Gray matter network measures are associated with cognitive decline in mild cognitive impairment

Disrupted single-subject gray matter networks are associated with cognitive decline and cortical atrophy in Alzheimer's disease

Background

Research has shown disrupted structural network measures related to cognitive decline and future cortical atrophy during the progression of Alzheimer's disease (AD). However, evidence regarding the individual variability of gray matter network measures and the associations with concurrent cognitive decline and cortical atrophy related to AD is still sparse.

Objective

To investigate whether alterations in single-subject gray matter networks are related to concurrent cognitive decline and cortical gray matter atrophy during AD progression.

Methods

We analyzed structural MRI data from 185 cognitively normal (CN), 150 mild cognitive impairment (MCI), and 153 AD participants, and calculated the global network metrics of gray matter networks for each participant. We examined the alterations of single-subject gray matter networks in patients with MCI and AD, and investigated the associations of network metrics with concurrent cognitive decline and cortical gray matter atrophy.

Results

The small-world properties including gamma, lambda, and sigma had lower values in the MCI and AD groups than the CN group. AD patients had reduced degree, clustering coefficient, and path length than the CN and MCI groups. We observed significant associations of cognitive ability with degree in the CN group, with gamma and sigma in the MCI group, and with degree, connectivity density, clustering coefficient, and path length in the AD group. There were significant correlation patterns between sigma values and cortical gray matter volume in the CN, MCI, and AD groups.

Conclusion

These findings suggest the individual variability of gray matter network metrics may be valuable to track concurrent cognitive decline and cortical atrophy during AD progression. This may contribute to a better understanding of cognitive decline and brain morphological alterations related to AD.

Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters

Objective

Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC).

Materials and methods

Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline.

Results

The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001).

Conclusion

The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.

Unlocking the Molecular Variations of a Micron-Scale Amyloid Plaque in an Early Stage Alzheimer's Disease by a Cellular-Resolution Mass Spectrometry Imaging Platform

Uncovering the molecular changes at the site where Aβ is deposited plays a critical role in advancing the diagnosis and treatment of Alzheimer's disease. However, there is currently a lack of a suitable label-free imaging method with a high spatial resolution for brain tissue analysis. In this study, we propose a modified desorption electrospray ionization (DESI) mass spectrometry imaging (MSI) method, called segmented temperature-controlled DESI (STC-DESI), to achieve high-resolution and high-sensitivity spatial metabolomics observation by precisely controlling desorption and ionization temperatures. By concentrating the spray plume and accelerating solvent evaporation at different temperatures, we achieved an impressive spatial resolution of 20 μm that enables direct observation of the heterogeneity around a single cell or an individual Aβ plaque and an exciting sensitivity that allows a variety of low-abundance metabolites and less ionizable neutral lipids to be detected. We applied this STC-DESI method to analyze the brains of transgenic AD mice and identified molecular changes associated with individual Aβ aggregates. More importantly, our study provides the first evidence that carnosine is significantly depleted and 5-caffeoylquinic acid (5-CQA) levels rise sharply around Aβ deposits. These observations highlight the potential of carnosine as a sensitive molecular probe for clinical magnetic resonance imaging diagnosis and the potential of 5-CQA as an efficient therapeutic strategy for Aβ clearance in the early AD stage. Overall, our findings demonstrate the effectiveness of our STC-DESI method and shed light on the potential roles of these molecules in AD pathology, specifically in cellular endocytosis, gray matter network disruption, and paravascular Aβ clearance.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

Cerebrospinal fluid proteomics in patients with Alzheimer's disease reveals five molecular subtypes with distinct genetic risk profiles

Alzheimer's disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood-brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine.

Examining the role of participant and study partner report in widely-used classification approaches of mild cognitive impairment in demographically-diverse community dwelling individuals: results from the Einstein aging study

Objective

The role of subjective cognitive concerns (SCC) as a diagnostic criterion for MCI remains uncertain and limits the development of a universally (or widely)-accepted MCI definition. The optimal MCI definition should define an at-risk state and accurately predict the development of incident dementia. Questions remain about operationalization of definitions of self- and informant-reported SCCs and their individual and joint associations with incident dementia.

Methods

The present study included Einstein Aging Study participants who were non-Hispanic White or Black, free of dementia at enrollment, had follow-up, and completed neuropsychological tests and self-reported SCC at enrollment to determine MCI status. Informant-reported SCC at baseline were assessed via the CERAD clinical history questionnaire. Self-reported SCC were measured using the CERAD, items from the EAS Health Self-Assessment, and the single memory item from the Geriatric Depression Scale. Cox proportional hazards models examined the association of different operationalizations of SCC with Petersen and Jak/Bondi MCI definitions on the risk of dementia, further controlling for age, sex, education, and race/ethnicity. Time-dependent sensitivity and specificity at specific time points for each definition, and Youden's index were calculated as an accuracy measure. Cox proportional hazards models were also used to evaluate the associations of combinations of self- and informant-reported SCC with the risk of incident dementia.

Results

91% of the sample endorsed at least one SCC. Youden's index showed that not including SCC in either Jak/Bondi or Petersen classifications had the best balance between sensitivity and specificity across follow-up. A subset of individuals with informants, on average, had a lower proportion of non-Hispanic Blacks and 94% endorsed at least one self-reported SCC. Both informant-reported and self-reported SCC were significantly associated with incident dementia.

