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

Weaker connectivity in resting state networks is associated with disinhibited eating in older adults

Background/objectives

Obesity affects more than forty percent of adults over the age of sixty. Aberrant eating styles such as disinhibition have been associated with the engagement of brain networks underlying executive functioning, attentional control, and interoception. However, these effects have been exclusively studied in young samples overlooking those most at risk of obesity related harm.

Methods

Here we assessed associations between resting-state functional connectivity and disinhibited eating (using the Three Factor Eating Questionnaire) in twenty-one younger (aged 19–34 years, BMI range: 18–31) and twenty older (aged 60–73 years, BMI range: 19–32) adults matched for BMI. The Alternative Healthy Eating Index was used to quantify diet quality.

Results

Older, compared to younger, individuals reported lower levels of disinhibited eating, consumed a healthier diet, and had weaker connectivity in the frontoparietal (FPN) and default mode (DMN) networks. In addition, associations between functional connectivity and eating behaviour differed between the two age groups. In older adults, disinhibited eating was associated with weaker connectivity in the FPN and DMN––effects that were absent in the younger sample. Importantly, these effects could not be explained by differences in habitual diet.

Conclusions

These findings point to a change in interoceptive signalling as part of the ageing process, which may contribute to behavioural changes in energy intake, and highlight the importance of studying this under researched population.

Multivariate patterns of brain-behavior associations across the adult lifespan

The nature of brain-behavior covariations with increasing age is poorly understood. In the current study, we used a multivariate approach to investigate the covariation between behavioral-health variables and brain features across adulthood. We recruited healthy adults aged 20–73 years-old (29 younger, mean age = 25.6 years; 30 older, mean age = 62.5 years), and collected structural and functional MRI (s/fMRI) during a resting-state and three tasks. From the sMRI, we extracted cortical thickness and subcortical volumes; from the fMRI, we extracted activation peaks and functional network connectivity (FNC) for each task. We conducted canonical correlation analyses between behavioral-health variables and the sMRI, or the fMRI variables, across all participants. We found significant covariations for both types of neuroimaging phenotypes (ps = 0.0004) across all individuals, with cognitive capacity and age being the largest opposite contributors. We further identified different variables contributing to the models across phenotypes and age groups. Particularly, we found behavior was associated with different neuroimaging patterns between the younger and older groups. Higher cognitive capacity was supported by activation and FNC within the executive networks in the younger adults, while it was supported by the visual networks’ FNC in the older adults. This study highlights how the brain-behavior covariations vary across adulthood and provides further support that cognitive performance relies on regional recruitment that differs between older and younger individuals.

Age- and Sex-Related Topological Organization of Human Brain Functional Networks and Their Relationship to Cognition

Age and sex associated with changes in the functional brain network topology and cognition in large population of older adults have been poorly understood. We explored this question further by examining differences in 11 resting-state graph theory measures with respect to age, sex, and their relationships with cognitive performance in 17,127 United Kingdom Biobank participants (mean = 62.83 ± 7.41 years). Age was associated with an overall decrease in the effectiveness of network communication (i.e., integration) and loss of functional specialization (i.e., segregation) of specific brain regions. Sex differences were also observed, with women showing more efficient networks, which were less segregated than in men (FDR adjusted p < 0.05). The age-related changes were also more apparent in men than in women, which suggests that men may be more vulnerable to cognitive decline with age. Interestingly, while network segregation and strength of limbic network were only nominally associated with cognitive performance, the network measures collectively were significantly associated with cognition (FDR adjusted p ≤ 0.002). This may imply that individual measures may be inadequate to capture much of the variance in the neural activity or its output and need further refinement. The complexity of the organization of the functional brain may be shaped by the age and sex of an individual, which ultimately may influence the cognitive performance of older adults. Age and sex stratification may be used to inform clinical neuroscience research to identify older adults at risk of cognitive dysfunction.

Exploring attention in the bilingualism continuum: A resting-state functional connectivity study

This study explores the effects of bilingualism on the subcomponents of attention using resting state functional connectivity analysis (rsFC). Unlike previous studies, measures of bilingualism - L2 Age of Acquisition (AOA), L2 exposure, and L2 proficiency - were examined along a continuum to study attentional mechanisms using rsFC instead of dichotomizing them. 20 seed regions were pre-selected for the three subcomponents of attention. The results showed a positive association between behavioral performance and rsFC for the seeds in alerting and orienting network; this was not true for the seeds in the executive control network. Secondly, rsFC for attention networks varied with different levels of bilingualism. The objective measures of bilingualism modulate all three attention networks. While the subjective measures such as L2 AOA modulates specific attention network. Thus, language performance in contrast to self-reported information, as a measure of bilingualism, has a greater potential to tap into the role of bilingualism in attentional processes.

