Richness in Functional Connectivity Depends on the Neuronal Integrity within the Posterior Cingulate Cortex

The brain's connectivity skeleton-a rich club of strongly interconnected members-was initially shown to exist in human structural networks, but recent evidence suggests a functional counterpart. This rich club typically includes key regions (or hubs) from multiple canonical networks, reducing the cost of inter-network communication. The posterior cingulate cortex (PCC), a hub node embedded within the default mode network, is known to facilitate communication between brain networks and is a key member of the "rich club." Here, we assessed how metabolic signatures of neuronal integrity and cortical thickness influence the global extent of a functional rich club as measured using the functional rich club coefficient (fRCC). Rich club estimation was performed on functional connectivity of resting state brain signals acquired at 3T in 48 healthy adult subjects. Magnetic resonance spectroscopy was measured in the same session using a point resolved spectroscopy sequence. We confirmed convergence of functional rich club with a previously established structural rich club. N-acetyl aspartate (NAA) in the PCC is significantly correlated with age (p = 0.001), while the rich club coefficient showed no effect of age (p = 0.106). In addition, we found a significant quadratic relationship between fRCC and NAA concentration in PCC (p = 0.009). Furthermore, cortical thinning in the PCC was correlated with a reduced rich club coefficient after accounting for age and NAA. In conclusion, we found that the fRCC is related to a marker of neuronal integrity in a key region of the cingulate cortex. Furthermore, cortical thinning in the same area was observed, suggesting that both cortical thinning and neuronal integrity in the hub regions influence functional integration of at a whole brain level

Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression

The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the German resting-state initiative for diagnostic biomarkers (psymri.org), comprising 53 AD patients and 118 age and sex matched healthy controls. Using all possible pairs of correlations between the time series of rs-fMRI signal from 84 functionally defined brain regions as the initial set of predictor variables, we calculated accuracy of group discrimination and consistency of feature selection with bootstrap cross-validation. Mean areas under the receiver operating characteristic curves as measure of diagnostic accuracy were 0.70 in unregularized and 0.80 in regularized regression. Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain regions involved in attention, executive control, and language processing. Stepwise logistic regression found no consistent network of AD related functional connectivity decline. Regularized regression has high potential to increase diagnostic accuracy and consistency of feature selection from multicollinear functional neuroimaging data in AD. Our findings suggest an extended network of functional alterations in AD, but the diagnostic accuracy of rs-fMRI in this multicenter setting did not reach the benchmark defined for a useful biomarker of AD

Sampling Rate Effects on Resting State fMRI Metrics

Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning

Anterior Thalamic High Frequency Band Activity Is Coupled with Theta Oscillations at Rest

Cross-frequency coupling (CFC) between slow and fast brain rhythms, in the form of phase–amplitude coupling (PAC), is proposed to enable the coordination of neural oscillatory activity required for cognitive processing. PAC has been identified in the neocortex and mesial temporal regions, varying according to the cognitive task being performed and also at rest. PAC has also been observed in the anterior thalamic nucleus (ATN) during memory processing. The thalamus is active during the resting state and has been proposed to be involved in switching between task-free cognitive states such as rest, in which attention is internally-focused, and externally-focused cognitive states, in which an individual engages with environmental stimuli. It is unknown whether PAC is an ongoing phenomenon during the resting state in the ATN, which is modulated during different cognitive states, or whether it only arises during the performance of specific tasks. We analyzed electrophysiological recordings of ATN activity during rest from seven patients who received thalamic electrodes implanted for treatment of pharmacoresistant focal epilepsy. PAC was identified between theta (4–6 Hz) phase and high frequency band (80–150 Hz) amplitude during rest in all seven patients, which diminished during engagement in tasks involving an external focus of attention. The findings are consistent with the proposal that theta–gamma coupling in the ATN is an ongoing phenomenon, which is modulated by task performance

Echoes of Affective Stimulation in Brain connectivity Networks

Affective experience has effects on subjective feelings, physiological indices, entails immediate activity changes in the brain, and even influences brain networks in a protracted manner. However, it is still unclear, how the functional connectivity (FC) interplay between major intrinsic connectivity networks upon affective stimulation depends on affective valence, and whether this is specific for affective experience, i.e., can be distinguished from cognitive task execution. Our study included fMRI scans during and after affective stimulation with sad and neutral movies and a working memory task complemented with measures of cardiovascular activity and mood. Via parcellation of the brain into default mode network (DMN), central executive network (CEN), and dorsal attention network, and application of network-based statistics, we identified subnetworks associated with changing psychological contexts. Specific effects for affective stimulation with negative valence were both reduced heart rate variability and mood, and upregulated FC of inter-CEN-DMN connections while intra-DMN connections were downregulated. Furthermore, results demonstrated a valence-specific dynamic carry-over effect in nodes of the CEN, which temporarily increased their FC strength after affective stimulation with negative valence and exhibited distinct temporal profiles. The reported effects were clearly distinguishable from those of a cognitive task and further elucidate the trajectory of affective experience

