The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline

Associations between the multitrajectory neuroplasticity of neuronavigated rTMS-mediated angular gyrus networks and brain gene expression in AD spectrum patients with sleep disorders

INTRODUCTION

The multifactorial influence of repetitive transcranial magnetic stimulation (rTMS) on neuroplasticity in neural networks is associated with improvements in cognitive dysfunction and sleep disorders. The mechanisms of rTMS and the transcriptional-neuronal correlation in Alzheimer's disease (AD) patients with sleep disorders have not been fully elucidated.

METHODS

Forty-six elderly participants with cognitive impairment (23 patients with low sleep quality and 23 patients with high sleep quality) underwent 4-week periods of neuronavigated rTMS of the angular gyrus and neuroimaging tests, and gene expression data for six post mortem brains were collected from another database. Transcription-neuroimaging association analysis was used to evaluate the effects on cognitive dysfunction and the underlying biological mechanisms involved.

RESULTS

Distinct variable neuroplasticity in the anterior and posterior angular gyrus networks was detected in the low sleep quality group. These interactions were associated with multiple gene pathways, and the comprehensive effects were associated with improvements in episodic memory.

DISCUSSION

Multitrajectory neuroplasticity is associated with complex biological mechanisms in AD-spectrum patients with sleep disorders.

HIGHLIGHTS

This was the first transcription-neuroimaging study to demonstrate that multitrajectory neuroplasticity in neural circuits was induced via neuronavigated rTMS, which was associated with complex gene expression in AD-spectrum patients with sleep disorders. The interactions between sleep quality and neuronavigated rTMS were coupled with multiple gene pathways and improvements in episodic memory. The present strategy for integrating neuroimaging, rTMS intervention, and genetic data provide a new approach to comprehending the biological mechanisms involved in AD.

Increased between-network connectivity: A risk factor for tau elevation and disease progression

One of the pathologic hallmarks of Alzheimer's disease (AD) is neurofibrillary tau tangles. Despite our knowledge that tau typically initiates in the medial temporal lobe (MTL), the mechanisms driving tau to spread beyond MTL remain unclear. Emerging evidence reveals distinct patterns of functional connectivity change during aging and preclinical AD: while connectivity within-network decreases, connectivity between-network increases. Building upon increased between-network connectivity, our study hypothesizes that this increase may play a critical role in facilitating tau spread in early stages. We conducted a longitudinal study over two to three years intervals on a cohort of 46 healthy elderly participants (mean age 64.23 ± 3.15 years, 26 females). Subjects were examined clinically and utilizing advanced imaging techniques that included resting-state functional MRI (rs-fMRI), structural magnetic resonance imaging (MRI), and a second-generation positron emission tomography (PET) tau tracer, 18F-MK6240. Through unsupervised agglomerative clustering and increase in between-network connectivity, we successfully identified individuals at increased risk of future tau elevation and AD progression. Our analysis revealed that individuals with increased between-network connectivity are more likely to experience more future tau deposition, entorhinal cortex thinning, and lower selective reminding test (SRT) delayed scores. Additionally, in the limbic network, we found a strong association between tau progression and increased between-network connectivity, which was mainly driven by beta-amyloid (Aβ) positive participants. These findings provide evidence for the hypothesis that an increase in between-network connectivity predicts future tau deposition and AD progression, also enhancing our understanding of AD pathogenesis in the preclinical stages.

Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study

Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.

Associations between differential connectivity patterns of executive control networks and APOE ɛ4 in the Alzheimer continuum

The APOE ɛ4 allele and age are risk factors for Alzheimer's disease (AD) and contribute to decreased executive function. However, the influence of APOE ɛ4 on the executive control network (ECN) in the AD continuum is still unclear. This study included 269 participants aged between 50 and 95 years old, based on ADNI data, including 104 cognitively normal (CN) individuals, 72 individuals with early mild cognitive impairment (EMCI), 55 individuals with late mild cognitive impairment (LMCI), and 38 AD patients. Within each disease group, participants were subdivided into APOE ɛ4 carriers and non-carriers. We explored brain regions within the ECN affected by the interactions between genes and disease states by resting-state functional magnetic resonance imaging (fMRI) and voxel-based two-way analysis of variance (ANOVA). Subsequently, functional connectivity (FC) between seeds and peak clusters were extracted and correlated with the cognitive performance. We found that the damages of carrying APOE ɛ4 in ECNs mainly distributed in the fronto-parietal and parietal-temporal systems. Functional network intergroup differences indicated increased intrafrontal and fronto-parietal connectivity at the early stage of AD and increased connectivity between the parietal lobe and related regions at late disease in these APOE ɛ4 carriers. Our conclusion is that the functional connectivity in the ECN exhibits different distinguishably patterns of impairment in the AD continuum under the influence of the APOE ɛ4 allele. Patients with different genotypes showed heterogeneity in functional network changes in the early stages of disease, which may be a potential biomarker for early AD.

Anterior-temporal hippocampal network mechanisms of left angular gyrus-navigated rTMS for memory improvement in aMCI: A sham-controlled study

INTRODUCTION

Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) of the left angular gyrus has been broadly investigated for the treatment of amnestic mild cognitive impairment (aMCI). Although abnormalities in two hippocampal networks, the anterior-temporal (AT) and posterior-medial (PM) networks, are consistent with aMCI and are potential therapeutic targets for rTMS, the underlying mechanisms of the therapeutic effects of rTMS on hippocampal network connections remain unknown. Here, we assessed the impact of left angular gyrus rTMS on activity in these networks and explored whether the treatment response was due to the distance between the clinically applied target (the group average optimal site) and the personalized target in patients with aMCI.

METHODS

Sixty subjects clinically diagnosed with aMCI participated in this study after 20 sessions of sham-controlled rTMS targeting the left angular gyrus. Resting-state functional magnetic resonance imaging and neuropsychological assessments were performed before and after rTMS. Functional connectivity alterations in the PM and AT networks were assessed using seed-based functional connectivity analysis and two-factor repeated measures analysis of variance (ANOVA). We then computed the correlations between the functional connectivity changes and clinical rating scales. Finally, we examined whether the Euclidean distance between the clinically applied and personalized targets predicted the subsequent treatment response.

RESULTS

Compared with the sham group, the active rTMS group showed rTMS-induced deactivation of functional connectivity within the medial temporal lobe-AT network, with a negative correlation with episodic memory score changes. Moreover, the active rTMS lowers the interdependency of changes in the PM and AT networks. Finally, the Euclidean distance between the clinically applied and personalized target distances could predict subsequent network lever responses in the active rTMS group.

CONCLUSIONS

Neuro-navigated rTMS selectively modulates widespread functional connectivity abnormalities in the PM and AT hippocampal networks in aMCI patients, and the modulation of hippocampal-AT network connectivity can efficiently reverse memory deficits. The results also highlight the necessity of personalized targets for fMRI.

Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences

Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.

Brain structure, amyloid, and behavioral features for predicting clinical progression in subjective cognitive decline

As a potential preclinical stage of Alzheimer's dementia, subjective cognitive decline (SCD) reveals a higher risk of future cognitive decline and conversion to dementia. However, it has not been clear whether SCD status increases the clinical progression of older adults in the context of amyloid deposition, cerebrovascular disease (CeVD), and psychiatric symptoms. We identified 99 normal controls (NC), 15 SCD individuals who developed mild cognitive impairment in the next 2 years (P-SCD), and 54 SCD individuals who did not (S-SCD) from ADNI database with both baseline and 2-year follow-up data. Total white matter hyperintensity (WMH), WMH in deep (DWMH) and periventricular (PWMH) regions, and voxel-wise grey matter volumes were compared among groups. Furthermore, using structural equation modelling method, we constructed path models to explore SCD-related brain changes longitudinally and to determine whether baseline SCD status, age, and depressive symptoms affect participants' clinical outcomes. Both SCD groups showed higher baseline amyloid PET SUVR, baseline PWMH volumes, and larger increase of PWMH volumes over time than NC. In contrast, only P-SCD had higher baseline DWMH volumes and larger increase of DWMH volumes over time than NC. No longitudinal differences in grey matter volume and amyloid was observed among NC, S-SCD, and P-SCD. Our path models demonstrated that SCD status contributed to future WMH progression. Further, baseline SCD status increases the risk of future cognitive decline, mediated by PWMH; baseline depressive symptoms directly contribute to clinical outcomes. In conclusion, both S-SCD and P-SCD exhibited more severe CeVD than NC. The CeVD burden increase was more pronounced in P-SCD. In contrast with the direct association of depressive symptoms with dementia severity progression, the effects of SCD status on future cognitive decline may manifest via CeVD pathologies. Our work highlights the importance of multi-modal longitudinal designs in understanding the SCD trajectory heterogeneity, paving the way for stratification and early intervention in the preclinical stage. PRACTITIONER POINTS: Both S-SCD and P-SCD exhibited more severe CeVD at baseline and a larger increase of CeVD burden compared to NC, while the burden was more pronounced in P-SCD. Baseline SCD status increases the risk of future PWMH and DWMH volume accumulation, mediated by baseline PWMH and DWMH volumes, respectively. Baseline SCD status increases the risk of future cognitive decline, mediated by baseline PWMH, while baseline depression status directly contributes to clinical outcome.