Longitudinal Changes in Resting-State Functional Connectivity and Gray Matter Volume Are Associated with Conversion to Hearing Impairment in Older Adults

BACKGROUND

Hearing loss was recently identified as a modifiable risk factor for dementia although the potential mechanisms explaining this relationship are unknown.

OBJECTIVE

The current study examined longitudinal change in resting-state fMRI functional connectivity and gray matter volume in individuals who developed a hearing impairment compared to those whose hearing remained normal.

METHODS

This study included 440 participants from the UK Biobank: 163 who had normal hearing at baseline and impaired hearing at follow-up (i.e., converters, mean age = 63.11±6.33, 53% female) and 277 who had normal hearing at baseline and maintained normal hearing at follow-up (i.e., non-converters, age = 63.31±5.50, 50% female). Functional connectivity was computed between a priori selected auditory seed regions (left and right Heschl's gyrus and cytoarchitectonic subregions Te1.0, Te1.1, and Te1.2) and select higher-order cognitive brain networks. Gray matter volume within these same regions was also obtained.

RESULTS

Converters had increased connectivity from left Heschl's gyrus to left anterior insula and from right Heschl's gyrus to right anterior insula, and decreased connectivity between right Heschl's gyrus and right hippocampus, compared to non-converters. Converters also had reduced gray matter volume in left hippocampus and left lateral visual cortex compared to non-converters.

CONCLUSION

These findings suggest that conversion to a hearing impairment is associated with altered brain functional connectivity and gray matter volume in the attention, memory, and visual processing regions that were examined in this study.

Along-tract analysis of the white matter is more informative about brain ageing, compared to whole-tract analysis

Diffusion Tensor Imaging (DTI) enabled the investigation of brain White Matter (WM), both qualitatively to study the macrostructure, and quantitatively to study the microstructure. The quantitative analyses are mostly performed at the whole-tract level, i.e., providing one measure of interest per tract; however, along-tract approaches may provide finer details of the quality of the WM tracts. In this study, using the DWI data collected from 40 young and 40 old individuals, we compared the DTI measures of FA, MD, AD, and RD, estimated by both whole-tract and along-tract approaches in 18 WM bundles, between the two groups. The results of the whole-tract quantitative analysis showed a statistically significant (p-FWER < 0.05) difference between the old and young groups in 6 tracts for FA, 8 tracts for MD, 1 tract for AD, and 7 tracts for RD. On the contrary, the along-tract approach showed differences between the two groups in 10 tracts for FA, 14 tracts for MD, 8 tracts for AD, and 11 tracts for RD. All the differences between the along-tract measures of the two groups had a large effect size (Cohen'd > 0.80). This study showed that the along-tract approach for the analysis of brain WM reveals changes in some WM tracts which had not shown any changes in the whole-tract approach, and therefore this finding emphasizes the utilization of the along-tract approach along with the whole-tract method for a more accurate study of the brain WM.

Attention network modulation via tRNS correlates with attention gain

Transcranial random noise stimulation (tRNS) can enhance vision in the healthy and diseased brain. Yet, the impact of multi-day tRNS on large-scale cortical networks is still unknown. We investigated the impact of tRNS coupled with behavioral training on resting-state functional connectivity and attention. We trained human subjects for 4 consecutive days on two attention tasks, while receiving tRNS over the intraparietal sulci, the middle temporal areas, or Sham stimulation. We measured resting-state functional connectivity of nodes of the dorsal and ventral attention network (DVAN) before and after training. We found a strong behavioral improvement and increased connectivity within the DVAN after parietal stimulation only. Crucially, behavioral improvement positively correlated with connectivity measures. We conclude changes in connectivity are a marker for the enduring effect of tRNS upon behavior. Our results suggest that tRNS has strong potential to augment cognitive capacity in healthy individuals and promote recovery in the neurological population.

Unveiling whole-brain dynamics in normal aging through Hidden Markov Models

During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs‐fMRI) data showing a link between aging and the increase of between‐networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data‐driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs‐fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph‐based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process.

