Reduced default mode network effective connectivity in healthy aging is modulated by years of education.

Aging is a major risk factor for neurodegenerative diseases like dementia and Alzheimer's disease. Even in non-pathological aging, decline in cognitive functioning is observed in the majority of the elderly population, necessitating the importance of studying the processes involved in healthy aging in order to identify brain biomarkers that promote the conservation of functioning. The default mode network (DMN) has been of special interest to aging research due to its vulnerability to atrophy and functional decline over the course of aging. Prior work has focused almost exclusively on functional (i.e. undirected) connectivity, yet converging findings are scarce. Therefore, we set out to use spectral dynamic causal modeling to investigate changes in the effective (i.e. directed) connectivity within the DMN and to discover changes in information flow in a sample of cognitively normal adults spanning from 48 to 89 years (n = 63). Age was associated to reduced verbal memory performance. Modeling of effective connectivity revealed a pattern of age-related downregulation of posterior DMN regions driven by inhibitory connections from the hippocampus and middle temporal gyrus. Additionally, there was an observed decline in the hippocampus' susceptibility to network inputs with age, effectively disconnecting itself from other regions. The estimated effective connectivity parameters were robust and able to predict the age in out of sample estimates in a leave-one-out cross-validation. Attained education moderated the effects of aging, largely reversing the observed pattern of inhibitory connectivity. Thus, medial prefrontal cortex, hippocampus and posterior DMN regions formed an excitatory cycle of extrinsic connections related to the interaction of age and education. This suggests a compensatory role of years of education in effective connectivity, stressing a possible target for interventions. Our findings suggest a connection to the concept of cognitive reserve, which attributes a protective effect of educational level on cognitive decline in aging (Stern, 2009).

The Effect of Second-Generation Antidepressant Treatment on the Memory of Patients With Major Depressive Disorder: A Meta-analysis Study With Structural Equation Models.

BACKGROUND: Major depressive disorder (MDD) has been linked to episodic memory deficits that may be improved after pharmacological treatment, but it is unclear whether there is a class of antidepressants that is more effective than others to ameliorate these deficits in MDD. In addition, the possible effects of clinical and sociodemographic variables on the improvement of MDD memory deficits after pharmacological treatment are currently unknown. Our aims are to study the possible neuropsychological effects of second-generation antidepressant classes on the episodic memory of MDD patients and to study the potential effects of clinical and demographic variables as moderators of the effects of antidepressants on the memory of depressed patients through a meta-analysis approach. PROCEDURES: Nine articles were included in our study. A structural equation model meta-analysis was performed. RESULTS: Our results suggest that selective serotonin reuptake inhibitors and serotonine-noradrenaline reuptake inhibitors would bring about a substantial improvement in the memory of depressed patients, whereas other antidepressant classes would cause rather modest effects. Our results also suggest that clinical and demographic variables play a very important role as mediators of memory improvement after MDD treatment. Thus, a relatively low level of symptom severity, a high degree of clinical improvement, a younger age, and more years of education were positively related to memory improvement after MDD treatment. CONCLUSIONS: Although antidepressant class is an important variable linked to memory improvement in MDD, overall, the degree of memory amelioration in depression is very closely related to clinical and demographic variables of patients with depression.

Overvaluation of weight and shape in obesity: a comparative study between people with and without binge eating disorder.

Introduction: The overvaluation of weight and shape is a diagnostic criterion in eating disorders, except in binge eating disorder (BED), where it has received less attention. This aspect is also not usually analyzed in people with overweight or obesity without an eating disorder. This research aims to identify the indicators of symptomatology, as well as those of self-construction and cognitive structure, that are associated with overvaluation in obesity, either alone or in conjunction with BED. Method: A sample of 102 overweight or obese participants was accessed. The sample was divided into four groups: one without overvaluation or BED (n = 33); a second with overvaluation and without BED (n = 21); a third with BED, but without overvaluation (n = 15), and a fourth with BED and overvaluation (n = 33). The groups completed instruments regarding eating symptomatology, anxiety, depression, and stress. In addition, they were administered the Repertory Grid Technique, a semi-structured interview to evaluate the cognitive structure involved in the construal of the self and others. Results: The factors of overvaluation and the presence of BED independently explained eating symptomatology, and the latter also showed a tendency to influence anxiety, depression, and stress. In terms of cognitive structure, weight polarization was explained by overvaluation, while BED was associated with a high presence of cognitive conflicts. In self-construction, BED was the factor that explained the differences, particularly in Self-Ideal discrepancy. Discussion: The results highlight the importance of overvaluation in obesity, even in the absence of BED. Its evaluation and treatment are recommended. Furthermore, in the case of BED, it is also advisable to evaluate the overvaluation of weight and shape since it can be a severity specifier.