Conclusion

Our findings suggest that the SCC criterion may not improve the predictive validity for dementia when included in widely-employed definitions of MCI. Consistent with some prior research, informant-reported SCC was more related to risk of incident dementia than self-reported SCC. Given that requiring informant report as a diagnostic criterion may unintentionally exclude health disparate groups, additional consideration is needed to determine how best to utilize informant-report in MCI diagnosis.

Topological reorganization of functional hubs in patients with Parkinson's disease with freezing of gait

BACKGROUND AND PURPOSE

Resting-state functional MRI (rs-fMRI) studies in Parkinson's disease (PD) patients with freezing of gait (FOG) have implicated dysfunctional connectivity over multiple resting-state networks (RSNs). While these findings provided network-specific insights and information related to the aberrant or altered regional functional connectivity (FC), whether these alterations have any effect on topological reorganization in PD-FOG patients is incompletely understood. Understanding the higher order functional organization, which could be derived from the "hub" and the "rich-club" organization of the functional networks, could be crucial to identifying the distinct and unique pattern of the network connectivity associated with PD-FOG.

METHODS

In this study, we use rs-fMRI data and graph theoretical approaches to explore the reorganization of RSN topology in PD-FOG when compared to those without FOG. We also compared the higher order functional organization derived using the hub and rich-club measures in the FC networks of these PD-FOG patients to understand whether there is a topological reorganization of these hubs in PD-FOG.

RESULTS

We found that the PD-FOG patients showed a noticeable reorganization of hub regions. Regions that are part of the prefrontal cortex, primary somatosensory, motor, and visuomotor coordination areas were some of the regions exhibiting altered hub measures in PD-FOG patients. We also found a significantly altered feeder and local connectivity in PD-FOG.

CONCLUSIONS

Overall, our findings demonstrate a widespread topological reorganization and disrupted higher order functional network topology in PD-FOG that may further assist in improving our understanding of functional network disturbances associated with PD-FOG.

Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review

Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.

Axonal damage and astrocytosis are biological correlates of grey matter network integrity loss: a cohort study in autosomal dominant Alzheimer disease

Brain development and maturation leads to grey matter networks that can be measured using magnetic resonance imaging. Network integrity is an indicator of information processing capacity which declines in neurodegenerative disorders such as Alzheimer disease (AD). The biological mechanisms causing this loss of network integrity remain unknown. Cerebrospinal fluid (CSF) protein biomarkers are available for studying diverse pathological mechanisms in humans and can provide insight into decline. We investigated the relationships between 10 CSF proteins and network integrity in mutation carriers (N=219) and noncarriers (N=136) of the Dominantly Inherited Alzheimer Network Observational study. Abnormalities in Aβ, Tau, synaptic (SNAP-25, neurogranin) and neuronal calcium-sensor protein (VILIP-1) preceded grey matter network disruptions by several years, while inflammation related (YKL-40) and axonal injury (NfL) abnormalities co-occurred and correlated with network integrity. This suggests that axonal loss and inflammation play a role in structural grey matter network changes.

Key points

Abnormal levels of fluid markers for neuronal damage and inflammatory processes in CSF are associated with grey matter network disruptions.The strongest association was with NfL, suggesting that axonal loss may contribute to disrupted network organization as observed in AD.Tracking biomarker trajectories over the disease course, changes in CSF biomarkers generally precede changes in brain networks by several years.

Hippocampus-centred grey matter covariance networks predict the development and reversion of mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) has been thought of as the transitional stage between normal ageing and Alzheimer's disease, involving substantial changes in brain grey matter structures. As most previous studies have focused on single regions (e.g. the hippocampus) and their changes during MCI development and reversion, the relationship between grey matter covariance among distributed brain regions and clinical development and reversion of MCI remains unclear.

METHODS

With samples from two independent studies (155 from the Beijing Aging Brain Rejuvenation Initiative and 286 from the Alzheimer's Disease Neuroimaging Initiative), grey matter covariance of default, frontoparietal, and hippocampal networks were identified by seed-based partial least square analyses, and random forest models were applied to predict the progression from normal cognition to MCI (N-t-M) and the reversion from MCI to normal cognition (M-t-N).

RESULTS

With varying degrees, the grey matter covariance in the three networks could predict N-t-M progression (AUC = 0.692-0.792) and M-t-N reversion (AUC = 0.701-0.809). Further analyses indicated that the hippocampus has emerged as an important region in reversion prediction within all three brain networks, and even though the hippocampus itself could predict the clinical reversion of M-t-N, the grey matter covariance showed higher prediction accuracy for early progression of N-t-M.

CONCLUSIONS

Our findings are the first to report grey matter covariance changes in MCI development and reversion and highlight the necessity of including grey matter covariance changes along with hippocampal degeneration in the early detection of MCI and Alzheimer's disease.

Sex differences in grey matter networks in dementia with Lewy bodies

Objectives

Sex differences permeate many aspects of dementia with Lewy bodies (DLB), including epidemiology, pathogenesis, disease progression, and symptom manifestation. However, less is known about potential sex differences in patterns of neurodegeneration in DLB. Here, we test whether grey matter networks also differ between female and male DLB patients. To assess the specificity of these sex differences to DLB, we additionally investigate sex differences in healthy controls (HCs).

