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Age-related changes in the brain connectivity of healthy older adults have been widely studied in recent years, with some differences in the obtained results. Most of these studies showed decreases in general functional connectivity, but they also found increases in some particular regions and areas. Frequently, these studies compared young individuals with older subjects, but few studies compared different age groups only in older populations. The purpose of this study is to analyze whole-brain functional connectivity in healthy older adult groups and its network characteristics through functional segregation. A total of 114 individuals, 48 to 89 years old, were scanned using resting-state functional magnetic resonance imaging in a resting state paradigm and were divided into six different age groups (< 60, 60-64, 65-69, 70-74, 75-79, ≥ 80 years old). A partial correlation analysis, a pooled correlation analysis and a study of 3-cycle regions with prominent connectivity were conducted. Our results showed progressive diminution in the functional connectivity among different age groups and this was particularly pronounced between 75 and 79 years old. The oldest group (≥ 80 years old) showed a slight increase in functional connectivity compared to the other groups. This occurred possibly because of compensatory mechanism in brain functioning. This study provides information on the brain functional characteristics of every age group, with more specific information on the functional progressive decline, and supplies methodological tools to study functional connectivity characteristics. Approval for the study was obtained from the ethics committee of the Comisión de Bioética de la Universidad de Barcelona (approval No. PSI2012-38257) on June 5, 2012, and from the ethics committee of the Barcelona's Hospital Clínic (approval No. 2009-5306 and 2011-6604) on October 22, 2009 and April 7, 2011 respectively.

Abstract Introduction We performed a systematic review and meta-analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment, using resting-state functional magnetic resonance imaging. Methods Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies. Results Thirty-four studies were included (1363 participants, average 40 per study). Consistent alterations in connectivity were found in the default mode, salience, and limbic networks in patients with AD dementia, mild cognitive impairment, or in both groups. We also identified a strong tendency in the literature toward specific examination of the default mode network. Discussion Convergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that highly connected hub regions in the brain might be an early target of AD.

BACKGROUND Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. METHODS Our research is based on resting‐state electroencephalography (EEG) and the current dataset includes 137 consensus‐diagnosed, community‐dwelling Black Americans (ages 60–90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time‐varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity‐based score. RESULTS The leave‐one‐out cross‐validation accuracy is 91.97% and 3‐fold accuracy is 91.17%. The 9 to 18 months’ progression trend prediction accuracy over an availability‐limited subset sample is 84.61%. CONCLUSION The EEG‐based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.

It is widely believed that democracy requires public support to survive. The empirical evidence for this hypothesis is weak, however, with existing tests resting on small cross-sectional samples and producing contradictory results. The underlying problem is that survey measures of support for democracy are fragmented across time, space, and different survey questions. In response, this article uses a Bayesian latent variable model to estimate a smooth country-year panel of democratic support for 135 countries and up to 29 years. The article then demonstrates a positive effect of support on subsequent democratic change, while adjusting for the possible confounding effects of prior levels of democracy and unobservable time-invariant factors. Support is, moreover, more robustly linked with the endurance of democracy than its emergence in the first place. As Lipset (1959) and Easton (1965) hypothesized over 50 years ago, public support does indeed help democracy survive.

Altered resting-state functional connectivity (rsFC) has been noted in large-scale functional networks in attention-deficit/hyperactivity disorder (ADHD). However, identifying consistent abnormalities of functional networks is difficult due to varied methods and results across studies. To integrate rsFC alterations and search for coherent patterns of intrinsic functional network impairments in ADHD, this research conducts a coordinate-based meta-analysis of voxel-wise seed-based rsFC studies comparing rsFC between ADHD patients and healthy controls. A total of 25 datasets from 21 studies including 700 ADHD patients and 580 controls were analyzed. We extracted the coordinates of seeds and between-group effects. Each seed was then categorized into a seed-network by its location within priori 7-network parcellations. Then, pooled meta-analyses were conducted for the default mode network (DMN), frontoparietal network (FPN) and affective network (AN) separately, but not for the ventral attention network (VAN), dorsal attention network (DAN), somatosensory network (SSN) and visual network due to a lack of primary studies. The results showed that ADHD was characterized by hyperconnectivity between the FPN and regions of the DMN and AN as well as hypoconnectivity between the FPN and regions of the VAN and SSN. These findings not only support the triple-network model of pathophysiology associated with ADHD but also extend this model by highlighting the involvement of the SSN and AN in the mechanisms of network interactions that may account for motor hyperactivity and impulsive symptoms.