Hypoconnectivity networks in schizophrenia patients: A voxel-wise meta-analysis of Rs-fMRI.

In recent years several meta-analyses regarding resting-state functional connectivity in patients with schizophrenia have been published. The authors have used different data analysis techniques: regional homogeneity, seed-based data analysis, independent component analysis, and amplitude of low frequencies. Hence, we aim to perform a meta-analysis to identify connectivity networks with different activation patterns between people diagnosed with schizophrenia and healthy controls using voxel-wise analysis. METHOD: We collected primary studies exploring whole brain connectivity by functional magnetic resonance imaging at rest in patients with schizophrenia compared with healthy controls. We identified 25 studies included high-quality studies that included 1285 patients with schizophrenia and 1279 healthy controls. RESULTS: The results indicate hypoactivation in the right precentral gyrus and the left superior temporal gyrus of patients with schizophrenia compared with healthy controls. CONCLUSIONS: These regions have been linked with some clinical symptoms usually present in Plea with schizophrenia, such as auditory verbal hallucinations, formal thought disorder, and the comprehension and production of gestures.

Abnormal degree centrality and functional connectivity in Down syndrome: A resting-state fMRI study.

Background/Objective: Neuroimaging studies have shown brain abnormalities in Down syndrome (DS) but have not clarified the underlying mechanisms of dysfunction. Here, we investigated the degree centrality (DC) abnormalities found in the DS group compared with the control group, and we conducted seed-based functional connectivity (FC) with the significant clusters found in DC. Moreover, we used the significant clusters of DC and the seed-based FC to elucidate differences between brain networks in DS compared with controls. Method: The sample comprised 18 persons with DS (M = 28.67, SD = 4.18) and 18 controls (M = 28.56, SD = 4.26). Both samples underwent resting-state functional magnetic resonance imaging. Results: DC analysis showed increased DC in the DS in temporal and right frontal lobe, as well as in the left caudate and rectus and decreased DC in the DS in regions of the left frontal lobe. Regarding seed-based FC, DS showed increased and decreased FC. Significant differences were also found between networks using Yeo parcellations, showing both hyperconnectivity and hypoconnectivity between and within networks. Conclusions: DC, seed-based FC and brain networks seem altered in DS, finding hypo- and hyperconnectivity depending on the areas. Network analysis revealed between- and within-network differences, and these abnormalities shown in DS could be related to the characteristics of the population.

The effect of second-generation antidepressant treatment on the attention and mental processing speed of patients with major depressive disorder: A meta-analysis study with structural equation models.

Major depressive disorder (MDD) has been linked to attention and mental processing speed deficits that can be improved after pharmacological treatment. However, it is unclear whether a class of antidepressants is more effective than others to ameliorate these deficits in MDD. Additionally, the possible effects of clinical and demographic variables on improving MDD attention and processing speed deficits after antidepressant treatment are unknown. We aimed to study the possible neuropsychological effects of second-generation antidepressant classes on the attention and processing speed of MDD patients and the potential influences of clinical and demographic variables as moderators of these effects using a meta-analytic approach. Twenty-five papers were included in our study. A structural equation model meta-analysis was performed. The improvement of attention and processing speed after pharmacological treatment is clinically relevant but incomplete. Selective serotonin reuptake inhibitors (SSRIs) and dual inhibitors are the drugs causing the greatest improvement in the processing speed of MDD patients. Antidepressant class is an important variable linked to processing speed improvement after MDD treatment. However, the degree of improvement in both cognitive functions is strongly influenced by some clinical and demographic variables of depressed patients, such are age and education of the MDD patients, the duration of the antidepressant treatment, and the depression status of the patients.