Methods

A total of 119 (68.7 ± 8.4 years) male and 45 female (69.9 ± 9.1 years) DLB patients from three European centres and the Mayo Clinic were included in this study. Additionally, we included 119 male and 45 female age-matched HCs from the Mayo Clinic. Grey matter volumes of 58 cortical, subcortical, cerebellar, and pontine brain regions derived from structural magnetic resonance images were corrected for age, intracranial volume, and centre. Sex-specific grey matter networks for DLB patients and HCs were constructed by correlating each pair of brain regions. Network properties of the correlation matrices were compared between sexes and groups. Additional analyses were conducted on W-scored data to identify DLB-specific findings.

Results

Networks of male HCs and male DLB patients were characterised by a lower nodal strength compared to their respective female counterparts. In comparison to female HCs, the grey matter networks of male HCs showed a higher global efficiency, modularity, and a lower number of modules. None of the global and nodal network measures showed significant sex differences in DLB.

Conclusions

The disappearance of sex differences in the structural grey matter networks of DLB patients compared to HCs may indicate a sex-dependent network vulnerability to the alpha-synuclein pathology in DLB. Future studies might investigate whether the differences in structural network measures are associated with differences in cognitive scores and clinical symptoms between the sexes.

Brain Network Organization and Aging

Despite recent substantial progress in neuroscience, the mechanisms and principles of the complex structure, functions, and the relationship between the brain and cognitive functions have not been fully understood. The modeling method of brain network can provide a new perspective for neuroscience research, and it is possible to provide new solutions to the related research problems. On this basis, the researchers define the concept of human brain connectome to highlight and emphasize the importance of network modeling methods in neuroscience. For example, using diffusion-weighted magnetic resonance imaging (dMRI) technology and fiber tractography methods, a white matter connection network of the whole brain can be constructed. From the perspective of brain function, functional magnetic resonance imaging (fMRI) data can build the brain functional connection network. A structural covariation modeling method is used to obtain a brain structure covariation network, and it appears to reflect developmental coordination or synchronized maturation between areas of the brain. In addition, network modeling and analysis methods can also be applied to other types of image data, such as positron emission tomography (PET), electroencephalogram (EEG), and magnetoencephalography (MEG). This chapter mainly reviews the research progress of researchers on brain structure, function, and other aspects at the network level in recent years.

Gray matter network properties show distinct associations with CSF p-tau 181 levels and amyloid status in individuals without dementia

Gray matter networks are altered with amyloid accumulation in the earliest stage of AD, and are associated with decline throughout the AD spectrum. It remains unclear to what extent gray matter network abnormalities are associated with hyperphosphorylated-tau (p-tau). We studied the relationship of cerebrospinal fluid (CSF) p-tau181 with gray matter networks in non-demented participants from the European Prevention of Alzheimer's Dementia (EPAD) cohort, and studied dependencies on amyloid and cognitive status. Gray matter networks were extracted from baseline structural 3D T1w MRI. P-tau181 and abeta were measured with the Roche cobas Elecsys System. We studied the associations of CSF biomarkers levels with several network's graph properties. We further studied whether the relationships of p-tau 181 and network measures were dependent on amyloid status and cognitive stage (CDR). We repeated these analyses for network properties at a regional level, where we averaged local network values across cubes within each of 116 areas as defined by the automated anatomical labeling (AAL) atlas. Amyloid positivity was associated with higher network size and betweenness centrality, and lower gamma, clustering and small-world coefficients. Higher CSF p-tau 181 levels were related to lower betweenness centrality, path length and lambda coefficients (all p < 0.01). Three-way interactions between p-tau181, amyloid status and CDR were found for path length, lambda and clustering (all p < 0.05): Cognitively unimpaired amyloid-negative participants showed lower path length and lambda values with higher CSF p-tau181 levels. Amyloid-positive participants with impaired cognition demonstrated lower clustering coefficients in association to higher CSF p-tau181 levels. Our results suggest that alterations in gray matter network clustering coefficient is an early and specific event in AD.

Structural Correlates of Overt Sentence Reading in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease

BACKGROUND

Overt sentence reading in mild cognitive impairment (MCI) and mild-tomoderate Alzheimer's disease (AD) has been associated with slowness of speech, characterized by a higher number of pauses, shorter speech units and slower speech rate and attributed to reduced working memory/ attention and language capacity.

OBJECTIVE

This preliminary case-control study investigates whether the temporal organization of speech is associated with the volume of brain regions involved in overt sentence reading and explores the discriminative ability of temporal speech parameters and standard volumetric MRI measures for the classification of MCI and AD.

METHODS

Individuals with MCI, mild-to-moderate AD, and healthy controls (HC) had a structural MRI scan and read aloud sentences varying in cognitive-linguistic demand (length). The association between speech features and regional brain volumes was examined by linear mixed-effect modeling. Genetic programming was used to explore the discriminative ability of temporal and MRI features.

RESULTS

Longer sentences, slower speech rate, and a higher number of pauses and shorter interpausal units were associated with reduced volumes of the reading network. Speech-based classifiers performed similarly to the MRI-based classifiers for MCI-HC (67% vs. 68%) and slightly better for AD-HC (80% vs. 64%) and AD-MCI (82% vs. 59%). Adding the speech features to the MRI features slightly improved the performance of MRI-based classification for AD-HC and MCI-HC but not HC-MCI.

