Abnormal salience network in normal aging and in amnestic mild cognitive impairment and Alzheimer's disease

The beneficial influence of night-shift napping on brain core cognition networks in nurses experiencing sleep deprivation: A preliminary resting-state fMRI study

PURPOSE

This study investigated the restorative effects of napping on cognitive brain networks in night-shift nurses experiencing sleep deprivation (SD).

METHODS

Functional magnetic resonance imaging data and neurocognitive assessments were collected from 20 nurses during three sessions (rested wakefulness (RW), SD, and night-shift napping (NS-NAP)). Functional connectivity (FC) was performed in three core cognitive networks, including the default-mode network (DMN), central executive network (CEN), and salience network (SN).

RESULTS

The SD session showed decreased FC across almost the entire DMN, while only showed increased FC in several key nodes of the CEN and SN. Napping partially mitigated SD-related FC alterations within the DMN and essentially restored FC abnormities in both the CEN and SN. Changes in neurocognitive performance observed between SD and NS-NAP sessions were correlated with alterations in FC within the DMN.

CONCLUSION

The functional integration of core neurocognitive networks can be restored to varying degrees through appropriate NS-NAP practices, potentially improving neurocognitive performance in nurses experiencing SD.

CLINICAL TRIAL NUMBER

not applicable.

Brain Activation Associated With Response to Psychotherapies for Late-Life Depression: A Task-Based fMRI Study

BACKGROUND

The course of late-life depression is associated with functioning of multiple brain networks. Understanding the brain mechanisms associated with response to psychotherapy can inform treatment development and a personalized treatment approach. This study examined how activation of key regions of the salience network, default mode network and reward systems is associated with response to psychotherapies for late-life depression.

METHODS

Thirty-three older adults with major depressive disorder were randomized to 9 weeks of Engage or Problem-Solving Therapy for late-life depression. Participants completed a Probabilistic Reversal Learning task in the MRI at baseline and Week 6. We focused on focal activation in regions of interest selected a priori: the subgenual cingulate cortex (sgACC; DMN); the dorsal anterior cingulate cortex (dACC; salience network and reward system); and the nucleus accumbens (NAcc; reward system). We applied mixed-effects regression models to examine whether brain activation was associated with psychotherapy response.

RESULTS

We found that at baseline, low activation of the dACC and the sgACC was associated with lower depression severity over 6 weeks of psychotherapy. In addition, we observed significant time*activation interactions, such that after 6 weeks of psychotherapy, lower dACC activation and higher NAcc and sgACC activation were each associated with lower depression severity. Further, we found that baseline slower response to negative feedback and faster response to positive feedback was associated with lower depression severity over 6 weeks of psychotherapy.

CONCLUSIONS

Our findings suggest that activation of reward, salience, and DMN regions may serve as markers of response during psychotherapy for late-life depression. Engagement of these networks may be linked to treatment outcome. Personalized psychotherapies can target individuals' brain profiles to improve outcomes for older adults with major depression.

ARTICLE SUMMARY

This study examined whether activation of regions of the reward, salience and default mode networks is associated with response to psychotherapies for late-life depression. We found that baseline low activation of the dACC and the sgACC was associated with lower depression severity during psychotherapy. We also found that at week 6, lower dACC activation and higher NAcc and sgACC activation were linked with lower depression severity. These regions may represent promising brain mechanisms for future personalized interventions.

Cognitive reserve linked to network-specific brain-ventricle coupling

Despite showing significant impact in cognitive preservation, the relationship between brain activity captured with functional Magnetic Resonance Imaging (fMRI) in gray matter and ventricular cerebrospinal fluid dynamics remains poorly understood. We analyzed 599 fMRI scans from 163 elderly participants at rest with varying degrees of cognitive impairment employing a unified phase coupling analysis that breaks from convention by incorporating both tissue and ventricular signal fluctuations. This whole-brain approach identified distinct brain-ventricle coupling modes that differentiate between cognitive status groups and correlate with specific cognitive abilities. Beyond the previously reported anti-phase coupling between global brain signals and ventricles-which we confirm occurs more frequently in cognitively normal controls-our analysis method uncovered additional coupling modes where signals in specific brain networks temporarily align with ventricle signals. At the cortical level, these modes reveal patterns corresponding to known resting-state networks: one overlapping with the Default Mode Network occurs significantly less frequently in Alzheimer's Disease patients, while another revealing the Frontoparietal Network correlates positively with memory scores. Our findings demonstrate that different brain-ventricle coupling modes correlate with specific cognitive domains, with particular modes predicting memory, executive function, and visuospatial abilities. The coupling between signals in brain ventricles and established resting-state networks challenges our current understanding of functional network formation, suggesting an integral link with brain fluid motion. This reconceptualization of brain dynamics through the lens of fluid-tissue interactions establishes a fundamental physical basis for cognitive preservation, suggesting that therapeutic interventions targeting these interactions may prove more effective than approaches focused solely on cellular or molecular mechanisms.

Astrocytes, via RTP801, contribute to cognitive decline by disrupting GABAergic-regulated connectivity and driving neuroinflammation in an Alzheimer's disease mouse model

INTRODUCTION

Alzheimer's disease (AD) pathogenesis involves astrocytic responses to extracellular amyloid beta deposits and phospho-tau neurofibrillary tangles, which drive inflammatory activation. RTP801, a stress-responsive protein, has been implicated in mediating neuroinflammation. Its levels are increased in AD hippocampal samples, correlating with disease severity and cognitive decline.

METHODS

Using astrocyte-specific RTP801 silencing in the hippocampus of 5xFAD mice, we evaluated cognition, neuroinflammation, and hippocampal connectivity by magnetic resonance spectroscopy (MRS) and resting-state functional connectivity analyses. Histological and biochemical analyses assessed microgliosis, astrogliosis, and inflammasome-related protein levels.

RESULTS

Astrocytic RTP801 silencing in 5xFAD mice preserved spatial memory, maintained hippocampal γ-aminobutyric acid (GABA) levels, and preserved resting-state brain networks. In addition, RTP801 silencing significantly reduced markers of microgliosis, astrogliosis, and inflammasome effectors.

DISCUSSION

Astrocytic RTP801 contributes to AD-associated cognitive decline by disrupting GABAergic-regulated connectivity and amplifying inflammatory responses. Targeting astrocytic RTP801 may therefore offer therapeutic potential to mitigate AD progression by preserving neural connectivity and reducing neuroinflammation.

HIGHLIGHTS

The 5xFAD mouse model of Alzheimer's disease presents higher levels of RTP801 in hippocampal astrocytes. Normalizing the levels of astrocytic RTP801 prevents cognitive decline and restores anxiety-like behavior in the 5xFAD mouse model. Knocking down astrocytic RTP801 preserves the resting-state functional connectivity in the 5xFAD mouse model. Astrocytic RTP801 mediates the loss of Parvalbumin+ interneurons, negatively affecting the levels of γ-aminobutyric acid (GABA) in the 5xFAD mouse model. Astrocytic RTP801 contributes to astro- and microgliosis and inflammasome expression in the 5xFAD mouse model.

Compensation versus deterioration across functional networks in amnestic mild cognitive impairment subtypes

Functional connectivity studies to detect neurophysiological correlates of amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease, have generated contradictory results in terms of compensation and deterioration, as most of the studies did not distinguish between the different aMCI subtypes: single-domain aMCI (sd-aMCI) and multiple-domain aMCI (md-aMCI). The present study aimed to characterize the neurophysiological correlates of aMCI subtypes by using resting-state functional magnetic resonance imaging. The study included sd-aMCI (n = 29), md-aMCI (n = 26), and control (n = 30) participants. The data were subjected to independent component analysis (ICA) to explore the default mode network (DMN) and the fronto-parietal control network (FPCN). Additionally, seed-based and moderation analyses were conducted to investigate the connectivity of the medial temporal lobe and functional networks. aMCI subtypes presented differences in functional connectivity relative to the control group: sd-aMCI participants displayed increased FPCN connectivity and reduced connectivity between the posterior parahippocampal gyrus (PHG) and medial structures; md-aMCI participants exhibited lower FPCN connectivity, higher anterior PHG connectivity with frontal structures and lower posterior PHG connectivity with central-parietal and temporo-occipital areas. Additionally, md-aMCI participants showed higher posterior PHG connectivity with structures of the DMN than both control and sd-aMCI participants, potentially indicating more severe cognitive deficits. The results showed gradual and qualitative neurofunctional differences between the aMCI subgroups, suggesting the existence of compensatory (sd-aMCI) and deterioration (md-aMCI) mechanisms in functional networks, mainly originated in the DMN. The findings support consideration of the subgroups as different stages of MCI within the Alzheimer disease continuum.

CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly population. The accurate classification of AD stages is essential for slowing disease progression and guiding effective interventions. However, limited sample sizes continue to present a significant challenge in classifying the stages of AD progression. Addressing this obstacle is crucial for improving diagnostic accuracy and optimizing treatment strategies for those affected by AD.

METHODS

In this study, we proposed cross-scale equilibrium pyramid coupling (CSEPC), which is a novel diagnostic algorithm designed for small-sample multimodal medical imaging data. CSEPC leverages scale equilibrium theory and modal coupling properties to integrate semantic features from different imaging modalities and across multiple scales within each modality. The architecture first extracts balanced multiscale features from structural MRI (sMRI) data and functional MRI (fMRI) data using a cross-scale pyramid module. These features are then combined through a contrastive learning-based cosine similarity coupling mechanism to capture intermodality associations effectively. This approach enhances the representation of both inter- and intramodal features while significantly reducing the number of learning parameters, making it highly suitable for small sample environments. We validated the effectiveness of the CSEPC model through experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and demonstrated its superior performance in diagnosing and staging AD.

RESULTS

Our experimental results demonstrate that the proposed model matches or exceeds the performance of models used in previous studies in AD classification. Specifically, the model achieved an accuracy of 85.67% and an area under the curve (AUC) of 0.98 in classifying the progression from mild cognitive impairment (MCI) to AD. To further validate its effectiveness, we used our method to diagnose different stages of AD. In both classification tasks, our approach delivered superior performance.

CONCLUSIONS

In conclusion, the performance of our model in various tasks has demonstrated its significant potential in the field of small-sample multimodal medical imaging classification, particularly AD classification. This advancement could significantly assist clinicians in effectively managing and intervening in the disease progression of patients with early-stage AD.

Classifying Mild Cognitive Impairment from Normal Cognition: fMRI Complexity Matches Tau PET Performance

Background

Tau protein accumulation is closely linked to synaptic and neuronal loss in Alzheimer's disease (AD), resulting in progressive cognitive decline. Although tau-PET imaging is a direct biomarker of tau pathology, it is costly, carries radiation risks, and is not widely accessible. Resting-state functional MRI (rs-fMRI) complexity-an entropy-based measure of BOLD signal variation-has been proposed as a non-invasive surrogate biomarker of early neuronal dysfunction associated with tau pathology.

Objectives

To determine whether fMRI-based brain complexity (sample entropy and multiscale entropy) can match or exceed tau-PET in classifying cognitively normal (CN) versus cognitively impaired (MCI/AD) individuals. And to investigate and compare the most influential network regions-of-interest (ROIs) for classification between fMRI complexity and tau-PET, thereby identifying key neuroanatomical correlates of AD-related changes.

Design

A cross-sectional study employing 3D convolutional neural network (CNN) classification with five-fold cross-validation and leave-one-network-out analysis.

Setting

Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Participants

One hundred forty-seven older adults (age 72.5 ± 7.5 years), including 95 CN, 45 MCI, and 7 AD.

Measurements

We created whole-brain complexity maps from rs-fMRI and standardized uptake value ratio (SUVR) maps from tau-PET. Each modality was separately fed into CNN classifiers. Region-based analyses (leave-one-network-out) were performed to identify critical ROIs for classification.

Results

fMRI complexity showed classification accuracy comparable to tau-PET yet surpassed it in F1-score (0.64 vs. 0.61) and area under the curve (AUC; 0.73 vs. 0.67). Salience and dorsal attention networks contributed most to fMRI-based classification, and a dorsal attention network contributed most to tau-PET-based classification.

Conclusions

fMRI complexity performs similarly to tau-PET in detecting cognitive impairment related to AD and identifies partially distinct critical ROIs, suggesting an alternative, radiation-free imaging biomarker for earlier detection and broader clinical application.

The brain selectively allocates energy to functional brain networks under cognitive control

Network energy has been conceptualized based on structural balance theory in the physics of complex networks. We utilized this framework to assess the energy of functional brain networks under cognitive control and to understand how energy is allocated across canonical functional networks during various cognitive control tasks. We extracted network energy from functional connectivity patterns of subjects who underwent fMRI scans during cognitive tasks involving working memory, inhibitory control, and cognitive flexibility, in addition to task-free scans. We found that the energy of the whole-brain network increases when exposed to cognitive control tasks compared to the task-free resting state, which serves as a reference point. The brain selectively allocates this elevated energy to canonical functional networks; sensory networks receive more energy to support flexibility for processing sensory stimuli, while cognitive networks relevant to the task, functioning efficiently, require less energy. Furthermore, employing network energy, as a global network measure, improves the performance of predictive modeling, particularly in classifying cognitive control tasks and predicting chronological age. Our results highlight the robustness of this framework and the utility of network energy in understanding brain and cognitive mechanisms, including its promising potential as a biomarker for mental conditions and neurological disorders.

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.

Eight brain structures mediate the age-related alterations of the working memory: forward and backward digit span tasks

Introduction

Working memory (WM) as one of the executive functions is an essential neurocognitive ability for daily life. Findings have suggested that aging is often associated with working memory and neural decline, but the brain structures and resting-state brain networks that mediate age-related differences in WM remain unclear.

Methods

A sample consisting of 252 healthy participants in the age range of 20 to 70years was used. Several cognitive tasks, including the n-back task and the forward and backward digit span tests were used. Also, resting-state functional imaging, as well as structural imaging using a 3T MRI scanner, were performed, resulting in 85 gray matter volumes and five resting-state networks, namely the anterior and posterior default mode, the right and left executive control, and the salience networks. Also, mediation analyses were used to investigate the role of gray matter volumes and resting-state networks in the relationship between age and WM.

Results

Behaviorally, aging was associated with decreased performance in the digit span task. Also, aging was associated with a decreased gray matter volume in 80 brain regions, and with a decreased activity in the anterior default mode network, executive control, and salience networks. Importantly, the path analysis showed that the GMV of the medial orbitofrontal, precentral, parieto-occipital, amygdala, middle occipital, posterior cingulate, and thalamus areas mediated the age-related differences in the forward digit span task, and the GMV of superior temporal gyrus mediated the age-related differences in the backward digit span task.

Discussion

This study identified the brain structures mediating the relationship between age and working memory, and we hope that our research provides an opportunity for early detection of individuals at risk of age-related memory decline.

Transcranial direct current stimulation and neuronal functional connectivity in MCI: role of individual factors associated to AD

Background

Alzheimer's disease (AD) encompasses a spectrum that may progress from mild cognitive impairment (MCI) to full dementia, characterized by amyloid-beta and tau accumulation. Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic option, but its efficacy in relation to individual genetic and biological risk factors remains underexplored.

Objective

To evaluate the effects of a two-week anodal tDCS regimen on the left dorsolateral prefrontal cortex, focusing on functional connectivity changes in neural networks in MCI patients resulting from various possible underlying disorders, considering individual factors associated to AD such as amyloid-beta deposition, APOE ϵ4 allele, BDNF Val66Met polymorphism, and sex.