Alterations in Brain Structure and Amplitude of Low-frequency after 8 weeks of Mindfulness Meditation Training in Meditation-Naïve Subjects

Increasing neuroimaging evidence suggests that mindfulness meditation expertise is related to different functional and structural configurations of the default mode network (DMN), the salience network (SN) and the executive network at rest. However, longitudinal studies observing resting network plasticity effects in brains of novices who started to practice meditation are scarce and generally related to one dimension, such as structural or functional effects. The purpose of this study was to investigate structural and functional brain network changes (e.g. DMN) after 40 days of mindfulness meditation training in novices and set these in the context of potentially altered depression symptomatology and anxiety. We found overlapping structural and functional effects in precuneus, a posterior DMN region, where cortical thickness increased and low-frequency amplitudes (ALFF) decreased, while decreased ALFF in left precuneus/posterior cingulate cortex correlates with the reduction of (CES-D) depression scores. In conclusion, regional overlapping of structural and functional changes in precuneus may capture different components of the complex changes of mindfulness meditation training

Brain parcellation choice affects disease-related topology differences increasingly from global to local network levels

Network-based analyses of deviant brain function have become extremely popular in psychiatric neuroimaging. Underpinning brain network analyses is the selection of appropriate regions of interest (ROIs). Although ROI selection is fundamental in network analysis, its impact on detecting disease effects remains unclear. We investigated the impact of parcellation choice when comparing results from different studies. We investigated the effects of anatomical (AAL) and literature-based (Dosenbach) parcellation schemes on comparability of group differences in 35 female patients with anorexia nervosa and 35 age- and sex-matched healthy controls. Global and local network properties, including network-based statistics (NBS), were assessed on resting state functional magnetic resonance imaging data obtained at 3T. Parcellation schemes were comparably consistent on global network properties, while NBS and local metrics differed in location, but not metric type. Location of local metric alterations varied for AAL (parietal and cingulate cortices) versus Dosenbach (insula, thalamus) parcellation approaches. However, consistency was observed for the occipital cortex. Patient-specific global network properties can be robustly observed using different parcellation schemes, while graph metrics characterizing impairments of individual nodes vary considerably. Therefore, the impact of parcellation choice on specific group differences varies depending on the level of network organization

Inverse correlation of fluctuations of cerebral blood and water concentrations in humans

Abstract Near-infrared spectroscopy (fNIRS) measures concentrations of oxygenated (HbO) and deoxygenated (HbR) hemoglobin in the brain. Recently, we demonstrated its potential also for measuring concentrations of cerebral water (cH₂O). We performed fNIRS measurements during rest to study fluctuations in concentrations of cH₂O, HbO and HbR in 33 well-rested healthy control subjects (HC) and 18 acutely sleep-deprived HC. Resting-state fNIRS signal was filtered in full-band, cardiac, respiratory, low-, and very-low-frequency bands. The sum of HbO and HbR constitutes the regional cerebral blood volume (CBV). CBV and cH₂O concentrations were analyzed via temporal correlation and phase synchrony. Fluctuation in concentrations of cH₂O and CBV was strongly anti-correlated across all frequency bands in both frontal and parietal cortices. Fluctuation in concentrations of cH₂O and CBV showed neither a completely synchronous nor a random phase relationship in both frontal and parietal cortices. Acutely sleep-deprived subjects did not show significant differences in temporal correlation or phase synchrony between fluctuations in cH₂O and CBV concentrations compared with well-rested HC. The reciprocal interrelation between fluctuations in CBV and cH₂O concentrations is consistent with the Munro–Kellie doctrine of constant intracranial volume. This coupling may constitute a functional mechanism underlying glymphatic circulation, which persists despite acutely disturbed sleep patterns

Echoes of Affective Stimulation in Brain connectivity Networks

Affective experience has effects on subjective feelings, physiological indices, entails immediate activity changes in the brain, and even influences brain networks in a protracted manner. However, it is still unclear, how the functional connectivity (FC) interplay between major intrinsic connectivity networks upon affective stimulation depends on affective valence, and whether this is specific for affective experience, i.e., can be distinguished from cognitive task execution. Our study included fMRI scans during and after affective stimulation with sad and neutral movies and a working memory task complemented with measures of cardiovascular activity and mood. Via parcellation of the brain into default mode network (DMN), central executive network (CEN), and dorsal attention network, and application of network-based statistics, we identified subnetworks associated with changing psychological contexts. Specific effects for affective stimulation with negative valence were both reduced heart rate variability and mood, and upregulated FC of inter-CEN-DMN connections while intra-DMN connections were downregulated. Furthermore, results demonstrated a valence-specific dynamic carry-over effect in nodes of the CEN, which temporarily increased their FC strength after affective stimulation with negative valence and exhibited distinct temporal profiles. The reported effects were clearly distinguishable from those of a cognitive task and further elucidate the trajectory of affective experience