Cognitive Decline and Reorganization of Functional Connectivity in Healthy Aging: The Pivotal Role of the Salience Network in the Prediction of Age and Cognitive Performances

The impact of cannabidiol placebo on amygdala-based neural responses to an acute stressor

BACKGROUND

Cannabidiol (CBD) impacts brain regions implicated in anxiety reactivity and stress reactivity (e.g., amygdala, anterior cingulate cortex (ACC), anterior insula (AI)); however, placebo-controlled studies are mixed regarding CBD's anxiolytic effects. We previously reported that CBD expectancy alone can alter subjective, physiological, and endocrine markers of stress/anxiety; however, it is unclear whether these findings reflect altered brain reactivity. This study evaluated whether CBD expectancy independently alters amygdala resting-state functional connectivity (rsFC) with the ACC and AI following acute stress.

METHOD

Thirty-eight (20 females) healthy adults were randomly assigned to receive accurate or inaccurate information regarding the CBD content of a CBD-free oil administered during a single experimental session. Following a baseline resting state MRI scan, participants administered their assigned oil sublingually, engaged in a stress task (serial subtraction with negative feedback) inside the scanner, and underwent another resting state MRI scan. Amygdala rsFC with the ACC and AI was measured during each scan, and the subjective state was assessed at six time points. Outcomes were analyzed using ANCOVA.

RESULTS

CBD expectancy (vs CBD-free expectancy) was associated with significantly weaker rsFC between the left amygdala and right ACC (p = 0.042), but did not systematically alter amygdala-AI rsFC (p-values > 0.05). We also replicated our previously reported CBD expectancy effects on subjective stress/anxiety in the scanner context.

CONCLUSION

CBD placebo effects may be sufficient to alter neural responses relevant to its purported anxiolytic and stress-relieving properties. Future work is needed to replicate these results and determine whether CBD expectancy and pharmacology interact to alter neural anxiety reactivity and stress reactivity.

The associations among glycemic control, heart variability, and autonomic brain function in healthy individuals: Age- and sex-related differences

INTRODUCTION

The purpose of this study was to clarify the relationships between glycemia and function of the autonomic nervous system (ANS), assessed via resting-state functional connectivity (FC) and heart-rate variability (HRV).

METHODS

Data for this study were extracted from the Leipzig Study for Mind-Body-Emotion Interactions, including 146 healthy adults (114 young, 32 older). Variables of interest were glycated hemoglobin (HbA1c), resting-state FC in the salience aspect of the central-autonomic (S-CAN) and salience network (SN) and HRV (RMSSD and high-frequency HRV (HF-HRV)).

RESULTS

HbA1c was inversely correlated with FC in the S-CAN but not SN. HbA1c was inversely correlated with HRV. Both RMSSD and log(HF-HRV) were correlated with FC in the S-CAN and SN. Age- (not sex-related) differences were observed in the Hb1Ac-FC associations (stronger in older adults) while sex- (not age-related) differences were observed in the HRV-FC (stronger in females).

CONCLUSIONS

These findings extend the diabetes literature to healthy adults in relating glycemia and brain function. The age- and sex-related differences in these relationships highlight the need to account for the potential effects of age and sex in future investigations.

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).

Fingerprints of decreased cognitive performance on fractal connectivity dynamics in healthy aging

Analysis of brain functional connectivity (FC) could provide insight in how and why cognitive functions decline even in healthy aging (HA). Despite FC being established as fluctuating over time even in the resting state (RS), dynamic functional connectivity (DFC) studies involving healthy elderly individuals and assessing how these patterns relate to cognitive performance are yet scarce. In our recent study we showed that fractal temporal scaling of functional connections in RS is not only reduced in HA, but also predicts increased response latency and reduced task solving accuracy. However, in that work we did not address changes in the dynamics of fractal connectivity (FrC) strength itself and its plausible relationship with mental capabilities. Therefore, here we analyzed RS electroencephalography recordings of the same subject cohort as previously, consisting of 24 young and 19 healthy elderly individuals, who also completed 7 different cognitive tasks after data collection. Dynamic fractal connectivity (dFrC) analysis was carried out via sliding-window detrended cross-correlation analysis (DCCA). A machine learning method based on recursive feature elimination was employed to select the subset of connections most discriminative between the two age groups, identifying 56 connections that allowed for classifying participants with an accuracy surpassing 92%. Mean of DCCA was found generally increased, while temporal variability of FrC decreased in the elderly when compared to the young group. Finally, dFrC indices expressed an elaborate pattern of associations-assessed via Spearman correlation-with cognitive performance scores in both groups, linking fractal connectivity strength and variance to increased response latency and reduced accuracy in the elderly population. Our results provide further support for the relevance of FrC dynamics in understanding age-related cognitive decline and might help to identify potential targets for future intervention strategies.

Age- and gender-related dispersion of brain networks across the lifespan

The effects of age and gender on large-scale resting-state networks (RSNs) reflecting within- and between-network connectivity in the healthy brain remain unclear. This study investigated how age and gender influence the brain network roles and topological properties underlying the ageing process. Ten RSNs were constructed based on 998 participants from the REST-meta-MDD cohort. Multivariate linear regression analysis was used to examine the independent and interactive influences of age and gender on large-scale RSNs and their topological properties. A support vector regression model integrating whole-brain network features was used to predict brain age across the lifespan and cognitive decline in an Alzheimer's disease spectrum (ADS) sample. Differential effects of age and gender on brain network roles were demonstrated across the lifespan. Specifically, cingulo-opercular, auditory, and visual (VIS) networks showed more incohesive features reflected by decreased intra-network connectivity with ageing. Further, females displayed distinctive brain network trajectory patterns in middle-early age, showing enhanced network connectivity within the fronto-parietal network (FPN) and salience network (SAN) and weakened network connectivity between the FPN-somatomotor, FPN-VIS, and SAN-VIS networks. Age - but not gender - induced widespread decrease in topological properties of brain networks. Importantly, these differential network features predicted brain age and cognitive impairment in the ADS sample. By showing that age and gender exert specific dispersion of dynamic network roles and trajectories across the lifespan, this study has expanded our understanding of age- and gender-related brain changes with ageing. Moreover, the findings may be useful for detecting early-stage dementia.