Methods

In a single-arm prospective study, 63 patients with MCI, including both amyloid-PET positive and negative cases, received 10 sessions of tDCS. We assessed intra- and inter-network functional connectivity (FC) using fMRI and analyzed interactions between tDCS effects and individual factors associated to AD.

Results

tDCS significantly enhanced intra-network FC within the Salience Network (SN) and inter-network FC between the Central Executive Network and SN, predominantly in APOE ϵ4 carriers. We also observed significant sex*tDCS interactions that benefited inter-network FC among females. Furthermore, the effects of multiple modifiers, particularly the interaction of the BDNF Val66Met polymorphism and sex, were evident, as demonstrated by increased intra-network FC of the SN in female Met non-carriers. Lastly, the effects of tDCS on FC did not differ between the group of 26 MCI patients with cerebral amyloid-beta deposition detected by flutemetamol PET and the group of 37 MCI patients without cerebral amyloid-beta deposition.

Conclusions

The study highlights the importance of precision medicine in tDCS applications for MCI, suggesting that individual genetic and biological profiles significantly influence therapeutic outcomes. Tailoring interventions based on these profiles may optimize treatment efficacy in early stages of AD.

Current role of magnetic resonance imaging on assessing and monitoring the efficacy of phototherapy

Phototherapy, also known as photobiological therapy, is a non-invasive and highly effective physical treatment method. Its broad use in clinics has led to significant therapeutic results. Phototherapy parameters, such as intensity, wavelength, and duration, can be adjusted to create specific therapeutic effects for various medical conditions. Meanwhile, Magnetic Resonance Imaging (MRI), with its diverse imaging sequences and excellent soft-tissue contrast, provides a valuable tool to understand the therapeutic effects and mechanisms of phototherapy. This review explores the clinical applications of commonly used phototherapy techniques, gives a brief overview of how phototherapy impacts different diseases, and examines MRI's role in various phototherapeutic scenarios. We argue that MRI is crucial for precise targeting, treatment monitoring, and prognosis assessment in phototherapy. Future research and applications will focus on personalized diagnosis and monitoring of phototherapy, expanding its applications in treatment and exploring multimodal imaging technology to enhance diagnostic and therapeutic precision and effectiveness.

Neural correlates underlying local and global processing during visual search across adulthood

Visual processing relies on the identification of both local and global features of visual stimuli. While well investigated at the behavioral level, the underlying brain mechanisms are less clear, especially in the context of aging. Using fMRI, we aimed to investigate the neural correlates underlying local and global processing in early and late adulthood. We recruited 77 healthy adults aged 19-77 who completed a visual search task based on 2-level hierarchical stimuli made of squares and/or circles. Participants were instructed to detect a target (a square) at either a local (small) or global (large) level of a hierarchical geometrical form, in the presence or absence of other hierarchical geometrical forms (distractors). At the behavioral level, we revealed high accuracy for all participants, but older participants were slower to detect local targets, specifically in presence of distractors. At the brain level, while both local and global processing were associated with occipital activation, local processing also recruited the anterior insula and dorsal anterior cingulate cortex, that are core regions of the salience network. However, while the presence of distractors in the local condition elicited specifically stronger activation within the right anterior insula for the young group, it was not observed for older participants. In addition, older participants showed less activation than younger participants in the occipital cortex, especially for the most complex conditions. Our findings suggest that the brain correlates underlying local and global processing change with aging, especially for complex visual patterns. These results are discussed in terms of top-down reduction effects from the salience network on primary visual areas, that may lead to specific difficulties to process local visual details in older adults.

Energy landscape analysis of brain network dynamics in Alzheimer's disease

Background

Alzheimer's disease (AD) is a common neurodegenerative dementia, characterized by abnormal dynamic functional connectivity (DFC). Traditional DFC analysis, assuming linear brain dynamics, may neglect the complexity of the brain's nonlinear interactions. Energy landscape analysis offers a holistic, nonlinear perspective to investigate brain network attractor dynamics, which was applied to resting-state fMRI data for AD in this study.

Methods

This study utilized resting-state fMRI data from 60 individuals, comparing 30 Alzheimer's patients with 30 controls, from the Alzheimer's Disease Neuroimaging Initiative. Energy landscape analysis was applied to the data to characterize the aberrant brain network dynamics of AD patients.

Results

The AD group stayed in the co-activation state for less time than the healthy control (HC) group, and a positive correlation was identified between the transition frequency of the co-activation state and behavior performance. Furthermore, the AD group showed a higher occurrence frequency and transition frequency of the cognitive control state and sensory integration state than the HC group. The transition between the two states was positively correlated with behavior performance.

Conclusion

The results suggest that the co-activation state could be important to cognitive processing and that the AD group possibly raised cognitive ability by increasing the occurrence and transition between the impaired cognitive control and sensory integration states.

Impaired olfactory identification in dementia-free individuals is associated with the functional abnormality of the precuneus

OBJECTIVE

Olfactory dysfunction indicates a higher risk of developing dementia. However, the potential structural and functional changes are still largely unknown.

METHODS

A total of 236 participants were enrolled, including 45 Alzheimer's disease (AD) individuals and 191dementia-free individuals. Detailed study methods, comprising neuropsychological assessment and olfactory identification test (University of Pennsylvania smell identification test, UPSIT), as well as structural and functional magnetic resonance imaging (MRI) were applied in this research. The dementia-free individuals were divided into two sub-groups based on olfactory score: dementia-free with olfactory dysfunction (DF-OD) sub-group and dementia-free without olfactory dysfunction (DF-NOD) sub-group. The results were analyzed for subsequent intergroup comparisons and correlations. The cognitive assessment was conducted again three years later.

RESULTS

(i) At dementia-free stage, there was a positive correlation between olfactory score and cognitive function. (ii) In dementia-free group, the volume of crucial brain structures involved in olfactory recognition and processing (such as amygdala, entorhinal cortex and basal forebrain volumes) are positively associated with olfactory score. (iii) Compared to the DF-NOD group, the DF-OD group showed a significant reduction in olfactory network (ON) function. (iv) Compared to DF-NOD group, there were significant functional connectivity (FC) decline between PCun_L(R)_4_1 in the precuneus of posterior default mode network (pDMN) and the salience network (SN) in DF-OD group, and the FC values decreased with falling olfactory scores. Moreover, in DF-OD group, the noteworthy reduction in FC were observed between PCun_L(R)_4_1 and amygdala, which was a crucial component of ON. (v) The AD conversion rate of DF-OD was 29.41%, while the DF-NOD group was 12.50%. The structural and functional changes in the precuneus were also observed in AD and were more severe.

CONCLUSIONS

In addition to the olfactory circuit, the precuneus is a critical structure in the odor identification process, whose abnormal function underlies the olfactory identification impairment of dementia-free individuals.

Magnetoencephalography-derived oscillatory microstate patterns across lifespan: the Cambridge centre for ageing and neuroscience cohort

The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary 'top-down' saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.