Prediction of Deep Brain Stimulation Outcome in Parkinson's Disease With Connectome Based on Hemispheric Asymmetry

Parkinson’s disease (PD) is a neurodegenerative disease that is associated with motor and non-motor symptoms and caused by lack of dopamine in the substantia nigra of the brain. Subthalamic nucleus deep brain stimulation (STN-DBS) is a widely accepted therapy of PD that mainly inserts electrodes into both sides of the brain. The effect of STN-DBS was mainly for motor function, so this study focused on the recovery of motor function for PD after DBS. Hemispherical asymmetry in the brain network is considered to be a potential indicator for diagnosing PD patients. This study investigated the value of hemispheric brain connection asymmetry in predicting the DBS surgery outcome in PD patients. Four types of brain connections, including left intra-hemispheric (LH) connection, right intra-hemispheric (RH) connection, inter-hemispheric homotopic (Ho) connection, and inter-hemispheric heterotopic (He) connection, were constructed based on the resting state functional magnetic resonance imaging (rs-fMRI) performed before the DBS surgery. We used random forest for selecting features and the Ridge model for predicting surgical outcome (i.e., improvement rate of motor function). The functional connectivity analysis showed that the brain has a right laterality: the RH networks has the best correlation (r = 0.37, p = 5.68E-03) between the predicted value and the true value among the above four connections. Moreover, the region-of-interest (ROI) analysis indicated that the medioventral occipital cortex (MVOcC)–superior temporal gyrus (STG) and thalamus (Tha)–precentral gyrus (PrG) contributed most to the outcome prediction model for DBS without medication. This result provides more support for PD patients to evaluate DBS before surgery.

Long-term prognosis and educational determinants of brain network decline in older adult individuals

Older adults with lower education are at greater risk for dementia. It is unclear which brain changes lead to these outcomes. Longitudinal imaging-based measures of brain structure and function were examined in adult individuals (baseline age, 45-86 years; two to five visits per participant over 1-9 years). College degree completion differentiates individual-based and neighborhood-based measures of socioeconomic status and disadvantage. Older adults (~65 years and over) without a college degree exhibit a pattern of declining large-scale functional brain network organization (resting-state system segregation) that is less evident in their college-educated peers. Declining brain system segregation predicts impending changes in dementia severity, measured up to 10 years past the last scan date. The prognostic value of brain network change is independent of Alzheimer's disease (AD)-related genetic risk (APOE status), the presence of AD-associated pathology (cerebrospinal fluid phosphorylated tau, cortical amyloid) and cortical thinning. These results demonstrate that the trajectory of an individual's brain network organization varies in relation to their educational attainment and, more broadly, is a unique indicator of individual brain health during older age.

Macroautophagy and normal aging of the nervous system: Lessons from animal models

Aging represents a cumulative form of cellular stress, which is thought to challenge many aspects of proteostasis. The non-dividing, long-lived neurons are particularly vulnerable to stress, and, not surprisingly, even normal aging is highly associated with a decline in brain function in humans, as well as in other animals. Macroautophagy is a fundamental arm of the proteostasis network, safeguarding proper protein turnover during different cellular states and against diverse cellular stressors. An intricate interplay between macroautophagy and aging is beginning to unravel, with the emergence of new tools, including those for monitoring autophagy in cultured neurons and in the nervous system of different organisms in vivo. Here, we review recent findings on the impact of aging on neuronal integrity and on neuronal macroautophagy, as they emerge from studies in invertebrate and mammalian models.

Regression-Based Normative Data for Independent and Cognitively Active Spanish Older Adults: Digit Span, Letters and Numbers, Trail Making Test and Symbol Digit Modalities Test

In this work, we developed normative data for the neuropsychological assessment of independent and cognitively active Spanish older adults over 55 years of age.

METHOD

Regression-based normative data were calculated from a sample of 103 non-depressed independent community-dwelling adults aged 55 or older (67% women). Raw data for Digit Span (DS), Letters and Numbers (LN), the Trail Making Test (TMT), and the Symbol Digit Modalities Test (SDMT) were regressed on age, sex, and education. The model predicting TMT-B scores also included TMT-A scores. Z-scores for the discrepancy between observed and predicted scores were used to identify low scores. The base rate of low scores for SABIEX normative data was compared to the base rate of low scores using published normative data obtained from the general population.

RESULTS

The effects of age, sex, and education varied across neuropsychological measures. Although the proportion of low scores was similar between normative datasets, there was no agreement in the identification of cognitively impaired individuals.

CONCLUSIONS

Normative data obtained from the general population might not be sensitive to identify low scores in cognitively active older adults, incorrectly classifying them as cognitively normal compared to the less-active population. We provide a friendly calculator for use in neuropsychological assessment in cognitively active Spanish people aged 55 or older.