Functional Connectivity and Microstructural Network Correlates of Interindividual Variability in Distinct Executive Functions of Healthy Older Adults

Executive functions, essential for daily life, are known to be impaired in older age. Some executive functions, including working memory updating and value-based decision-making, are specifically sensitive to age-related deterioration. While their neural correlates in young adults are well-described, a comprehensive delineation of the underlying brain substrates in older populations, relevant to identify targets for modulation against cognitive decline, is missing. Here, we assessed letter updating and Markov decision-making task performance to operationalize these trainable functions in 48 older adults. Resting-state functional magnetic resonance imaging was acquired to quantify functional connectivity (FC) in task-relevant frontoparietal and default mode networks. Microstructure in white matter pathways mediating executive functions was assessed with diffusion tensor imaging and quantified by tract-based fractional anisotropy (FA). Superior letter updating performance correlated with higher FC between dorsolateral prefrontal cortex and left frontoparietal and hippocampal areas, while superior Markov decision-making performance correlated with decreased FC between basal ganglia and right angular gyrus. Furthermore, better working memory updating performance was related to higher FA in the cingulum bundle and the superior longitudinal fasciculus. Stepwise linear regression showed that cingulum bundle FA added significant incremental contribution to the variance explained by fronto-angular FC alone. Our findings provide a characterization of distinct functional and structural connectivity correlates associated with performance of specific executive functions. Thereby, this study contributes to the understanding of the neural correlates of updating and decision-making functions in older adults, paving the way for targeted modulation of specific networks by modulatory techniques such as behavioral interventions and non-invasive brain stimulation.

The effects of typical ageing on cognitive control: recent advances and future directions

Cognitive control is one of the most fundamental aspects of human life. Its ageing is an important contemporary research area due to the needs of the growing ageing population, such as prolonged independence and quality of life. Traditional ageing research argued for a global decline in cognitive control with age, typically characterised by slowing processing speed and driven by changes in the frontal cortex. However, recent advances questioned this perspective by demonstrating high heterogeneity in the ageing data, domain-specific declines, activity changes in resting state networks, and increased functional connectivity. Moreover, improvements in neuroimaging techniques have enabled researchers to develop compensatory models of neural reorganisation that helps negate the effects of neural losses and promote cognitive control. In this article on typical ageing, we review recent behavioural and neural findings related to the decline in cognitive control among older adults. We begin by reviewing traditional perspectives and continue with how recent work challenged those perspectives. In the discussion section, we propose key areas of focus for future research in the field.

Phenotyping Superagers Using Resting-State fMRI

BACKGROUND AND PURPOSE

Superagers are defined as older adults with episodic memory performance similar or superior to that in middle-aged adults. This study aimed to investigate the key differences in discriminative networks and their main nodes between superagers and cognitively average elderly controls. In addition, we sought to explore differences in sensitivity in detecting these functional activities across the networks at 3T and 7T MR imaging fields.

MATERIALS AND METHODS

Fifty-five subjects 80 years of age or older were screened using a detailed neuropsychological protocol, and 31 participants, comprising 14 superagers and 17 cognitively average elderly controls, were included for analysis. Participants underwent resting-state-fMRI at 3T and 7T MR imaging. A prediction classification algorithm using a penalized regression model on the measurements of the network was used to calculate the probabilities of a healthy older adult being a superager. Additionally, ORs quantified the influence of each node across preselected networks.

RESULTS

The key networks that differentiated superagers and elderly controls were the default mode, salience, and language networks. The most discriminative nodes (ORs > 1) in superagers encompassed areas in the precuneus posterior cingulate cortex, prefrontal cortex, temporoparietal junction, temporal pole, extrastriate superior cortex, and insula. The prediction classification model for being a superager showed better performance using the 7T compared with 3T resting-state-fMRI data set.

CONCLUSIONS

Our findings suggest that the functional connectivity in the default mode, salience, and language networks can provide potential imaging biomarkers for predicting superagers. The 7T field holds promise for the most appropriate study setting to accurately detect the functional connectivity patterns in superagers.

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.

Wearing a KN95/FFP2 facemask induces subtle yet significant brain functional connectivity modifications restricted to the salience network

Background

The use of facemasks is one of the consequences of the coronavirus disease 2019 (COVID-19) pandemic. We used resting-state functional magnetic resonance imaging (fMRI) to search for subtle changes in brain functional connectivity, expected notably related to the high-level salience network (SN) and default mode network (DMN).

Methods

Prospective crossover design resting 3-T fMRI study with/without wearing a tight FFP2/KN95 facemask, including 23 community-dwelling male healthy controls aged 29.9 ± 6.9 years (mean ± standard deviation). Physiological parameters, respiration frequency, and heart rate were monitored. The data analysis was performed using the CONN toolbox.

Results

Wearing an FFP2/KN95 facemask did not impact respiration or heart rate but resulted in a significant reduction in functional connectivity between the SN as the seed region and the left middle frontal and precentral gyrus. No difference was found when the DMN, sensorimotor, visual, dorsal attention, or language networks were used as seed regions. In the absence of significant changes of physiological parameter respiration and heart rate, and in the absence of changes in lower-level functional networks, we assume that those subtle modifications are cognitive consequence of wearing facemasks.

Conclusions

The effect of wearing a tight FFP2/KN95 facemask in men is limited to high-level functional networks. Using the SN as seed network, we observed subtle yet significant decreases between the SN and the left middle frontal and precentral gyrus. Our observations suggest that wearing a facemask may change the patterns of functional connectivity with the SN known to be involved in communication, social behavior, and self-awareness.

Trait dialectical thinking is associated with the strength of functional coupling between the dACC and the default mode network

Dialectical thinking is an overarching and sophisticated thinking style that involves accepting and resolving contradictions. The current study examined whether the dispositional tendency of dialectical thinking is mediated by organizational patterns of intrinsic brain networks. Based on previous theoretical and empirical works, we hypothesized that the dorsal anterior cingulate cortex (dACC), the hub for conflict processing, shows increased couplings with nodes in the default mode network (DMN). A sample of 380 young and healthy participants completed a self-reported measure of dialectical thinking and underwent resting-state functional magnetic resonance imaging scanning. Results of seed-based correlational ROI and whole-brain analyses supported our hypothesis that trait dialectical thinking was positively correlated with the strength of the dACC-DMN couplings. These findings demonstrate the possibility of identifying network-level neural representations of sociocultural orientations.

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.

Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis

Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial resolutions (also known as scales), together with performance scores across multiple neurobehavioral domains, including sensation, motor skills, and cognition. Provided by the Human Connectome Project, we make use of three cohorts: 640 participants for model training, 160 subjects for validation, and 200 subjects for model performance testing thus enhancing prediction generalization. Our modeling consists of two main stages, namely dimensionality reduction in brain network features at multiple scales, followed by canonical correlation analysis, which determines an optimal linear combination of connectivity features to predict multiple behavioral performance scores. To assess the differences in the predictive power of each modality, we separately applied three different strategies: structural unimodal, functional unimodal, and multimodal, that is, structural in combination with functional features of the brain network. Our results show that the multimodal association outperforms any of the unimodal analyses. Then, to answer which human brain structures were most involved in predicting multiple behavioral scores, we simulated different synthetic scenarios in which in each case we completely deleted a brain structure or a complete resting state network, and recalculated performance in its absence. In deletions, we found critical structures to affect performance when predicting single behavioral domains, but this occurred in a lesser manner for prediction of multi-domain behavior. Overall, our results confirm that although there are synergistic contributions between brain structure and function that enhance behavioral prediction, brain networks may also be mutually redundant in predicting multidomain behavior, such that even after deletion of a structure, the connectivity of the others can compensate for its lack in predicting behavior.