See no evil: Attentional bias toward threat is diminished in aged monkeys

Prior evidence demonstrates that relative to younger adults, older human adults exhibit attentional biases toward positive and/or away from negative socioaffective stimuli (i.e., the age-related positivity effect). Whether or not the effect is phylogenetically conserved is currently unknown and its biopsychosocial origins are debated. To address this gap, we evaluated how visual processing of socioaffective stimuli differs in aged, compared to middle-aged, rhesus monkeys (Macaca mulatta) using eye tracking in two experimental designs that are directly comparable to those historically used for evaluating attentional biases in humans. Results of our study demonstrate that while younger rhesus possesses robust attentional biases toward threatening pictures of conspecifics' faces, aged animals evidence no such bias. Critically, these biases emerged only when threatening faces were paired with neutral and not ostensibly "positive" faces, suggesting social context modifies the effect. Results of our study suggest that the evolutionarily shared mechanisms drive age-related decline in visual biases toward negative stimuli in aging across primate species. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The co-activation pattern between the DMN and other brain networks affects the cognition of older adults: evidence from naturalistic stimulation fMRI data

Brain function changes affect cognitive functions in older adults, yet the relationship between cognition and the dynamic changes of brain networks during naturalistic stimulation is not clear. Here, we recruited the young, middle-aged and older groups from the Cambridge Center for Aging and Neuroscience to investigate the relationship between dynamic metrics of brain networks and cognition using functional magnetic resonance imaging data during movie-watching. We found six reliable co-activation pattern (CAP) states of brain networks grouped into three pairs with opposite activation patterns in three age groups. Compared with young and middle-aged adults, older adults dwelled shorter time in CAP state 4 with deactivated default mode network (DMN) and activated salience, frontoparietal and dorsal-attention networks (DAN), and longer time in state 6 with deactivated DMN and activated DAN and visual network, suggesting altered dynamic interaction between DMN and other brain networks might contribute to cognitive decline in older adults. Meanwhile, older adults showed easier transfer from state 6 to state 3 (activated DMN and deactivated sensorimotor network), suggesting that the fragile antagonism between DMN and other cognitive networks might contribute to cognitive decline in older adults. Our findings provided novel insights into aberrant brain network dynamics associated with cognitive decline.

Eye Tracking During Visual Paired-Comparison Tasks: A Systematic Review and Meta-Analysis of the Diagnostic Test Accuracy for Detecting Cognitive Decline

Background

Alzheimer's disease and mild cognitive impairment (MCI) progress silently, making early diagnosis challenging, especially in less educated populations. The visual paired comparison (VPC) task, utilizing eye-tracking movement (ETM) technology, offers a promising alternative for early detection of memory decline.

Objective

This systematic review and meta-analysis evaluated the efficacy of the VPC task, utilizing ETM as a tool for assessing age-related cognitive changes.

Methods

A comprehensive search across five databases and grey literature focused on healthy and impaired memory participants assessed through the ETM-based VPC task. The primary outcomes were novelty preference scores and eye movement metrics. The risk of bias of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Random-effects meta-analyses calculated Hedges' g effect size. Sensitivity and specificity of the VPC were meta-analytically pooled.

Results

The systematic review included 12 articles, involving 1,022 participants (aged 18 to 90 years, with education ranging from 6.5 to 20.0 years), with a low risk of bias and minimal applicability concerns across all items. Five studies contributed to the meta-analysis, revealing a significant effect favoring the VPC task for recognition memory detection (k = 9, g = -1.03). Pooled sensitivity and specificity analyses demonstrated VPC effectiveness as a recognition memory assessment tool (0.84 and 0.75, respectively).

Conclusions

The VPC task, utilizing ETM, may serve as a biomarker for early memory decline detection. Its use as a digital eye-tracking tool presents a possible alternative to traditional tests, warranting further research for application in neurodegenerative disease diagnosis.

Sex differences in the effects of trait anxiety and age on resting-state functional connectivities of the amygdala

Background

Numerous studies characterized how resting-state functional connectivities (rsFCs) of the amygdala were disrupted in emotional disorders and varied with emotional traits, including anxiety. With trait anxiety known to diminish with age, a critical issue concerns disambiguating the effects of age and anxiety on amygdala rsFCs in studying the neural bases of individual differences in anxiety.

Methods

Two-hundred adults (83 women) 19-85 years of age underwent fMRI and assessment for trait anxiety. Amygdala rsFC correlates were identified using multiple regression with age and anxiety in the same model for all and separately in men and women. The rsFC correlates were examined for age-anxiety interaction.

Results

Anxiety was negatively correlated with amygdala-temporooccipital gyri rsFC in all and in men alone. In women, amgydala rsFC with the thalamus/pallidum, angular/supramarginal gyri, inferior temporal gyrus, and posterior insula correlated positively and rsFC with calcarine cortex and caudate correlated negatively with anxiety. We also observed sex differences in age correlation of amgydala-posterior cingulate cortex/precuneus and -insula/temporoparietal rsFCs, with stronger associations in women. In women alone, anxiety and age interacted to determine amygdala rsFC with the thalamus/pallidum, calcarine cortex, and caudate, with older age associated with stronger correlation between anxiety and the rsFCs.

Limitations

The findings need to be validated in an independent sample and further explored using task-based data.

Conclusion

Highlighting anxiety- and age- specific as well as interacting correlates of amygdala rsFCs and sex differences in the correlates, the findings may shed light on the neural markers of anxiety.

Higher Fat-Related Body Composition Measurement and Lower Resting-State Inter-Network Functional Connectivity of APOE ε4 Carrier in Mild Cognitive Impairment Patients With Aβ Deposition

OBJECTIVE

We aimed to evaluate the impact of interaction between APOE ε4 carrier status and body composition measurements on intra- and inter-regional functional connectivity (FC) in mild cognitive impairment (MCI) patients with Aβ deposition.

METHODS

MCI patients with and without APOE ε4 allele (carrier, n=86; non-carrier, n=95) underwent neuropsychological battery, resting-state functional magnetic resonance imaging scans, positron emission tomography scans with [18F]flutemetamol, and bioelectrical impedance analysis for measuring body composition. We employed a priori defined regions of interest to investigate the intra- and inter-network FC profiles of default mode network (DMN), central executive network (CEN), and salience network.

RESULTS

There was a significant interaction of APOE ε4 carrier status with body fat mass index, visceral fat area, and waist-hip circumference ratio for inter-network FC between DMN and CEN, contributing higher fat-related body composition measurements in the APOE ε4 carrier with lower DMN-CEN FC.

CONCLUSION

The present results highlight the detrimental effect of APOE ε4 carrier status on the associations between the fat-related body composition measurements and FC in the MCI patients with Aβ accumulation.

Cortical Iron Accumulation as an Imaging Marker for Neurodegeneration in Clinical Cognitive Impairment Spectrum: A Quantitative Susceptibility Mapping Study

OBJECTIVE

Cortical iron deposition has recently been shown to occur in Alzheimer's disease (AD). In this study, we aimed to evaluate how cortical gray matter iron, measured using quantitative susceptibility mapping (QSM), differs in the clinical cognitive impairment spectrum.

MATERIALS AND METHODS

This retrospective study evaluated 73 participants (mean age ± standard deviation, 66.7 ± 7.6 years; 52 females and 21 males) with normal cognition (NC), 158 patients with mild cognitive impairment (MCI), and 48 patients with AD dementia. The participants underwent brain magnetic resonance imaging using a three-dimensional multi-dynamic multi-echo sequence on a 3-T scanner. We employed a deep neural network (QSMnet+) and used automatic segmentation software based on FreeSurfer v6.0 to extract anatomical labels and volumes of interest in the cortex. We used analysis of covariance to investigate the differences in susceptibility among the clinical diagnostic groups in each brain region. Multivariable linear regression analysis was performed to study the association between susceptibility values and cognitive scores including the Mini-Mental State Examination (MMSE).

RESULTS

Among the three groups, the frontal (P < 0.001), temporal (P = 0.004), parietal (P = 0.001), occipital (P < 0.001), and cingulate cortices (P < 0.001) showed a higher mean susceptibility in patients with MCI and AD than in NC subjects. In the combined MCI and AD group, the mean susceptibility in the cingulate cortex (β = -216.21, P = 0.019) and insular cortex (β = -276.65, P = 0.001) were significant independent predictors of MMSE scores after correcting for age, sex, education, regional volume, and APOE4 carrier status.

CONCLUSION

Iron deposition in the cortex, as measured by QSMnet+, was higher in patients with AD and MCI than in NC participants. Iron deposition in the cingulate and insular cortices may be an early imaging marker of cognitive impairment related neurodegeneration.