The characteristic pattern of functional connectivity density influenced by postmenopausal females in the preclinical stage of dementia

Subjective cognitive decline (SCD) is considered an early indicator of Alzheimer's disease. Previous evidence suggests that postmenopausal females are at heightened risk for developing dementia. However, the potential effects of gender (i.e. postmenopausal female) on functional connectivity density (FCD) in individuals with SCD are not well understood. A total of 56 healthy controls and 57 subjects with SCD were included. The short-range and long-range FCD (srFCD and lrFCD) mapping of each participant was calculated. The interactive effect of gender × diagnosis on the FCD was explored by two-way analysis of variance. The interaction effect of gender × diagnosis on lrFCD was primarily in the right middle frontal gyrus (MFG). The older males with SCD exhibited significantly enhanced lrFCD in the right MFG relative to other subgroups. The lrFCD of the right MFG was positively associated with cognitive performance in older females with SCD. Cognition-related functional terms were significantly related to the right MFG. Decreased lrFCD of the right MFG in cognitively normal older women may explain why postmenopausal females have a higher risk for progression to dementia than men. Furthermore, this altered pattern could be applied to identify individuals with a high risk for dementia.

Prediction of disease progression in individuals with subjective cognitive decline using brain network analysis

OBJECTIVE

The objective of this study is to explore potential differences in brain functional networks at baseline between individuals with progressive subjective cognitive decline (P-SCD) and stable subjective cognitive decline (S-SCD), as well as to identify potential indicators that can effectively distinguish between P-SCD and S-SCD.

METHODS

Alzheimer's Disease Neuroimaging Initiative (ADNI) database was utilized to enroll SCD individuals with a follow-up period of over 3 years. This study included 39 individuals with S-SCD, 15 individuals with P-SCD, and 45 cognitively normal (CN) individuals. Brain functional networks were constructed based on the AAL template, and graph theory analysis was performed to determine the topological properties.

RESULTS

For global metric, the S-SCD group exhibited stronger small-worldness with reduced connectivity among nearby nodes and accelerated compensatory information transfer capacity. For nodal efficiency, the S-SCD group showed increased connectivity in bilateral posterior cingulate gyri (PCG). However, for nodal local efficiency, the P-SCD group exhibited significantly reduced connectivity in the right cerebellar Crus I compared with the S-SCD group.

CONCLUSION

There are differences in brain functional networks at baseline between P-SCD and S-SCD groups. Furthermore, the right cerebellar Crus I region may be a potentially useful brain area to distinguish between P-SCD and S-SCD.

The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment

Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer's disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.

Predictors of progression from subjective cognitive decline to objective cognitive impairment: A systematic review and meta-analysis of longitudinal studies

BACKGROUND

Subjective cognitive decline is one of the first symptoms of dementia. With increasing awareness of brain health and a rising prevalence of dementia, a growing number of individuals seek medical assistance for purely subjective cognitive decline. However, only individuals with specific characteristics tend to experience clinical progression.

OBJECTIVES

This study aims to summarize the predictors of objective cognitive impairment in individuals with subjective cognitive decline and to identify those at higher risk of clinical progression.

DESIGN

Systematic review and meta-analysis.

METHODS

We systematically searched 11 electronic databases from inception to February 1, 2023, for longitudinal studies investigating factors associated with the clinical progression of subjective cognitive decline. Effect sizes were pooled using fixed-effects and random-effects models. Leveraging the results of the meta-analysis, we developed two risk prediction models for objective cognitive impairment.

RESULTS

Forty-six cohort studies were included in the systematic review, of which 28 met the meta-analysis criteria. Fifteen predictors were identified, including 4 biomarkers (amyloid β deposition, lower Hulstaert Formula scores, apolipoprotein e4, and hippocampus atrophy), four epidemiological factors (older age at baseline, impaired instrumental activity of daily living, depression, and anxiety), and seven neuropsychological factors (participants in clinical settings, older age at onset, stable symptom, concerns, cognitive decline confirmed by informant, severe symptoms, and poor performance on Trail Making Test B). Based on the meta-analysis results, we developed two risk prediction models. The first model (Model1) incorporates epidemiological and neuropsychological factors, distinguishing individuals with low and medium risk. The second model (Model2) includes additional biomarkers to enhance predictive performance and identify individuals at high risk.

CONCLUSIONS

This study provides a comprehensive characterization of individuals undergoing clinical progression from subjective cognitive decline to mild cognitive impairment or dementia. The developed models support the prediction of progression risk in both memory clinic and community settings, aiding in the early identification of individuals at risk of disease conversion and facilitating the translation of evidence into clinical practice.

REGISTRATION

The systematic review and meta-analysis have been registered in PROSPERO (CRD 42023392476).

TWEETABLE ABSTRACT

Factors for predicting progression from subjective cognitive decline to objective cognitive impairment: evidence from longitudinal studies.

Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis

Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer's disease

BACKGROUND

Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer's disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response.

METHODS

Sixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline.

RESULTS

We found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54).

CONCLUSION

Our findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response.

RELEVANCE STATEMENT

Dynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer's disease.

KEY POINTS

• rTMS at the left angular gyrus could induce cognitive improvement. • rTMS could suppress primary network connections to compensate for higher-order networks. • Dynamic reorganization of brain networks could predict individual treatment response to rTMS.

Altered morphological connectivity mediated white matter hyperintensity-related cognitive impairment

White matter hyperintensities (WMH) are widely observed in older adults and are closely associated with cognitive impairment. However, the underlying neuroimaging mechanisms of WMH-related cognitive dysfunction remain unknown. This study recruited 61 WMH individuals with mild cognitive impairment (WMH-MCI, n = 61), 48 WMH individuals with normal cognition (WMH-NC, n = 48) and 57 healthy control (HC, n = 57) in the final analyses. We constructed morphological networks by applying the Kullback-Leibler divergence to estimate interregional similarity in the distributions of regional gray matter volume. Based on morphological networks, graph theory was applied to explore topological properties, and their relationship to WMH-related cognitive impairment was assessed. There were no differences in small-worldness, global efficiency and local efficiency. The nodal local efficiency, degree centrality and betweenness centrality were altered mainly in the limbic network (LN) and default mode network (DMN). The rich-club analysis revealed that WMH-MCI subjects showed lower average strength of the feeder and local connections than HC (feeder connections: P = 0.034; local connections: P = 0.042). Altered morphological connectivity mediated the relationship between WMH and cognition, including language (total indirect effect: -0.010; 95 % CI: -0.024, -0.002) and executive (total indirect effect: -0.010; 95 % CI: -0.028, -0.002) function. The altered topological organization of morphological networks was mainly located in the DMN and LN and was associated with WMH-related cognitive impairment. The rich-club connection was relatively preserved, while the feeder and local connections declined. The results suggest that single-subject morphological networks may capture neurological dysfunction due to WMH and could be applied to the early imaging diagnostic protocol for WMH-related cognitive impairment.