Functional network topology in drug resistant and well-controlled idiopathic generalized epilepsy: a resting state functional MRI study

Despite an increasing number of drug treatment options for people with idiopathic generalized epilepsy (IGE), drug resistance remains a significant issue and the mechanisms underlying it remain poorly understood. Previous studies have largely focused on potential cellular or genetic explanations for drug resistance. However, epilepsy is understood to be a network disorder and there is a growing body of literature suggesting altered topology of large-scale resting networks in people with epilepsy compared with controls. We hypothesize that network alterations may also play a role in seizure control. The aim of this study was to compare resting state functional network structure between well-controlled IGE (WC-IGE), drug resistant IGE (DR-IGE) and healthy controls. Thirty-three participants with IGE (10 with WC-IGE and 23 with DR-IGE) and 34 controls were included. Resting state functional MRI networks were constructed using the Functional Connectivity Toolbox (CONN). Global graph theoretic network measures of average node strength (an equivalent measure to mean degree in a network that is fully connected), node strength distribution variance, characteristic path length, average clustering coefficient, small-world index and average betweenness centrality were computed. Graphs were constructed separately for positively weighted connections and for absolute values. Individual nodal values of strength and betweenness centrality were also measured and ‘hub nodes’ were compared between groups. Outcome measures were assessed across the three groups and between both groups with IGE and controls. The IGE group as a whole had a higher average node strength, characteristic path length and average betweenness centrality. There were no clear differences between groups according to seizure control. Outcome metrics were sensitive to whether negatively correlated connections were included in network construction. There were no clear differences in the location of ‘hub nodes’ between groups. The results suggest that, irrespective of seizure control, IGE interictal network topology is more regular and has a higher global connectivity compared to controls, with no alteration in hub node locations. These alterations may produce a resting state network that is more vulnerable to transitioning to the seizure state. It is possible that the lack of apparent influence of seizure control on network topology is limited by challenges in classifying drug response. It is also demonstrated that network topological features are influenced by the sign of connectivity weights and therefore future methodological work is warranted to account for anticorrelations in graph theoretic studies.

Oligodendrocytes in the aging brain

More than half of the human brain volume is made up of white matter: regions where axons are coated in myelin, which primarily functions to increase the conduction speed of axon potentials. White matter volume significantly decreases with age, correlating with cognitive decline. Much research in the field of non-pathological brain aging mechanisms has taken a neuron-centric approach, with relatively little attention paid to other neural cells. This review discusses white matter changes, with focus on oligodendrocyte lineage cells and their ability to produce and maintain myelin to support normal brain homoeostasis. Improved understanding of intrinsic cellular changes, general senescence mechanisms, intercellular interactions and alterations in extracellular environment which occur with aging and impact oligodendrocyte cells is paramount. This may lead to strategies to support oligodendrocytes in aging, for example by supporting myelin synthesis, protecting against oxidative stress and promoting the rejuvenation of the intrinsic regenerative potential of progenitor cells. Ultimately, this will enable the protection of white matter integrity thus protecting cognitive function into the later years of life.

Feasibility of Contrasting Brain Connectivity Patterns in Cognitive and Motor Cerebral Networks to Clinical Outcomes in Patients Surviving Acute Respiratory Failure: A Pilot Study

BACKGROUND

There is a paucity of research regarding the feasibility and association of cerebral cortex function to patient outcomes after acute respiratory failure (ARF).

PURPOSE

To determine the feasibility of functional connectivity measures and examine the association of functional connectivity to a multifaceted battery of outcomes in survivors of ARF.

METHODS

Eight ARF patients (age:58±3.7, ICU days:10.4±8.6) completed functional magnetic resonance imaging (fMRI), cognitive, physical-function, anxiety, depression, and driving simulator tests at one month post-hospital discharge. Pearson's correlations assessed the relationship between functional connectivity within the default mode network (FPN), sensorimotor network (SMN), and frontoparietal network (FPN) to outcomes.

RESULTS

Low physical-function (r=0.75, p=0.03) and divided-attention (r=-0.86, p=0.03) during the driving simulator task correlated with low FPN connectivity. Low SMN connectivity demonstrated relationships to slower gait speed (r=0.82, p=0.01) and low short physical performance battery (SPPB) scores (r=0.81, p=0.01).

CONCLUSIONS

fMRI is feasible to assess ARF patients' post-ICU limitations, as low post-ARF brain connectivity may be linked to low physical function, providing potential development of therapeutic interventions.