Lower Dorsal Lateral Prefrontal Cortex Functional Connectivity in Late-Life Depression With Suicidal Ideation

OBJECTIVE

The dorsal lateral prefrontal cortex (DLPFC) has been identified as a neuromodulation target for alleviating suicidal ideation. Dysfunctional DLPFC has been implicated in suicidality in depression. This study aimed to investigate the functional connectivity (FC) of the DLPFC in late-life depression (LLD) with suicidal ideation.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data from 32 LLD patients with suicidal ideation (LLD-S), 41 LLD patients without suicidal ideation (LLD-NS), and 54 healthy older adults (HOA) were analyzed using DLPFC seed-based FC analyses. Group differences in FC were examined, and machine learning was applied to explore the potential of DLPFC-FC for classifying LLD-S from LLD-NS.

RESULTS

Abnormal DLPFC-FC patterns were observed in LLD-S, characterized by lower connectivity with the angular gyrus, precuneus, and superior frontal gyrus compared to LLD-NS and healthy controls. A classification model based on the identified DLPFC-FC achieved an accuracy of 75%.

CONCLUSION

The lower FC of DLPFC networks may contribute to the neurobiological mechanism of suicidal ideation in late-life depression. These findings may facilitate suicide prevention for LLD by providing potential neuroimaging markers and network-based neuromodulation targets. However, further confirmation with larger sample sizes and experimental designs is warranted.

Bayesian workflow for the investigation of hierarchical classification models from tau-PET and structural MRI data across the Alzheimer's disease spectrum

Background

Alzheimer's disease (AD) diagnosis in its early stages remains difficult with current diagnostic approaches. Though tau neurofibrillary tangles (NFTs) generally follow the stereotypical pattern described by the Braak staging scheme, the network degeneration hypothesis (NDH) has suggested that NFTs spread selectively along functional networks of the brain. To evaluate this, we implemented a Bayesian workflow to develop hierarchical multinomial logistic regression models with increasing levels of complexity of the brain from tau-PET and structural MRI data to investigate whether it is beneficial to incorporate network-level information into an ROI-based predictive model for the presence/absence of AD.

Methods

This study included data from the Translational Biomarkers in Aging and Dementia (TRIAD) longitudinal cohort from McGill University's Research Centre for Studies in Aging (MCSA). Baseline and 1 year follow-up structural MRI and [18F]MK-6240 tau-PET scans were acquired for 72 cognitive normal (CN), 23 mild cognitive impairment (MCI), and 18 Alzheimer's disease dementia subjects. We constructed the four following hierarchical Bayesian models in order of increasing complexity: (Model 1) a complete-pooling model with observations, (Model 2) a partial-pooling model with observations clustered within ROIs, (Model 3) a partial-pooling model with observations clustered within functional networks, and (Model 4) a partial-pooling model with observations clustered within ROIs that are also clustered within functional brain networks. We then investigated which of the models had better predictive performance given tau-PET or structural MRI data as an input, in the form of a relative annualized rate of change.

Results

The Bayesian leave-one-out cross-validation (LOO-CV) estimate of the expected log pointwise predictive density (ELPD) results indicated that models 3 and 4 were substantially better than other models for both tau-PET and structural MRI inputs. For tau-PET data, model 3 was slightly better than 4 with an absolute difference in ELPD of 3.10 ± 1.30. For structural MRI data, model 4 was considerably better than other models with an absolute difference in ELPD of 29.83 ± 7.55 relative to model 3, the second-best model.

Conclusion

Our results suggest that representing the data generating process in terms of a hierarchical model that encompasses both ROI-level and network-level heterogeneity leads to better predictive ability for both tau-PET and structural MRI inputs over all other model iterations.

Sex-Related Disparities in the Resting State Functional Connectivity of the Locus Coeruelus and Salience Network in Preclinical Alzheimer's Disease

Recent studies have demonstrated the pivotal role of locus coeruleus (LC) and salience network (SN) resting state functional connectivity (rsFC) changes in the early stage of Alzheimer's disease (AD). Moreover, sex has been a crucial point of discussion in understanding AD pathology. We aimed to demonstrate the sex-related disparities in the functional connectivity (FC) of the SN and LC in preclinical AD. A total of 89 cognitively normal patients with evidence of amyloid beta (Aβ) accumulation ([18F] flutemetamol +) were recruited in the study. A seed-to-voxel analysis was conducted to measure the LC and SN rsFC differences between sexes. In addition, sex by Aβ interactive effects on FC values were analyzed with a general linear model. There were statistically significant sex by regional standardized uptake value ratio (SUVR) interactions in the LC FC with the parietal, frontal, and occipital cortices. Moreover, there was a significant sex by global SUVR interaction in the SN FC with the temporal cortex. The findings suggest that there are differential patterns of LC FC and SN FC in males and females with preclinical AD, which interact with regional Aβ deposition.

Resting-State Functional Connectivity Difference in Alzheimer's Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement

The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer's disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We used the threshold-free cluster enhancement (TFCE) method to explore the connectivity from resting-state functional MR images (rs-fMRIs). We conducted the study on a total of 32 AD, 32 HC, and 31 MCI subjects. We modeled the brain as a graph-based network to study these impairments, and pairwise Pearson's correlation-based functional connectivity was used to construct the brain network. The study found that connections in the sensory motor network (SMN), dorsal attention network (DAN), salience network (SAN), default mode network (DMN), and cerebral network were severely affected in AD and MCI. The disruption in these networks may serve as potential biomarkers for distinguishing AD and MCI from HC. The study suggests that alterations in functional connectivity in these networks may contribute to cognitive deficits observed in AD and MCI. Additionally, a negative correlation was observed between the global clinical dementia rating (CDR) score and the Z-score of functional connectivity within identified clusters in AD subjects. These findings provide compelling evidence suggesting that the neurodegenerative disruption of functional magnetic resonance imaging (fMRI) connectivity is extensively distributed across multiple networks in individuals diagnosed with AD.

APOE ε4 influences within and between network functional connectivity in posterior cortical atrophy and logopenic progressive aphasia

INTRODUCTION

Presence of apolipoprotein E (APOE) ε4 has shown greater predisposition to medial temporal involvement in posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA). Little is known about its influence on memory network connectivity, a network comprised of medial temporal structures.

METHODS

Fifty-eight PCA and 82 LPA patients underwent structural and resting state functional magnetic resonance imaging (MRI). Bayesian hierarchical linear models assessed the influence of APOE ε4 on within and between-network connectivity for five networks.

RESULTS

APOE ε4 carriers showed reduced memory and language within-network connectivity in LPA and increased salience within-network connectivity in PCA compared to non-carriers. Between-network analysis showed evidence of reduced DMN connectivity in APOE ε4 carriers, with reduced DMN-to-salience and DMN-to-language network connectivity in PCA, and reduced DMN-to-visual network connectivity in LPA.

DISCUSSION

The APOE genotype influences brain connectivity, both within and between-networks, in atypical Alzheimer's disease. However, there was evidence that the modulatory effects of APOE differ across phenotype.

HIGHLIGHTS

APOE genotype is associated with reductions in within-network connectivity for the memory and language networks in LPA APOE genotype is associated with reductions in language-to-visual connectivity in LPA and PCA APOE genotype has no effect on the memory network in PCA.

Hypoconnectivity within the cingulo-opercular network in patients with mild cognitive impairment in Chinese communities

INTRODUCTION

At rest, the brain's higher cognitive systems engage in correlated activity patterns, forming networks. With mild cognitive impairment (MCI), it is essential to understand how functional connectivity within and between resting-state networks changes. This study used resting-state functional connectivity to identify significant differences within and between the cingulo-opercular network (CON) and default mode network (DMN).

METHODS

We assessed cognitive function in patients using the Chinese version of the Alzheimer's disease assessment scale-Cognitive subscale (ADAS-Cog). A group of MCI subjects (ages 60-83 years, n = 45) was compared to age-matched healthy controls (n = 70). Resting-state functional connectivity was used to determine functional connectivity strength within and between the CON and DMN.