Sleep mediates the effect of stressful environments on youth development of impulsivity: The moderating role of within default mode network resting-state functional connectivity

OBJECTIVES

Youth raised in stressful environments are at increased risk for developing impulsive traits, which are a robust precursor of problem behaviors. Sleep may mediate the link between stress and problem behaviors as it is both sensitive to stress and essential for neurocognitive development underlying behavioral control during adolescence. The default mode network (DMN) is a brain network implicated in stress regulation and sleep. Yet, it is poorly understood how individual differences in resting-state DMN moderate the effect of stressful environments on impulsivity via sleep problems.

METHODS

Three waves of data spanning 2 years were obtained from the Adolescent Brain and Cognitive Development Study, a national longitudinal sample of 11,878 children (Mage at baseline = 10.1; 47.8% female). Structural equation modeling was used to test (a) the mediating role of sleep at T3 in the link between stressful environments at baseline and impulsivity at T5 and (b) the moderation of this indirect association by baseline levels of within-DMN resting-state functional connectivity.

RESULTS

Sleep problems, shorter sleep duration, and longer sleep latency significantly mediated the link between stressful environments and youth impulsivity. Youth with elevated within-DMN resting-state functional connectivity showed intensified associations between stressful environments and impulsivity via shorter sleep duration.

CONCLUSION

Our findings suggest that sleep health can be a target for preventive intervention and thereby mitigate the link between stressful environments and increased levels of youth impulsivity.

Altered within- and between-network functional connectivity in atypical Alzheimer's disease

Posterior cortical atrophy and logopenic progressive aphasia are atypical clinical presentations of Alzheimer's disease. Resting-state functional connectivity studies have shown functional network disruptions in both phenotypes, particularly involving the language network in logopenic progressive aphasia and the visual network in posterior cortical atrophy. However, little is known about how connectivity differs both within and between brain networks in these atypical Alzheimer's disease phenotypes. A cohort of 144 patients was recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, USA, and underwent structural and resting-state functional MRI. Spatially preprocessed data were analysed to explore the default mode network and the salience, sensorimotor, language, visual and memory networks. The data were analysed at the voxel and network levels. Bayesian hierarchical linear models adjusted for age and sex were used to analyse within- and between-network connectivity. Reduced within-network connectivity was observed in the language network in both phenotypes, with stronger evidence of reductions in logopenic progressive aphasia compared to controls. Only posterior cortical atrophy showed reduced within-network connectivity in the visual network compared to controls. Both phenotypes showed reduced within-network connectivity in the default mode and sensorimotor networks. No significant change was noted in the memory network, but a slight increase in the salience within-network connectivity was seen in both phenotypes compared to controls. Between-network analysis in posterior cortical atrophy showed evidence of reduced visual-to-language network connectivity, with reduced visual-to-salience network connectivity, compared to controls. An increase in visual-to-default mode network connectivity was noted in posterior cortical atrophy compared to controls. Between-network analysis in logopenic progressive aphasia showed evidence of reduced language-to-visual network connectivity and an increase in language-to-salience network connectivity compared to controls. Findings from the voxel-level and network-level analysis were in line with the Bayesian hierarchical linear model analysis, showing reduced connectivity in the dominant network based on diagnosis and more crosstalk between networks in general compared to controls. The atypical Alzheimer's disease phenotypes were associated with disruptions in connectivity, both within and between brain networks. Phenotype-specific differences in connectivity patterns were noted in the visual network for posterior cortical atrophy and the language network for logopenic progressive aphasia.

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.

Multi-networks connectivity at baseline predicts the clinical efficacy of left angular gyrus-navigated rTMS in the spectrum of Alzheimer's disease: A sham-controlled study

INTRODUCTION

Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is effective in alleviating cognitive deficits in Alzheimer's disease (AD). However, the strategy for target determination and the mechanisms for cognitive improvement remain unclear.

METHODS

One hundred and thirteen elderly subjects were recruited in this study, including both cross-sectional (n = 79) and longitudinal experiments (the rTMS group: n = 24; the sham group: n = 10). The cross-sectional experiment explored the precise intervention target based on the cortical-hippocampal network. The longitudinal experiment investigated the clinical efficacy of neuro-navigated rTMS treatment over a four-week period and explored its underlying neural mechanism using seed-based and network-based analysis. Finally, we applied connectome-based predictive modeling to predict the rTMS response using these functional features at baseline.

RESULTS

RTMS at a targeted site of the left angular gyrus (MNI: -45, -67, 38) significantly induced cognitive improvement in memory and language function (p < 0.001). The improved cognition correlated with the default mode network (DMN) subsystems. Furthermore, the connectivity patterns of DMN subsystems (r = 0.52, p = 0.01) or large-scale networks (r = 0.85, p = 0.001) at baseline significantly predicted the Δ language cognition after the rTMS treatment. The connectivity patterns of DMN subsystems (r = 0.47, p = 0.019) or large-scale networks (r = 0.80, p = 0.001) at baseline could predict the Δ memory cognition after the rTMS treatment.

CONCLUSION

These findings suggest that neuro-navigated rTMS targeting the left angular gyrus could improve cognitive function in AD patients. Importantly, dynamic regulation of the intra- and inter-DMN at baseline may represent a potential predictor for favorable rTMS treatment response in patients with cognitive impairment.

Reconfiguration of brain network dynamics underlying spatial deficits in subjective cognitive decline

Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.

Altered functional connectivity of the default mode and dorsal attention network in subjective cognitive decline

Subjective cognitive decline (SCD) is a clinical condition in which performance on standardized cognitive tests does not indicate impairment like mild cognitive impairment (MCI), but self-experienced cognitive capacity persistently declines. We aimed to explore the functional connectivity (FC) characteristics of SCD subjects compared to healthy controls and MCI patients. Resting-state functional MRI was performed on 152 elderly subjects: 65 normal controls, 62 SCD subjects, and 25 MCI patients. A seed-based FC analysis was performed to compare groups. The major brain regions of the default mode network (DMN) and dorsal attention network (DAN), large-scale brain networks disrupted in patients with dementia, were selected as seed regions. As a result, the SCD group showed stronger FC than the MCI group between DMN seeds and the supramarginal gyrus. Both the SCD and MCI groups showed stronger FC between the left lateral parietal cortex and right dorsolateral prefrontal cortex. In the FC analysis centred on DAN seeds, both the SCD and MCI groups showed weaker FC of the right posterior intraparietal sulcus in the left anterior cingulate cortex and the left insula, compared to those in the control group. Within the SCD group, hyperconnectivity between the right lateral parietal cortex and left supramarginal gyrus was significantly correlated with better performance on the Controlled Oral Word Association Test. In conclusion, the SCD group showed several DMN- and DAN-related FC alterations, similar to the MCI group, but with distinct hyperconnectivity between DMN seeds and the supramarginal gyrus. In particular, SCD has DMN-related FC patterns distinct from those of MCI that are associated with verbal fluency retention.

Distinct reserve capacity impacts on default-mode network in response to left angular gyrus-navigated repetitive transcranial magnetic stimulation in the prodromal Alzheimer disease

Default-mode network (DMN) may be the earliest affected network and is associated with cognitive decline in Alzheimer's disease (AD). Repetitive transcranial magnetic stimulation (rTMS) may help to modulate DMN plasticity. Still, stimulation effects substantially vary across studies and individuals. Global left frontal cortex (gLFC) connectivity, a substitute for reserve capacity, may contribute to the heterogeneous physiological effects of neuro-navigated rTMS. This study investigated the effects of left angular gyrus-navigated rTMS on DMN connectivity in different reserve capacity participants. gLFC connectivity, was computed through resting-state fMRI correlations. Thirty-one prodromal AD patients were divided into low connection group (LCG) and high connection group (HCG) by the median of gLFC connectivity. Distinct reserve capacity impacts on DMN in response to rTMS were identified in these two groups. Then, brain-behavior relationships were examined. gLFC connectivity within a certain range is directly proportional to cognitive reserve ability (i.e., LCG), and the effectiveness of functional connectivity beyond this range decreases (i.e, HCG). Moreover, LCG exhibited increased DMN connectivity and significantly positive memory improvements, while HCG showed a contrary connectivity decline and maintained or slightly improved their cognitive function after neuro-navigated rTMS treatment. The prodromal AD patients with the distinct reserve capacity may benefit differently from left angular gyrus-navigated rTMS, which may lead to increasing attention in defining personalized medicine approach of AD.