Functional connectivity within an anxiety network and associations with anxiety symptom severity in middle-aged adults with and without autism

Anxiety is highly prevalent in autism spectrum disorders (ASDs). However, few functional magnetic resonance imaging (fMRI) studies of ASDs have focused on anxiety (and fewer still on anxiety in middle-aged adults). Thus, relationships between atypical connectivity and anxiety in this population are poorly understood. The current study contrasted functional connectivity within anxiety network regions across adults (40-64 years) with and without autism, and tested for group by functional connectivity interactions on anxiety. Twenty-two adults with ASDs (16 males) and 26 typical control (TC) adults (22 males) completed the Beck Anxiety Inventory and a resting-state fMRI scan. An anxiety network consisting of 12 regions of interest was defined, based on a meta-analysis in TC individuals and two studies on anxiety in ASDs. We tested for main effects of group and group by anxiety interactions on connectivity within this anxiety network, controlling for head motion using ANCOVA. Results are reported at an FDR adjusted threshold of q < 0.1 (corrected) and p < 0.05 (uncorrected). Adults with ASDs showed higher anxiety and underconnectivity within the anxiety network, mostly involving bilateral insula. Connectivity within the anxiety network in the ASD group showed distinct relationships with anxiety symptoms that did not relate to ASD symptom severity. Functional connectivity involving the bilateral posterior insula was positively correlated with anxiety in the ASD (but not the TC) group. Increased anxiety in middle-aged adults with ASD is associated with atypical functional connectivity, predominantly involving bilateral insula. Results were not related to ASD symptom severity suggesting independence of anxiety-related effects. LAY SUMMARY: Anxiety is very common in adults with autism but the brain basis of this difference is not well understood. We compared functional connectivity between anxiety-related brain regions in middle-aged adults with and without autism. Adults with autism were more anxious and showed weaker functional connections between these regions. Some relationships between functional connectivity and higher anxiety were specific to the autism group. Results suggest that anxiety functions differently in autism.

Association of Lifestyle Activities with Functional Brain Connectivity and Relationship to Cognitive Decline among Older Adults

This study examines the relationship of engagement in different lifestyle activities to connectivity in large-scale functional brain networks, and whether network connectivity modifies cognitive decline, independent of brain amyloid levels. Participants (N = 153, mean age = 69 years, including N = 126 with amyloid imaging) were cognitively normal when they completed resting-state functional magnetic resonance imaging, a lifestyle activity questionnaire, and cognitive testing. They were followed with annual cognitive tests up to 5 years (mean = 3.3 years). Linear regressions showed positive relationships between cognitive activity engagement and connectivity within the dorsal attention network, and between physical activity levels and connectivity within the default-mode, limbic, and frontoparietal control networks, and global within-network connectivity. Additionally, higher cognitive and physical activity levels were independently associated with higher network modularity, a measure of functional network specialization. These associations were largely independent of APOE4 genotype, amyloid burden, global brain atrophy, vascular risk, and level of cognitive reserve. Moreover, higher connectivity in the dorsal attention, default-mode, and limbic networks, and greater global connectivity and modularity were associated with reduced cognitive decline, independent of APOE4 genotype and amyloid burden. These findings suggest that changes in functional brain connectivity may be one mechanism by which lifestyle activity engagement reduces cognitive decline.

Segregation of functional networks is associated with cognitive resilience in Alzheimer's disease

Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer's disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal ageing, we tested the hypothesis that higher segregation of the brain's connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer's disease. Using resting-state functional MRI, we assessed both resting-state functional MRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: (i) 108 individuals with autosomal dominantly inherited Alzheimer's disease and 71 non-carrier controls; and (ii) 156 amyloid-PET-positive subjects across the spectrum of sporadic Alzheimer's disease and 184 amyloid-negative controls. In the autosomal dominant Alzheimer's disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer's sample, disease stage was assessed by temporal lobe tau-PET (i.e. composite across Braak stage I and III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer's disease, we found higher functional MRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (P = 0.007). Similarly, for patients with sporadic Alzheimer's disease, higher functional MRI-assessed system segregation was associated with less decrement in global cognition (P = 0.001) and episodic memory (P = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer's disease.

Resting-state networks in the course of aging-differential insights from studies across the lifespan vs. amongst the old

Resting-state functional connectivity (RSFC) has widely been used to examine reorganization of functional brain networks during normal aging. The extraction of generalizable age trends, however, is hampered by differences in methodological approaches, study designs and sample characteristics. Distinct age ranges of study samples thereby represent an important aspect between studies especially due to the increase in inter-individual variability over the lifespan. The current review focuses on comparing age-related differences in RSFC in the course of the whole adult lifespan versus later decades of life. We summarize and compare studies assessing age-related differences in within- and between-network RSFC of major resting-state brain networks. Differential effects of the factor age on resting-state networks can be identified when comparing studies focusing on younger versus older adults with studies investigating effects within the older adult population. These differential effects pertain to higher order and primary processing resting-state networks to a varying extent. Especially during later decades of life, other factors beyond age might come into play to understand the high inter-individual variability in RSFC.