RESULTS

Compared to healthy controls, the MCI group showed significantly lower functional connectivity within the CON (F = 10.76, df = 1, p = 0.001, FDR adjusted p = 0.003). Additionally, the MCI group displayed no distinct differences in functional connectivity within DMN (F = 0.162, df = 1, p = 0.688, FDR adjusted p = 0.688) and between CON and DMN (F = 2.270, df = 1, p = 0.135, FDR adjusted p = 0.262). Moreover, we found no correlation between ADAS-Cog and within- or between-connectivity metrics among subjects with MCI.

CONCLUSIONS

Our findings indicate that specific patterns of hypoconnectivity within CON circuitry may characterize MCI relative to healthy controls. This work improves our understanding of network dysfunction underlying MCI and could inform more targeted treatment.

Amyloid β-based therapy for Alzheimer's disease: challenges, successes and future

Amyloid β protein (Aβ) is the main component of neuritic plaques in Alzheimer's disease (AD), and its accumulation has been considered as the molecular driver of Alzheimer's pathogenesis and progression. Aβ has been the prime target for the development of AD therapy. However, the repeated failures of Aβ-targeted clinical trials have cast considerable doubt on the amyloid cascade hypothesis and whether the development of Alzheimer's drug has followed the correct course. However, the recent successes of Aβ targeted trials have assuaged those doubts. In this review, we discussed the evolution of the amyloid cascade hypothesis over the last 30 years and summarized its application in Alzheimer's diagnosis and modification. In particular, we extensively discussed the pitfalls, promises and important unanswered questions regarding the current anti-Aβ therapy, as well as strategies for further study and development of more feasible Aβ-targeted approaches in the optimization of AD prevention and treatment.

Local Brain Network Alterations and Olfactory Impairment in Alzheimer's Disease: An fMRI and Graph-Based Study

Alzheimer's disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance to analyze the differences in AD brain network characteristics. A total of 23 patients with AD and 18 normal controls were recruited for resting-state functional magnetic resonance imaging (fMRI), clinical neuropsychological examinations and the University of Pennsylvania Smell Identification Test (UPSIT). Between-group differences in the topological properties of the local network were compared. Pearson correlations were explored based on differential brain regions and olfactory performance. Statistical analysis revealed a correlation of the degree of cognitive impairment with olfactory recognition function. Local node topological properties were significantly altered in many local brain regions in the AD group. The nodal clustering coefficients of the bilateral temporal pole: middle temporal gyrus (TPOmid), degree centrality of the left insula (INS.L), degree centrality of the right middle temporal gyrus (MTG.R), and betweenness centrality of the left middle temporal gyrus (MTG.L) were related to olfactory performance. Alterations in local topological properties combined with the olfactory impairment can allow early identification of abnormal olfactory-related regions, facilitating early AD screening.

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.

A Residual Marker of Cognitive Reserve Is Associated with Resting-State Intrinsic Functional Connectivity Along the Alzheimer's Disease Continuum

BACKGROUND

Cognitive reserve (CR) explains inter-individual differences in the impact of the neurodegenerative burden on cognitive functioning. A residual model was proposed to estimate CR more accurately than previous measures. However, associations between residual CR markers (CRM) and functional connectivity (FC) remain unexplored.

OBJECTIVE

To explore the associations between the CRM and intrinsic network connectivity (INC) in resting-state networks along the neuropathological-continuum of Alzheimer's disease (ADN).

METHODS

Three hundred eighteen participants from the DELCODE cohort were stratified using cerebrospinal fluid biomarkers according to the A(myloid-β)/T(au)/N(eurodegeneration) classification. CRM was calculated utilizing residuals obtained from a multilinear regression model predicting cognition from markers of disease burden. Using an independent component analysis in resting-state fMRI data, we measured INC of resting-state networks, i.e., default mode network (DMN), frontoparietal network (FPN), salience network (SAL), and dorsal attention network. The associations of INC with a composite memory score and CRM and the associations of CRM with the seed-to-voxel functional connectivity of memory-related were tested in general linear models.

RESULTS

CRM was positively associated with INC in the DMN in the entire cohort. The A+T+N+ group revealed an anti-correlation between the SAL and the DMN. Furthermore, CRM was positively associated with anti-correlation between memory-related regions in FPN and DMN in ADN and A+T/N+.

CONCLUSION

Our results provide evidence that INC is associated with CRM in ADN defined as participants with amyloid pathology with or without cognitive symptoms, suggesting that the neural correlates of CR are mirrored in network FC in resting-state.

Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study

(1) Background: Alzheimer's disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer's pathophysiology, even in the early stages of the disease.

Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques

The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network's connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy.

Association of functional connectivity of the executive control network or default mode network with cognitive impairment in older adults with remitted major depressive disorder or mild cognitive impairment

Major depressive disorder (MDD) is associated with an increased risk of developing dementia. The present study aimed to better understand this risk by comparing resting state functional connectivity (rsFC) in the executive control network (ECN) and the default mode network (DMN) in older adults with MDD or mild cognitive impairment (MCI). Additionally, we examined the association between rsFC in the ECN or DMN and cognitive impairment transdiagnostically. We assessed rsFC alterations in ECN and DMN in 383 participants from five groups at-risk for dementia-remitted MDD with normal cognition (MDD-NC), non-amnestic mild cognitive impairment (naMCI), remitted MDD + naMCI, amnestic MCI (aMCI), and remitted MDD + aMCI-and from healthy controls (HC) or individuals with Alzheimer's dementia (AD). Subject-specific whole-brain functional connectivity maps were generated for each network and group differences in rsFC were calculated. We hypothesized that alteration of rsFC in the ECN and DMN would be progressively larger among our seven groups, ranked from low to high according to their risk for dementia as HC, MDD-NC, naMCI, MDD + naMCI, aMCI, MDD + aMCI, and AD. We also regressed scores of six cognitive domains (executive functioning, processing speed, language, visuospatial memory, verbal memory, and working memory) on the ECN and DMN connectivity maps. We found a significant alteration in the rsFC of the ECN, with post hoc testing showing differences between the AD group and the HC, MDD-NC, or naMCI groups, but no significant alterations in rsFC of the DMN. Alterations in rsFC of the ECN and DMN were significantly associated with several cognitive domain scores transdiagnostically. Our findings suggest that a diagnosis of remitted MDD may not confer functional brain risk for dementia. However, given the association of rs-FC with cognitive performance (i.e., transdiagnostically), rs-FC may help in stratifying this risk among people with MDD and varying degrees of cognitive impairment.

Functional connectivity and mild behavioral impairment in dementia-free elderly

Background

Mild behavioral impairment (MBI) is a syndrome that uses later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a group at high risk for incident dementia. MBI is associated with neurodegenerative disease markers in advance of syndromic dementia. Functional connectivity (FC) correlates of MBI are understudied and could provide further insights into mechanisms early in the disease course. We used resting-state functional magnetic resonance imaging (rs-fMRI) to test the hypothesis that FC within the default mode network (DMN) and salience network (SN) of persons with MBI (MBI+) is reduced, relative to those without (MBI-).

Methods

From two harmonized dementia-free cohort studies, using a score of ≥6 on the MBI Checklist to define MBI status, 32 MBI+ and 63 MBI- individuals were identified (mean age: 71.7 years; 54.7% female). Seed-based connectivity analysis was implemented in each MBI group using the CONN fMRI toolbox (v20.b), with the posterior cingulate cortex (PCC) as the seed region within the DMN and anterior cingulate cortex (ACC) as the seed within the SN. The average time series from the PCC and ACC were used to determine FC with other regions within the DMN (medial prefrontal cortex, lateral inferior parietal cortex) and SN (anterior insula, supramarginal gyrus, rostral prefrontal cortex), respectively. Age, sex, years of education, and Montreal Cognitive Assessment scores were included as model covariates. The false discovery rate approach was used to correct for multiple comparisons, with a p-value of .05 considered significant.

Results

For the DMN, MBI+ individuals exhibited reduced FC between the PCC and the medial prefrontal cortex, compared to MBI-. For the SN, MBI+ individuals exhibited reduced FC between the ACC and left anterior insula.

