloid and cortical thickness in mild cognitive impairment

How beta-amyloid accumulation influences brain atrophy in Alzheimer's disease remains contentious with conflicting findings. We aimed to elucidate the correlations of regional longitudinal atrophy with cross-sectional regional and global amyloid in individuals with mild cognitive impairment and no cognitive impairment. We hypothesized that greater cortical thinning over time correlated with greater amyloid deposition, particularly within Alzheimer's disease characteristic regions in mild cognitive impairment, and weaker or no correlations in those with no cognitive impairment. 45 patients with mild cognitive impairment and 12 controls underwent a cross-sectional [¹¹C]-Pittsburgh Compound B PET and two retrospective longitudinal structural imaging (follow-up: 23.65 ± 2.04 months) to assess global/regional amyloid and regional cortical thickness, respectively. Separate linear mixed models were constructed to evaluate relationships of either global or regional amyloid with regional cortical thinning longitudinally. In patients with mild cognitive impairment, regional amyloid in the right banks of the superior temporal sulcus was associated with longitudinal cortical thinning in the right medial orbitofrontal cortex (P = 0.04 after False Discovery Rate correction). In the mild cognitive impairment group, greater right banks amyloid burden and less cortical thickness in the right medial orbitofrontal cortex showed greater visual and verbal memory decline over time, which was not observed in controls. Global amyloid was not associated with longitudinal cortical thinning in any locations in either group. Our findings indicate an increasing influence of amyloid on neurodegeneration and memory along the preclinical to prodromal spectrum. Future multimodal studies that include additional biomarkers will be well-suited to delineate the interplay between various pathological processes and amyloid and memory decline, as well as clarify their additive or independent effects along the disease deterioration.

Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer's disease continuum

Background

Large-scale neuronal network breakdown underlies memory impairment in Alzheimer's disease (AD). However, the differential trajectories of the relationships between network organisation and memory across pathology and cognitive stages in AD remain elusive. We determined whether and how the influences of individual-level structural and metabolic covariance network integrity on memory varied with amyloid pathology across clinical stages without assuming a constant relationship.

Methods

Seven hundred and eight participants from the Alzheimer's Disease Neuroimaging Initiative were studied. Individual-level structural and metabolic covariance scores in higher-level cognitive and hippocampal networks were derived from magnetic resonance imaging and [18F] fluorodeoxyglucose positron emission tomography using seed-based partial least square analyses. The non-linear associations between network scores and memory across cognitive stages in each pathology group were examined using sparse varying coefficient modelling.

Results

We showed that the associations of memory with structural and metabolic networks in the hippocampal and default mode regions exhibited pathology-dependent differential trajectories across cognitive stages using sparse varying coefficient modelling. In amyloid pathology group, there was an early influence of hippocampal structural network deterioration on memory impairment in the preclinical stage, and a biphasic influence of the angular gyrus-seeded default mode metabolic network on memory in both preclinical and dementia stages. In non-amyloid pathology groups, in contrast, the trajectory of the hippocampus-memory association was opposite and weaker overall, while no metabolism covariance networks were related to memory. Key findings were replicated in a larger cohort of 1280 participants.

Conclusions

Our findings highlight potential windows of early intervention targeting network breakdown at the preclinical AD stage.

Funding

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). We also acknowledge the funding support from the Duke NUS/Khoo Bridge Funding Award (KBrFA/2019-0020) and NMRC Open Fund Large Collaborative Grant (OFLCG09May0035).

Goal-specific brain MRI harmonization

There is significant interest in pooling magnetic resonance image (MRI) data from multiple datasets to enable mega-analysis. Harmonization is typically performed to reduce heterogeneity when pooling MRI data across datasets. Most MRI harmonization algorithms do not explicitly consider downstream application performance during harmonization. However, the choice of downstream application might influence what might be considered as study-specific confounds. Therefore, ignoring downstream applications during harmonization might potentially limit downstream performance. Here we propose a goal-specific harmonization framework that utilizes downstream application performance to regularize the harmonization procedure. Our framework can be integrated with a wide variety of harmonization models based on deep neural networks, such as the recently proposed conditional variational autoencoder (cVAE) harmonization model. Three datasets from three different continents with a total of 2787 participants and 10,085 anatomical T1 scans were used for evaluation. We found that cVAE removed more dataset differences than the widely used ComBat model, but at the expense of removing desirable biological information as measured by downstream prediction of mini mental state examination (MMSE) scores and clinical diagnoses. On the other hand, our goal-specific cVAE (gcVAE) was able to remove as much dataset differences as cVAE, while improving downstream cross-sectional prediction of MMSE scores and clinical diagnoses.

Deep phenotyping of Alzheimer's disease leveraging electronic medical records identifies sex-specific clinical associations

Alzheimer's Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches.

Hippocampus-Based Dynamic Functional Connectivity Mapping in the Early Stages of Alzheimer's Disease

BACKGROUND

The hippocampus with varying degrees of atrophy was a crucial neuroimaging feature resulting in the declining memory and cognitive function in Alzheimer's disease (AD). However, the abnormal dynamic functional connectivity (DFC) in both white matter (WM) and gray matter (GM) from the left and right hippocampus remains unclear.

OBJECTIVE

To explore the abnormal DFC within WM and GM from the left and right hippocampus across the different stages of AD.

METHODS

Current study employed the OASIS-3 dataset including 43 mild cognitive impairment (MCI), 71 pre-mild cognitive impairment (pre-MCI), and matched 87 normal cognitive (NC). Adopting the FMRIB's Integrated Registration and Segmentation Tool, we obtained the left and right hippocampus mask. Based on above hippocampus mask as seed point, we calculated the DFC between left/right hippocampus and all voxel time series within whole brain. One-way ANOVA analysis was performed to estimate the abnormal DFC among MCI, pre-MCI, and NC groups.

RESULTS

We found that MCI and pre-MCI groups showed the common abnormalities of DFC in the Temporal_Mid_L, Cingulum_Mid_L, and Thalamus_L. Specific abnormalities were found in the Cerebelum_9_L and Precuneus of MCI group and Vermis_8 and Caudate_L of pre-MCI group. In addition, we found that DFC within WM regions also showed the common low DFC for the Cerebellum anterior lobe-WM, Corpus callosum, and Frontal lobe-WM in MCI and pre-MCI group.

CONCLUSION

Our findings provided a novel information for discover the pathophysiological mechanisms of AD and indicate WM lesions were also an important cause of cognitive decline in AD.