Altered rich-club organization of brain functional network in autism spectrum disorder

Despite numerous research showing the association between brain network abnormalities and autism spectrum disorder (ASD), contrasting findings have been reported from broad functional underconnectivity to broad overconnectivity. Thus, the significance of rich-hub organizations in the brain functional connectome of individuals with ASD remains largely unknown. High-quality data subset of ASD (n = 45) and healthy controls (HC; n = 47) children (7-15 years old) were retrieved from the ABIDE data set, and rich-club organization and network-based statistic (NBS) were assessed from resting-state functional magnetic resonance imaging (rs-fMRI). The rich-club organization functional network (normalized rich-club coefficients >1) was observed in all subjects under a range of thresholds. Compared with HC, ASD patients had higher degree of feeder connections and lower degree of local connections (degree of feeder connections: ASD = 259.20 ± 32.97, HC = 244.98 ± 30.09, p = 0.041; degree of local connections: ASD = 664.02 ± 39.19, HC = 679.89 ± 34.05, p = 0.033) but had similar in rich-club connections. Further, nonparametric NBS analysis showed the presence of abnormal connectivity in the functional network of ASD individuals. Our findings indicated that local connection might be more vulnerable, and feeder connection may compensate for its disruption in ASD, enhancing our understanding on the mechanism of functional connectome dysfunction in ASD.

Morphological and Structural Network Analysis of Sporadic Alzheimer's Disease Brains Based on the APOE4 Gene

BACKGROUND

Alzheimer's disease (AD) is an increasingly common type of dementia. Apolipoprotein E (APOE) gene is a strong risk factor for AD.

OBJECTIVE

Here, we explored alterations in grey matter structure (GMV) and networks in AD, as well as the effects of the APOEɛ4 allele on neuroimaging regions based on structural magnetic resonance imaging (sMRI).

METHODS

All subjects underwent an sMRI scan. GMV and cortical thickness were calculated using voxel-based morphological analysis, and structural networks were constructed based on graph theory analysis to compare differences between AD and normal controls.

RESULTS

The volumes of grey matter in the bilateral inferior temporal gyrus, right middle temporal gyrus, right inferior parietal lobule, right limbic lobe, right frontal lobe, left anterior cingulate gyrus, and bilateral olfactory cortex of patients with AD were significantly decreased. The cortical thickness in patients with AD was significantly reduced in the left lateral occipital lobe, inferior parietal lobe, orbitofrontal region, precuneus, superior parietal gyrus, right precentral gyrus, middle temporal gyrus, pars opercularis gyrus, insular gyrus, superior marginal gyrus, bilateral fusiform gyrus, and superior frontal gyrus. In terms of local properties, there were significant differences between the AD and control groups in these areas, including the right bank, right temporalis pole, bilateral middle temporal gyrus, right transverse temporal gyrus, left postcentral gyrus, and left parahippocampal gyrus.

CONCLUSION

There were significant differences in the morphological and structural covariate networks between AD patients and healthy controls under APOEɛ4 allele effects.

R4Alz-Revised: A Tool Able to Strongly Discriminate 'Subjective Cognitive Decline' from Healthy Cognition and 'Minor Neurocognitive Disorder'

BACKGROUND

The diagnosis of the minor neurocognitive diseases in the clinical course of dementia before the clinical symptoms' appearance is the holy grail of neuropsychological research. The R4Alz battery is a novel and valid tool that was designed to assess cognitive control in people with minor cognitive disorders. The aim of the current study is the R4Alz battery's extension (namely R4Alz-R), enhanced by the design and administration of extra episodic memory tasks, as well as extra cognitive control tasks, towards improving the overall R4Alz discriminant validity.

METHODS

The study comprised 80 people: (a) 20 Healthy adults (HC), (b) 29 people with Subjective Cognitive Decline (SCD), and (c) 31 people with Mild Cognitive Impairment (MCI). The groups differed in age and educational level.

RESULTS

Updating, inhibition, attention switching, and cognitive flexibility tasks discriminated SCD from HC (p ≤ 0.003). Updating, switching, cognitive flexibility, and episodic memory tasks discriminated SCD from MCI (p ≤ 0.001). All the R4Alz-R's tasks discriminated HC from MCI (p ≤ 0.001). The R4Alz-R was free of age and educational level effects. The battery discriminated perfectly SCD from HC and HC from MCI (100% sensitivity-95% specificity and 100% sensitivity-90% specificity, respectively), whilst it discriminated excellently SCD from MCI (90.3% sensitivity-82.8% specificity).

CONCLUSION

SCD seems to be stage a of neurodegeneration since it can be objectively evaluated via the R4Alz-R battery, which seems to be a useful tool for early diagnosis.

Preclinical Brain Network Abnormalities in Patients with Subjective Cognitive Decline

BACKGROUND

Alzheimer's disease (AD) is the most common form of dementia worldwide. Currently there are no disease modifying treatments available. Detecting subjects with increased risk to develop dementia is essential for future clinical trials. Subjective cognitive decline (SCD) is a condition defining individuals who perceive a decrease in their own cognitive functioning in the absence of any detectable deficit on neuropsychological testing. SCD individuals show AD-related biomarkers abnormalities in cerebrospinal fluid.

OBJECTIVE

The aim of the present study was to assess brain functional connectivity (FC) changes in SCD individuals.

METHODS

23 SCD and 33 healthy subjects (HS) underwent an extensive neuropsychological assessment and 3T-MRI scanning including a T1-w volume and resting-state fMRI (RS-fMRI) to assess brain atrophy and brain FC.

RESULTS

No between-group differences in grey matter volumes were detected. SCD subjects compared to HS showed both increased and decreased FC in the executive and parietal networks. Associations between cognitive measures, mainly assessing working memory, and FC within brain networks were found both in SCD and HS separately.

CONCLUSIONS

SCD individuals showed FC abnormalities in networks involving fronto-parietal areas that may account for their lower visuo-spatial working memory performances. Dysfunctions in executive-frontal networks may be responsible for the cognitive decline subjectively experienced by SCD individuals despite the normal scores observed by formal neuropsychological assessment. The present study contributes to consider SCD individuals in an early AD stage with an increased risk of developing the disease in the long term.

Graph Analysis of Functional Brain Topology Using Minimum Spanning Tree in Subjective Cognitive Decline

BACKGROUND

Subjects with subjective cognitive decline (SCD) are proposed as a potential population to screen for Alzheimer's disease (AD).

OBJECTIVE

Investigating brain topologies would help to mine the neuromechanisms of SCD and provide new insights into the pathogenesis of AD.

METHODS

Objectively cognitively unimpaired subjects from communities who underwent resting-state BOLD-fMRI and clinical assessments were included. The subjects were categorized into SCD and normal control (NC) groups according to whether they exhibited self-perceived cognitive decline and were worried about it. The minimum spanning tree (MST) of the functional brain network was calculated for each subject, based on which the efficiency and centrality of the brain network organization were explored. Hippocampal/parahippocampal volumes were also detected to reveal whether the early neurodegeneration of AD could be seen in SCD.

RESULTS

A total of 49 subjects in NC and 95 subjects in SCD group were included in this study. We found the efficiency and centrality of brain network organization, as well as the hippocampal/parahippocampal volume were preserved in SCD. Besides, SCD exhibited normal cognitions, including memory, language, and execution, but increased depressive and anxious levels. Interestingly, language and execution, instead of memory, showed a significant positive correlation with the maximum betweenness centrality of the functional brain organization and hippocampal/parahippocampal volume. Neither depressive nor anxious scales exhibited correlations with the brain functional topologies or hippocampal/parahippocampal volume.

CONCLUSION

SCD exhibited preserved efficiency and centrality of brain organization. In clinical practice, language and execution as well as depression and anxiety should be paid attention in SCD.