Conclusion

MBI in dementia-free older adults is associated with reduced FC in networks known to be disrupted in dementia. Our results complement the evidence linking MBI with Alzheimer's disease biomarkers.

Highlights

Resting-state functional magnetic resonance imaging was completed in 95 dementia-free persons from FAVR and COMPASS-ND studies.Participants were stratified by informant-rated Mild Behavioral Impairment Checklist (MBI-C) score, ≥6 for MBI+.MBI+ participants showed reduced functional connectivity (FC) within the default mode network and salience network.These FC changes are consistent with those seen in early-stage Alzheimer's disease.MBI may help identify persons with early-stage neurodegenerative disease.

Brain Network Organization and Aging

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

Deep brain stimulation of the nucleus basalis of Meynert modulates hippocampal-frontoparietal networks in patients with advanced Alzheimer's disease

BACKGROUND

Deep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) has shown potential for the treatment of mild-to-moderate Alzheimer's disease (AD). However, there is little evidence of whether NBM-DBS can improve cognitive functioning in patients with advanced AD. In addition, the mechanisms underlying the modulation of brain networks remain unclear. This study was aimed to assess the cognitive function and the resting-state connectivity following NBM-DBS in patients with advanced AD.

METHODS

Eight patients with advanced AD underwent bilateral NBM-DBS and were followed up for 12 months. Clinical outcomes were assessed by neuropsychological examinations using the Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale. Resting-state functional magnetic resonance imaging and positron emission tomography data were also collected.

RESULTS

The cognitive functioning of AD patients did not change from baseline to the 12-month follow-up. Interestingly, the MMSE score indicated clinical efficacy at 1 month of follow-up. At this time point, the connectivity between the hippocampal network and frontoparietal network tended to increase in the DBS-on state compared to the DBS-off state. Additionally, the increased functional connectivity between the parahippocampal gyrus (PHG) and the parietal cortex was associated with cognitive improvement. Further dynamic functional network analysis showed that NBM-DBS increased the proportion of the PHG-related connections, which was related to improved cognitive performance.

CONCLUSION

The results indicated that NBM-DBS improves short-term cognitive performance in patients with advanced AD, which may be related to the modulation of multi-network connectivity patterns, and the hippocampus plays an important role within these networks.

TRIAL REGISTRATION

ChiCTR, ChiCTR1900022324. Registered 5 April 2019-Prospective registration. https://www.chictr.org.cn/showproj.aspx?proj=37712.

Subjective cognitive decline predicts lower cingulo-opercular network functional connectivity in individuals with lower neurite density in the forceps minor: Cingulo-opercular network in SCD

Cognitive complaints of attention/concentration problems are highly frequent in older adults with subjective cognitive decline (SCD). Functional connectivity in the cingulo-opercular network (CON-FC) supports cognitive control, tonic alertness, and visual processing speed. Thus, those complaints in SCD may reflect a decrease in CON-FC. Frontal white-matter tracts such as the forceps minor exhibit age- and SCD-related alterations and, therefore, might influence the CON-FC decrease in SCD. Here, we aimed to determine whether SCD predicts an impairment in CON-FC and whether neurite density in the forceps minor modulates that effect. To do so, we integrated cross-sectional and longitudinal analyses of multimodal data in a latent growth curve modeling approach. Sixty-nine healthy older adults (13 males; 68.33 ± 7.95 years old) underwent resting-state functional and diffusion-weighted magnetic resonance imaging, and the degree of SCD was assessed at baseline with the memory functioning questionnaire (greater score indicating more SCD). Forty-nine of the participants were further enrolled in two follow-ups, each about 18 months apart. Baseline SCD did not predict CON-FC after three years or its rate of change (p-values > 0.092). Notably, however, the forceps minor neurite density did modulate the relation between SCD and CON-FC (intercept; b = 0.21, 95% confidence interval, CI, [0.03, 0.39], p = 0.021), so that SCD predicted a greater CON-FC decrease in older adults with relatively lower neurite density in the forceps minor. The neurite density of the forceps minor, in turn, negatively correlated with age. These results suggest that CON-FC alterations in SCD are dependent upon the forceps minor neurite density. Accordingly, these results imply modifiable age-related factors that could help delay or mitigate both age and SCD-related effects on brain connectivity.

Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls

Purpose

Conventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer's disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer's disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls.

Methods

We used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies-Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05.

Results

We identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04).

Conclusion

We show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.

Repeated High-Definition Transcranial Direct Current Stimulation Modulated Temporal Variability of Brain Regions in Core Neurocognitive Networks Over the Left Dorsolateral Prefrontal Cortex in Mild Cognitive Impairment Patients

Background: Early intervention of amnestic mild cognitive impairment (aMCI) may be the most promising way for delaying or even preventing the progression to Alzheimer’s disease. Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that has been recognized as a promising approach for the treatment of aMCI. Objective: In this paper, we aimed to investigate the modulating mechanism of tDCS on the core neurocognitive networks of brain. Methods: We used repeated anodal high-definition transcranial direct current stimulation (HD-tDCS) over the left dorsolateral prefrontal cortex and assessed the effect on cognition and dynamic functional brain network in aMCI patients. We used a novel method called temporal variability to depict the characteristics of the dynamic brain functional networks. Results: We found that true anodal stimulation significantly improved cognitive performance as measured by the Montreal Cognitive Assessment after simulation. Meanwhile, the Mini-Mental State Examination scores showed a clear upward trend. More importantly, we found significantly altered temporal variability of dynamic functional connectivity of regions belonging to the default mode network, central executive network, and the salience network after true anodal stimulation, indicating anodal HD-tDCS may enhance brain function by modulating the temporal variability of the brain regions. Conclusion: These results imply that ten days of anodal repeated HD-tDCS over the LDLPFC exerts beneficial effects on the temporal variability of the functional architecture of the brain, which may be a potential neural mechanism by which HD-tDCS enhances brain functions. Repeated HD-tDCS may have clinical uses for the intervention of brain function decline in aMCI patients.

Abnormal information interaction in multilayer directed network based on cross-frequency integration of mild cognitive impairment and Alzheimer’s disease

Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) have been reported to result in abnormal cross-frequency integration. However, previous studies have failed to consider specific abnormalities in receiving and outputting information among frequency bands during integration. Here, we investigated heterogeneity in receiving and outputting information during cross-frequency integration in patients. The results showed that during cross-frequency integration, information interaction first increased and then decreased, manifesting in the heterogeneous distribution of inter-frequency nodes for receiving information. A possible explanation was that due to damage to some inter-frequency hub nodes, intra-frequency nodes gradually became new inter-frequency nodes, whereas original inter-frequency nodes gradually became new inter-frequency hub nodes. Notably, damage to the brain regions that receive information between layers was often accompanied by a strengthened ability to output information and the emergence of hub nodes for outputting information. Moreover, an important compensatory mechanism assisted in the reception of information in the cingulo-opercular and auditory networks and in the outputting of information in the visual network. This study revealed specific abnormalities in information interaction and compensatory mechanism during cross-frequency integration, providing important evidence for understanding cross-frequency integration in patients with MCI and AD.

Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI

Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also affected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject's age and classified them into different age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r² of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how different functional regions of the brain are correlated. We also analyzed how functional regions contribute differently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing age.

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).

Static and Dynamic Functional Connectivity Alterations in Alzheimer's Disease and Neuropsychiatric Diseases

To date, numerous studies have documented various alterations in resting brain activity in Alzheimer's disease (AD) and other neuropsychiatric diseases. In particular, disease-related alterations of functional connectivity (FC) in the resting state networks (RSN) have been documented. Altered FC in RSN is useful not only for interpreting the phenotype of diseases but also for diagnosing the diseases. More recently, several studies proposed the dynamics of resting-brain activity as a useful marker for detecting altered RSNs related to AD and other diseases. In contrast to previous studies, which focused on FC calculated using an entire fMRI scan (static FC), these newer studies focused the on temporal dynamics of FC within the scan (dynamic FC) to provide more sensitive measures to characterize RSNs. However, despite the increasing popularity of dFC, several studies cautioned that the results obtained in commonly used analyses for dFC require careful interpretation. In this mini-review, we review recent studies exploring alterations of static and dynamic functional connectivity in AD and other neuropsychiatric diseases. We then discuss how to utilize and interpret dFC for studying resting brain activity in diseases.