CONCLUSION

The temporal organization of speech in overt sentence reading reflects underlying volume reductions. It may represent a sensitive marker for early assessment of structural changes and cognitive- linguistic deficits associated with healthy aging, MCI, and AD.

Early Alzheimer's disease-like reductions in gray matter and cognitive function with aging in nonhuman primates

Introduction

Age-related neuropathology associated with sporadic Alzheimer's disease (AD) often develops well before the onset of symptoms. Given AD's long preclinical period, translational models are needed to identify early signatures of pathological decline.

Methods

Using structural magnetic resonance imaging and cognitive assessments, we examined the relationships among age, cognitive performance, and neuroanatomy in 48 vervet monkeys (Chlorocebus aethiops sabaeus) ranging from young adults to very old.

Results

We found negative associations of age with cortical gray matter volume (P = .003) and the temporal-parietal cortical thickness meta-region of interest (P = .001). Additionally, cortical gray matter volumes predicted working memory at approximately 1-year follow-up (correct trials at the 20s delay [P = .008]; correct responses after longer delays [P = .004]).

Discussion

Cortical gray matter diminishes with age in vervets in regions relevant to AD, which may increase risk of cognitive impairment. This study lays the groundwork for future investigations to test therapeutics to delay or slow pathological decline.

Individual-specific networks for prediction modelling – A scoping review of methods

Background

Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.

Methods

We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase.

Results

Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.

Conclusion

The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01544-6.

Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline

Individuals with prodromal Alzheimer's disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer's disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1-42 levels from the Amsterdam Dementia Cohort and the Alzheimer's Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer's Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer's Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer's disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer's Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer's disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

Rich-Club Connectivity of the Structural Covariance Network Relates to Memory Processes in Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN).

OBJECTIVE

To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club.

METHODS

To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression.

RESULTS

Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume.

CONCLUSION

Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy.

Tau-related grey matter network breakdown across the Alzheimer's disease continuum

BACKGROUND

Changes in grey matter covariance networks have been reported in preclinical and clinical stages of Alzheimer's disease (AD) and have been associated with amyloid-β (Aβ) deposition and cognitive decline. However, the role of tau pathology on grey matter networks remains unclear. Based on previously reported associations between tau pathology, synaptic density and brain structural measures, tau-related connectivity changes across different stages of AD might be expected. We aimed to assess the relationship between tau aggregation and grey matter network alterations across the AD continuum.

METHODS

We included 533 individuals (178 Aβ-negative cognitively unimpaired (CU) subjects, 105 Aβ-positive CU subjects, 122 Aβ-positive patients with mild cognitive impairment, and 128 patients with AD dementia) from the BioFINDER-2 study. Single-subject grey matter networks were extracted from T1-weighted images and graph theory properties including degree, clustering coefficient, path length, and small world topology were calculated. Associations between tau positron emission tomography (PET) values and global and regional network measures were examined using linear regression models adjusted for age, sex, and total intracranial volume. Finally, we tested whether the association of tau pathology with cognitive performance was mediated by grey matter network disruptions.

RESULTS

Across the whole sample, we found that higher tau load in the temporal meta-ROI was associated with significant changes in degree, clustering, path length, and small world values (all p < 0.001), indicative of a less optimal network organisation. Already in CU Aβ-positive individuals associations between tau burden and lower clustering and path length were observed, whereas in advanced disease stages elevated tau pathology was progressively associated with more brain network abnormalities. Moreover, the association between higher tau load and lower cognitive performance was only partly mediated (9.3 to 9.5%) through small world topology.

CONCLUSIONS

Our data suggest a close relationship between grey matter network disruptions and tau pathology in individuals with abnormal amyloid. This might reflect a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions with tau pathology, indicative of a more random network topology across different AD stages.

Insight does not come at random: Individual gray matter networks relate to clinical and cognitive insight in schizophrenia

BACKGROUND

Impaired clinical and cognitive insight are prevalent in schizophrenia and relate to poorer outcome. Good insight has been suggested to depend on social cognitive and metacognitive abilities requiring global integration of brain signals. Impaired insight has been related to numerous focal gray matter (GM) abnormalities distributed across the brain suggesting dysconnectivity at the global level. In this study, we test whether global integration deficiencies reflected in gray matter network connectivity underlie individual variations in insight.

METHODS

We used graph theory to examine whether individual GM-network metrics relate to insight in patients with a psychotic disorder (n = 114). Clinical insight was measured with the Schedule for the Assessment of Insight-Expanded and item G12 of the Positive and Negative Syndrome Scale, and cognitive insight with the Beck Cognitive Insight Scale. Individual GM-similarity networks were created from GM-segmentations of T1-weighted MRI-scans. Graph metrics were calculated using the Brain Connectivity Toolbox.

RESULTS

Networks of schizophrenia patients with poorer clinical insight showed less segregation (i.e. clustering coefficient) into specialized subnetworks at the global level. Schizophrenia patients with poorer cognitive insight showed both less segregation and higher connectedness (i.e. lower path length) of their brain networks, making their network topology more "random".

CONCLUSIONS

Our findings suggest less segregated processing of information in patients with poorer cognitive and clinical insight, in addition to higher connectedness in patients with poorer cognitive insight. The ability to take a critical perspective on one's symptoms (clinical insight) or views (cognitive insight) might depend especially on segregated specialized processing within distinct subnetworks.