Normal aging in mice is associated with a global reduction in cortical spectral power and network-specific declines in functional connectivity

Normal aging is associated with a variety of neurologic changes including declines in cognition, memory, and motor activity. These declines correlate with neuronal changes in synaptic structure and function. Degradation of brain network activity and connectivity represents a likely mediator of age-related functional deterioration resulting from these neuronal changes. Human studies have demonstrated both general decreases in spontaneous cortical activity and disruption of cortical networks with aging. Current techniques used to study cerebral network activity are hampered either by limited spatial resolution (e.g. electroencephalography, EEG) or limited temporal resolution (e.g., functional magnetic resonance imaging, fMRI). Here we utilize mesoscale imaging of neuronal activity in Thy1-GCaMP6f mice to characterize neuronal network changes in aging with high spatial resolution across a wide frequency range. We show that while evoked activity is unchanged with aging, spontaneous neuronal activity decreases across a wide frequency range (0.01–4 Hz) involving all regions of the cortex. In contrast to this global reduction in cortical power, we found that aging is associated with functional connectivity (FC) deterioration of select networks including somatomotor, cingulate, and retrosplenial nodes. These changes are corroborated by reductions in homotopic FC and node degree within somatomotor and visual cortices. Finally, we found that whole-cortex delta power and delta band node degree correlate with exploratory activity in young but not aged animals. Together these data suggest that aging is associated with global declines in spontaneous cortical activity and focal deterioration of network connectivity, and that these reductions may be associated with age-related behavioral declines.

Abnormal EEG Signal Energy in the Elderly: A Wavelet Analysis of Event-related Potentials During a Stroop Task

BACKGROUND

Previous work showed that elderly with excess in theta activity in their resting state electroencephalogram (EEG) are at higher risk of cognitive decline than those with a normal EEG. By using event-related potentials (ERP) during a counting Stroop task, our prior work showed that elderly with theta excess have a large P300 component compared with normal EEG group. This increased activity could be related to a higher EEG signal energy used during this task.

NEW METHOD

By wavelet analysis applied to ERP obtained during a counting Stroop task we quantified the energy in the different frequency bands of a group of elderly with altered EEG.

RESULTS

In theta and alpha bands, the total energy was higher in elderly subjects with theta excess, specifically in the stimulus categorization window (258-516 ms). Both groups solved the task with similar efficiency.

COMPARISON WITH EXISTING METHODS

The traditional ERP analysis in elderly compares voltage among conditions and groups for a given time windows, while the frequency composition is not usually examined. We complemented our previous ERP analysis using a wavelet methodology. Furthermore, we showed the advantages of wavelet analysis over Short Time Fourier Transform when exploring EEG signal during this task.

CONCLUSIONS

The higher EEG signal energy in ERP might reflect undergoing neurobiological mechanisms that allow the elderly with theta excess to cope with the cognitive task with similar behavioral results as the normal EEG group. This increased energy could promote a metabolic and cellular dysregulation causing a greater decline in cognitive function.

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology underlying successful aging. We aimed to investigate the unique patterns of functional brain connectome of superagers and develop predictive models to differentiate superagers from typical agers based on machine learning methods. We obtained resting-state functional magnetic resonance imaging (rsfMRI) data and cognitive measures from 32 superagers and 58 typical agers. The accuracies of three machine learning methods including the linear support vector machine classifier (SV), the random forest classifier (RF), and the logistic regression classifier (LR) in predicting superagers were comparable (SV = 0.944, RF = 0.944, LR = 0.944); however, RF achieved the highest area under the curve (AUC; 0.979). An ensemble learning method combining the three classifiers achieved the highest AUC (0.986). The most discriminative nodes for predicting superagers encompassed areas in the precuneus; posterior cingulate gyrus; insular cortex; and superior, middle, and inferior frontal gyrus, which were located in default, salient, and multiple-demand networks. Thus, rsfMRI data can provide high accuracy for predicting superagers, thereby capturing and describing the unique characteristics of their functional brain connectome.

The central executive network and executive function in healthy and persons with schizophrenia groups: a meta-analysis of structural and functional MRI

This meta-analysis evaluated the extent to which executive function can be understood with structural and functional magnetic resonance imaging. Studies included structural in schizophrenia (k = 8; n = 241) and healthy controls (k = 12; n = 1660), and functional in schizophrenia (k = 4; n = 104) and healthy controls (k = 12; n = 712). Results revealed a positive association in the brain behavior relationship when pooled across schizophrenia and control samples for structural (pr = 0.27) and functional (pr = 0.29) modalities. Subgroup analyses revealed no significant difference for functional neuroimaging (pr = .43, 95%CI = -.08-.77, p = .088) but with structural neuroimaging (pr = .37, 95%CI = -.08-.69, p = .015) the association to executive functions is lower in the control group. Subgroup analyses also revealed no significant differences in the strength of the brain-behavior relationship in the schizophrenia group (pr = .59, 95%CI = .58-.61, p = .881) or the control group (pr = 0.19, 95%CI = 0.18-0.19, p = 0.920), suggesting concordance.

Functional Abnormality Associated With Tau Deposition in Alzheimer's Disease - A Hybrid Positron Emission Tomography/MRI Study

Objective: To investigate the characteristics of tau deposition and its impact on functional connectivity (FC) in Alzheimer’s disease (AD).

Methods: Hybrid PET/MRI scans with [¹⁸F]-THK5317 and neuropsychological assessments were undertaken in 26 participants with AD and 19 healthy controls (HC). The standardized uptake value ratio (SUVR) of [¹⁸F]-THK5317 PET imaging was compared between the AD and HC groups. Significant clusters that revealed higher tau deposition in the AD group compared to the HC group were selected as regions of interest (ROI) for FC analysis. We evaluated the difference in the FC between the two groups for each ROI pair. The clinical and radiological characteristics were compared between the AD patients with negative FC and AD patients with positive FC for exploratory analysis.