Altered spontaneous brain activity in Down syndrome and its relation with cognitive outcome.

Although Down syndrome (DS) is the most common genetic cause of neurodevelopmental delay, few neuroimaging studies have explored this population. This investigation aimed to study whole-brain resting-state spontaneous brain activity using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) strategies to find differences in spontaneous brain activity among young people with DS and controls and to correlate these results with cognitive outcomes. The sample comprised 18 persons with DS (age mean = 28.67, standard deviation = 4.18) and 18 controls (age mean = 28.56, standard deviation = 4.26). fALFF and ReHo analyses were performed, and the results were correlated with other cognitive variables also collected (KBIT-2 and verbal fluency test). Increased activity was found in DS using fALFF in areas involving the frontal and temporal lobes and left cerebellum anterior lobe. Decreased activity in DS was found in the left parietal and occipital lobe, the left limbic lobe and the left cerebellum posterior lobe. ReHo analysis showed increased activity in certain DS areas of the left frontal lobe and left rectus, as well as the inferior temporal lobe. The areas with decreased activity in the DS participants were regions of the frontal lobe and the right limbic lobe. Altered fALFF and ReHo were found in the DS population, and this alteration could predict the cognitive abilities of the participants. To our knowledge, this is the first study to explore regional spontaneous brain activity in a population with DS. Moreover, this study suggests the possibility of using fALFF and ReHo as biomarkers of cognitive function, which is highly important given the difficulties in cognitively evaluating this population to assess dementia. More research is needed, however, to demonstrate its utility.

Spontaneous brain activity in healthy aging: An overview through fluctuations and regional homogeneity.

Introduction: This study aims to explore whole-brain resting-state spontaneous brain activity using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) strategies to find differences among age groups within a population ranging from middle age to older adults. Methods: The sample comprised 112 healthy persons (M = 68.80, SD = 7.99) aged 48-89 who were split into six age groups (< 60, 60-64, 65-69, 70-74, 75-79, and ≥ 80). Fractional amplitude of low-frequency fluctuation and ReHo analyses were performed and were compared among the six age groups, and the significant results commonly found across groups were correlated with the gray matter volume of the areas and the age variable. Results: Increased activity was found using fALFF in the superior temporal gyrus and inferior frontal gyrus when comparing the first group and the fifth. Regarding ReHo analysis, Group 6 showed increased ReHo in the temporal lobe (hippocampus), right and left precuneus, right caudate, and right and left thalamus depending on the age group. Moreover, significant correlations between age and fALFF and ReHo clusters, as well as with their gray matter volume were found, meaning that the higher the age, the higher the regional synchronization, the lower the fALFF activation, and the lower gray matter of the right thalamus. Conclusion: Both techniques have been shown to be valuable and usable tools for disentangling brain changes in activation in a very low interval of years in healthy aging.

Abnormalities in the default mode network in late-life depression: A study of resting-state fMRI.

Background/Objective: Neuroimaging studies have reported abnormalities in the examination of functional connectivity in late-life depression (LLD) in the default mode network (DMN). The present study aims to study resting-state functional connectivity within the DMN in people diagnosed with late-life major depressive disorder (MDD) compared to healthy controls (HCs). Moreover, we would like to differentiate these same connectivity patterns between participants with high vs. low anxiety levels. Method: The sample comprised 56 participants between the ages of 60 and 75; 27 of them were patients with a diagnosis of MDD. Patients were further divided into two samples according to anxiety level: the four people with the highest anxiety level and the five with the lowest anxiety level. Clinical aspects were measured using psychological questionnaires. Each participant underwent functional magnetic resonance imaging (fMRI) acquisition in different regions of interest (ROIs) of the DMN. Results: There was a greater correlation between pairs of ROIs in the control group than in patients with LLD, being this effect preferentially observed in patients with higher anxiety levels. Conclusions: There are differences in functional connectivity within the DMN depending on the level of psychopathology. This can be reflected in these correlations and in the number of clusters and how the brain lateralizes (clustering).

Diagnosis of late-life depression using structural equation modeling and dynamic effective connectivity during resting fMRI.