The intensity and connectivity of spontaneous brain activity in a language network relate to aging and language

Neuroimaging studies often either look at functional activation in response to an explicit task, or functional connectivity (i.e., interregional correlations) during resting-state. Few studies have looked at the intensity of brain activity or its relationship with age, behavior, and language. The current study investigated both intensity (i.e., the Amplitude of Low-Frequency Fluctuations, ALFF) and the functional connectivity of spontaneous brain activity during rest and their relationship with age and language. A life-span sample of individuals (N = 152) completed a battery of neuropsychological tests to assess basic cognitive functions and resting-state functional MRI data to assess spontaneous brain activity. Focusing on an extend language network, the mean ALFF and total degree were calculated for this network. We found that increased age was associated with more intense activity (i.e., higher ALFF) but lower within-network connectivity. Additionally, these increases in activity within the language network during resting-state were related to worse language ability, particularly in younger adults, supporting a dedifferentiation account of cognition. Our results support the utility of using resting-state data as an indicator of cognition and support the role of ALFF as a potential biomarker in characterizing the relationships between resting-state brain activity, age, and cognition.

Atlas55+: Brain Functional Atlas of Resting-State Networks for Late Adulthood

Currently, several human brain functional atlases are used to define the spatial constituents of the resting-state networks (RSNs). However, the only brain atlases available are derived from samples of young adults. As brain networks are continuously reconfigured throughout life, the lack of brain atlases derived from older populations may influence RSN results in late adulthood. To address this gap, the aim of the study was to construct a reliable brain atlas derived only from older participants. We leveraged resting-state functional magnetic resonance imaging data from three cohorts of healthy older adults (total N = 563; age = 55-95 years) and a younger-adult cohort (N = 128; age = 18-35 years). We identified the major RSNs and their subdivisions across all older-adult cohorts. We demonstrated high spatial reproducibility of these RSNs with an average spatial overlap of 67%. Importantly, the RSNs derived from the older-adult cohorts were spatially different from those derived from the younger-adult cohort (P = 2.3 × 10-3). Lastly, we constructed a novel brain atlas, called Atlas55+, which includes the consensus of the major RSNs and their subdivisions across the older-adult cohorts. Thus, Atlas55+ provides a reliable age-appropriate template for RSNs in late adulthood and is publicly available. Our results confirm the need for age-appropriate functional atlases for studies investigating aging-related brain mechanisms.

Vascular risk factors are associated with a decline in resting-state functional connectivity in cognitively unimpaired individuals at risk for Alzheimer's disease: Vascular risk factors and functional connectivity changes

Resting-state functional connectivity is suggested to be cross-sectionally associated with both vascular burden and Alzheimer's disease (AD) pathology. However, evidence is lacking regarding longitudinal changes in functional connectivity. This study includes 247 cognitively unimpaired individuals with a family history of sporadic AD (185 women/ 62 men; mean [SD] age of 63 [5.3] years). Plasma total-, HDL-, and LDL-cholesterol and systolic and diastolic blood pressure were measured at baseline. Global (whole-brain) brain functional connectivity and connectivity from canonical functional networks were computed from resting-state functional MRI obtained at baseline and ~3.5 years of annual follow-ups, using a predefined functional parcellation. A subsample underwent Aβ- and tau-PET (n=91). Linear mixed-effects models demonstrated that global functional connectivity increased over time across the entire sample. In contrast, higher total-cholesterol and LDL-cholesterol levels were associated with greater reduction of functional connectivity in the default-mode network over time. In addition, higher diastolic blood pressure was associated with global functional connectivity reduction. The associations were similar when the analyses were repeated using two other functional brain parcellations. Aβ and tau deposition in the brain were not associated with changes in functional connectivity over time in the subsample. These findings provide evidence that vascular burden is associated with a decrease in functional connectivity over time in older adults with elevated risk for AD. Future studies are needed to determine if the impact of vascular risk factors on functional brain changes precede the impact of AD pathology on functional brain changes.

Resting-state functional brain connectivity predicts cognitive performance: An exploratory study on a time-based prospective memory task

Resting-state functional brain connectivity (rsFC) is in wide use for the investigation of a variety of cognitive neuroscience phenomena. In the first phase of this study, we explored the changes in EEG-reconstructed rsFC in young vs. older adults, in the both the open-eyes (OE) and the closed-eyes (CE) conditions. The results showed significant differences in several rsFC network metrics in the two age groups, confirming and detailing established knowledge that aging modulates brain functional organisation. In the study's second phase we investigated the role of rsFC architecture on cognitive performance through a time-based Prospective Memory task involving participants who monitored the passage of time to perform a specific action at an appropriate time in the future. Regression models revealed that the monitoring strategy (i.e. the number of clock checks) can be predicted by rsFC graph metric, specifically, eccentricity and betweenness in the OE condition, and assortativity in the CE condition. These results show for the first time how metrics qualifying functional brain connectivity at rest can account for the differences in the way individuals strategically handle cognitive loads in the Prospective Memory domain.