Results: The bilateral inferior lateral temporal lobe, dorsal prefrontal cortex, precuneus, posterior cingulate cortex, hippocampus, and occipital lobe showed significantly higher [¹⁸F]-THK5317 accumulation in AD patients. Decreased FC in regions with higher SUVR was observed in AD patients, and the FC strength was negatively correlated with regional SUVR. Patients with a positive FC exhibited older ages, better cognitive performances, and a lower SUVR than patients with a negative FC.

Conclusions: An impact of tau deposition was observed on FC at the individual level in AD patients. Our findings suggested that the combination of tau-PET and rs-fMRI might help predict AD progression.

Altered neural networks and cognition in a hereditary colon cancer

Familial Adenomatous Polyposis (FAP) is an autosomal dominant disorder caused by mutation of the APC gene presenting with numerous colorectal adenomatous polyps and a near 100% risk of colon cancer. Preliminary research findings from our group indicate that FAP patients experience significant deficits across many cognitive domains. In the current study, fMRI brain metrics in a FAP population and matched controls were used to further the mechanistic understanding of reported cognitive deficits. This research identified and characterized any possible differences in resting brain networks and associations between neural network changes and cognition from 34 participants (18 FAP patients, 16 healthy controls). Functional connectivity analysis was performed using FSL with independent component analysis (ICA) to identify functional networks. Significant differences between cases and controls were observed in 8 well-established resting state networks. With the addition of an aggregate cognitive measure as a covariate, these differences were virtually non-existent, indicating a strong correlation between cognition and brain activity at the network level. The data indicate robust and pervasive effects on functional neural network activity among FAP patients and these effects are likely involved in cognitive deficits associated with this disease.

Prefrontal Plasticity after a 3-Month Exercise Intervention in Older Adults Relates to Enhanced Cognitive Performance

This study examined exercise intervention effects on older adults' brain structures and function. Brain data were analyzed from 47 healthy adults between 61 and 82 years of age who, in a previous study, showed cognitive improvement following a 3-month intervention. The participants were assigned to a motor exercise intervention group (n = 24), performing exercise training programs for a 12-week period, or a waiting control group (n = 23), abstaining from any exercise program. Structural analysis of the frontal cortex and hippocampus revealed increased gray matter volume and/or thickness in several prefrontal areas in the intervention group and reduced hippocampal gray matter volume in the control group. Importantly, the volume increase in the middle frontal sulcus in the intervention group was associated with a general cognitive improvement after the intervention. Functional analysis showed that the prefrontal functional connectivity during a working memory task differently changed in response to the intervention or waiting in the two groups. The functional connectivity decreased in the intervention group, whereas the corresponding connectivity increased in the control group, which was associated with maintaining cognitive performance. The current longitudinal findings indicate that short-term exercise intervention can induce prefrontal plasticity associated with cognitive performance in older adults.

Brain-based mechanisms of late-life depression: Implications for novel interventions

Late-life depression (LLD) is a particularly debilitating illness. Older adults suffering from depression commonly experience poor outcomes in response to antidepressant treatments, medical comorbidities, and declines in daily functioning. This review aims to further our understanding of the brain network dysfunctions underlying LLD that contribute to disrupted cognitive and affective processes and corresponding clinical manifestations. We provide an overview of a network model of LLD that integrates the salience network, the default mode network (DMN) and the executive control network (ECN). We discuss the brain-based structural and functional mechanisms of LLD with an emphasis on their link to clinical subtypes that often fail to respond to available treatments. Understanding the brain networks that underlie these disrupted processes can inform the development of targeted interventions for LLD. We propose behavioral, cognitive, or computational approaches to identifying novel, personalized interventions that may more effectively target the key cognitive and affective symptoms of LLD.

Emulative, coherent, and causal dynamics between large-scale brain networks are neurobiomarkers of Accelerated Cognitive Ageing in epilepsy

Accelerated cognitive ageing (ACA) is an ageing co-morbidity in epilepsy that is diagnosed through the observation of an evident IQ decline of more than 1 standard deviation (15 points) around the age of 50 years old. To understand the mechanism of action of this pathology, we assessed brain dynamics with the use of resting-state fMRI data. In this paper, we present novel and promising methods to extract brain dynamics between large-scale resting-state networks: the emulative power, wavelet coherence, and granger causality between the networks were extracted in two resting-state sessions of 24 participants (10 ACA, 14 controls). We also calculated the widely used static functional connectivity to compare the methods. To find the best biomarkers of ACA, and have a better understanding of this epilepsy co-morbidity we compared the aforementioned between-network neurodynamics using classifiers and known machine learning algorithms; and assessed their performance. Results show that features based on the evolutionary game theory on networks approach, the emulative powers, are the best descriptors of the co-morbidity, using dynamics associated with the default mode and dorsal attention networks. With these dynamic markers, linear discriminant analysis could identify ACA patients at 82.9% accuracy. Using wavelet coherence features with decision-tree algorithm, and static functional connectivity features with support vector machine, ACA could be identified at 77.1% and 77.9% accuracy respectively. Granger causality fell short of being a relevant biomarker with best classifiers having an average accuracy of 67.9%. Combining the features based on the game theory, wavelet coherence, Granger-causality, and static functional connectivity- approaches increased the classification performance up to 90.0% average accuracy using support vector machine with a peak accuracy of 95.8%. The dynamics of the networks that lead to the best classifier performances are known to be challenged in elderly. Since our groups were age-matched, the results are in line with the idea of ACA patients having an accelerated cognitive decline. This classification pipeline is promising and could help to diagnose other neuropsychiatric disorders, and contribute to the field of psychoradiology.

Age differences in functional network reconfiguration with working memory training

Demanding cognitive functions like working memory (WM) depend on functional brain networks being able to communicate efficiently while also maintaining some degree of modularity. Evidence suggests that aging can disrupt this balance between integration and modularity. In this study, we examined how cognitive training affects the integration and modularity of functional networks in older and younger adults. Twenty three younger and 23 older adults participated in 10 days of verbal WM training, leading to performance gains in both age groups. Older adults exhibited lower modularity overall and a greater decrement when switching from rest to task, compared to younger adults. Interestingly, younger but not older adults showed increased task-related modularity with training. Furthermore, whereas training increased efficiency within, and decreased participation of, the default-mode network for younger adults, it enhanced efficiency within a task-specific salience/sensorimotor network for older adults. Finally, training increased segregation of the default-mode from frontoparietal/salience and visual networks in younger adults, while it diffusely increased between-network connectivity in older adults. Thus, while younger adults increase network segregation with training, suggesting more automated processing, older adults persist in, and potentially amplify, a more integrated and costly global workspace, suggesting different age-related trajectories in functional network reorganization with WM training.