White matter network damage mediates association between cerebrovascular disease and cognition

To determine whether white matter network disruption mediates the association between MRI markers of cerebrovascular disease (CeVD) and cognitive impairment. Participants (n = 253, aged ≥60 years) from the Epidemiology of Dementia in Singapore study underwent neuropsychological assessments and MRI. CeVD markers were defined as lacunes, white matter hyperintensities (WMH), microbleeds, cortical microinfarcts, cortical infarcts and intracranial stenosis (ICS). White matter microstructure damage was measured as fractional anisotropy and mean diffusivity by tract based spatial statistics from diffusion tensor imaging. Cognitive function was summarized as domain-specific Z-scores.Lacunar counts, WMH volume and ICS were associated with worse performance in executive function, attention, language, verbal and visual memory. These three CeVD markers were also associated with white matter microstructural damage in the projection, commissural, association, and limbic fibers. Path analyses showed that lacunar counts, higher WMH volume and ICS were associated with executive and verbal memory impairment via white matter disruption in commissural fibers whereas impairment in the attention, visual memory and language were mediated through projection fibers.Our study shows that the abnormalities in white matter connectivity may underlie the relationship between CeVD and cognition. Further longitudinal studies are needed to understand the cause-effect relationship between CeVD, white matter damage and cognition.

Brain Functional Changes in Stroke Following Rehabilitation Using Brain-Computer Interface-Assisted Motor Imagery With and Without tDCS: A Pilot Study

Brain-computer interface-assisted motor imagery (MI-BCI) or transcranial direct current stimulation (tDCS) has been proven effective in post-stroke motor function enhancement, yet whether the combination of MI-BCI and tDCS may further benefit the rehabilitation of motor functions remains unknown. This study investigated brain functional activity and connectivity changes after a 2 week MI-BCI and tDCS combined intervention in 19 chronic subcortical stroke patients. Patients were randomized into MI-BCI with tDCS group and MI-BCI only group who underwent 10 sessions of 20 min real or sham tDCS followed by 1 h MI-BCI training with robotic feedback. We derived amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) from resting-state functional magnetic resonance imaging (fMRI) data pre- and post-intervention. At baseline, stroke patients had lower ALFF in the ipsilesional somatomotor network (SMN), lower ReHo in the contralesional insula, and higher ALFF/Reho in the bilateral posterior default mode network (DMN) compared to age-matched healthy controls. After the intervention, the MI-BCI only group showed increased ALFF in contralesional SMN and decreased ALFF/Reho in the posterior DMN. In contrast, no post-intervention changes were detected in the MI-BCI + tDCS group. Furthermore, higher increases in ALFF/ReHo/FC measures were related to better motor function recovery (measured by the Fugl-Meyer Assessment scores) in the MI-BCI group while the opposite association was detected in the MI-BCI + tDCS group. Taken together, our findings suggest that brain functional re-normalization and network-specific compensation were found in the MI-BCI only group but not in the MI-BCI + tDCS group although both groups gained significant motor function improvement post-intervention with no group difference. MI-BCI and tDCS may exert differential or even opposing impact on brain functional reorganization during post-stroke motor rehabilitation; therefore, the integration of the two strategies requires further refinement to improve efficacy and effectiveness.

Causal structural covariance network revealing atrophy progression in Alzheimer's disease continuum

The structural covariance network (SCN) has provided a perspective on the large‐scale brain organization impairment in the Alzheimer's Disease (AD) continuum. However, the successive structural impairment across brain regions, which may underlie the disrupted SCN in the AD continuum, is not well understood. In the current study, we enrolled 446 subjects with AD, mild cognitive impairment (MCI) or normal aging (NA) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The SCN as well as a casual SCN (CaSCN) based on Granger causality analysis were applied to the T1‐weighted structural magnetic resonance images of the subjects. Compared with that of the NAs, the SCN was disrupted in the MCI and AD subjects, with the hippocampus and left middle temporal lobe being the most impaired nodes, which is in line with previous studies. In contrast, according to the 194 subjects with records on CSF amyloid and Tau, the CaSCN revealed that during AD progression, the CaSCN was enhanced. Specifically, the hippocampus, thalamus, and precuneus/posterior cingulate cortex (PCC) were identified as the core regions in which atrophy originated and could predict atrophy in other brain regions. Taken together, these findings provide a comprehensive view of brain atrophy in the AD continuum and the relationships among the brain atrophy in different regions, which may provide novel insight into the progression of AD.

Task-related brain functional network reconfigurations relate to motor recovery in chronic subcortical stroke

Stroke leads to both regional brain functional disruptions and network reorganization. However, how brain functional networks reconfigure as task demand increases in stroke patients and whether such reorganization at baseline would facilitate post-stroke motor recovery are largely unknown. To address this gap, brain functional connectivity (FC) were examined at rest and motor tasks in eighteen chronic subcortical stroke patients and eleven age-matched healthy controls. Stroke patients underwent a 2-week intervention using a motor imagery-assisted brain computer interface-based (MI-BCI) training with or without transcranial direct current stimulation (tDCS). Motor recovery was determined by calculating the changes of the upper extremity component of the Fugl–Meyer Assessment (FMA) score between pre- and post-intervention divided by the pre-intervention FMA score. The results suggested that as task demand increased (i.e., from resting to passive unaffected hand gripping and to active affected hand gripping), patients showed greater FC disruptions in cognitive networks including the default and dorsal attention networks. Compared to controls, patients had lower task-related spatial similarity in the somatomotor–subcortical, default–somatomotor, salience/ventral attention–subcortical and subcortical–subcortical connections, suggesting greater inefficiency in motor execution. Importantly, higher baseline network-specific FC strength (e.g., dorsal attention and somatomotor) and more efficient brain network reconfigurations (e.g., somatomotor and subcortical) from rest to active affected hand gripping at baseline were related to better future motor recovery. Our findings underscore the importance of studying functional network reorganization during task-free and task conditions for motor recovery prediction in stroke.

Development and validation of the Uniform Data Set (v3.0) executive function composite score (UDS3-EF)

INTRODUCTION

Cognitive composite scores offer a means of precisely measuring executive functioning (EF).

METHODS

We developed the Uniform Data Set v3.0 EF composite score (UDS3-EF) in 3507 controls from the National Alzheimer's Coordinating Center dataset using item-response theory and applied nonlinear and linear demographic adjustments. The UDS3-EF was validated with other neuropsychological tests and brain magnetic resonance imaging from independent research cohorts using linear models.