Task-based functional connectivity in aging: How task and connectivity methodology affect discovery of age effects

INTRODUCTION

Past studies have found that healthy aging has a significant effect on the organization and function of networks in the human brain. Many of these studies have examined how functional connectivity during one task or at rest is affected by aging; however, few studies have systematically examined how the effect of age on functional connectivity may vary as a function of choice of in-scanner task.

METHODS

The present study included healthy adults between the ages of 20 and 80 and examined a variety of metrics of functional connectivity during performance of 11 in-scanner tasks, falling into 4 cognitive domains: vocabulary, processing speed, fluid reasoning, and episodic memory. Functional connectivity was assessed at three levels: average correlations within and between 10 networks, system segregation (sensorimotor vs. association networks), and whole-brain graph theory metrics (global efficiency and modularity).

RESULTS

Results showed that the effect of age on these metrics differed as a function of task-for example, age had a more consistent effect on functional connectivity metrics computed during fluid reasoning tasks; however, there was less of an effect of age on functional connectivity metrics computed during tasks of episodic memory. Further, some of these measures showed relationships with behavioral performance on the in-scanner task, with different networks playing a role in the different cognitive domains.

CONCLUSION

These findings suggest that while aging may be generally associated with reductions in within- and between-network connectivity, system segregation, global efficiency, and modularity, the magnitude and presence of these effects varies by in-scanner task.

Age-related differences in resting-state and task-based network characteristics and cognition: a lifespan sample

Aging is often associated with cognitive and neural decline, but how these factors interact is still not fully understood. Recently, functional connectivity, or the degree to which brain regions are concurrently active, has provided insight into age-related differences. However, functional connectivities during task and rest differ and few studies have examined how these relate to a broad range of cognitive functions. The present study investigated the effect of age on cognition, whole-brain functional connectivity during resting-state and task, and their relationships across the adult lifespan. Cognition was broadly assessed using a battery of cognitive assessments and mean network characteristics were calculated across the whole brain. Behaviorally, increased age was associated with worse recall, executive function, and verbal working memory abilities but better language performance. Neurally, increased age was associated with lower overall within- and between-network functional connectivities during both rest and task, and these age-connectivity relationships were stronger during task performance. Connectivity was also related to cognition, and for all participants, these relationships were strongest during rest. Specifically, higher resting-state between-network functional connectivity was associated with poorer cognition for all adults and poorer language ability among older adults. Collectively, these findings demonstrate that while age effects were strongest during the task, resting-state functional connectivity was most closely tied to cognition. Moreover, these results are theoretically consistent with dedifferentiation accounts of cognition and aging and show that less differentiated functional connectivities are associated with cognitive costs for both older and younger adults.

Connectome-based models can predict processing speed in older adults

Decrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the fast-PS and slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process.

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.

A Longitudinal Study of Changes in Resting-State Functional Magnetic Resonance Imaging Functional Connectivity Networks During Healthy Aging

Background: Vast increases in life expectancy over the last century have led to shifts in population demographics and the emergence of a largely aged population, globally. This has led to a need to understand neurobiological changes associated with healthy aging. Studies on age-related changes in functional connectivity networks have largely been cross-sectional and focused on the default mode network (DMN). The current study investigated longitudinal changes in functional connectivity in multiple resting-state networks over 4 years of aging in cognitively normal older adults. Methods: Resting-state functional magnetic resonance imaging scans from older adults (n = 16) who maintained "cognitive normal" status over 4 years were retrieved at baseline and follow-up from the Alzheimer's Disease Neuroimaging Initiative database. A seed-based approach was executed in Functional MRI of the Brain Software Library (FSL) to examine significant changes in functional connectivity within the DMN, frontoparietal network (FPN), and salience network (SN) within subjects over time. Results: Results indicated significantly (p < 0.05, corrected) reduced functional connectivity in the FPN and SN, but not in the DMN at year 4 compared with baseline in older adults who were cognitively stable. Conclusions: The current study highlights the importance of a longitudinal approach for understanding changes in functional connectivity. The findings also underscore the need to examine multiple networks within the same participants, given that changes were apparent in the FPN and SN but not in the DMN. Future studies should also examine changes in internetwork connectivity as well as shifts in structural connectivity over time. Impact statement Investigations of age-related changes in functional connectivity have largely been cross-sectional and focused on the default mode network (DMN). The current study examined the DMN as well as the frontoparietal network (FN) and salience network (SN), in a group of healthy aging adults over four years. The results revealed decreased functional connectivity over time, in the FN and SN, but not the DMN. These findings provide insights about the healthy aging brain. They also underscore the need to broaden the scope of functional connectivity analyses beyond the DMN and highlight the use of longitudinal methods.