Genetic factors influencing a neurobiological substrate for psychiatric disorders

A retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex-areas that constitute hub nodes of the salience network-represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n = 2271), followed by meta-analysis and replication in a fifth cohort (n = 865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

Neurocognition applied to psychotherapy: A brief theoretical proposal based on the complex neural network perspective

Impairments on executive functions, attention, memory, and self-perception had been systematically associated and document across several psychological disorders. Individuals with anxiety, depression, and schizophrenia spectrum disorders tend to manifest difficulties in response modulation/inhibition, cognitive flexibility, selective attention, updating autobiographical memory patterns, and maintenance in the sense of self and boundaries of others. Difficulties in cognitive, emotional, behavioral, and interpersonal functions in intrapsychic and interpsychic mental domains may be theoretically related to the maladaptive functioning of several neural networks. Frontal-Parietal Executive Network (FPEN), Salience Network (SN), Amygdaloid-Hippocampal Memory Network (AHMN), and Default Mode-Network (DMN) are four major complex neural pathways associated with these neurocognitive processes, sharing some neuroanatomical elements. These shared elements may support a latent factor that accounts for the common neurocognitive symptomatology across several psychopathological conditions. Based on these preliminary observations a new theoretical neurocognitive syndrome is hypothesized, potentially a productive target for clinical case conceptualization. Several articulations bettween neurocognition and psychotherapy are discussed and a new assessment measure is proposed.

How Do You Feel Now? The Salience Network Functional Connectivity in End-Stage Renal Disease

Objective

The network connectivity basis of cognitive declines in end-stage renal disease (ESRD) remains unclear. A triple-network model of the salience (SN), executive control, and default mode networks has been suggested to be critical for efficient cognition. Here, we aimed to test the hypothesis that SN may play a role in cognitive impairment in patients with ESRD.

Materials and Methods

We investigated functional connectivity (FC) alterations within the SN between 43 ESRD patients (19 females/24 males, 46 ± 10 years) and 43 healthy controls (HC) (19 females/24 males, 47 ± 10 years), and performed linear support vector machine (LSVM) analysis on significant FC pairs within the SN to discriminate the two groups, and tested the accuracy of the classifier. Association and mediation analyses were conducted among the significant FC pairs within the SN nodes, clinical indicators, and neuropsychological tests scores.

Results

We identified significant between-group FC pairs within the SN and fairly good classification efficiency with significant accuracy (72.09%, p < 0.001). We found that FC between the right supramarginal gyrus and right anterior insula (AISL) was positively correlated with MoCA (r = 0.4010, p = 0.008); FC between the dorsal anterior cingulate cortex (dACC) and left AISL was positively correlated with the level of hemoglobin (r = 0.4979, p < 0.001). Mediation analysis found that the indirect effect of hemoglobin on forward digit span test scores via the FC between the dACC and right AISL (p < 0.05).

Conclusion

Disrupted SN connectivity may help explain cognitive declines in ESRD patients and act as a potential early biomarker. Moreover, the SN connectivity may interact with anemia to promote cognitive impairment.

Altered Resting State Brain Networks and Cognition in Familial Adenomatous Polyposis

Familial Adenomatous Polyposis (FAP) is an autosomal dominant disorder caused by mutation of the APC gene presenting with numerous colorectal adenomatous polyps and a near 100% risk of colon cancer. Preliminary research findings from our group indicate that FAP patients experience significant deficits across many cognitive domains. In the current study, fMRI brain metrics in a FAP population and matched controls were used to further the mechanistic understanding of reported cognitive deficits. This research identified and characterized any possible differences in resting brain networks and associations between neural network changes and cognition from 34 participants (18 FAP patients, 16 healthy controls). Functional connectivity analysis was performed using FSL with independent component analysis (ICA) to identify functional networks. Significant differences between cases and controls were observed in 8 well-established resting state networks. With the addition of an aggregate cognitive measure as a covariate, these differences were virtually non-existent, indicating a strong correlation between cognition and brain activity at the network level. The data indicate robust and pervasive effects on functional neural network activity among FAP patients and these effects are likely involved in cognitive deficits associated with this disease.

Age differences in predicting working memory performance from network-based functional connectivity

Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity.

Salience and default-mode network connectivity during threat and safety processing in older adults

The appropriate assessment of threat and safety is important for decision‐making but might be altered in old age due to neurobiological changes. The literature on threat and safety processing in older adults is sparse and it is unclear how healthy ageing affects the brain's functional networks associated with affective processing. We measured skin conductance responses as an indicator of sympathetic arousal and used functional magnetic resonance imaging and independent component analysis to compare young and older adults' functional connectivity in the default mode (DMN) and salience networks (SN) during a threat conditioning and extinction task. While our results provided evidence for differential threat processing in both groups, they also showed that functional connectivity within the SN – but not the DMN – was weaker during threat processing in older compared to young adults. This reduction of within‐network connectivity was accompanied by an age‐related decrease in low frequency spectral power in the SN and a reduction in inter‐network connectivity between the SN and DMN during threat and safety processing. Similarly, we found that skin conductance responses were generally lower in older compared to young adults. Our results are the first to demonstrate age‐related changes in brain activation during aversive conditioning and suggest that the ability to adaptively filter affective information is reduced in older adults.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder

A mechanistic understanding of the pathology of psychiatric disorders has been hampered by extensive heterogeneity in biology, symptoms, and behavior within diagnostic categories that are defined subjectively. We investigated whether leveraging individual differences in information-processing impairments in patients with post-traumatic stress disorder (PTSD) could reveal phenotypes within the disorder. We found that a subgroup of patients with PTSD from two independent cohorts displayed both aberrant functional connectivity within the ventral attention network (VAN) as revealed by functional magnetic resonance imaging (fMRI) neuroimaging and impaired verbal memory on a word list learning task. This combined phenotype was not associated with differences in symptoms or comorbidities, but nonetheless could be used to predict a poor response to psychotherapy, the best-validated treatment for PTSD. Using concurrent focal noninvasive transcranial magnetic stimulation and electroencephalography, we then identified alterations in neural signal flow in the VAN that were evoked by direct stimulation of that network. These alterations were associated with individual differences in functional fMRI connectivity within the VAN. Our findings define specific neurobiological mechanisms in a subgroup of patients with PTSD that could contribute to the poor response to psychotherapy.