RESULTS

Final model fit was good-to-excellent: comparative fit index = 0.99; root mean squared error of approximation = 0.057. UDS3-EF scores differed across validation cohorts (controls > mild cognitive impairment > Alzheimer's disease-dementia ≈ behavioral variant frontotemporal dementia; P < 0.001). The UDS3-EF correlated most strongly with other EF tests (βs = 0.50 to 0.85, Ps < 0.001) and more with frontal, parietal, and temporal lobe gray matter volumes (βs = 0.18 to 0.33, Ps ≤ 0.004) than occipital gray matter (β = 0.12, P = 0.04). The total sample needed to detect a 40% reduction in UDS3-EF change (n = 286) was ≈40% of the next best measure (F-words; n = 714).

CONCLUSIONS

The UDS3-EF is well suited to quantify EF in research and clinical trials and offers psychometric and practical advantages over its component tests.

The Influence of Cerebral Small Vessel Disease on Static and Dynamic Functional Network Connectivity in Subjects Along Alzheimer's Disease Continuum

Background: Alzheimer's disease (AD) is a chronic neurodegenerative disorder frequently accompanied by cerebral small vessel disease (CSVD). However, the influence of CSVD on the brain functional connectivity in subjects along the AD continuum is still largely unknown. The current study combined the static and dynamic functional network connectivity (FNC) to explore the underlying mechanism. Materials and Methods: In this study, we included 182 healthy controls, 27 individuals with subjective cognitive decline (SCD), 27 with SCD+CSVD, 104 with mild cognitive impairment (MCI), 123 with MCI+CSVD, 16 with AD, and 62 with AD+CSVD. We examined the static and dynamic FNC within the default mode, salience, and cognitive control domains. We also assessed the association between atypical FNC patterns and cognitive impairments, as well as the pathologies. Results: Static FNC results showed progressively increased within-domain connectivity and decreased between-domain connectivity along the AD continuum, especially in CSVD subjects. Dynamic FNC in CSVD subjects showed more occurrences in a highly modularized state and fewer occurrences in the diffusely connected state. Further analysis showed that neuropathology and CSVD burden divergently affect the FNC changes. Conclusions: The overall results demonstrate divergent abnormalities of FNC in CSVD and non-CSVD individuals along the AD continuum, which were divergently affected by neuropathology and CSVD burden. Specifically, those with CSVD show more static and dynamic FNC impairments, associated with cognitive decline. These findings may advance our understanding of the effect of CSVD on AD onset and progression, and provide potential hints for clinical treatment.

Dynamic Connectivity Alteration Facilitates Cognitive Decline in Alzheimer's Disease Spectrum

Introduction: It is unknown the alterations in the dynamic networks of the brain and the underlying molecular pathological mechanism of Alzheimer's disease (AD) spectrum. Here, we aim to explore the association between alterations in the dynamic brain networks' trajectory and cognitive decline in the AD spectrum. Methods: One hundred sixty subjects were recruited from the ADNI database, including 49 early mild cognitive impairment, 28 late mild cognitive impairment, 24 AD patients, and 59 cognitively normal. All participants completed the resting-state functional magnetic resonance imaging scan and neuropsychological tests. We integrated a new method combining large-scale network analysis and canonical correlation analysis to explore the dynamic spatiotemporal patterns within- and between resting-state networks (RSNs) and their significance in the AD spectrum. Results: All RSNs represented an increase in connectivity within networks by enhancing inner cohesive ability, while 7 out of 10 RSNs were characterized by a decrease in connectivity between networks, which indicated a weakened connector among networks from the early stage to dementia. This dichotomous mode presenting large-scale dynamic network abnormality was significantly correlated with the levels of molecular biomarkers of AD, and cognitive performance, as well as with the accumulating effects of 10 identified AD-related genetic risk factors. Discussion: These findings deepen our understanding of the associated mechanism underlying large-scale network disruption, linking known molecular biomarkers and phenotypic variations in the AD spectrum.

Distinct network topology in Alzheimer's disease and behavioral variant frontotemporal dementia

Background

Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) cause distinct atrophy and functional disruptions within two major intrinsic brain networks, namely the default network and the salience network, respectively. It remains unclear if inter-network relationships and whole-brain network topology are also altered and underpin cognitive and social–emotional functional deficits.

Methods

In total, 111 participants (50 AD, 14 bvFTD, and 47 age- and gender-matched healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessments. Functional connectivity was derived among 144 brain regions of interest. Graph theoretical analysis was applied to characterize network integration, segregation, and module distinctiveness (degree centrality, nodal efficiency, within-module degree, and participation coefficient) in AD, bvFTD, and healthy participants. Group differences in graph theoretical measures and empirically derived network community structures, as well as the associations between these indices and cognitive performance and neuropsychiatric symptoms, were subject to general linear models, with age, gender, education, motion, and scanner type controlled.

Results

Our results suggested that AD had lower integration in the default and control networks, while bvFTD exhibited disrupted integration in the salience network. Interestingly, AD and bvFTD had the highest and lowest degree of integration in the thalamus, respectively. Such divergence in topological aberration was recapitulated in network segregation and module distinctiveness loss, with AD showing poorer modular structure between the default and control networks, and bvFTD having more fragmented modules in the salience network and subcortical regions. Importantly, aberrations in network topology were related to worse attention deficits and greater severity in neuropsychiatric symptoms across syndromes.

Conclusions

Our findings underscore the reciprocal relationships between the default, control, and salience networks that may account for the cognitive decline and neuropsychiatric symptoms in dementia.

Cerebral microinfarcts affect brain structural network topology in cognitively impaired patients

Cerebral microinfarcts (CMIs), a novel cerebrovascular marker, are prevalent in Alzheimer's disease (AD) and associated with cognitive impairment. Nonetheless, the underlying mechanism of how CMIs influence cognition remains uncertain. We hypothesized that cortical-CMIs disrupted structural connectivity in the higher-order cognitive networks, leading to cognitive impairment. We analyzed diffusion-MRI data of 92 AD (26 with cortical-CMIs) and 110 cognitive impairment no dementia patients (CIND, 28 with cortical-CMIs). We compared structural network topology between groups with and without cortical-CMIs in AD/CIND, and tested whether structural connectivity mediated the association between cortical-CMIs and cognition. Cortical-CMIs correlated with impaired structural network topology (i.e. lower efficiency/degree centrality in the executive control/dorsal attention networks in CIND, and lower clustering coefficient in the default mode/dorsal attention networks in AD), which mediated the association of cortical-CMIs with visuoconstruction dysfunction. Our findings provide the first in vivo human evidence that cortical-CMIs impair cognition in elderly via disrupting structural connectivity.