A Graph Theory Approach to Clarifying Aging and Disease Related Changes in Cognitive Networks

In accordance with the physiological networks that underlie it, human cognition is characterized by both the segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, can be conceptualized as a network of functions. A network approach to cognition has previously revealed topological differences in cognitive profiles between healthy and disease populations. The present study, therefore, used graph theory to determine variation in cognitive profiles across healthy aging and cognitive impairment. A comprehensive neuropsychological test battery was administered to 415 participants. This included three groups of healthy adults aged 18-39 (n = 75), 40-64 (n = 75), and 65 and over (n = 70) and three patient groups with either amnestic (n = 75) or non-amnestic (n = 60) mild cognitive impairment or Alzheimer's type dementia (n = 60). For each group, cognitive networks were created reflective of test-to-test covariance, in which nodes represented cognitive tests and edges reflected statistical inter-nodal significance (p < 0.05). Network metrics were derived using the Brain Connectivity Toolbox. Network-wide clustering, local efficiency and global efficiency of nodes showed linear differences across the stages of aging, being significantly higher among older adults when compared with younger groups. Among patients, these metrics were significantly higher again when compared with healthy older controls. Conversely, average betweenness centralities were highest in middle-aged participants and lower among older adults and patients. In particular, compared with controls, patients demonstrated a distinct lack of centrality in the domains of semantic processing and abstract reasoning. Network composition in the amnestic mild cognitive impairment group was similar to the network of Alzheimer's dementia patients. Using graph theoretical methods, this study demonstrates that the composition of cognitive networks may be measurably altered by the aging process and differentially impacted by pathological cognitive impairment. Network alterations characteristic of Alzheimer's disease in particular may occur early and be distinct from alterations associated with differing types of cognitive impairment. A shift in centrality between domains may be particularly relevant in identifying cognitive profiles indicative of underlying disease. Such techniques may contribute to the future development of more sophisticated diagnostic tools for neurodegenerative disease.

Two-Year Follow-Up Study of the Relationship Between Brain Structure and Cognitive Control Function Across the Adult Lifespan

Age-related decline in cognitive control and general slowing are prominent phenomena in aging research. These declines in cognitive functions have been shown to also involve age-related decline in brain structure. However, most evidence in support of these associations is based on cross-sectional data. Therefore, the aim of this study is to contrast cross-sectional and longitudinal analyses to re-examine if the relationship between age-related brain structure and cognitive function are similar between the two approaches. One hundred and two participants completed two sessions with an average interval of 2 years. All participants were assessed by questionnaires, a series of cognitive tasks, and they all underwent neuroimaging acquisition. The main results of this study show that the majority of the conclusions regarding age effect in cognitive control function and processing speed in the literature can be replicated based on the cross-sectional data. Conversely, when we followed up individuals over an average interval of 2 years, then we found much fewer significant relationships between age-related change in gray matter structure of the cognitive control network and age-related change in cognitive control function. Furthermore, there was no "initial age" effect in the relationships between age-related changes in brain structure and cognitive function. This finding suggests that the "aging" relationship between brain structure and cognitive function over a short period of time are independent of "initial age" difference at time point 1. The result of this study warrants the importance of longitudinal research for aging studies to elucidate actual aging processes on cognitive control function.

Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease: Relevance for Early Detection Approaches

Background: Increasing efforts have focused on the establishment of novel biomarkers for the early detection of Alzheimer’s disease (AD) and prediction of Mild Cognitive Impairment (MCI)-to-AD conversion. Behavioral changes over the course of healthy ageing, at disease onset and during disease progression, have been recently put forward as promising markers for the detection of MCI and AD. The present study examines whether the temporal characteristics of speech in a collaborative referencing task are associated with cognitive function and the volumes of brain regions involved in speech production and known to be reduced in MCI and AD pathology. We then explore the discriminative ability of the temporal speech measures for the classification of MCI and AD.

Method: Individuals with MCI, mild-to-moderate AD and healthy controls (HCs) underwent a structural MRI scan and a battery of neuropsychological tests. They also engaged in a collaborative referencing task with a caregiver. The associations between the conversational speech timing features, cognitive function (domain-specific) and regional brain volumes were examined by means of linear mixed-effect modeling. Genetic programming was used to explore the discriminative ability of the conversational speech features.

Results: MCI and mild-to-moderate AD are characterized by a general slowness of speech, attributed to slower speech rate and slower turn-taking in conversational settings. The speech characteristics appear to be reflective of episodic, lexico-semantic, executive functioning and visuospatial deficits and underlying volume reductions in frontal, temporal and cerebellar areas.

Conclusion: The implementation of conversational speech timing-based technologies in clinical and community settings may provide additional markers for the early detection of cognitive deficits and structural changes associated with MCI and AD.