Altered Patterns of Functional Connectivity and Causal Connectivity in Salience Subnetwork of Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment

The subjective cognitive decline (SCD) may last for decades prior to the onset of dementia and has been proposed as a risk population for development to amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD). Disruptions of functional connectivity and causal connectivity (CC) in the salience network (SN) are generally perceived as prominent hallmarks of the preclinical AD. Nevertheless, the alterations in anterior SN (aSN), and posterior SN (pSN) remain unclear. Here, we hypothesized that both the functional connectivity (FC) and CC of the SN subnetworks, comprising aSN and pSN, were distinct disruptive in the SCD and aMCI. We utilized resting-state functional magnetic resonance imaging to investigate the altered FC and CC of the SN subnetworks in 28 healthy controls, 23 SCD subjects, and 29 aMCI subjects. In terms of altered patterns of FC in SN subnetworks, aSN connected to the whole brain was significantly increased in the left orbital superior frontal gyrus, left insula lobule, right caudate lobule, and left rolandic operculum gyrus (ROG), whereas decreased FC was found in the left cerebellum superior lobule and left middle temporal gyrus when compared with the HC group. Notably, no prominent statistical differences were obtained in pSN. For altered patterns of CC in SN subnetworks, compared to the HC group, the aberrant connections in aMCI group were separately involved in the right cerebellum inferior lobule (CIL), right supplementary motor area (SMA), and left ROG, whereas the SCD group exhibited more regions of aberrant connection, comprising the right superior parietal lobule, right CIL, left inferior parietal lobule, left post-central gyrus (PG), and right angular gyrus. Especially, SCD group showed increased CC in the right CIL and left PG, whereas the aMCI group showed decreased CC in the left pre-cuneus, corpus callosum, and right SMA when compared to the SCD group. Collectively, our results suggest that analyzing the altered FC and CC observed in SN subnetworks, served as impressible neuroimaging biomarkers, may supply novel insights for designing preclinical interventions in the preclinical stages of AD.

Baseline Functional Connectivity Predicts Connectivity Changes Due to a Small Dose of Midazolam in Older Adults

BACKGROUND

In the perioperative context, benzodiazepines are widely used as anxiolytics. They affect cognition in general, but it is unclear whether the effects of a small dose of the short-acting benzodiazepine midazolam can be assessed objectively. To address this scientific question, we conducted a prospective observational study in adults 55-73 years of age. Using both validated psychometric and functional imaging techniques, we determined whether a 2-mg intravenous (IV) dose of midazolam affects cognitive function.

METHODS

We measured the effect of 2 mg IV of midazolam with both the well-established Repeatable Battery for the Assessment of Neuropsychological Status test and resting-state functional magnetic imaging (rs-fMRI) in older adults.

RESULTS

Midazolam reduces immediate and delayed memory and has a profound and robust effect on rs-fMRI. Baseline resting-state connectivity predicts memory decline after midazolam administration.

CONCLUSIONS

Observed effects of midazolam on brain networks were statistically significant even in a small group of volunteers. If validated by other investigators, resting-state brain connectivity may have utility as a measure to predict sensitivity to midazolam in older adults.

Age-Related Decreases in Interhemispheric Resting-State Functional Connectivity and Their Relationship With Executive Function

Age-related alterations of functional brain networks contribute to cognitive decline. Current theories indicate that age-related intrinsic brain functional reorganization may be a critical marker of cognitive aging. Yet, little is known about how intrinsic interhemispheric functional connectivity changes with age in adults, and how this relates to critical executive functions. To address this, we examined voxel-mirrored homotopic connectivity (VMHC), a metric that quantifies interhemispheric communication, in 93 healthy volunteers (age range: 19-85) with executive function assessment using the Delis-Kaplan Executive Function System (D-KEFS) scales. Resting functional MRI data were analyzed to assess VMHC, and then a multiple linear regression model was employed to evaluate the relationship between age and the whole-brain VMHC. We observed age-related reductions in VMHC of ventromedial prefrontal cortex (vmPFC) and hippocampus in the medial temporal lobe subsystem, dorsal anterior cingulate cortex and insula in salience network, and inferior parietal lobule in frontoparietal control network. Performance on the color-word inhibition task was associated with VMHC of vmPFC and insula, and VMHC of vmPFC mediated the relationship between age and CWIT inhibition reaction times. The percent ratio of correct design scores in design fluency test correlated positively with VMHC of the inferior parietal lobule. The current study suggests that brain interhemispheric functional alterations may be a promising new avenue for understanding age-related cognitive decline.

Functional dedifferentiation of associative resting state networks in older adults - A longitudinal study

Healthy aging is associated with weaker functional connectivity within resting state brain networks and stronger functional interaction between these networks. This phenomenon has been characterized as reduced functional segregation and has been investigated mainly in cross-sectional studies. Here, we used a longitudinal dataset which consisted of four occasions of resting state fMRI and psychometric cognitive ability data, collected from a sample of healthy older adults (baseline N = 232, age range: 64-87 y, age M = 70.8 y), to investigate the functional segregation of several well-defined resting state networks encompassing the whole brain. We characterized the ratio of within-network and between-network correlations via the well-established segregation index. Our findings showed a decrease over a 4-year interval in the functional segregation of the default mode, frontoparietal control and salience ventral attention networks. In contrast, we showed an increase in the segregation of the limbic network over the same interval. More importantly, the rate of change in functional segregation of the frontoparietal control network was associated with the rate of change in processing speed. These findings support the hypothesis of functional dedifferentiation in healthy aging as well as its role in cognitive function in elderly.

Cognitive Control Network Homogeneity and Executive Functions in Late-Life Depression

BACKGROUND

Late-life depression is characterized by network abnormalities, especially within the cognitive control network. We used alternative functional connectivity approaches, regional homogeneity (ReHo) and network homogeneity, to investigate late-life depression functional homogeneity. We examined the association between cognitive control network homogeneity and executive functions.

METHODS

Resting-state functional magnetic resonance imaging data were analyzed for 33 older adults with depression and 43 healthy control subjects. ReHo was performed as the correlation between each voxel and the 27 neighbor voxels. Network homogeneity was calculated as global brain connectivity restricted to 7 networks. T-maps were generated for group comparisons. We measured cognitive performance and executive functions with the Dementia Rating Scale, Trail-Making Test (A and B), Stroop Color Word Test, and Digit Span Test.

RESULTS

Older adults with depression showed increased ReHo in the bilateral dorsal anterior cingulate cortex (dACC) and the right middle temporal gyrus, with no significant findings for network homogeneity. Hierarchical linear regression models showed that higher ReHo in the dACC predicted better performance on Trail-Making Test B (p < .001; R2 = .49), Digit Span Backward (p < .05; R2 = .23), and Digit Span Total (p < .05; R2 = .23). Used as a seed, the dACC cluster of higher ReHo showed lower functional connectivity with bilateral precuneus.