The resting-state topological organization damage of language-related brain regions in post-stroke cognitive impairment

The topology of brain networks is the foundation of cognition. We hypothesized that stroke damaged topological organization resulting in cognitive impairment. The aim was to explore the damage pattern of the resting-state topology in post-stroke cognitive impairment (PSCI) patients. Thirty-seven patients with PSCI and thirty-seven gender- and age-matched healthy controls (HC) were recruited. The structural and functional data were collected from all subjects. The degree centrality (DC), betweenness centrality (BC), and global properties of brain networks were analyzed between groups. Spearman correlation analysis was performed between topological properties that changed significantly and clinical cognitive function scale scores. Compared with HC, the PSCI patients had significantly reduced DC in language-related brain regions and significantly higher DC in the right frontal lobe, hippocampus, and paracentral lobule. The decreased BC was located in the left caudate, thalamus, temporal, and frontal lobes. The increased BC was detected in the left cuneus and right precuneus. In addition, PSCI exhibited increased characteristic path length and decreased small-worldness. PSCI patients had impaired functional topology of the language-related brain regions, mainly in the left hemisphere. The enhanced processing and relaying information of some right high-order cognitive brain regions may be a compensatory mechanism. However, the whole brain's function integration was reduced, and there was an imbalance between efficiency and consumption.

Corneal confocal microscopy differentiates patients with Parkinson’s disease with and without autonomic involvement

Autonomic dysregulation in Parkinson’s disease (PD) can precede motor deficits and is associated with reduced quality of life, disease progression, and increased mortality. Objective markers of autonomic involvement in PD are limited. Corneal confocal microscopy (CCM) is a rapid ophthalmic technique that can quantify small nerve damage in a range of peripheral and autonomic neuropathies. Here we investigated whether CCM can be used to assess autonomic symptoms in PD. Based on the scale for outcomes in Parkinson’s disease for autonomic symptoms (SCOPA-AUT), patients with PD were classified into those without autonomic symptoms (AutD-N), with single (AutD-S), and multiple (AutD-M) domain autonomic dysfunction. Corneal nerve fiber pathology was quantified using CCM, and the relationship with autonomic symptoms was explored. The study enrolled 71 PD patients and 30 control subjects. Corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), corneal nerve fiber length (CNFL), and CNBD/CNFD ratio were lower in PD patients with autonomic symptoms compared to those without autonomic symptoms. Autonomic symptoms correlated positively with CNFD (r = −0.350, p = 0.004), and were not related to Levodopa equivalent daily dose (r = 0.042, p = 0.733) after adjusting for age, disease severity, disease duration or cognitive function. CCM parameters had high sensitivity and specificity in distinguishing patients with PD with and without autonomic symptoms. PD patients with autonomic symptoms have corneal nerve loss, and CCM could serve as an objective ophthalmic imaging technique to identify patients with PD and autonomic symptoms.

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).

Altered pattern analysis and identification of subjective cognitive decline based on morphological brain network

Subjective cognitive decline (SCD) is considered the first stage of Alzheimer's disease (AD). Accurate diagnosis and the exploration of the pathological mechanism of SCD are extremely valuable for targeted AD prevention. However, there is little knowledge of the specific altered morphological network patterns in SCD individuals. In this present study, 36 SCD cases and 34 paired-matched normal controls (NCs) were recruited. The Jensen-Shannon distance-based similarity (JSS) method was implemented to construct and derive the attributes of multiple brain connectomes (i.e., morphological brain connections and global and nodal graph metrics) of individual morphological brain networks. A t-test was used to discriminate between the selected nodal graph metrics, while the leave-one-out cross-validation (LOOCV) was used to obtain consensus connections. Comparisons were performed to explore the altered patterns of connectome features. Further, the multiple kernel support vector machine (MK-SVM) was used for combining brain connectomes and differentiating SCD from NCs. We showed that the consensus connections and nodal graph metrics with the most discriminative ability were mostly found in the frontal, limbic, and parietal lobes, corresponding to the default mode network (DMN) and frontoparietal task control (FTC) network. Altered pattern analysis demonstrated that SCD cases had a tendency for modularity and local efficiency enhancement. Additionally, using the MK-SVM to combine the features of multiple brain connectomes was associated with optimal classification performance [area under the curve (AUC): 0.9510, sensitivity: 97.22%, specificity: 85.29%, and accuracy: 91.43%]. Therefore, our study highlighted the combination of multiple connectome attributes based on morphological brain networks and offered a valuable method for distinguishing SCD individuals from NCs. Moreover, the altered patterns of multidimensional connectome attributes provided a promising insight into the neuroimaging mechanism and early intervention in SCD subjects.

The fractional amplitude of low-frequency fluctuations signals related to amyloid uptake in high-risk populations—A pilot fMRI study

Background

Patients with type 2 diabetes mellitus (T2DM) and subjective cognitive decline (SCD) have a higher risk to develop Alzheimer's Disease (AD). Resting-state-functional magnetic resonance imaging (rs-fMRI) was used to document neurological involvement in the two groups from the aspect of brain dysfunction. Accumulation of amyloid-β (Aβ) starts decades ago before the onset of clinical symptoms and may already have been associated with brain function in high-risk populations. However, this study aims to compare the patterns of fractional amplitude of low-frequency fluctuations (fALFF) maps between cognitively normal high-risk groups (SCD and T2DM) and healthy elderly and evaluate the association between regional amyloid deposition and local fALFF signals in certain cortical regions.

Materials and methods

A total of 18 T2DM, 11 SCD, and 18 healthy elderlies were included in this study. The differences in the fALFF maps were compared between HC and high-risk groups. Regional amyloid deposition and local fALFF signals were obtained and further correlated in two high-risk groups.

Results

Compared to HC, the altered fALFF signals of regions were shown in SCD such as the left posterior cerebellum, left putamen, and cingulate gyrus. The T2DM group illustrated altered neural activity in the superior temporal gyrus, supplementary motor area, and precentral gyrus. The correlation between fALFF signals and amyloid deposition was negative in the left anterior cingulate cortex for both groups. In the T2DM group, a positive correlation was shown in the right occipital lobe and left mesial temporal lobe.

Conclusion

The altered fALFF signals were demonstrated in high-risk groups compared to HC. Very early amyloid deposition in SCD and T2DM groups was observed to affect the neural activity mainly involved in the default mode network (DMN).

Alternation of the Rich-Club Organization of Individual Brain Metabolic Networks in Parkinson's Disease

Objective

The diagnosis of Parkinson's disease (PD) remains challenging. Although 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has revealed the metabolic abnormalities associated with PD at systemic levels, the underlying rich-club organization of the metabolic connectome in these patients remains largely unknown.

Materials and Methods

The data of 49 PD patients and 49 well-matched healthy controls (HCs) were retrieved and assessed. An individual metabolic connectome based on the standard uptake value (SUV) was built using the Jensen-Shannon Divergence Similarity Estimation (JSSE) method to compare the rich-club properties between PD patients and HC.

Results

Our results showed the rich-club organization of metabolic networks (normalized rich-club coefficients > 1) in the PD and HC group were within a range of thresholds. Further, patients with PD demonstrated lower strength and degree in rich-club connections compared with HCs (strength: HCs = 55.70 ± 8.52, PDs = 52.03 ± 10.49, p = 0.028; degree: HCs = 56.55 ± 8.60, PDs = 52.85 ± 10.62, p = 0.029), but difference between their feeder and local connections was not significant.

Conclusion

Individual metabolic networks combined with rich club analysis indicated that PD patients had decreased rich club connections but similar feeder and local connections compared with HCs, indicating rich club connections as a promising marker for early diagnosis of PD.

Discrimination of subjective cognitive decline from healthy control based on glucose-oxygen metabolism network coupling features and machine learning

BACKGROUND

Our previous studies have proved that preclinical Alzheimer's disease (AD) which including subjective cognitive decline (SCD) stage, can be distinguished from normal control (NC) by glucose-oxygen metabolism coupling at the voxel level, but whether the coupling at the network level worked has not been studied. Therefore, this study aimed to explore the coupling relationship between brain glucose metabolic connectivity network and oxygen functional connectivity network, and whether its feasibility as a biomarker to discriminate SCD from healthy control (HC).

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) and glucose positron emission tomography (PET) based on hybrid PET/MRI scans were used to investigate metabolism-oxygen metabolism coupling in 56 SCD individuals and 54 HCs. Network coupling features were selected by logistic regression-recursive feature elimination (LR-RFE), and then a linear support vector machine (SVM) was used to distinguish SCD and HC by using 5-fold cross-validation.