Systemic physiological dynamics, such as heart rate variability (HRV) and respiration volume per time (RVT), are known to account for significant variance in the blood oxygen level dependent (BOLD) signal of resting‐state functional magnetic resonance imaging (rsfMRI). However, synchrony between these cardiorespiratory changes and the BOLD signal could be due to neuronal (i.e., autonomic activity inducing changes in heart rate and respiration) or vascular (i.e., cardiorespiratory activity facilitating hemodynamic changes and thus the BOLD signal) effects and the contributions of these effects may differ spatially, temporally, and spectrally. In this study, we characterize these brain–body dynamics using a wavelet analysis in rapidly sampled rsfMRI data with simultaneous pulse oximetry and respiratory monitoring of the Human Connectome Project. Our time–frequency analysis across resting‐state networks (RSNs) revealed differences in the coherence of the BOLD signal and heartbeat interval (HBI)/RVT dynamics across frequencies, with unique profiles per network. Somatomotor (SMN), visual (VN), and salience (VAN) networks demonstrated the greatest synchrony with both systemic physiological signals when compared to other networks; however, significant coherence was observed in all RSNs regardless of direct autonomic involvement. Our phase analysis revealed distinct frequency profiles of percentage of time with significant coherence between BOLD and systemic physiological signals for different phase offsets across RSNs, suggesting that the phase offset and temporal order of signals varies by frequency. Lastly, our analysis of temporal variability of coherence provides insight on potential influence of autonomic state on brain–body communication. Overall, the novel wavelet analysis enables an efficient characterization of the dynamic relationship between cardiorespiratory activity and the BOLD signal in spatial, temporal, and spectral dimensions to inform our understanding of autonomic states and improve our interpretation of the BOLD signal. We characterized the dynamic relationship between cardiorespiratory activity and the blood oxygen level dependent (BOLD) signal in spatial, temporal, and spectral dimensions using a wavelet analysis to inform our understanding of autonomic states and improve our interpretation of the BOLD signal. We identified unique frequency profiles for different phase offsets in instances of coherence between the BOLD signal and systemic physiological dynamics.

Background To explore the long‐term efficacy and resting‐state functional magnetic resonance imaging (fMRI) changes of lateral habenula nucleus (LHb) deep brain stimulation (DBS; LHb‐DBS) for treatment‐resistant bipolar disorder (TRBD). Methods An 18‐year‐old woman with TRBD received bilateral LHb‐DBS. We assessed changes in Hamilton Depression Scale‐17 (HDRS‐17), Bech‐Rafaelsen Melancholia Scale (BRMS), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Scale (PSQI) scores from preoperative baseline to postoperative continuous 24‐month follow‐up. Brain activity and resting‐state functional connectivity (rsFC) were examined off‐stimulation at 0.6 and 15 months post‐LHb‐DBS. Overall improvement and adverse events were analyzed. Results Continuous 24‐month follow‐up showed average improvements from baseline of 65.33%, 54.90%, 63.33%, and 48.72% for HDRS‐17, BRMS, HAMA, and PSQI scores, respectively. At the final follow‐up, improvement was 96.00%, 88.24%, 84.85%, and 69.23%, respectively. Resting‐state fMRI results revealed an increase in fractional amplitude of low‐frequency fluctuations (fALFF) within the putamen, ventral tegmental area (VTA), and substantia nigra pars compacta (SNc) over 15 months of continuous bilateral LHb stimulation when DBS was off. From baseline to 15 months, fALFF in the putamen, VTA, and SNc increased by 1.68%, 6.36%, and 1.10%, respectively. Consistently reduction in rsFC was observed between the left nucleus accumbens (NAcc) and left hippocampus. Over the 15 months of continuous stimulation, rsFC decreased by 72% from baseline. Conclusions Long‐term LHb‐DBS can control symptoms and improve the quality of life in patients with TRBD. This may be attributed to an increase in fALFF in the putamen, VTA, and SNc, and a reduction in rsFC between the left NAcc and left hippocampus.

This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. For that purpose, 5 min of resting‐state MEG activity were recorded in 16 FRDA patients (mean age: 27 years, range: 12–51 years; 10 females) and matched healthy subjects. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). Post hoc median‐split, nonparametric permutations and Spearman rank correlations were used for statistics. In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states compared with FRDA patients with an ASO > 11 years or healthy subjects. The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. This study demonstrates that ASO is the main determinant of alterations in the sub‐second dynamics of posterior associative neocortices in FRDA patients and substantiates a direct link between sub‐second network activity and functional brain integration over long timescales. This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states.

We tested whether the resting state functional connectivity of the motor system changed during 4weeks of motor skill learning using functional magnetic resonance imaging (fMRI). Ten healthy volunteers learned to produce a sequential finger movement by daily practice of the task over a 4week period. Changes in the resting state motor network were examined before training (Week 0), two weeks after the onset of training (Week 2), and immediately at the end of the training (Week 4). The resting state motor system was analyzed using group independent component analysis (ICA). Statistical Parametric Mapping (SPM) second-level analysis was conducted on independent z-maps generated by the group ICA. Three regions, namely right postcentral gyrus, and bilateral supramarginal gyri were found to be sensitive to the training duration. Specifically, the strength of resting state functional connectivity in the right postcentral gyrus and right supramarginal gyrus increased from Week 0 to Week 2, during which the behavioral performance improved significantly, and decreased from Week 2 to Week 4, during which there was no more significant improvement in behavioral performance. The strength of resting state functional connectivity in left supramarginal gyrus increased throughout the training. These results confirm changes in the resting state network during slow-learning stage of motor skill learning, and support the premise that the resting state networks play a role in improving performance.

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078-0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p < 0.05). Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain's functioning at resting state.