Gray Matter Deterioration Pattern During Alzheimer's Disease Progression: A Regions-of-Interest Based Surface Morphometry Study

Accurate detection of the regions of Alzheimer's disease (AD) lesions is critical for early intervention to effectively slow down the progression of the disease. Although gray matter volumetric abnormalities are commonly detected in patients with mild cognition impairment (MCI) and patients with AD, the gray matter surface-based deterioration pattern associated with the progression of the disease from MCI to AD stages is largely unknown. To identify group differences in gray matter surface morphometry, including cortical thickness, the gyrification index (GI), and the sulcus depth, 80 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were split into healthy controls (HCs; N = 20), early MCIs (EMCI; N = 20), late MCIs (LMCI; N = 20), and ADs (N = 20). Regions-of-interest (ROI)-based surface morphometry was subsequently studied and compared across the four stage groups to characterize the gray matter deterioration during AD progression. Co-alteration patterns (Spearman's correlation coefficient) across the whole brain were also examined. Results showed that patients with MCI and AD exhibited a significant reduction in cortical thickness (p < 0.001) mainly in the cingulate region (four subregions) and in the temporal (thirteen subregions), parietal (five subregions), and frontal (six subregions) lobes compared to HCs. The sulcus depth of the eight temporal, four frontal, four occipital, and eight parietal subregions were also significantly affected (p < 0.001) by the progression of AD. The GI was shown to be insensitive to AD progression (only three subregions were detected with a significant difference, p < 0.001). Moreover, Spearman's correlation analysis confirmed that the co-alteration pattern of the cortical thickness and sulcus depth indices is predominant during AD progression. The findings highlight the relevance between gray matter surface morphometry and the stages of AD, laying the foundation for in vivo tracking of AD progression. The co-alteration pattern of surface-based morphometry would improve the researchers' knowledge of the underlying pathologic mechanisms in AD.

Apolipoprotein E allele 4 effects on Single-Subject Gray Matter Networks in Mild Cognitive Impairment

There is evidence that gray matter networks are disrupted in Mild Cognitive Impairment (MCI) and associated with cognitive impairment and faster disease progression. However, it remains unknown how these alterations are related to the presence of Apolipoprotein E isoform E4 (ApoE4), the most prominent genetic risk factor for late-onset Alzheimer's disease (AD). To investigate this topic at the individual level, we explore the impact of ApoE4 and the disease progression on the Single-Subject Gray Matter Networks (SSGMNets) using the graph theory approach. Our data sample comprised 200 MCI patients selected from the ADNI database, classified as non-Converters and Converters (will progress into AD). Each group included 50 ApoE4-positive ('Carriers', ApoE4 + ) and 50 ApoE4-negative ('non-Carriers', ApoE4-). The SSGMNets were estimated from structural MRIs at two-time points: baseline and conversion. We investigated whether altered network topological measures at baseline and their rate of change (RoC) between baseline and conversion time points were associated with ApoE4 and disease progression. We also explored the correlation of SSGMNets attributes with general cognition score (MMSE), memory (ADNI-MEM), and CSF-derived biomarkers of AD (Aβ42, T-tau, and P-tau). Our results showed that ApoE4 and the disease progression modulated the global topological network properties independently but not in their RoC. MCI converters showed a lower clustering index in several regions associated with neurodegeneration in AD. The SSGMNets' topological organization was revealed to be able to predict cognitive and memory measures. The findings presented here suggest that SSGMNets could indeed be used to identify MCI ApoE4 Carriers with a high risk for AD progression.

Topological alterations in older adults with temporal lobe epilepsy are distinct from amnestic mild cognitive impairment

Epilepsy incidence and prevalence peaks in older adults, yet systematic studies of brain aging and epilepsy remain limited. We investigated topological network disruption in older adults with temporal lobe epilepsy (TLE; age > 55 years). Additionally, we examined the potential network disruption overlap between TLE and amnestic mild cognitive impairment (aMCI), the prodromal stage of Alzheimer disease. Measures of network integration ("global path efficiency") and segregation ("transitivity" and "modularity") were calculated from cortical thickness covariance from 73 TLE subjects, 79 aMCI subjects, and 70 healthy controls. Compared to controls, TLE patients demonstrated abnormal measures of segregation (increased transitivity and decreased modularity) and integration (decreased global path efficiency). aMCI patients also displayed increased transitivity and decreased global path efficiency, but these differences were less pronounced than in TLE. At the local level, TLE patients demonstrated decreased local path efficiency focused in the bilateral temporal lobes, whereas aMCI patients had a more frontal-parietal distribution. These results suggest that network disruption at the global and local level is present in both disorders, but global disruption may be a particularly salient feature in older adults with TLE. These findings motivate further research into whether these network changes have distinct cognitive correlates or are progressive in older adults with epilepsy.

Grey matter network trajectories across the Alzheimer's disease continuum and relation to cognition

Biomarkers are needed to monitor disease progression in Alzheimer's disease. Grey matter network measures have such potential, as they are related to amyloid aggregation in cognitively unimpaired individuals and to future cognitive decline in predementia Alzheimer's disease. Here, we investigated how grey matter network measures evolve over time within individuals across the entire Alzheimer's disease cognitive continuum and whether such changes relate to concurrent decline in cognition. We included 190 cognitively unimpaired, amyloid normal (controls) and 523 individuals with abnormal amyloid across the cognitive continuum (preclinical, prodromal, Alzheimer's disease dementia) from the Alzheimer's Disease Neuroimaging Initiative and calculated single-subject grey matter network measures (median of five networks per individual over 2 years). We fitted linear mixed models to investigate how network measures changed over time and whether such changes were associated with concurrent changes in memory, language, attention/executive functioning and on the Mini-Mental State Examination. We further assessed whether associations were modified by baseline disease stage. We found that both cognitive functioning and network measures declined over time, with steeper rates of decline in more advanced disease stages. In all cognitive stages, decline in network measures was associated with concurrent decline on the Mini-Mental State Examination, with stronger effects for individuals closer to Alzheimer's disease dementia. Decline in network measures was associated with concurrent cognitive decline in different cognitive domains depending on disease stage: In controls, decline in networks was associated with decline in memory and language functioning; preclinical Alzheimer's disease showed associations of decline in networks with memory and attention/executive functioning; prodromal Alzheimer's disease showed associations of decline in networks with cognitive decline in all domains; Alzheimer's disease dementia showed associations of decline in networks with attention/executive functioning. Decline in grey matter network measures over time accelerated for more advanced disease stages and was related to concurrent cognitive decline across the entire Alzheimer's disease cognitive continuum. These associations were disease stage dependent for the different cognitive domains, which reflected the respective cognitive stage. Our findings therefore suggest that grey matter measures are helpful to track disease progression in Alzheimer's disease.