RESULTS

The classification average accuracy of network coupling had reached 76.36% with a standard deviation of 9.85% (with a sensitivity of 77.82%±15.13% and a specificity of 75.30%±15.15%). After receiver operating characteristic (ROC) analysis, the average area under curve (AUC) of network coupling was 0.788 (95% confidence interval [Formula: see text]).

CONCLUSION

This study provided a new perspective for exploring network coupling. The proposed classification method highlighted the potential clinical application by combing glucose-oxygen metabolism coupling and machine learning in identifying SCD.

Investigating the overlapping associations of prior concussion, default mode connectivity, and executive function-based symptoms

Growing evidence suggests that younger athletes with greater concussion history are more likely to endorse greater subjective cognitive (e.g., executive function) symptoms, but not perform worse on objective cognitive testing. We sought to identify biological correlates of elevated cognitive symptoms in 100 healthy, collegiate-aged athletes with varying degrees of concussion history. Associations between concussion history with subjectively-rated executive function were assessed with generalized linear models. Using resting state fMRI, we examined associations between concussion history and between-and within-network connectivity across three networks integral to executive function; default mode network (DMN), frontoparietal network (FPN), and ventral attention network (VAN). Relationships of between-and within-network connectivity with subjective executive function were assessed. Although the large majority of participants did not report clinically relevant levels of executive difficulties, there was a significant association between concussion history and higher behavioral regulation-related symptoms; B = .04[.01, .07], p = .011. A significant elevation in total within-network connectivity was observed among those with a greater concussion history, B = .02[.002, .03], p = .028, which was primarily driven by a positive association between concussion history and within DMN connectivity, B = .02[.004, .04], p = .014. Higher behavioral regulation-related symptoms were associated with greater total within-network connectivity, B = 0.57[0.18, 0.96], p = .005, and increased within-network connectivity for the DMN, B = .49[.12, .86], p = .010). The current study identified a distinct biological correlate, increased within-DMN connectivity, which was associated with both a greater history of concussion and greater behavioral regulation symptoms. Future studies are required to determine the degree to which these changes associated with concussion history may evolve toward objective cognitive decline over the lifespan.

Differential Abnormality in Functional Connectivity Density in Preclinical and Early-Stage Alzheimer's Disease

Background

Both subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) have a high risk of progression to Alzheimer's disease (AD). While most of the available evidence described changes in functional connectivity (FC) in SCD and aMCI, there was no confirmation of changes in functional connectivity density (FCD) that have not been confirmed. Therefore, the purpose of this study was to investigate the specific alterations in resting-state FCD in SCD and aMCI and further assess the extent to which these changes can distinguish the preclinical and early-stage AD.

Methods

A total of 57 patients with SCD, 59 patients with aMCI, and 78 healthy controls (HC) were included. The global FCD, local FCD, and long-range FCD were calculated for each voxel to identify brain regions with significant FCD alterations. The brain regions with abnormal FCD were then used as regions of interest for FC analysis. In addition, we calculated correlations between neuroimaging alterations and cognitive function and performed receiver-operating characteristic analyses to assess the diagnostic effect of the FCD and FC alterations on SCD and aMCI.

Results

FCD mapping revealed significantly increased global FCD in the left parahippocampal gyrus (PHG.L) and increased long-range FCD in the left hippocampus for patients with SCD when compared to HCs. However, when compared to SCD, patients with aMCI showed significantly decreased global FCD and long-range FCD in the PHG.L. The follow-up FC analysis further revealed significant variations between the PHG.L and the occipital lobe in patients with SCD and aMCI. In addition, patients with SCD also presented significant changes in FC between the left hippocampus, the left cerebellum anterior lobe, and the inferior temporal gyrus. Moreover, changes in abnormal indicators in the SCD and aMCI groups were significantly associated with cognitive function. Finally, combining FCD and FC abnormalities allowed for a more precise differentiation of the clinical stages.

Conclusion

To our knowledge, this study is the first to investigate specific alterations in FCD and FC for both patients with SCD and aMCI and confirms differential abnormalities that can serve as potential imaging markers for preclinical and early-stage Alzheimer's disease (AD). Also, it adds a new dimension of understanding to the diagnosis of SCD and aMCI as well as the evaluation of disease progression.

Automatic Classification of Subjects of the PSEN1-E280A Family at Risk of Developing Alzheimer’s Disease Using Machine Learning and Resting State Electroencephalography

Background: The study of genetic variant carriers provides an opportunity to identify neurophysiological changes in preclinical stages. Electroencephalography (EEG) is a low-cost and minimally invasive technique which, together with machine learning, provide the possibility to construct systems that classify subjects that might develop Alzheimer’s disease (AD). Objective: The aim of this paper is to evaluate the capacity of the machine learning techniques to classify healthy Non-Carriers (NonCr) from Asymptomatic Carriers (ACr) of PSEN1-E280A variant for autosomal dominant Alzheimer’s disease (ADAD), using spectral features from EEG channels and brain-related independent components (ICs) obtained using independent component analysis (ICA). Methods: EEG was recorded in 27 ACr and 33 NonCr. Statistical significance analysis was applied to spectral information from channels and group ICA (gICA), standardized low-resolution tomography (sLORETA) analysis was applied over the IC as well. Strategies for feature selection and classification like Chi-square, mutual informationm and support vector machines (SVM) were evaluated over the dataset. Results: A test accuracy up to 83% was obtained by implementing a SVM with spectral features derived from gICA. The main findings are related to theta and beta rhythms, generated in the parietal and occipital regions, like the precuneus and superior parietal lobule. Conclusion: Promising models for classification of preclinical AD due to PSEN-1-E280A variant can be trained using spectral features, and the importance of the beta band and precuneus region is highlighted in asymptomatic stages, opening up the possibility of its use as a screening methodology.

Dynamics of the Brain Functional Network Associated With Subjective Cognitive Decline and Its Relationship to Apolipoprotein E €4 Alleles

The aim of our study was to explore the dynamic functional alterations in the brain in patients with subjective cognitive decline (SCD) and their relationship to apolipoprotein E (APOE) €4 alleles. In total, 95 SCD patients and 49 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Then, the mean time series of 90 cortical or subcortical regions were extracted based on anatomical automatic labeling (AAL) atlas from the preprocessed rs-fMRI data. The static functional connectome (SFC) and dynamic functional connectome (DFC) were constructed and compared using graph theory methods and leading eigenvector dynamics analysis (LEiDA), respectively. The SCD group displayed a shorter lifetime (p = 0.003, false discovery rate corrected) and lower probability (p = 0.009, false discovery rate corrected) than the HC group in a characteristic dynamic functional network mainly involving the bilateral insular and temporal neocortex. No significant differences in the SFC were detected between the two groups. Moreover, the lower probability in the SCD group was found to be negatively correlated with the number of APOE ε4 alleles (r = −0.225, p = 0.041) in a partial correlation analysis with years of education as a covariate. Our results suggest that the DFC may be a more sensitive parameter than the SFC and can be used as a potential biomarker for the early detection of SCD.

Rich-Club Organization Disturbances of the Individual Morphological Network in Subjective Cognitive Decline

Background

Subjective cognitive decline (SCD) was considered to be the preclinical stage of Alzheimer's disease (AD). However, less is known about the altered rich-club organizations of the morphological networks in individuals with SCD.

Methods

This study included 53 individuals with SCD and 54 well-matched healthy controls (HC) from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Individual-level brain morphological networks were constructed by estimating the Jensen-Shannon distance-based similarity in the distribution of regional gray matter volume. Rich-club properties were then detected, followed by statistical comparison.

Results

The characteristic rich-club organization of morphological networks (normalized rich-club coefficients > 1) was observed for both the SCD and HC groups under a range of thresholds. The SCD group showed a reduced normalized rich-club coefficient compared with the HC group. The SCD group exhibited the decreased strength and degree of rich-club connections than the HC group (strength: HC = 79.93, SCD = 74.37, p = 0.028; degree: HC = 85.28, SCD = 79.34, p = 0.027). Interestingly, the SCD group showed an increased strength of local connections than the HC group (strength: HC = 1982.16, SCD = 2003.38, p = 0.036).