Longitudinal functional brain network reconfiguration in healthy aging

Healthy aging is associated with changes in cognitive performance and functional brain organization. In fact, cross-sectional studies imply lower modularity and significant heterogeneity in modular architecture across older subjects. Here, we used a longitudinal dataset consisting of four occasions of resting-state-fMRI and cognitive testing (spanning 4 years) in 150 healthy older adults. We applied a graph-theoretic analysis to investigate the time-evolving modular structure of the whole-brain network, by maximizing the multilayer modularity across four time points. Global flexibility, which reflects the tendency of brain nodes to switch between modules across time, was significantly higher in healthy elderly than in a temporal null model. Further, global flexibility, as well as network-specific flexibility of the default mode, frontoparietal control, and somatomotor networks, were significantly associated with age at baseline. These results indicate that older age is related to higher variability in modular organization. The temporal metrics were not associated with simultaneous changes in processing speed or learning performance in the context of memory encoding. Finally, this approach provides global indices for longitudinal change across a given time span and it may contribute to uncovering patterns of modular variability in healthy and clinical aging populations.

Differential Effects of Confluent and Nonconfluent White Matter Hyperintensities on Functional Connectivity in Mild Cognitive Impairment

Background: White matter hyperintensities (WMHs) indicate active small vessel disease. Emerging evidence suggests that confluent WMH (C-WMH) results in greater cognitive impairment compared with nonconfluent WMH (NC-WMH) visualized as punctate lesions. However, the mechanism linking C-WMH and early cognitive impairment is not clearly understood. Aims: To investigate the effects of C-WMH and NC-WMH on whole-brain functional connectivity (FC) across 138 regions of interest (ROIs) and cognition in 63 subjects with mild cognitive impairment (MCI). Methods: MCI subjects were classified as C-WMH or NC-WMH using the Staals criteria on the Fazekas WMH scale. Group-level ROI-to-ROI FC trends and differences based on WMH subtypes were computed using standard resting-state functional magnetic resonance imaging analysis. Global cognitive performance was measured with mini-mental state examination (MMSE). Results: Subjects with C-WMH exhibited increased inter-regional FC in the fronto-parietal, fronto-occipital, parieto-occipital, and temporo-parietal regions of the salience, dorsal-attention, default-mode, and visual networks compared with NC-WMH. Increased intra-regional FC was also observed within the frontal and parietal lobes in C-WMH. In addition to widespread increased FC in C-WMH, a few regions in the fronto-temporal and intra-frontal areas demonstrated reduced FC in C-WMH compared with NC-WMH. Analyses of cognitive correlates demonstrated increased parieto-occipital FC to be negatively associated with MMSE in the C-WMH. The increased parieto-occipital FC may be related to loss of higher order inhibitory control in the parieto-occipital regions induced by C-WMH or alternatively a compensatory mechanism to FC alterations induced by C-WMH. Conclusion: C-WMH in subjects with MCI is associated with widespread increase in intra- and inter-regional FC. These findings provide novel insights into divergent FC related to confluence of WMH in MCI. Impact statement White matter hyperintensities (WMHs) have been demonstrated to be a major risk factor for progressive cognitive decline. However, the relationship between structural and functional brain changes related to different types of WMH lesions as well as different stages of WMH progression remains unclear. In this study, we demonstrate that confluent WMH is significantly associated with divergent functional connectivity changes in patients with mild cognitive impairment (MCI). This finding may allow identification of MCI subjects who are adversely affected by WMH and thus provides a window of opportunity to introduce pharmacological and nonpharmacological interventional measures.

Cognition in Vascular Aging and Mild Cognitive Impairment

Cardiovascular health declines with age, due to vascular risk factors, and this leads to an increasing risk of cognitive decline. Mild cognitive impairment (MCI) is defined as the negative cognitive changes beyond what is expected in normal aging. The purpose of the study was to compare older adults with vascular risk factors (VRF), MCI patients, and healthy controls (HC) in main dimensions of cognitive control. The sample comprised a total of 109 adults, aged 50 to 85 (M = 66.09, S.D. = 9.02). They were divided into three groups: 1) older adults with VRF, 2) MCI patients, and 3) healthy controls (HC). VRF and MCI did not differ significantly in age, educational level, or gender as was the case with HC. The tests used mainly examine inhibition, cognitive flexibility, and working memory processing. Results showed that the VRF group had more Set Loss Errors in drawing designs indicating deficits in establishing cognitive set and in cognitive shifting. MCI patients displayed lower performance in processing. Hence, different types of specific impairments emerge in vascular aging and MCI, and this may imply that discrete underlying pathologies may play a role in the development of somewhat different profiles of cognitive decline.

Degeneration of structural brain networks is associated with cognitive decline after ischaemic stroke

Over one-third of stroke patients has long-term cognitive impairment. The likelihood of cognitive dysfunction is poorly predicted by the location or size of the infarct. The macro-scale damage caused by ischaemic stroke is relatively localized, but the effects of stroke occur across the brain. Structural covariance networks represent voxelwise correlations in cortical morphometry. Atrophy and topographical changes within such distributed brain structural networks may contribute to cognitive decline after ischaemic stroke, but this has not been thoroughly investigated. We examined longitudinal changes in structural covariance networks in stroke patients and their relationship to domain-specific cognitive decline. Seventy-three patients (mean age, 67.41 years; SD = 12.13) were scanned with high-resolution magnetic resonance imaging at sub-acute (3 months) and chronic (1 year) timepoints after ischaemic stroke. Patients underwent a number of neuropsychological tests, assessing five cognitive domains including attention, executive function, language, memory and visuospatial function at each timepoint. Individual-level structural covariance network scores were derived from the sub-acute grey-matter probabilistic maps or changes in grey-matter probability maps from sub-acute to chronic using data-driven partial least squares method seeding at major nodes in six canonical high-order cognitive brain networks (i.e. dorsal attention, executive control, salience, default mode, language-related and memory networks). We then investigated co-varying patterns between structural covariance network scores within canonical distributed brain networks and domain-specific cognitive performance after ischaemic stroke, both cross-sectionally and longitudinally, using multivariate behavioural partial least squares correlation approach. We tested our models in an independent validation data set with matched imaging and behavioural testing and using split-half validation. We found that distributed degeneration in higher-order cognitive networks was associated with attention, executive function, language, memory and visuospatial function impairment in sub-acute stroke. From the sub-acute to the chronic timepoint, longitudinal structural co-varying patterns mirrored the baseline structural covariance networks, suggesting synchronized grey-matter volume decline occurred within established networks over time. The greatest changes, in terms of extent of distributed spatial co-varying patterns, were in the default mode and dorsal attention networks, whereas the rest were more focal. Importantly, faster degradation in these major cognitive structural covariance networks was associated with greater decline in attention, memory and language domains frequently impaired after stroke. Our findings suggest that sub-acute ischaemic stroke is associated with widespread degeneration of higher-order structural brain networks and degradation of these structural brain networks may contribute to longitudinal domain-specific cognitive dysfunction.