BACKGROUND: Late-life depression (LLD) is characterized by cognitive and social impairments. Determining neurobiological alterations in connectivity in LLD by means of fMRI may lead to a better understanding of the neural basis underlying this disorder and more precise diagnostic markers. The primary objective of this paper is to identify a structural model that best explains the dynamic effective connectivity (EC) of the default mode network (DMN) in LLD patients compared to controls. METHODS: Twenty-seven patients and 29 healthy controls underwent resting-state fMRI during a period of eight minutes. In both groups, jackknife correlation matrices were generated with six ROIs of the DMN that constitute the posterior DMN (pDMN). The different correlation matrices were used as input to estimate each structural equation model (SEM) for each subject in both groups incorporating dynamic effects. RESULTS: The results show that the proposed LLD diagnosis algorithm achieves perfect accuracy in classifying LLD patients and controls. This differentiation is based on three aspects: the importance of ROIs 4 and 6, which seem to be the most distinctive among the subnetworks; the shape that the specific connections adopt in their networks, or in other words, the directed connections that are established among the ROIs in the pDMN for each group; and the number of dynamic effects that seem to be greater throughout the six ROIs studied [t = 54.346; df = 54; p < .001; 95 % CI difference = 5.486-5.906]. LIMITATIONS: The sample size was moderate, and the participants continued their current medications. CONCLUSIONS: The network models that we developed describe a pattern of dynamic activation in the pDMN that may be considered a possible biomarker for LLD, which may allow early diagnosis of this disorder.

THE EFFECT OF SECOND-GENERATION ANTIDEPRESSANT TREATMENT ON THE EXECUTIVE FUNCTIONS OF PATIENTS WITH MAJOR DEPRESSIVE DISORDER: A META-ANALYSIS STUDY WITH STRUCTURAL EQUATION MODELS.

Major depressive disorder (MDD) has been linked to executive functions (EF) deficits that can be improved after pharmacological treatment, but it is unclear whether there is a class of antidepressants that is more effective than others to ameliorate these deficits in MDD. Additionally, the possible effects of clinical and demographic variables on the improvement of MDD EF deficits after pharmacological treatment are currently unknown. Our aim was to study the possible neuropsychological effects of second-generation antidepressant classes on the EF of MDD patients and the potential influence of clinical and demographic variables as moderators of these effects through a meta-analytic approach. Twenty-one papers were included in our study. A structural equation model meta-analysis was performed. The improvement of EF after pharmacological treatment is clinically relevant, but it is incomplete. This effect is influenced by age and years of education of the patients. Selective serotonin reuptake inhibitors (SSRIs) and dual inhibitors are the drugs causing the greatest improvement in EF of MDD patients. Antidepressant class is an important variable linked to EF improvement after MDD treatment, but the degree of improvement in these cognitive functions is strongly influenced by some clinical and demographic variables of patients with depression.

Task-Related Brain Connectivity Activation Functional Magnetic Resonance Imaging in Intellectual Disability Population: A Meta-Analytic Study.

Introduction: Neuroimaging studies of intellectual disability (ID) have been published over the last three decades, but the findings are often inconsistent, and therefore, the neural correlates of ID remain elusive. This article aims to study the different publications in task-functional magnetic resonance imaging (fMRI) and different ID populations to make a qualitative and quantitative analysis on this field. Methods: After duplicates were removed, only 10 studies matching our inclusion criteria were incorporated. Moreover, a quality assessment of the included studies was done. Qualitative results of the different articles were analyzed, separated by type of task and type of ID. Seed-based d mapping (SDM) software was used. Results: The right temporal gyrus was more activated in control subjects than in ID. Concretely, the right temporal gyrus is implicated in many cognitive domains as semantic memory processing and language. Moreover, it can be highly influenced by the type of task used in every study. Heterogeneity was not detected. A jackknife sensitivity analysis was also estimated to improve the analysis reliability, and both results were confirmed. Conclusions: More task-fMRI studies on ID must be published to add larger samples to address the pathophysiological questions more directly. Impact statement In this article, the state-of-the-art in the field of functional magnetic resonance imaging (fMRI) and intellectual disability (ID) is reviewed. Moreover, we perform a meta-analysis of every article's results to summarize the principal outcomes in the field. It is very relevant because it has become the first meta-analytic study to overcome all the principal studies published in fMRI and ID to find the principal neurological substrates while the subjects are performing a task.