The relationship between inflammation and neurocognitive dysfunction in obstructive sleep apnea syndrome

Obstructive sleep apnea syndrome (OSAS), a state of sleep disorder, is characterized by repetitive apnea, chronic hypoxia, oxygen desaturation, and hypercapnia. Previous studies have revealed that intermittent hypoxia (IH) conditions in OSAS patients elicited neuron injury (especially in the hippocampus and cortex), leading to cognitive dysfunction, a significant and extraordinary complication of OSAS patients. The repeated courses of airway collapse and obstruction in OSAS patients resulted in apnea and arousal during sleep, leading to IH and excessive daytime sleepiness (EDS) and subsequently contributing to the development of inflammation. IH-mediated inflammation could further trigger various types of cognitive dysfunction. Many researchers have found that, besides continuous positive airway pressure (CPAP) treatment and surgery, anti-inflammatory substances might alleviate IH-induced neurocognitive dysfunction. Clarifying the role of inflammation in IH-mediated cognitive impairment is crucial for potentially valuable therapies and future research in the related domain. The objective of this article was to critically review the relationship between inflammation and cognitive deficits in OSAS.

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset -9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer's disease, which is alike sporadic Alzheimer's disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer's disease.

Single-subject gray matter networks predict future cortical atrophy in preclinical Alzheimer's disease

The development of preventive strategies in early-stage Alzheimer's disease (AD) requires measures that can predict future brain atrophy. Gray matter network measures are related to amyloid burden in cognitively normal older individuals and predict clinical progression in preclinical AD. Here, we show that within individuals with preclinical AD, gray matter network measures predict hippocampal atrophy rates, whereas other AD biomarkers (total gray matter volume, cerebrospinal fluid total tau, and Mini-Mental State Examination) do not. Furthermore, in brain areas where amyloid is known to start aggregating (i.e. anterior cingulate and precuneus), disrupted network measures predict faster atrophy in other distant areas, mostly involving temporal regions, which are associated with AD. When repeating analyses in age-matched, cognitively unimpaired individuals without amyloid or tau pathology, we did not find any associations between network measures and hippocampal atrophy, suggesting that the associations are specific for preclinical AD. Our findings suggest that disrupted gray matter networks may indicate a treatment opportunity in preclinical AD individuals but before the onset of irreversible atrophy and cognitive impairment.

Reduced Global Efficiency and Random Network Features in Patients with Relapsing-Remitting Multiple Sclerosis with Cognitive Impairment

BACKGROUND AND PURPOSE

Graph theory uses structural similarity to analyze cortical structural connectivity. We used a voxel-based definition of cortical covariance networks to quantify and assess the relationship of network characteristics to cognition in a cohort of patients with relapsing-remitting MS with and without cognitive impairment.

MATERIALS AND METHODS

We compared subject-specific structural gray matter network properties of 18 healthy controls, 25 patients with MS with cognitive impairment, and 55 patients with MS without cognitive impairment. Network parameters were compared, and predictive value for cognition was assessed, adjusting for confounders (sex, education, gray matter volume, network size and degree, and T1 and T2 lesion load). Backward stepwise multivariable regression quantified predictive factors for 5 neurocognitive domain test scores.

RESULTS

Greater path length (r = -0.28, P < .0057) and lower normalized path length (r = 0.36, P < .0004) demonstrated a correlation with average cognition when comparing healthy controls with patients with MS. Similarly, MS with cognitive impairment demonstrated a correlation between lower normalized path length (r = 0.40, P < .001) and reduced average cognition. Increased normalized path length was associated with better performance for processing (P < .001), learning (P < .001), and executive domain function (P = .0235), while reduced path length was associated with better executive (P = .0031) and visual domains. Normalized path length improved prediction for processing (R ² = 43.6%, G² = 20.9; P < .0001) and learning (R ² = 40.4%, G² = 26.1; P < .0001) over a null model comprising confounders. Similarly, higher normalized path length improved prediction of average z scores (G² = 21.3; P < .0001) and, combined with WM volume, explained 52% of average cognition variance.

CONCLUSIONS

Patients with MS and cognitive impairment demonstrate more random network features and reduced global efficiency, impacting multiple cognitive domains. A model of normalized path length with normal-appearing white matter volume improved average cognitive z score prediction, explaining 52% of variance.

