The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy.

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the Interictal Suppression Hypothesis (ISH) posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-12Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analyzed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the Interictal Suppression Hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the Interictal Suppression Hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.

Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition.

Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts ("semantic cognition"). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.SIGNIFICANCE STATEMENT The present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., "semantic cognition"). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.

Indirect structural changes and reduced controllability after temporal lobe epilepsy resection.

OBJECTIVE: To understand the potential behavioral and cognitive effects of mesial temporal resection for temporal lobe epilepsy (TLE) a method is required to characterize network-wide functional alterations caused by a discrete structural disconnection. The objective of this study was to investigate network-wide alterations in brain dynamics of patients with TLE before and after surgical resection of the seizure focus using average regional controllability (ARC), a measure of the ability of a node to influence network dynamics. METHODS: Diffusion-weighted imaging (DWI) data were acquired in 27 patients with drug-resistant unilateral mesial TLE who underwent selective amygdalohippocampectomy. Imaging data were acquired before and after surgery and a presurgical and postsurgical structural connectome was generated from whole-brain tractography. Edge-wise strength, node strength, and node ARC were compared before and after surgery. Direct and indirect edge-wise strength changes were identified using patient-specific simulated resections. Direct edges were defined as primary edges disconnected by the resection zone itself. Indirect edges were secondary measured edge strength changes. Changes in node strength and ARC were then related to both direct and indirect edge changes. RESULTS: We found nodes with significant postsurgical changes in both node strength and ARC surrounding the resection zone (paired t tests, p < .05, Bonferroni corrected). ARC identified additional postsurgical changes in nodes outside of the resection zone within the ipsilateral occipital lobe, which were associated with indirect edge-wise strength changes of the postsurgical network (Fisher's exact test, p < .001). These indirect edge-wise changes were facilitated through the "hub" nodes including the thalamus, putamen, insula, and precuneus. SIGNIFICANCE: Discrete network disconnection from TLE resection results in widespread structural and functional changes not predicted by disconnection alone. These can be well characterized by dynamic controllability measures such as ARC and may be useful for investigating changes in brain function that may contribute to seizure recurrence and behavioral or cognitive changes after surgery.

Structural disconnection relates to functional changes after temporal lobe epilepsy surgery.

Epilepsy surgery consists of surgical resection of the epileptic focus and is recommended for patients with drug-resistant focal epilepsy. However, focal brain lesions can lead to effects in distant brain regions. Similarly, the focal resection in temporal lobe epilepsy surgery has been shown to lead to functional changes distant from the resection. Here we hypothesize that there are changes in brain function caused by temporal lobe epilepsy surgery in regions distant from the resection that are due to their structural disconnection from the resected epileptic focus. Therefore, the goal of this study was to localize changes in brain function caused by temporal lobe epilepsy surgery and relate them to the disconnection from the resected epileptic focus. This study takes advantage of the unique opportunity that epilepsy surgery provides to investigate the effects of focal disconnections on brain function in humans, which has implications in epilepsy and broader neuroscience. Changes in brain function from pre- to post-epilepsy surgery were quantified in a group of temporal lobe epilepsy patients (n = 36) using a measure of resting state functional MRI activity fluctuations. We identified regions with significant functional MRI changes that had high structural connectivity to the resected region in healthy controls (n = 96) and patients based on diffusion MRI. The structural disconnection from the resected epileptic focus was then estimated using presurgical diffusion MRI and related to the functional MRI changes from pre- to post-surgery in these regions. Functional MRI activity fluctuations increased from pre- to post-surgery in temporal lobe epilepsy in the two regions most highly structurally connected to the resected epileptic focus in healthy controls and patients-the thalamus and the fusiform gyrus ipsilateral to the side of surgery (PFWE < 0.05). Broader surgeries led to larger functional MRI changes in the thalamus than more selective surgeries (P < 0.05), but no other clinical variables were related to functional MRI changes in either the thalamus or fusiform. The magnitude of the functional MRI changes in both the thalamus and fusiform increased with a higher estimated structural disconnection from the resected epileptic focus when controlling for the type of surgery (P < 0.05). These results suggest that the structural disconnection from the resected epileptic focus may contribute to the functional changes seen after epilepsy surgery. Broadly, this study provides a novel link between focal disconnections in the structural brain network and downstream effects on function in distant brain regions.

Dynamic vagal-mediated connectivity of cortical and subcortical central autonomic hubs predicts chronotropic response to submaximal exercise in healthy adults.