Functional brain connectivity changes across the human life span: From fetal development to old age

The dynamic of the temporal correlations between brain areas, called functional connectivity (FC), undergoes complex transformations through the life span. In this review, we aim to provide an overview of these changes in the nonpathological brain from fetal life to advanced age. After a brief description of the main methods, we propose that FC development can be divided into four main phases: first, before birth, a strong change in FC leads to the emergence of functional proto-networks, involving mainly within network short-range connections. Then, during the first years of life, there is a strong widespread organization of networks which starts with segregation processes followed by a continuous increase in integration. Thereafter, from adolescence to early adulthood, a refinement of existing networks in the brain occurs, characterized by an increase in integrative processes until about 40 years. Middle age constitutes a pivotal period associated with an inversion of the functional brain trajectories with a decrease in segregation process in conjunction to a large-scale reorganization of between network connections. Studies suggest that these processes are in line with the development of cognitive and sensory functions throughout life as well as their deterioration. During aging, results support the notion of dedifferentiation processes, which refer to the decrease in functional selectivity of the brain regions, resulting in more diffuse and less specialized FC, associated with the disruption of cognitive functions with age. The inversion of developmental processes during aging is in accordance with the developmental models of neuroanatomy for which the latest matured regions are the first to deteriorate.

A multidimensional and multi-feature framework for cardiac interoception

Interoception (the sensing of inner-body signals) is a multi-faceted construct with major relevance for basic and clinical neuroscience research. However, the neurocognitive signatures of this domain (cutting across behavioral, electrophysiological, and fMRI connectivity levels) are rarely reported in convergent or systematic fashion. Additionally, various controversies in the field might reflect the caveats of standard interoceptive accuracy (IA) indexes, mainly based on heartbeat detection (HBD) tasks. Here we profit from a novel IA index (md) to provide a convergent multidimensional and multi-feature approach to cardiac interoception. We found that outcomes from our IA-md index are associated with -and predicted by- canonical markers of interoception, including the hd-EEG-derived heart-evoked potential (HEP), fMRI functional connectivity within interoceptive hubs (insular, somatosensory, and frontal networks), and socio-emotional skills. Importantly, these associations proved more robust than those involving current IA indexes. Furthermore, this pattern of results persisted when taking into consideration confounding variables (gender, age, years of education, and executive functioning). This work has relevant theoretical and clinical implications concerning the characterization of cardiac interoception and its assessment in heterogeneous samples, such as those composed of neuropsychiatric patients.

Age-related neural dedifferentiation and cognition

This review focuses on possible contributions of neural dedifferentiation to age-related cognitive decline. Neural dedifferentiation is held to reflect a breakdown in the functional specificity of brain regions and networks that compromises the fidelity of neural representations supporting episodic memory and related cognitive functions. The evidence for age-related dedifferentiation is robust when it is operationalized as neural selectivity for different categories of perceptual stimuli or as decreased segregation or modularity of resting-state functional brain networks. Neural dedifferentiation for perceptual categories appears to demonstrate a negative, age-invariant relationship with performance on tests of memory and fluid processing. Whether this pattern extends to network-level measures of dedifferentiation cannot currently be determined due to insufficient evidence. The existing data highlight the importance of further examination of neural dedifferentiation as a factor contributing to episodic memory and to cognitive performance more generally.

Associations between personality and whole-brain functional connectivity at rest: Evidence across the adult lifespan

INTRODUCTION

Personality is associated with cognitive, emotional, and social functioning, and can play a role in age-related cognitive decline and dementia risk; however, little is known about the brain dynamics underlying personality characteristics, and whether they are moderated by age.

METHODS

We investigated the associations between personality and resting-state functional MRI data from 365 individuals across the adult lifespan (20-80 years). Participants completed the 50-item International Personality Item Pool and a resting-state imaging protocol on a 3T MRI scanner. Within-network connectivity values were computed based on predefined networks. Regression analyzes were conducted in order to investigate personality-connectivity associations, as well as moderation by age. All models controlled for potential confounders (such as age, sex, education, IQ, and the other personality traits).

RESULTS

We found that openness was positively associated with connectivity in the default-mode network, neuroticism was negatively associated with both the ventral and dorsal attention networks, and agreeableness was negatively associated with the dorsal attention network. In addition, age moderated the association between conscientiousness and the frontoparietal network, indicating that this association become stronger in older age.

CONCLUSIONS

Our findings demonstrate that personality is associated with brain connectivity, which may contribute to identifying personality profiles that play a role in protection against or risk for age-related brain changes and dementia.