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

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

Resting-state default mode network connectivity in young individuals with Down syndrome.

BACKGROUND: Down syndrome (DS) is a chromosomal disorder that causes intellectual disability. Few studies have been conducted on functional connectivity using resting-state fMRI (functional magnetic resonance imaging) signals or more specifically, on the relevant structure and density of the default mode network (DMN). Although data on this issue have been reported in adult DS individuals (age: >45 years), the DMN properties in young DS individuals have not been studied. The aim of this study was to describe the density and structure of the DMN network from fMRI signals in young DS (age: <36 years). METHOD: A sample of 22 young people with DS between the ages of 16 and 35 (M = 25.5 and SD = 5.1) was recruited in various centers for people with intellectual disability (ID). In addition to sociodemographic data, a six-minute fMRI session was recorded with a 3. T Philips Ingenia scanner. A control group of 22 young people, matched by age and gender, was obtained from the Human Connectome Project (to compare the networks properties between groups). RESULTS: The values of the 48 ROIs that configured the DMN were obtained, and the connectivity graphs for each subject, the average connectivity graph for each group, the clustering and degree values for each ROI, and the average functional connectivity network were estimated. CONCLUSIONS: A higher density of overactivation was identified in DS group in the ventral, sensorimotor, and visual DMN networks, although within a framework of a wide variability of connectivity patterns in comparison with the control group network. These results extend our understanding of the functional connectivity networks pattern and intrasubject variability in DS.

Classical and Bayesian estimation in the logistic regression model applied to diagnosis of child attention deficit hyperactivity disorder.

The limitations inherent to classical estimation of the logistic regression models are known. The Bayesian approach in statistical analysis is an alternative to be considered, given that it makes it possible to introduce prior information about the phenomenon under study. The aim of the present work is to analyze binary and multinomial logistic regression simple models estimated by means of a Bayesian approach in comparison to classical estimation. To that effect, Child Attention Deficit Hyperactivity Disorder (ADHD) clinical data were analyzed. The sample included 286 participants of 6-12 years (78% boys, 22% girls) with ADHD positive diagnosis in 86.7% of the cases. The results show a reduction of standard errors associated to the coefficients obtained from the Bayesian analysis, thus bringing a greater stability to the coefficients. Complex models where parameter estimation may be easily compromised could benefit from this advantage.

Eliminate the effect of severity of the Personal Outcomes Scale: Linear regression in persons with intellectual disability.

BACKGROUND: The Personal Outcomes Scale (POS) is used to assess quality of life (QoL) in people with intellectual disability (ID) but the results are influenced by the severity of the disability. To address this issue, we present the standardization of the Spanish adaptation of the POS. One of the limitations of the Classical Test Theory is the differential effect in some items due to the effect of an external variable. For this reason, we propose the use of multiple linear regressions. METHOD: We used a sample of 529 people with ID, along with 522 professionals and 462 parents who were administered the POS in order to carry out an in-depth psychometric study. For the analysis, we used the correction strategy by multiple linear regressions to generate centiles corrected for the severity effect of ID. RESULTS: Following this technique, the results show the centiles from the raw POS score corrected for the effect of the severity of ID. CONCLUSIONS: This standardization technique is a feasible option to provide a QoL scale without biased results due to the severity of ID.

Using fMRI to Assess Brain Activity in People With Down Syndrome: A Systematic Review.

Background: In the last few years, many investigations have focused on brain activity in general and in populations with different pathologies using non-invasive techniques such as electroencefalography (EEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and magnetic resonance imaging (MRI). However, the use of non-invasive techniques to detect brain signals to evaluate the cognitive activity of people with Down syndrome (DS) has not been sufficiently addressed. The objective of this study is to describe the state-of-the-art in fMRI techniques for recording brain signals in people with DS. Method: A systematic review was performed based on PRISMA recommendations; only nine papers on this topic have been published. Three independent researchers selected all relevant information from each paper. Analyses of information concordance showed a high value of agreement between researchers. Results: Although few relevant works have been published, the use of fMRI in people with DS is becoming an appropriate option to study brain function in this population. Of the nine identified papers, five used task designs, and four used resting-state paradigms. Conclusion: Thus, we emphasize the need to incorporate rigorous cognitive activity procedures in evaluations of the DS population. We suggest several factors (such as head correction movements and paired sample techniques) that must be considered when designing an fMRI study with a task or a resting-state paradigm in a DS population.