BACKGROUND: Despite accumulation of a substantial body of literature supporting the role of exercise on frontal lobe functioning, relatively less is understood of the interconnectivity of ventromedial prefrontal cortical (vmPFC) regions that underpin cardio-autonomic regulation predict cardiac chronotropic competence (CC) in response to sub-maximal exercise. METHODS: Eligibility of 161 adults (mean age = 48.6, SD = 18.3, 68% female) was based upon completion of resting state brain scan and sub-maximal bike test. Sliding window analysis of the resting state signal was conducted over 45-s windows, with 50% overlap, to assess how changes in photoplethysmography-derived HRV relate to vmPFC functional connectivity with the whole brain. CC was assessed based upon heart rate (HR) changes during submaximal exercise (HR change /HRmax (206-0.88 × age) - HRrest). RESULTS: During states of elevated HRV the vmPFC showed greater rsFC with an 83-voxel region of the hypothalamus (p < 0.001, uncorrected). Beta estimates of vmPFC connectivity extracted from a 6-mm sphere around this region emerged as the strongest predictor of CC (b = 0.283, p <.001) than age, BMI, and resting HRV F(8,144) = 6.30, p <.001. CONCLUSION: Extensive glutamatergic innervation of the hypothalamus by the vmPFC allows for top-down control of the hypothalamus and its various autonomic efferents which facilitate chronotropic response during sub-maximal exercise.

Emotion Downregulation Targets Interoceptive Brain Regions While Emotion Upregulation Targets Other Affective Brain Regions.

Researchers generally agree that when upregulating and downregulating emotion, control regions in the prefrontal cortex turn up or down activity in affect-generating brain areas. However, the "affective dial hypothesis" that turning up and down emotions produces opposite effects in the same affect-generating regions is untested. We tested this hypothesis by examining the overlap between the regions activated during upregulation and those deactivated during downregulation in 54 male and 51 female humans. We found that upregulation and downregulation both recruit regulatory regions, such as the inferior frontal gyrus and dorsal anterior cingulate gyrus, but act on distinct affect-generating regions. Upregulation increased activity in regions associated with emotional experience, such as the amygdala, anterior insula, striatum, and anterior cingulate gyrus as well as in regions associated with sympathetic vascular activity, such as periventricular white matter, while downregulation decreased activity in regions receiving interoceptive input, such as the posterior insula and postcentral gyrus. Nevertheless, participants' subjective sense of emotional intensity was associated with activity in overlapping brain regions (dorsal anterior cingulate, insula, thalamus, and frontal pole) across upregulation and downregulation. These findings indicate that upregulation and downregulation rely on overlapping brain regions to control and assess emotions but target different affect-generating brain regions.SIGNIFICANCE STATEMENT Many contexts require modulating one's own emotions. Identifying the brain areas implementing these regulatory processes should advance understanding emotional disorders and designing potential interventions. The emotion regulation field has an implicit assumption we call the affective dial hypothesis: both emotion upregulation and downregulation modulate the same emotion-generating brain areas. Countering the hypothesis, our findings indicate that up- and down-modulating emotions target different brain areas. Thus, the mechanisms underlying emotion regulation might differ more than previously appreciated for upregulation versus downregulation. In addition to their theoretical importance, these findings are critical for researchers attempting to target activity in particular brain regions during an emotion regulation intervention.

Vigilance associates with the low-dimensional structure of fMRI data.

The human brain exhibits rich dynamics that reflect ongoing functional states. Patterns in fMRI data, detected in a data-driven manner, have uncovered recurring configurations that relate to individual and group differences in behavioral, cognitive, and clinical traits. However, resolving the neural and physiological processes that underlie such measurements is challenging, particularly without external measurements of brain state. A growing body of work points to underlying changes in vigilance as one driver of time-windowed fMRI connectivity states, calculated on the order of tens of seconds. Here we examine the degree to which the low-dimensional spatial structure of instantaneous fMRI activity is associated with vigilance levels, by testing whether vigilance-state detection can be carried out in an unsupervised manner based on individual BOLD time frames. To investigate this question, we first reduce the spatial dimensionality of fMRI data, and apply Gaussian Mixture Modeling to cluster the resulting low-dimensional data without any a priori vigilance information. Our analysis includes long-duration task and resting-state scans that are conducive to shifts in vigilance. We observe a close alignment between low-dimensional fMRI states (data-driven clusters) and measurements of vigilance derived from concurrent electroencephalography (EEG) and behavior. Whole-brain coactivation analysis revealed cortical anti-correlation patterns that resided primarily during higher behavioral- and EEG-defined levels of vigilance, while cortical activity was more often spatially uniform in states corresponding to lower vigilance. Overall, these findings indicate that vigilance states may be detected in the low-dimensional structure of fMRI data, even within individual time frames.

Increasing coordination and responsivity of emotion-related brain regions with a heart rate variability biofeedback randomized trial.