Indoor incense burning impacts cognitive functions and brain functional connectivity in community older adults

To investigate (1) the effects of indoor incense burning upon cognition over 3 years; (2) the associations between indoor incense burning with the brain’s structure and functional connectivity of the default mode network (DMN); and (3) the interactions between indoor incense burning and vascular disease markers upon cognitive functions. Community older adults without stroke or dementia were recruited (n = 515). Indoor incense use was self-reported as having burnt incense at home ≥ weekly basis over the past 5 years. Detailed neuropsychological battery was administered at baseline (n = 227) and the Montreal Cognitive Assessment at baseline and year 3 (n = 515). MRI structural measures and functional connectivity of the DMN were recorded at baseline. Demographic and vascular risk factors and levels of outdoor pollutants were treated as covariates. Indoor incense burning was associated with reduced performance across multiple cognitive domains at baseline and year 3 as well as decreased connectivity in the DMN. It interacted with diabetes mellitus, hyperlipidemia and white matter hyperintensities to predict poorer cognitive performance. Indoor incense burning is (1) associated with poorer cognitive performance over 3 years; (2) related to decreased brain connectivity; and (3) it interacts with vascular disease to predispose poor cognitive performance.

Distinct BOLD variability changes in the default mode and salience networks in Alzheimer's disease spectrum and associations with cognitive decline

Optimal levels of intrinsic Blood-Oxygenation-Level-Dependent (BOLD) signal variability (variability hereafter) are important for normative brain functioning. However, it remains largely unknown how network-specific and frequency-specific variability changes along the Alzheimer's disease (AD) spectrum and relates to cognitive decline. We hypothesized that cognitive impairment was related to distinct BOLD variability alterations in two brain networks with reciprocal relationship, i.e., the AD-specific default mode network (DMN) and the salience network (SN). We examined variability of resting-state fMRI data at two characteristic slow frequency-bands of slow4 (0.027-0.073 Hz) and slow5 (0.01-0.027 Hz) in 96 AD, 98 amnestic mild cognitive impairment (aMCI), and 48 age-matched healthy controls (HC) using two commonly used pre-processing pipelines. Cognition was measured with a neuropsychological assessment battery. Using both global signal regression (GSR) and independent component analysis (ICA), results generally showed a reciprocal DMN-SN variability balance in aMCI (vs. AD and/or HC), although there were distinct frequency-specific variability patterns in association with different pre-processing approaches. Importantly, lower slow4 posterior-DMN variability correlated with poorer baseline cognition/smaller hippocampus and predicted faster cognitive decline in all patients using both GSR and ICA. Altogether, our findings suggest that reciprocal DMN-SN variability balance in aMCI might represent an early signature in neurodegeneration and cognitive decline along the AD spectrum.

Longitudinal trajectory of Amyloid-related hippocampal subfield atrophy in nondemented elderly

Hippocampal atrophy and abnormal β‐Amyloid (Aβ) deposition are established markers of Alzheimer's disease (AD). Nonetheless, longitudinal trajectory of Aβ‐associated hippocampal subfield atrophy prior to dementia remains unclear. We hypothesized that elevated Aβ correlated with longitudinal subfield atrophy selectively in no cognitive impairment (NCI), spreading to other subfields in mild cognitive impairment (MCI). We analyzed data from two independent longitudinal cohorts of nondemented elderly, including global PET‐Aβ in AD‐vulnerable cortical regions and longitudinal subfield volumes quantified with a novel auto‐segmentation method (FreeSurfer v.6.0). Moreover, we investigated associations of Aβ‐related progressive subfield atrophy with memory decline. Across both datasets, we found a converging pattern that higher Aβ correlated with faster CA1 volume decline in NCI. This pattern spread to other hippocampal subfields in MCI group, correlating with memory decline. Our results for the first time suggest a longitudinal focal‐to‐widespread trajectory of Aβ‐associated hippocampal subfield atrophy over disease progression in nondemented elderly.

Breadth and depth of working memory and executive function compromises in mild cognitive impairment and their relationships to frontal lobe morphometry and functional competence

INTRODUCTION

The extent of working memory (WM) and executive function (EF) impairment in mild cognitive impairment (MCI) is not well-characterized.

METHODS

We compared 48 patients with MCI, 124 noncognitively impaired elderly healthy controls, and 57 patients with Alzheimer's disease (AD) on multiple WM/EF measures, frontal lobe integrity indexes, and functioning.

RESULTS

Patients with MCI demonstrated worse performance on nearly all WM/EF tests. This profile of impairment was refined in a factor analysis that identified three primary WM/EF constructs: WM storage; speed and controlled visual search; and manipulation of information and problem solving. EF impairments were associated with reductions in prefrontal cortical thickness. WM/EF accounted for over 50% of the variance in functional competence.

DISCUSSION

In MCI, WM/EF impairments are far from rare, based on specific compromises to frontal cortex circuitry, and are associated with loss of everyday functioning. WM/EF impairments, even at this potentially prodromal stage of AD, have clinically deleterious consequences.