Relationship between Quality of Life and the Complexity of Default Mode Network in Resting State Functional Magnetic Resonance Image in Down Syndrome.

BACKGROUND: The study of the Default Mode Network (DMN) has been shown to be sensitive for the recognition of connectivity patterns between the brain areas involved in this network. It has been hypothesized that the connectivity patterns in this network are related to different cognitive states. PURPOSE: In this study, we explored the relationship that can be estimated between these functional connectivity patterns of the DMN with the Quality-of-Life levels in people with Down syndrome, since no relevant data has been provided for this population. METHODS: 22 young people with Down syndrome were evaluated; they were given a large evaluation battery that included the Spanish adaptation of the Personal Outcome Scale (POS). Likewise, fMRI sequences were obtained on a 3T resonator. For each subject, the DMN functional connectivity network was studied by estimating the indicators of complexity networks. The variability obtained in the Down syndrome group was studied by taking into account the Quality-of-Life distribution. RESULTS: There is a negative correlation between the complexity of the connectivity networks and the Quality-of-Life values. CONCLUSIONS: The results are interpreted as evidence that, even at rest, connectivity levels are detected as already shown in the community population and that less intense connectivity levels correlate with higher levels of Quality of Life in people with Down syndrome.

Resting-state functional dynamic connectivity and healthy aging: A sliding-window network analysis.

BACKGROUND: Graph theory has been widely used to study structural and functional brain connectivity changes in healthy aging, and occasionally with clinical samples; in both cases, during task-related and resting-state experiments. Recent studies have focused their interest on dynamic changes during a resting-state fMRI register in order to identify differences in non-stationary patterns associated with the aging process. The objective of this study was to characterize resting-state fMRI network dynamics in order to study the healthy aging process. METHOD: 114 healthy older adults were measured in a resting-state paradigm using fMRI. A sliding-window approach to graph theory was used to measure the mean degree, average path length, clustering coefficient, and small-worldness of each subnetwork, and the impact of age and time in each graph measure was assessed. RESULTS: A combined effect of age and time was detected in mean degree, average path length, and small-worldness, where participants aged 75 to 79 showed a curvilinear trend with reduced network density and increased small-world coefficient in the middle of the register. CONCLUSION: An effect of age was observed on average path length, with younger participants showing slightly lower scores.

Resting-State Functional Connectivity Dynamics in Healthy Aging: An Approach Through Network Change Point Detection.

This study aims at assessing the impact of age on the short-term temporal dynamics of the topological properties of the undirected and weighted whole-brain functional connectivity (FC) networks. We studied the association between the participant's age and the number of significant change points detected through the Network Change Point Detection algorithm. Secondary, we defined state as the resting-state functional magnetic resonance imaging (rs-fMRI) subsequence between two significant change points, obtaining the FC network in each state and participant and characterized their network topological properties. The data comprise the rs-fMRI sequences of 114 healthy individuals combined from 3 different studies conducted at the Department of Medicine, School of Medicine and Health Sciences, University of Barcelona. Participants were healthy people in the absence of any pathology that could interfere with the scanning procedures, as well as any chronic illness that implied a short-lived situation. Topological properties of everyone's FC networks were characterized by their network strength, transitivity, characteristic path length, and small-worldness, analyzing the effect of age in those observed distributions. To that effect, we constructed a mixed linear model for each network topological property with age, state, and state duration as the linear predictors. Several statistically significant relationships have been estimated between the indicators of the FC networks that show a certain regular pattern of change in the networks during the time of registration at the resting fMRI paradigm. These dynamic changes seem to be related to the age of each group studied. Healthy aging could be characterized by FC dynamics patterns.