CONCLUSIONS

Higher ReHo within the dACC and right middle temporal gyrus distinguish older adults with depression from control subjects. The correlations with executive function performance support increased ReHo in the dACC as a meaningful measure of the organization of the cognitive control network and a potential compensatory mechanism. Lower functional connectivity between the dACC and the precuneus in late-life depression suggests that clusters of increased ReHo may be functionally segregated.

Premorbid Alterations of Spontaneous Brain Activity in Elderly Patients With Early Post-operative Cognitive Dysfunction: A Pilot Resting-State Functional MRI Study

Background: Elderly patients with pre-existing cognitive impairment are susceptible to post-operative cognitive dysfunction (POCD). In this study, we investigated whether there is pre-existing local homogeneity and functional connectivity alteration in the brain before surgery for POCD patients as compared to that in non-POCD patients.

Methods: Eighty elderly patients undergoing major thoracic or abdominal surgeries were recruited. Resting-state functional MRI was scanned at least 1 day before surgery. Neuropsychological tests (NPTs) were performed before surgery and at discharge, respectively. Pre-operative regional homogeneity (ReHo) and resting-state functional connectivity (RSFC) were compared between POCD patients and non-POCD patients, respectively. Partial correlation between NPTs and ReHo or RSFC was analyzed by adjusting for confounding factors.

Results: Significant difference (P < 0.001, Gaussian Random Field (GRF) correction which is a multiple comparisons correction method at cluster level, cluster size > 49) in ReHo between POCD patients and non-POCD patients was detected in right hippocampus/parahippocampus. Pre-operative RSFC between right hippocampus/parahippocampus and right middle/inferior temporal gyrus increased in POCD patients (P < 0.001, GRF correction for multiple comparisons) when compared with that in non-POCD patients.RSFC significantly correlated with composite Z-score (r = 0.46, 95% CI [0.234, 0.767], P = 0.002) or Digit Symbol Substitution Test Z-scores (r = 0.31, 95% CI [0.068, 0.643], P = 0.046) after adjusting for confounding factors.

Conclusions: The results suggest that premorbid alterations of spontaneous brain activity might exist in elderly patients who develop early POCD. The neural mechanism by which patients with pre-operative abnormal spontaneous activity are susceptible to POCD requires further study.

The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain

Recent studies have found a deleterious effect of age on a wide variety of measures of functional connectivity, and some hints at a relationship between connectivity at rest and cognitive functioning. However, few studies have combined multiple functional connectivity methods, or examined them over a wide range of adult ages, to try to uncover which metrics and networks seem to be particularly sensitive to age-related decline across the adult lifespan. The present study utilized multiple resting state functional connectivity methods in a sample of adults from 20–80 years old to gain a more complete understanding of the effect of aging on network function and integrity. Whole-brain results showed that aging results in weakening average within-network connectivity, lower system segregation and local efficiency, and higher participation coefficient. Network-level results suggested that nearly every primary sensory and cognitive network faces some degree of age-related decline, including reduced within-network connectivity, higher network-based participation coefficient, and reduced network-level local efficiency. Further, some of these connectivity metrics showed relationships with cognitive performance. Thus, these results suggest that a multi-method analysis of functional connectivity data may be critical to capture the full effect of aging on the health of brain networks.

Default Mode Network, Meditation, and Age-Associated Brain Changes: What Can We Learn from the Impact of Mental Training on Well-Being as a Psychotherapeutic Approach?

Aging is a physiological process accompanied by cognitive decline, principally in memory and executive functions. Alterations in the connectivity of the default mode network (DMN) have been found to participate in cognitive decline, as well as in several neurocognitive disorders. The DMN has antisynchronic activity with attentional networks (task-positive networks (TPN)), which are critical to executive function and memory. Findings pointing to the regulation of the DMN via activation of TPN suggest that it can be used as a strategy for neuroprotection. Meditation is a noninvasive and nonpharmacological technique proven to increase meta-awareness, a cognitive ability which involves the control of both networks. In this review, we discuss the possibility of facilitating healthy aging through the regulation of networks through meditation. We propose that by practicing specific types of meditation, cognitive decline could be slowed, promoting a healthy lifestyle, which may enhance the quality of life for the elderly.

Steeper aging-related declines in cognitive control processes among adults with bipolar disorders

BACKGROUND

Little is known about the specificity of executive functioning (EF) decline in older adults with bipolar disorders (OABD), or the impact of bipolar disorders (BD) on the timing and slope of age-related declines in EF processes implicated in both BD etiology and normative aging-cognitive control (CC). This cross-sectional study investigated age-related CC decline in BD.

METHODS

Participants were 43 adults with BD (M age = 61.5, SD = 15.8; 86% male) and 45 Controls (M age = 65.2, SD = 12.2; 98% male). Two-way ANOVAs examined the effects of median-age-split and diagnostic groups on cognitive processes with established BD deficits-CC processes (mental flexibility and response inhibition), verbal learning, and verbal fluency.

RESULTS

The median-split-age-by-diagnostic-group interaction was significant for mental flexibility; OABD performed significantly worse than younger adults with BD and younger and older Controls. Exploratory multivariate adaptive regression spline characterized non-linear nature of aging-slope changes in mental flexibility for each diagnostic group, yielding an inflection point at older age and steeper subsequent decline in OABD versus Controls.

LIMITATIONS

This study is limited by a small sample (particularly for select neuropsychological measures) of mostly Caucasian men and BD diagnoses based on clinical interview and medical records review.

CONCLUSIONS

Compared to healthy older adults, OABD showed steeper age-related decline in mental flexibility-select EF processes that depend on the integrity of the CC system. Preliminary evidence links CC integrity to daily functioning in OABD; accelerated aging decline in CC may pose a mechanism for high risk of functional impairment and dementia in OABD.

The Transitions Between Dynamic Micro-States Reveal Age-Related Functional Network Reorganization

Normal dynamic change in human brain occurs with age increasing, yet much remains unknown regarding how brain develops, matures, and ages. Functional connectivity analysis of the resting-state brain is a powerful method for revealing the intrinsic features of functional networks, and micro-states, which are the intrinsic patterns of functional connectivity in dynamic network courses, and are suggested to be more informative of brain functional changes. The aim of this study is to explore the age-related changes in these micro-states of dynamic functional network. Three healthy groups were included: the young (ages 21-32 years), the adult (age 41-54 years), and the old (age 60-86 years). Sliding window correlation method was used to construct the dynamic connectivity networks, and then the micro-states were individually identified with clustering analysis. The distribution of age-related connectivity variations in several intrinsic networks for each micro-state was analyzed then. The micro-states showed substantial age-related changes in the transitions between states but not in the dwelling time. Also there was no age-related reorganization observed within any micro-state. But there were reorganizations observed in the transition between them. These results suggested that the identified micro-states represented certain underlying connectivity patterns in functional brain system, which are similar to the intrinsic cognitive networks or resources. In addition, the dynamic transitions between these states were probable mechanisms of reorganization or compensation in functional brain networks with age increasing.