White Matter Hyperintensities Relate to Basal Ganglia Functional Connectivity and Memory Performance in aMCI and SVMCI

Cerebral small vessel diseases play a crucial role in both vascular and non-vascular dementias. The location of white matter hyperintensities (WMHs), a neuroimaging marker of cerebral small vessel disease, has been found to vary between different types of dementias, and those in the basal ganglia (BG) have been particularly associated with vascular cognitive impairment (VCI). However, anatomical variation of WMHs across BG nuclei and its effect on brain network dysconnectivity has not been clearly elucidated. The study sample consisted of 40 patients with amnestic mild cognitive impairment (aMCI), 40 with subcortical vascular MCI (SVMCI), and 40 healthy control subjects. We examined the volume of WMH using T2-weighted magnetic resonance imaging. We also assessed the disturbances in BG-cortical communication by measuring resting-state functional connectivity (rsFC) from the functional magnetic resonance imaging signal. WMHs were more pronounced in the SVMCI group particularly in the caudate regions. In SVMCI patients, while higher WMHs in the dorsal caudate correlated with weaker FC with executive control regions and worse immediate recall performance, WMHs in the ventral caudate were associated with weaker FC with anterior default mode regions and worse delayed recall performance. In contrast, in aMCI patients, BG WMHs were not correlated with their changes in functional connectivity changes, which showed weaker connectivity with almost all BG structures, rather than restricting to specific BG subdivisions as observed in the SVMCI group. Our findings demonstrate that heterogeneously distributed BG WMHs are associated with changes in functional network interactions and verbal episodic memory performance only in SVMCI patients, which establishes a link between cerebrovascular-related structural abnormality, functional integrity of BG circuits, and episodic memory impairments in SVMCI, and may reflect a differential role of the cerebrovascular pathology in disrupting network-level communications and cognition between Alzheimer's and subcortical vascular dementia.

Time-efficient physical training for enhancing cardiovascular function in midlife and older adults: promise and current research gaps

Cardiovascular diseases (CVD) remain the leading cause of death in developed societies, and "midlife" (50-64 yr) and older (65+) men and women bear the great majority of the burden of CVD. Much of the increased risk of CVD in this population is attributable to CV dysfunction, including adverse changes in the structure and function of the heart, increased systolic blood pressure, and arterial dysfunction. The latter is characterized by increased arterial stiffness and vascular endothelial dysfunction. Conventional aerobic exercise training, as generally recommended in public health guidelines, is an effective strategy to preserve or improve CV function with aging. However, <40% of midlife and older adults meet aerobic exercise guidelines, due in part to time availability-related barriers. As such, there is a need to develop evidence-based time-efficient exercise interventions that promote adherence and optimize CV function in these groups. Two promising interventions that may meet these criteria are interval training and inspiratory muscle strength training (IMST). Limited research suggests these modes of training may improve CV function with time commitments of ≤60 min/wk. This review will summarize the current evidence for interval training and IMST to improve CV function in midlife/older adults and identify key research gaps and future directions.

Regional White Matter Hyperintensity Influences Grey Matter Atrophy in Mild Cognitive Impairment

The association between cerebrovascular disease pathology (measured by white matter hyperintensities, WMH) and brain atrophy in early Alzheimer's disease (AD) remain to be elucidated. Thus, we investigated how WMH influence neurodegeneration and cognition in prodromal and clinical AD. We examined 51 healthy controls, 35 subjects with mild cognitive impairment (MCI), and 30 AD patients. We tested how total and regional WMH is related to specific grey matter volume (GMV) reductions in MCI and AD compared to controls. Stepwise regression analysis was further performed to investigate the association of GMV and regional WMH volume with global cognition. We found that total WMH volume was highest in AD but showed the strongest association with lower GMV in MCI. Frontal and parietal WMH had the most extensive influence on GMV loss in MCI. Additionally, parietal lobe WMH volume (but not hippocampal atrophy) was significantly associated with global cognition in MCI while smaller hippocampal volume (but not WMH volume) was associated with lower global cognition in AD. Thus, although WMH volume was highest in AD subjects, it had a more pervasive influence on brain structure and cognitive impairment in MCI. Our study thus highlights the importance of early detection of cerebrovascular disease, as its intervention at the MCI stage might potentially slow down neurodegeneration.

Amyloid and cerebrovascular burden divergently influence brain functional network changes over time

Objective

To examine the effects of baseline Alzheimer disease and cerebrovascular disease markers on longitudinal default mode network (DMN) and executive control network (ECN) functional connectivity (FC) changes in mild cognitive impairment (MCI).

Methods

We studied 30 patients with amnestic MCI (aMCI) and 55 patients with subcortical vascular MCI (svMCI) with baseline Pittsburgh Compound B (PiB)–PET scans and longitudinal MRI scans. Participants were followed up clinically with annual MRI for up to 4 years (aMCI: 26 with 2 timepoints, 4 with 3 timepoints; svMCI: 13 with 2 timepoints, 16 with 3 timepoints, 26 with 4 timepoints).

Results

β-Amyloid (Aβ) burden was associated with longitudinal DMN FC declines, while cerebrovascular burden was associated with longitudinal ECN FC changes. When patients were divided into PiB+ and PiB− groups, PiB+ patients showed longitudinal DMN FC declines, while patients with svMCI showed longitudinal ECN FC increases. Direct comparisons between the 2 groups without mixed pathology (aMCI PiB+ and svMCI PiB−) recapitulated this divergent pattern: aMCI PiB+ patients showed steeper longitudinal DMN FC declines, while svMCI PiB− patients showed steeper longitudinal ECN FC increases. Finally, using baseline PiB uptake and lacune numbers as continuous variables, baseline PiB uptake showed inverse U-shape associations with longitudinal DMN FC changes in both MCI subtypes, while baseline lacune numbers showed mainly inverse U-shape relationships with longitudinal ECN FC changes in patients with svMCI.

Conclusions

Our findings underscore the divergent effects of Aβ and cerebrovascular burden on longitudinal FC changes in the DMN and ECN in the predementia stage, which reflect the underlying pathology and may be used to track early changes in Alzheimer disease and cerebrovascular disease.