A cross-sectional and longitudinal study on the protective effect of bilingualism against dementia using brain atrophy and cognitive measures

Background

Evidence from previous studies suggests that bilingualism contributes to cognitive reserve because bilinguals manifest the first symptoms of Alzheimer’s disease (AD) up to 5 years later than monolinguals. Other cross-sectional studies demonstrate that bilinguals show greater amounts of brain atrophy and hypometabolism than monolinguals, despite sharing the same diagnosis and suffering from the same symptoms. However, these studies may be biased by possible pre-existing between-group differences.

Methods

In this study, we used global parenchymal measures of atrophy and cognitive tests to investigate the protective effect of bilingualism against dementia cross-sectionally and prospectively, using a sample of bilinguals and monolinguals in the same clinical stage and matched on sociodemographic variables.

Results

Our results suggest that the two groups did not differ in their cognitive status at baseline, but bilinguals had less parenchymal volume than monolinguals, especially in areas related to brain atrophy in dementia. In addition, a longitudinal prospective analysis revealed that monolinguals lost more parenchyma and had more cognitive decline than bilinguals in a mean follow-up period of 7 months.

Conclusion

These results provide the first prospective evidence that bilingualism may act as a neuroprotective factor against dementia and could be considered a factor in cognitive reserve.

Topological Disruption of Structural Brain Networks in Patients With Cognitive Impairment Following Cerebellar Infarction

Cerebellar lesions can lead to a series of cognitive and emotional disorders by influencing cerebral activity via cerebro-cerebellar loops. To explore changes in cognitive function and structural brain networks in patients with posterior cerebellar infarction, we conducted the current study using diffusion-weighted MRI (32 cerebellar infarction patients, 29 controls). Moreover, a series of neuropsychological tests were used to assess the subject's cognitive performance. We found cognitive impairment following cerebellar infarction involving multiple cognitive domains, including memory, executive functions, visuospatial abilities, processing speed and language functions, and brain topological abnormalities, including changes in clustering coefficients, shortest path lengths, global efficiency, local efficiencies, betweenness centrality and nodal efficiencies. Our results indicated that measures of local efficiency, mainly in the precuneus, cingulate gyrus and frontal-temporal cortex, were significantly reduced with posterior cerebellar infarction. At the same time, The correlation analysis suggested thatthe abnormal alterations in the right PCG, bilateral DCG, right PCUN may play a core role in the cognitive impairment following cerebellar infarctions. The differences in topological features of the structural brain networks within the cerebro-cerebellar circuits may provide a new approach to explore the pathophysiological mechanisms of cognitive impairment following cerebellar infarction.

Gray matter T1-w/T2-w ratios are higher in Alzheimer's disease

Myelin determines the conduction of neuronal signals along axonal connections in networks of the brain. Loss of myelin integrity in neuronal circuits might result in cognitive decline in Alzheimer's disease (AD). Recently, the ratio of T1-weighted by T2-weighted MRI has been used as a proxy for myelin content in gray matter of the cortex. With this approach, we investigated whether AD dementia patients show lower cortical myelin content (i.e., a lower T1-w/T2-w ratio value). We selected structural T1-w and T2-w MR images of 293 AD patients and 172 participants with normal cognition (NC). T1-w/T2-w ratios were computed for the whole brain and within 90 automated anatomical labeling atlas regions using SPM12, compared between groups and correlated with the neuronal injury marker tau in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE). In contrast to our hypothesis, AD patients showed higher whole brain T1-w/T2-w ratios than NC, and regionally in 31 anatomical areas (p < .0005; d = 0.21 to 0.48), predominantly in the inferior parietal lobule, angular gyrus, anterior cingulate, and precuneus. Regional higher T1-w/T2-w values were associated with higher CSF tau concentrations (p < .0005; r = .16 to .22) and worse MMSE scores (p < .0005; r = -.16 to -.21). These higher T1-w/T2-w values in AD seem to contradict previous pathological findings of demyelination and disconnectivity in AD. Future research should further investigate the biological processes reflected by increases in T1-w/T2-w values.

Gray matter structural covariance networks changes along the Alzheimer's disease continuum

Alzheimer's disease (AD) has a long neuropathological accumulation phase before the onset of dementia. Such AD neuropathological deposition between neurons impairs the synaptic communication, resulting in networks disorganization. Our study aimed to explore the evolution patterns of gray matter structural covariance networks (SCNs) along AD continuum. Based on the AT(N) (i.e., Amyloid/Tau/Neurodegeneration) pathological classification system, we classified subjects into four groups using cerebrospinal fluid amyloid-beta1-42 (A) and phosphorylated tau protein181 (T). We identified 101 subjects with normal AD biomarkers (A-T-), 40 subjects with Alzheimer's pathologic change (A + T-), 101 subjects with biological AD (A + T+) and 91 AD with dementia (demented subjects with A + T+). We used four regions of interest to anchor default mode network (DMN, medial temporal subsystem and midline core subsystem), salience network (SN) and executive control network (ECN). Finally, we used a multi-regression model-based linear-interaction analysis to assess the SCN changes. Along the disease progression, DMN and SN showed increased structural association at the early stage while decreased structural association at the late stage. Moreover, ECN showed progressively increased structural association as AD neuropathological profiles progress. In conclusion, this study found the dynamic trajectory of SCNs changes along the AD continuum and support the network disconnection hypothesis underlying AD neuropathological progression. Further, SCN may potentially serve as an effective AD biomarker.

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome

Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown.

Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters.

Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%.

Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.

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.

Stability of graph theoretical measures in structural brain networks in Alzheimer's disease

Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer's disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.