Triple Network Model-Based Analysis on Abnormal Core Brain Functional Network Dynamics in Different Stage of Amnestic Mild Cognitive Impairment

Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage of Alzheimer’s disease (AD) because it has the same clinical symptoms as AD but with lower severity. Studies have confirmed that patients with aMCI are more likely to develop to AD. Although studies on resting state functional connectivity have revealed the abnormal organization of brain networks, the dynamic changes of the functional connectivity across the scans have been ignored. Objective: Dynamic functional connectivity is a novel method to reveal the temporal variation of brain networks. This paper aimed to investigate the dynamic characteristics of brain functional connectivity in the early and late phases of aMCI. Methods: Based on the “triple network” model, we used the sliding time window approach to construct dynamical functional networks and then analyzed the dynamic characteristics of the functional connectivity across the entire scan. Results: The results showed that patients with aMCI had longer dwell times in weaker network connection than in the strong network. The transitions between different states become more frequent, and the stability of the patient’s brain core network deteriorates. This study also found the correlation between the altered dynamic properties of the core functional networks and the patient’s clinical Mini-Mental State Examination assessment scale sores. Conclusion: This study revealed that the characteristics of dynamic functional networks constructed by the core cognitive networks varied in distinct ways at different stages of aMCI, which could provide a new idea for exploring the neuro-mechanisms of neurological disorders.

Impact of APOE ε4 Carrier Status on Associations Between Subthreshold, Positive Amyloid-β Deposition, Brain Function, and Cognitive Performance in Cognitively Normal Older Adults: A Prospective Study

Background

A growing body of evidence suggests a deteriorating effect of subthreshold amyloid-beta (Aβ) accumulation on cognition before the onset of clinical symptoms of Alzheimer's disease (AD). Despite the association between the Aβ-dependent pathway and the APOE ε4 allele, the impact of this allele on the progression from the subthreshold Aβ deposits to cognitive function impairment is unclear. Furthermore, the comparative analysis of positive Aβ accumulation in the preclinical phase is lacking.

Objective

This study aimed to explore the differential effect of the APOE ε4 carrier status on the association between Aβ deposition, resting-state brain function, and cognitive performance in cognitively normal (CN) older adults, depending on the Aβ burden status.

Methods

One hundred and eighty-two older CN adults underwent resting-state functional magnetic resonance imaging, [18F] flutemetamol (FMM) positron emission tomography, a neuropsychological battery, and APOE genotyping. We evaluated the resting-state brain function by measuring the local and remote functional connectivity (FC) and measured the remote FC in the default-mode network (DMN), central-executive network (CEN), and salience network (SN). In addition, the subjects were dichotomized into those with subthreshold and positive Aβ deposits using a neocortical standardized uptake value ratio with the cut-off value of 0.62, which was calculated with respect to the pons.

Results

The present result showed that APOE ε4 carrier status moderated the relationship between Aβ deposition, local and remote resting-state brain function, and cognitive performance in each CN subthreshold and positive Aβ group. We observed the following: (i) the APOE ε4 carrier status-Aβ deposition and APOE ε4 carrier status-local FC interaction for the executive and memory function; (ii) the APOE ε4 carrier status-regional Aβ accumulation interaction for the local FC; and (iv) the APOE ε4 carrier status-local FC interaction for the remote inter-network FC between the DMN and CEN, contributing higher cognitive performance in the APOE ε4 carrier with higher inter-network FC. Finally, these results were modulated according to Aβ positivity.

Conclusion

This study is the first attempt to thoroughly examine the influence of the APOE ε4 carrier status from the subthreshold to positive Aβ accumulation during the preclinical phase.

Disrupted coupling between salience network segregation and glucose metabolism is associated with cognitive decline in Alzheimer's disease - A simultaneous resting-state FDG-PET/fMRI study

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Hybrid PET/MRI was used to explore network segregation and glucose metabolism in AD.

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DMN, CEN, and SN showed reduced segregation in AD.

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In salience network, segregation coupled with glucose metabolism in CN group.

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The coupled segregation and glucose metabolism in CN disappeared in MCI and AD.

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Reduced segregation and hypometabolism were associated with cognitive impairments.

The aberrant organization and functioning of three core neurocognitive networks (NCNs), i.e., default-mode network (DMN), central executive network (CEN), and salience network (SN), are among the prominent features in Alzheimer’s disease (AD). The dysregulation of both intra- and inter-network functional connectivities (FCs) of the three NCNs contributed to AD-related cognitive and behavioral abnormalities. Brain functional network segregation, integrating intra- and inter-network FCs, is essential for maintaining the energetic efficiency of brain metabolism. The association of brain functional network segregation, together with glucose metabolism, with age-related cognitive decline was recently shown. Yet how these joint functional-metabolic biomarkers relate to cognitive decline along with mild cognitive impairment (MCI) and AD remains to be elucidated. In this study, under the framework of the triple-network model, we performed a hybrid FDG-PET/fMRI study to evaluate the concurrent changes of resting-state brain intrinsic FCs and glucose metabolism of the three NCNs across cognitively normal (CN) (N = 24), MCI (N = 21), and AD (N = 21) groups. Lower network segregation and glucose metabolism were observed in all three NCNs in patients with AD. More interestingly, in the SN, the coupled relationship between network segregation and glucose metabolism existed in the CN group (r = 0.523, p = 0.013) and diminished in patients with MCI (r = 0.431, p = 0.065) and AD (r = 0.079, p = 0.748). Finally, the glucose metabolism of the DMN (r = 0.380, p = 0.017) and the network segregation of the SN (r = 0.363, p = 0.023) were significantly correlated with the general cognitive status of the patients. Our findings suggest that the impaired SN segregation and its uncoupled relationship with glucose metabolism contribute to the cognitive decline in AD.

Functional connectome analysis in Mild Cognitive Impairment: Comparing AD continuum and Suspected Non-Alzheimer Pathology

INTRODUCTION

Research in brain resting-state functional connectivity (FC) analysis in mild cognitive impairment (MCI) has conflicting results. This work intends to find differences in resting-state FC of MCI subjects due to Alzheimer´s disease continuum (MCI-AD) or suspected non-Alzheimer pathology (MCI-SNAP).

METHODS

92 subjects over 55 years old were enrolled. MCI and controls were grouped using clinical dementia rating and neuropsychological data. CSF biomarkers were collected from MCI subjects, resulting in 32 MCI-AD, 25 MCI-SNAP, and 35 controls. A ROI-to-ROI analysis was carried out looking at inter and intranetwork interactions selecting the following networks: default mode (DMN), salience (SN), visuospatial (VN), and executive. Pearson correlation coefficients, converted to Z-scores were compared by T-tests with alpha set to 0.05, FDR corrected.

RESULTS

Groups were similar in age, education and demographic measures, there were no differences in neuropsychological data between the MCI groups. The ROI-to-ROI analysis MCI-AD versus MCI-SNAP showed no differences. MCI-AD versus controls showed decreased FC between ROIs of the SN and between ROIs from SN and VN. MCI-SNAP versus controls showed increased FC between a ROI of DMN and VN.

DISCUSSION

SN, DMN, and VN are multimodal networks with high value/high cost and may be more vulnerable to AD pathogenic processes. SN and VN were affected in the MCI-AD group, with maintained anticorrelation between DMN and VN. This may indicate subthreshold DMN dysfunction. The result in MCI-SNAP, although discrete, reflects a rearrangement of brain FC, as DMN and VN are expected to be anticorrelated. More research is necessary to confirm these findings.