Longitudinal Changes in the Cerebral Cortex Functional Organization of Healthy Elderly

Healthy aging is accompanied by disruptions in the functional modular organization of the human brain. Cross-sectional studies have shown age-related reductions in the functional segregation and distinctiveness of brain networks. However, less is known about the longitudinal changes in brain functional modular organization and their associations with aging-related cognitive decline. We examined age- and aging-related changes in functional architecture of the cerebral cortex using a dataset comprising a cross-sectional healthy young cohort of 57 individuals (mean ± SD age, 23.71 ± 3.61 years, 22 males) and a longitudinal healthy elderly cohort of 72 individuals (mean ± baseline age, 68.22 ± 5.80 years, 39 males) with 2-3 time points (18-24 months apart) of task-free fMRI data. We found both cross-sectional (elderly vs young) and longitudinal (in elderly) global decreases in network segregation (decreased local efficiency), integration (decreased global efficiency), and module distinctiveness (increased participation coefficient and decreased system segregation). At the modular level, whereas cross-sectional analyses revealed higher participation coefficient across all modules in the elderly compared with young participants, longitudinal analyses revealed focal longitudinal participation coefficient increases in three higher-order cognitive modules: control network, default mode network, and salience/ventral attention network. Cross-sectionally, elderly participants also showed worse attention performance with lower local efficiency and higher mean participation coefficient, and worse global cognitive performance with higher participation coefficient in the dorsal attention/control network. These findings suggest that healthy aging is associated with whole-brain connectome-wide changes in the functional modular organization of the brain, accompanied by loss of functional segregation, particularly in higher-order cognitive networks.SIGNIFICANCE STATEMENT Cross-sectional studies have demonstrated age-related reductions in the functional segregation and distinctiveness of brain networks. However, longitudinal aging-related changes in brain functional modular architecture and their links to cognitive decline remain relatively understudied. Using graph theoretical and community detection approaches to study task-free functional network changes in a cross-sectional young and longitudinal healthy elderly cohort, we showed that aging was associated with global declines in network segregation, integration, and module distinctiveness, and specific declines in distinctiveness of higher-order cognitive networks. Further, such functional network deterioration was associated with poorer cognitive performance cross-sectionally. Our findings suggest that healthy aging is associated with system-level changes in brain functional modular organization, accompanied by functional segregation loss particularly in higher-order networks specialized for cognition.

Altered functional connectivity in patients with subcortical ischemic vascular disease: A resting-state fMRI study

Patients with subcortical ischemic vascular disease (SIVD) may hold a high risk of cognitive impairment (CI) by affecting the functional connectivity (FC) of resting-state networks (RSNs). Current studies have mainly focused on the patients with CI but have ignored the prodromal stage when people suffered subcortical vascular damage, but without CI. Independent component analysis (ICA) of rs-fMRI could detect altered FC in RSNs at the early stage of the disease. 81 SIVD patients with CI (SVCI = 29) and without CI (pre-SVCI = 25), and 27 normal controls (NCs) were scanned with rs-fMRI, analyzed by ICA and assessed by neuropsychological examinations. We found significantly altered FC within the RSNs of sensorimotor network (SMN), posterior default mode networks (pDMN), right frontoparietal network (rFPN) and language network (LN) (P < 0.05, AlphaSim corrected). The pre-SVCI group showed significantly increased FC in brain regions of the multiple RSNs when compared with the other two groups. The mean values extracted from the right inferior frontal gyrus (IFG.R) and the left posterior cingulate gyrus (PCG.L) were significantly correlated with clock drawing test (CDT). The right precentral/postcentral gyrus (PreCG.R/PoCG.R) and the right supramarginal gyrus (SMG.R) were positively correlated with Stroop-1 Test. We concluded the FC in RSNs had already been changed at the early stage of the disease as the maladaptive response or compensatory reallocation of the cognitive resources. The ICA of rs-fMRI can be applied as a potential approach to identify the underlying mechanisms of SIVD.

White matter microstructural abnormalities and default network degeneration are associated with early memory deficit in Alzheimer's disease continuum

Instead of assuming a constant relationship between brain abnormalities and memory impairment, we aimed to examine the stage-dependent contributions of multimodal brain structural and functional deterioration to memory impairment in the Alzheimer’s disease (AD) continuum. We assessed grey matter volume, white matter (WM) microstructural measures (free-water (FW) and FW-corrected fractional anisotropy), and functional connectivity of the default mode network (DMN) in 54 amnestic mild cognitive impairment (aMCI) and 46 AD. We employed a novel sparse varying coefficient model to investigate how the associations between abnormal brain measures and memory impairment varied throughout disease continuum. We found lower functional connectivity in the DMN was related to worse memory across AD continuum. Higher widespread white matter FW and lower fractional anisotropy in the fornix showed a stronger association with memory impairment in the early aMCI stage; such WM-memory associations then decreased with increased dementia severity. Notably, the effect of the DMN atrophy occurred in early aMCI stage, while the effect of the medial temporal atrophy occurred in the AD stage. Our study provided evidence to support the hypothetical progression models underlying memory dysfunction in AD cascade and underscored the importance of FW increases and DMN degeneration in early stage of memory deficit.

Alzheimer's disease neuroimaging

PURPOSE OF REVIEW

The aim of this study was to discuss the contribution of neuroimaging studies to our understanding of Alzheimer's disease. We now have the capability of measuring both tau and beta-amyloid (Aβ) proteins in the brain, which together with more traditional neuroimaging modalities, has led the field to focus on using neuroimaging to better characterize disease mechanisms underlying Alzheimer's disease.

RECENT FINDINGS

Studies have utilized tau and Aβ PET, as well as [18F]fluorodeoxyglucose PET, and structural and functional MRI, to investigate the following topics: phenotypic variability in Alzheimer's disease , including how neuroimaging findings are related to clinical phenotype and age; multimodality analyses to investigate the relationships between different neuroimaging modalities and what that teaches us about disease mechanisms; disease staging by assessing neuroimaging changes in the very earliest phases of the disease in cognitively normal individuals and individuals carrying an autosomal dominant Alzheimer's disease mutation; and influence of other comorbidities and proteins to the disease process.

SUMMARY

The findings shed light on the role of tau and Aβ, as well as age and other comorbidities, in the neurodegenerative process in Alzheimer's disease. This knowledge will be crucial in the development of better disease biomarkers and targeted therapeutic approaches.

Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment

According to the network-based neurodegeneration hypothesis, neurodegenerative diseases target specific large-scale neural networks, such as the default mode network, and may propagate along the structural and functional connections within and between these brain networks. Cognitive impairment no dementia (CIND) represents an early prodromal stage but few studies have examined brain topological changes within and between brain structural and functional networks. To this end, we studied the structural networks [diffusion magnetic resonance imaging (MRI)] and functional networks (task-free functional MRI) in CIND (61 mild, 56 moderate) and healthy older adults (97 controls). Structurally, compared with controls, moderate CIND had lower global efficiency, and lower nodal centrality and nodal efficiency in the thalamus, somatomotor network, and higher-order cognitive networks. Mild CIND only had higher nodal degree centrality in dorsal parietal regions. Functional differences were more subtle, with both CIND groups showing lower nodal centrality and efficiency in temporal and somatomotor regions. Importantly, CIND generally had higher structural-functional connectome correlation than controls. The higher structural-functional topological similarity was undesirable as higher correlation was associated with poorer verbal memory, executive function, and visuoconstruction. Our findings highlighted the distinct and progressive changes in brain structural-functional networks at the prodromal stage of neurodegenerative diseases.