Impact of sex and reproductive status on memory circuitry structure and function in early midlife using structural covariance analysis

Research on age-related memory alterations traditionally targets individuals aged ≥65 years. However, recent studies emphasize the importance of early aging processes. We therefore aimed to characterize variation in brain gray matter structure in early midlife as a function of sex and menopausal status. Subjects included 94 women (33 premenopausal, 29 perimenopausal, and 32 postmenopausal) and 99 demographically comparable men from the New England Family Study. Subjects were scanned with a high-resolution T1 sequence on a 3 T whole body scanner. Sex and reproductive-dependent structural differences were evaluated using Box's M test and analysis of covariances (ANCOVAs) for gray matter volumes. Brain regions of interest included dorsolateral prefrontal cortex (DLPFC), inferior parietal lobule (iPAR), anterior cingulate cortex (ACC), hippocampus (HIPP), and parahippocampus. While we observed expected significant sex differences in volume of hippocampus with women of all groups having higher volumes than men relative to cerebrum size, we also found significant differences in the covariance matrices of perimenopausal women compared with postmenopausal women. Associations between ACC and HIPP/iPAR/DLPFC were higher in postmenopausal women and correlated with better memory performance. Findings in this study underscore the importance of sex and reproductive status in early midlife for understanding memory function with aging.

Dimensional Complexity of the Resting Brain in Healthy Aging, Using a Normalized MPSE

Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE). It allows functional brain complexity analyses at varying levels of EV energy, independent from global shifts in data variance. Especially the restriction of the EV spectrum to the first dimensions, carrying the most prominent data variance, can act as a filter to reveal the most discriminant factors of dependent variables. Here we look at the effects of healthy aging on the dimensional complexity of brain activity. We employ a large open access dataset, providing a great number of high quality fast multiband recordings. Using nMPSE on whole brain, regional, network and searchlight approaches, we were able to find many age related changes, i.e., in sensorimotoric and right inferior frontal brain regions. Our results implicate that research on dimensional complexity of functional MRI recordings promises to be a unique resource for understanding brain function and for the extraction of biomarkers.

Cognitive Improvement in Healthy Older Adults Can Parallel That of Younger Adults Following Lifestyle Modification: Support for Cognitive Reserve During Aging

Executive function was assayed following a nutritional supplementation in healthy adults using the Trail Making Test. Comparison with published normative scores demonstrated that cohorts from 35-74 years of age displayed similar relative improvement compared to their own baseline performance. These findings support early, pro-active lifestyle modifications to maintain cognitive performance during aging and further demonstrate the persistence of cognitive reserve in healthy older adults.

Functional segregation loss over time is moderated by APOE genotype in healthy elderly

We investigated the influence of the apolipoprotein E-ɛ4 allele (APOE-ɛ4) on longitudinal age-related changes in brain functional connectivity (FC) and cognition, in view of mixed cross-sectional findings. One hundred and twenty-two healthy older adults (aged 58-79; 25 APOE-ɛ4 carriers) underwent task-free fMRI scans at baseline. Seventy-eight (16 carriers) had at least one follow-up (every 2 years). Changes in intra- and internetwork FCs among the default mode (DMN), executive control (ECN), and salience (SN) networks, as well as cognition, were quantified using linear mixed models. Cross-sectionally, APOE-ɛ4 carriers had lower functional connectivity between the ECN and SN than noncarriers. Carriers also showed a stronger age-dependent decrease in visuospatial memory performance. Longitudinally, carriers had steeper increase in inter-ECN-DMN FC, indicating loss of functional segregation. The longitudinal change in processing speed performance was not moderated by APOE-ɛ4 genotype, but the brain-cognition association was. In younger elderly, faster loss of segregation was correlated with greater decline in processing speed regardless of genotype. In older elderly, such relation remained for noncarriers but reversed for carriers. APOE-ɛ4 may alter aging by accelerating the change in segregation between high-level cognitive systems. Its modulation on the longitudinal brain-cognition relationship was age-dependent.

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on "resting-state" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.

Age-Related Decline in the Variation of Dynamic Functional Connectivity: A Resting State Analysis

Normal aging is typically characterized by abnormal resting-state functional connectivity (FC), including decreasing connectivity within networks and increasing connectivity between networks, under the assumption that the FC over the scan time was stationary. In fact, the resting-state FC has been shown in recent years to vary over time even within minutes, thus showing the great potential of intrinsic interactions and organization of the brain. In this article, we assumed that the dynamic FC consisted of an intrinsic dynamic balance in the resting brain and was altered with increasing age. Two groups of individuals (N = 36, ages 20–25 for the young group; N = 32, ages 60–85 for the senior group) were recruited from the public data of the Nathan Kline Institute. Phase randomization was first used to examine the reliability of the dynamic FC. Next, the variation in the dynamic FC and the energy ratio of the dynamic FC fluctuations within a higher frequency band were calculated and further checked for differences between groups by non-parametric permutation tests. The results robustly showed modularization of the dynamic FC variation, which declined with aging; moreover, the FC variation of the inter-network connections, which mainly consisted of the frontal-parietal network-associated and occipital-associated connections, decreased. In addition, a higher energy ratio in the higher FC fluctuation frequency band was observed in the senior group, which indicated the frequency interactions in the FC fluctuations. These results highly supported the basis of abnormality and compensation in the aging brain and might provide new insights into both aging and relevant compensatory mechanisms.

A posterior-to-anterior shift of brain functional dynamics in aging

Convergent evidence from task-based functional magnetic resonance imaging (fMRI) studies suggests a posterior-to-anterior shift as an adaptive compensatory scaffolding mechanism for aging. This study aimed to investigate whether brain functional dynamics at rest follow the same scaffolding mechanism for aging using a large Chinese sample aged from 22 to 79 years (n = 277). We defined a probability of brain regions being hubs over a period of time to characterize functional hub dynamic, and defined variability of the functional connectivity to characterize dynamic functional connectivity using resting-state fMRI. Our results revealed that both functional hub dynamics and dynamic functional connectivity posited an age-related posterior-to-anterior shift. Specifically, the posterior brain region showed attenuated dynamics, while the anterior brain regions showed augmented dynamics in aging. Interestingly, our analysis further indicated that the age-related episodic memory decline was associated with the age-related decrease in the brain functional dynamics of the posterior regions. Hence, these findings provided a new dimension to view the scaffolding mechanism for aging based on the brain functional dynamics.