Conclusion

Rich-club organization disturbances of morphological networks in individuals with SCD reveal a distinct pattern between the rich-club and peripheral regions. This altered rich-club organization pattern provides novel insights into the underlying mechanism of SCD and could be used to investigate prevention strategies at the preclinical stage of AD.

Brain Functional Network Analysis of Patients with Primary Angle-Closure Glaucoma

Objectives. Recent resting-state functional magnetic resonance imaging (fMRI) studies have focused on glaucoma-related neuronal degeneration in structural and spontaneous functional brain activity. However, there are limited studies regarding the differences in the topological organization of the functional brain network in patients with glaucoma. In this study, we aimed to assess both potential alterations and the network efficiency in the functional brain networks of patients with primary angle-closure glaucoma (PACG). Methods. We applied resting-state fMRI data to construct the functional connectivity network of 33 patients with PACG ( $54.21\pm 7.21\text{years}$ ) and 33 gender- and age-matched healthy controls ( $52.42\pm 7.80\text{years}$ ). The differences in the global and regional topological brain network properties between the two groups were assessed using graph theoretical analysis. Partial correlations between the altered regional values and clinical parameters were computed for patients with PACG. Results. No significant differences in global topological measures were identified between the two groups. However, significant regional alterations were identified in the patients with PACG, including differences within visual and nonvisual (somatomotor and cognition-emotion) regions. The normalized clustering coefficient and normalized local efficiency of the right superior parietal gyrus were significantly correlated with the retinal fiber layer thickness (RNFLT) and the vertical cup to disk ratio (V C/D). In addition, the normalized node betweenness of the left middle frontal gyrus (orbital portion) was significantly correlated with the V C/D in the patients with PACG. Conclusions. Our results suggest that regional inefficiency with decrease and compensatory increase in local functional properties of visual and nonvisual nodes preserved the brain network of the PACG at the global level.

Effect of APOEε4 on Functional Brain Network in Patients with Subjective Cognitive Decline: A Resting State Functional MRI Study

PURPOSE

Subjective cognitive decline (SCD) is the earliest symptom stage of Alzheimer's disease (AD), and the APOEε4 allele is the strongest genetic risk factor for sporadic AD. Based on graph theory, the resting state functional connectivity (rsFC) in SCD patients with APOEε4 was studied to explore the effect of APOEε4 on the rsFC network properties of SCD patients.

PATIENTS AND METHODS

This cross-sectional study included MRI image data from 19 SCD patients with APOEε4 (SCD+), 29 SCD patients without APOEε4 (SCD-), and 30 normal control (NC-) individuals without APOEε4. We generated a binary matrix based on anatomical automatic labeling (AAL) 90 atlas to construct the functional network. We then calculated the whole brain network characteristics and intracerebral node characteristics by graph theory.

RESULTS

For the whole brain network characteristics, all three groups showed small-worldness. The SCD+ group had increased compensatory information transfer speed and enhanced integration capability. This group also had high heterogeneity for intracerebral node characteristics, mainly in the default mode network, left superior occipital gyrus, and bilateral putamen.

CONCLUSION

APOEε4 effects the functional brain network in patients with SCD and may be a potential indicator for the identification of SCD.

Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer's disease: A cross-validation study with Chinese and ADNI cohorts

OBJECTIVE

Disease-related metabolic brain patterns have been verified for a variety of neurodegenerative diseases including Alzheimer's disease (AD). This study aimed to explore and validate the pattern derived from cognitively normal controls (NCs) in the Alzheimer's continuum.

METHODS

This study was based on two cohorts; one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Sino Longitudinal Study on Cognitive Decline (SILCODE). Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) and [18F]florbetapir-PET imaging. Participants were binary-grouped based on β-amyloid (Aβ) status, and the positivity was defined as Aβ+. Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate the "at-risk AD-related metabolic pattern (ARADRP)" for NCs. The pattern expression score was obtained and compared between the groups, and receiver operating characteristic curves were drawn. Notably, we conducted cross-validation to verify the robustness and correlation analyses to explore the relationships between the score and AD-related pathological biomarkers.

RESULTS

Forty-eight Aβ+ NCs and 48 Aβ- NCs were included in the ADNI cohort, and 25 Aβ+ NCs and 30 Aβ- NCs were included in the SILCODE cohort. The ARADRPs were identified from the combined cohorts and the two separate cohorts, characterized by relatively lower regional loadings in the posterior parts of the precuneus, posterior cingulate, and regions of the temporal gyrus, as well as relatively higher values in the superior/middle frontal gyrus and other areas. Patterns identified from the two separate cohorts showed some regional differences, including the temporal gyrus, basal ganglia regions, anterior parts of the precuneus, and middle cingulate. Cross-validation suggested that the pattern expression score was significantly higher in the Aβ+ group of both cohorts (p < 0.01), and contributed to the diagnosis of Aβ+ NCs (with area under the curve values of 0.696-0.815). The correlation analysis revealed that the score was related to tau pathology measured in cerebrospinal fluid (p-tau: p < 0.02; t-tau: p < 0.03), but not Aβ pathology assessed with [18F]florbetapir-PET (p > 0.23).

CONCLUSIONS

ARADRP exists for NCs, and the acquired pattern expression score shows a certain ability to discriminate Aβ+ NCs from Aβ- NCs. The SSM/PCA method is expected to be helpful in the ultra-early diagnosis of AD in clinical practice.

Corneal nerve fiber loss relates to cognitive impairment in patients with Parkinson’s disease

Cognitive impairment in Parkinson’s disease (PD) adversely influences quality of life. There is currently no available biomarker to predict cognitive decline in PD. Corneal confocal microscopy (CCM) has been used as a non-invasive tool for quantifying small nerve damage in PD. The present study investigated whether corneal nerve measures were associated with cognitive function in PD. Patients with PD were classified into those with normal cognitive function (PD-CN), mild cognitive impairment (PD-MCI), and dementia (PDD). Corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), and corneal nerve fiber length (CNFL) were quantified with CCM and compared with a control group. Sixty-five PD patients and thirty controls were studied. CNFD was decreased and CNBD was increased in PD patients compared to controls (P < 0.05). CNBD and CNBD/CNFD ratio was higher in PD-CN compared to controls. CNFD was positively correlated with the Montreal cognitive assessment (MoCA) score (r = 0.683, P < 0.001), but negatively associated with unified Parkinson disease rating scale (UPDRS)-part III (r = −0.481, P < 0.001) and total UPDRS scores (r = −0.401, P = 0.001) in PD patients. There was no correlation between CNFD and Levodopa equivalent daily dose (LEDD) (r = 0.176, P = 0.161). CNFD, CNBD, CNFL, and CNBD/CNFD ratio was lower with increasing Hoehn and Yahr stage. PD patients show evidence of corneal nerve loss compared with controls and corneal nerve parameters are associated with the severity of cognitive and motor dysfunction in PD. CCM could serve as an objective in vivo ophthalmic imaging technique to assess neurodegeneration in PD.

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE

BACKGROUND

Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown.

OBJECTIVE

In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry.

METHODS

A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied.

RESULTS

Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p < 0.001). The above results appeared in both the whole brain and DMN.

CONCLUSION

There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

Aberrant Topological Patterns of Structural Covariance Networks in Cognitively Normal Elderly Adults With Mild Behavioral Impairment

Mild behavioral impairment (MBI), characterized by the late-life onset of sustained and meaningful neuropsychiatric symptoms, is increasingly recognized as a prodromal stage of dementia. However, the underlying neural mechanisms of MBI remain unclear. Here, we examined alterations in the topological organization of the structural covariance networks of patients with MBI (N = 32) compared with normal controls (N = 38). We found that the gray matter structural covariance networks of both the patients with MBI and controls exhibited a small-world topology evidenced by sigma value larger than one. The patients with MBI had significantly decreased clustering coefficients at several network densities and local efficiency at densities ranging from 0.05 to 0.26, indicating decreased local segregation. No significant differences in the characteristic path length, gamma value, sigma value, or global efficiency were detected. Locally, the patients with MBI showed significantly decreased nodal betweenness centrality in the left middle frontal gyrus, right inferior frontal gyrus (opercular part), and left Heschl gyrus and increased betweenness centrality in the left gyrus rectus, right insula, bilateral precuneus, and left thalamus. Moreover, the difference in the bilateral precuneus survived after correcting for multiple comparisons. In addition, a different number and distribution of hubs was identified in patients with MBI, showing more paralimbic hubs than observed in the normal controls. In conclusion, we revealed abnormal topological patterns of the structural covariance networks in patients with MBI and offer new insights into the network dysfunctional mechanisms of MBI.