Cerebrovascular disease influences functional and structural network connectivity in patients with amnestic mild cognitive impairment and Alzheimer's disease

BACKGROUND

Patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) show functional and structural connectivity alterations in the default mode network (DMN) while cerebrovascular disease (CeVD) shows functional and structural connectivity changes in the executive control network (ECN). Such disruptions are associated with memory and executive function impairment, respectively. Concurrent AD and CeVD pathology is associated with a higher rate of cognitive decline and differential neurodegenerative patterns. Together, such findings are likely reflective of different underlying pathology in AD with and without CeVD. However, few studies have examined the effect of CeVD on network functional connectivity (task-free functional magnetic resonance imaging (fMRI)) and structural connectivity (diffusion MRI) of the DMN and ECN in aMCI and AD using a hypothesis-driven multiple seed-based approach.

METHODS

We examined functional and structural connectivity network changes in 39 aMCI, 50 aMCI+CeVD, 47 AD, 47 AD+CeVD, and 65 healthy controls (HCs) and their associations with cognitive impairment in the executive/attention and memory domains.

RESULTS

We demonstrate divergent DMN and ECN functional connectivity changes in CeVD and non-CeVD subjects. Compared with controls, intra-DMN hippocampal functional connectivity reductions were observed in both AD and AD+CeVD, while intra-DMN parietal and medial prefrontal-parietal functional connectivity was higher in AD+CeVD and aMCI+CeVD, but lower in AD. Intra-ECN frontal functional connectivity increases and fronto-parietal functional connectivity decreases occurred in CeVD but not non-CeVD subjects. Such functional connectivity alterations were related with cognitive impairment in a dissociative manner: intra-DMN functional connectivity changes were associated with worse cognition primarily in non-CeVD groups, while intra-ECN functional connectivity changes were associated with worse cognition primarily in CeVD groups. Additionally, CeVD and non-CeVD groups showed overlapping and distinct alterations in inter-network DMN-ECN functional connectivity depending on disease severity. In contrast to functional connectivity, CeVD groups had greater network structural connectivity damage compared with non-CeVD groups in both aMCI and AD patients. Network structural connectivity damage was associated with worse cognition.

CONCLUSIONS

We demonstrate differential functional and structural network changes between aMCI and AD patients with and without CeVD through diverging and deleterious network-based degeneration underlying domain-specific cognitive impairment.

Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice

Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis. We therefore used rsfMRI to investigate QPPs in old TG2576 mice, a model of amyloidosis, and age-matched controls. Multiple QPPs were determined and compared across groups. Using linear regression, we removed their contribution from the functional scans and assessed how they reflected functional connectivity. Lastly, we used elastic net regression to determine if QPPs improved disease classification. We present three prominent findings: (1) Compared to controls, TG2576 mice were marked by opposing neural dynamics in which DMN areas were anti-correlated and displayed diminished anti-correlation with the TPN. (2) QPPs reflected lowered DMN functional connectivity in TG2576 mice and revealed significantly decreased DMN-TPN anti-correlations. (3) QPP-derived measures significantly improved classification compared to conventional functional connectivity measures. Altogether, our findings provide insight into the neural dynamics of aberrant network connectivity in AD and indicate that QPPs might serve as a translational diagnostic tool.

Cognition and dementia in older patients with epilepsy

With advances in healthcare and an ageing population, the number of older adults with epilepsy is set to rise substantially across the world. In developed countries the highest incidence of epilepsy is already in people over 65 and, as life expectancy increases, individuals who developed epilepsy at a young age are also living longer. Recent findings show that older persons with epilepsy are more likely to suffer from cognitive dysfunction and that there might be an important bidirectional relationship between epilepsy and dementia. Thus some people with epilepsy may be at a higher risk of developing dementia, while individuals with some forms of dementia, particularly Alzheimer's disease and vascular dementia, are at significantly higher risk of developing epilepsy. Consistent with this emerging view, epidemiological findings reveal that people with epilepsy and individuals with Alzheimer's disease share common risk factors. Recent studies in Alzheimer's disease and late-onset epilepsy also suggest common pathological links mediated by underlying vascular changes and/or tau pathology. Meanwhile electrophysiological and neuroimaging investigations in epilepsy, Alzheimer's disease, and vascular dementia have focused interest on network level dysfunction, which might be important in mediating cognitive dysfunction across all three of these conditions. In this review we consider whether seizures promote dementia, whether dementia causes seizures, or if common underlying pathophysiological mechanisms cause both. We examine the evidence that cognitive impairment is associated with epilepsy in older people (aged over 65) and the prognosis for patients with epilepsy developing dementia, with a specific emphasis on common mechanisms that might underlie the cognitive deficits observed in epilepsy and Alzheimer's disease. Our analyses suggest that there is considerable intersection between epilepsy, Alzheimer's disease and cerebrovascular disease raising the possibility that better understanding of shared mechanisms in these conditions might help to ameliorate not just seizures, but also epileptogenesis and cognitive dysfunction.

Pleiotropy and promiscuity in pharmacogenomics for the treatment of Alzheimer's disease and related risk factors

Patients with Alzheimer's disease are current consumers of polypharmacy with a high risk for drug–drug interactions. Antidementia drugs and other pharmacological treatments for vascular risk factors associated with dementia exert pleiotropic effects which are promiscuously regulated by different gene products. The aim of this review is to highlight the influence of genes involved in pharmacogenetics (i.e., pathogenic, mechanistic, metabolic, transporter and pleiotropic genes) as major determinants of response to treatment in Alzheimer's disease. Patients harboring poor or ultrarapid geno-phenotypes display more irregular profiles in drug efficacy and safety than extensive or intermediate metabolizers. Polymorphic variants of genes associated with lipid metabolism influence the therapeutic response to hypolipemic agents. Understanding these effects is very useful for optimizing polytherapy in dementia.