Altered resting-state functional networks in patients with premenstrual syndrome: a graph-theoretical based study

Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Previous studies have indicated alterations of brain functional connectivity in PMS patients. However, little is known about the overall organization of brain network in PMS patients. Functional magnetic resonance imaging data deriving from 20 PMS patients and 21 healthy controls (HCs). Pearson correlation between mean time-series was used to estimate connectivity matrix between each paired regions of interest, and the connectivity matrix for each participant was then binarized. Graph theory analysis was applied to assess each participant's global and local topological properties of brain functional network. Correlation analysis was performed to evaluate relationships between the daily rating of severity of problems (DRSP) and abnormal network properties. PMS patients had lower small-worldness values than HCs. PMS-related alterations of nodal properties were mainly found in the posterior cingulate cortex, precuneus and angular gyrus. The PMS-related abnormal connectivity components were mainly associated with the thalamus, putamen and middle cingulate cortex. In the PMS group, the DRSP score were negatively correlated with the area under the curves of nodal local efficiency in the posterior cingulate cortex. Our study suggests that the graph-theory method may be one potential tool to detect disruptions of brain connections and may provide important evidence for understanding the PMS from the disrupted network organization perspective.

Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline

Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer’s Disease (AD). Previous studies have suggested that multimodal brain networks play an important role in the early diagnosis and mechanisms underlying SCD. However, most of the previous studies focused on a single modality, and lacked correlation analysis between different modal biomarkers and brain regions. In order to further explore the specific characteristic of the multimodal brain networks in the stage of SCD, 22 individuals with SCD and 20 matched healthy controls (HCs) were recruited in the present study. We constructed the individual morphological, structural and functional brain networks based on 3D-T1 structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. A t-test was used to select the connections with significant difference, and a multi-kernel support vector machine (MK-SVM) was applied to combine the selected multimodal connections to distinguish SCD from HCs. Moreover, we further identified the consensus connections of brain networks as the most discriminative features to explore the pathological mechanisms and potential biomarkers associated with SCD. Our results shown that the combination of three modal connections using MK-SVM achieved the best classification performance, with an accuracy of 92.68%, sensitivity of 95.00%, and specificity of 90.48%. Furthermore, the consensus connections and hub nodes based on the morphological, structural, and functional networks identified in our study exhibited abnormal cortical-subcortical connections in individuals with SCD. In addition, the functional networks presented more discriminative connections and hubs in the cortical-subcortical regions, and were found to perform better in distinguishing SCD from HCs. Therefore, our findings highlight the role of the cortical-subcortical circuit in individuals with SCD from the perspective of a multimodal brain network, providing potential biomarkers for the diagnosis and prediction of the preclinical stage of AD.

Changes of Regional Neural Activity Homogeneity in Preclinical Alzheimer's Disease: Compensation and Dysfunction

INTRODUCTION

Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease and may develop into amnestic mild cognitive impairment (aMCI). Finding suitable biomarkers is the key to accurately identifying SCD. Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies on SCD patients showed functional connectivity disorders. Our goal was to explore whether local neurological homogeneity changes in SCD patients, the relationship between these changes and cognitive function, and similarities of neurological homogeneity changes between SCD and aMCI patients.

MATERIALS AND METHODS

37 cases of the healthy control (HC) group, 39 cases of the SCD group, and 28 cases of the aMCI group were included. Participants underwent rs-fMRI examination and a set of neuropsychological test batteries. Regional homogeneity (ReHo) was calculated and compared between groups. ReHo values were extracted from meaningful regions in the SCD group, and the correlation between ReHo values with the performance of neuropsychological tests was analyzed.

RESULTS

Our results showed significant changes in the ReHo among groups. In the SCD group compared with the HC group, part of the parietal lobe, frontal lobe, and occipital lobe showed decreased ReHo, and the temporal lobe, part of the parietal lobe and the frontal lobe showed increased ReHo. The increased area of ReHo was negatively correlated with the decreased area, and was related to decrease on multiple neuropsychological tests performance. Simultaneously, the changed areas of ReHo in SCD patients are similar to aMCI patients, while aMCI group's neuropsychological test performance was significantly lower than that of the SCD group.

CONCLUSION

There are significant changes in local neurological homogeneity in SCD patients, and related to the decline of cognitive function. The increase of neurological homogeneity in the temporal lobe and adjacent area is negatively correlated with cognitive function, reflecting compensation for local neural damage. These changes in local neurological homogeneity in SCD patients are similar to aMCI patients, suggesting similar neuropathy in these two stages. However, the aMCI group's cognitive function was significantly worse than that of the SCD group, suggesting that this compensation is limited. In summary, regional neural activity homogeneity may be a potential biomarker for identifying SCD and measuring the disease severity.

Complexity Analysis of the Default Mode Network Using Resting-State fMRI in Down Syndrome: Relationships Highlighted by a Neuropsychological Assessment

Background: Studies on complexity indicators in the field of functional connectivity derived from resting-state fMRI (rs-fMRI) in Down syndrome (DS) samples and their possible relationship with cognitive functioning variables are rare. We analyze how some complexity indicators estimated in the subareas that constitute the default mode network (DMN) might be predictors of the neuropsychological outcomes evaluating Intelligence Quotient (IQ) and cognitive performance in persons with DS. Methods: Twenty-two DS people were assessed with the Kaufman Brief Test of Intelligence (KBIT) and Frontal Assessment Battery (FAB) tests, and fMRI signals were recorded in a resting state over a six-minute period. In addition, 22 controls, matched by age and sex, were evaluated with the same rs-fMRI procedure. Results: There was a significant difference in complexity indicators between groups: the control group showed less complexity than the DS group. Moreover, the DS group showed more variance in the complexity indicator distributions than the control group. In the DS group, significant and negative relationships were found between some of the complexity indicators in some of the DMN networks and the cognitive performance scores. Conclusions: The DS group is characterized by more complex DMN networks and exhibits an inverse relationship between complexity and cognitive performance based on the negative parameter estimates.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Alterations in Dynamic Functional Connectivity in Individuals With Subjective Cognitive Decline

Purpose: To investigate the dynamic functional connectivity (DFC) and static parameters of graph theory in individuals with subjective cognitive decline (SCD) and the associations of DFC and topological properties with cognitive performance. Methods: Thirty-three control subjects and 32 SCD individuals were enrolled in this study, and neuropsychological evaluations and resting-state functional magnetic resonance imaging scanning were performed. Thirty-three components were selected by group independent component analysis to construct 7 functional networks. Based on the sliding window approach and k-means clustering, distinct DFC states were identified. We calculated the temporal properties of fractional windows in each state, the mean dwell time in each state, and the number of transitions between each pair of DFC states. The global and local static parameters were assessed by graph theory analysis. The differences in DFC and topological metrics, and the associations of the altered neuroimaging measures with cognitive performance were assessed. Results: The whole cohort demonstrated 4 distinct connectivity states. Compared to the control group, the SCD group showed increased fractional windows and an increased mean dwell time in state 4, characterized by hypoconnectivity both within and between networks. The SCD group also showed decreased fractional windows and a decreased mean dwell time in state 2, dominated by hyperconnectivity within and between the auditory, visual and somatomotor networks. The number of transitions between state 1 and state 2, between state 2 and state 3, and between state 2 and state 4 was significantly reduced in the SCD group compared to the control group. No significant differences in global or local topological metrics were observed. The altered DFC properties showed significant correlations with cognitive performance. Conclusion: Our findings indicated DFC network reconfiguration in the SCD stage, which may underlie the early cognitive decline in SCD subjects and serve as sensitive neuroimaging biomarkers for the preclinical detection of individuals with incipient Alzheimer's disease.