Heart rate variability is a robust biomarker of emotional well-being, consistent with the shared brain networks regulating emotion regulation and heart rate. While high heart rate oscillatory activity clearly indicates healthy regulatory brain systems, can increasing this oscillatory activity also enhance brain function? To test this possibility, we randomly assigned 106 young adult participants to one of two 5-week interventions involving daily biofeedback that either increased heart rate oscillations (Osc+ condition) or had little effect on heart rate oscillations (Osc- condition) and examined effects on brain activity during rest and during regulating emotion. While there were no significant changes in the right amygdala-medial prefrontal cortex (MPFC) functional connectivity (our primary outcome), the Osc+ intervention increased left amygdala-MPFC functional connectivity and functional connectivity in emotion-related resting-state networks during rest. It also increased down-regulation of activity in somatosensory brain regions during an emotion regulation task. The Osc- intervention did not have these effects. In this healthy cohort, the two conditions did not differentially affect anxiety, depression, or mood. These findings indicate that modulating heart rate oscillatory activity changes emotion network coordination in the brain.

Characterization of resting functional MRI activity alterations across epileptic foci and networks.

Brain network alterations have been studied extensively in patients with mesial temporal lobe epilepsy (mTLE) and other focal epilepsies using resting-state functional magnetic resonance imaging (fMRI). However, little has been done to characterize the basic fMRI signal alterations caused by focal epilepsy. Here, we characterize how mTLE affects the fMRI signal in epileptic foci and networks. Resting-state fMRI and diffusion MRI were collected from 47 unilateral mTLE patients and 96 healthy controls. FMRI activity, quantified by amplitude of low-frequency fluctuations, was increased in the epileptic focus and connected regions in mTLE. Evidence for spread of this epileptic fMRI activity was found through linear relationships of regional activity across subjects, the association of these relationships with functional connectivity, and increased activity along white matter tracts. These fMRI activity increases were found to be dependent on the epileptic focus, where the activity was related to disease severity, suggesting the focus to be the origin of these pathological alterations. Furthermore, we found fMRI activity decreases in the default mode network of right mTLE patients with different properties than the activity increases found in the epileptic focus. This work provides insights into basic fMRI signal alterations and their potential spread across networks in focal epilepsy.

Whole-Brain Functional Dynamics Track Depressive Symptom Severity.

Depressive symptoms are reported by 20% of the population and are related to altered functional integrity of large-scale brain networks. The link between moment-to-moment brain function and depressive symptomatology, and the implications of these relationships for clinical and community populations alike, remain understudied. The present study examined relationships between functional brain dynamics and subclinical-to-mild depressive symptomatology in a large community sample of adults with and without psychiatric diagnoses. This study used data made available through the Enhanced Nathan Kline Institute-Rockland Sample; 445 participants between 18 and 65 years of age completed a 10-min resting-state functional MRI scan. Coactivation pattern analysis was used to examine the dimensional relationship between depressive symptoms and whole-brain states. Elevated levels of depressive symptoms were associated with increased frequency and dwell time of the default mode network, a brain network associated with self-referential thought, evaluative judgment, and social cognition. Furthermore, increased depressive symptom severity was associated with less frequent occurrences of a hybrid brain network implicated in cognitive control and goal-directed behavior, which may impair the inhibition of negative thinking patterns in depressed individuals. These findings demonstrate how temporally dynamic techniques offer novel insights into time-varying neural processes underlying subclinical and clinically meaningful depressive symptomatology.

Interindividual Signatures of fMRI Temporal Fluctuations.

The complexity and variability of human brain activity, such as quantified from Functional Magnetic Resonance Imaging (fMRI) time series, have been widely studied as potential markers of healthy and pathological states. However, the extent to which fMRI temporal features exhibit stable markers of inter-individual differences in brain function across healthy young adults is currently an open question. In this study, we draw upon two widely used time-series measures-a nonlinear complexity measure (sample entropy; SampEn) and a spectral measure of low-frequency content (fALFF)-to capture dynamic properties of resting-state fMRI in a large sample of young adults from the Human Connectome Project. We observe that these two measures are closely related, and that both generate reproducible patterns across brain regions over four different fMRI runs, with intra-class correlations of up to 0.8. Moreover, we find that both metrics can uniquely differentiate subjects with high identification rates (ca. 89%). Canonical correlation analysis revealed a significant relationship between multivariate brain temporal features and behavioral measures. Overall, these findings suggest that regional profiles of fMRI temporal characteristics may provide stable markers of individual differences, and motivate future studies to further probe relationships between fMRI time series metrics and behavior.

Brain Dynamics Underlying Cognitive Flexibility Across the Lifespan.

The neural mechanisms contributing to flexible cognition and behavior and how they change with development and aging are incompletely understood. The current study explored intrinsic brain dynamics across the lifespan using resting-state fMRI data (n = 601, 6-85 years) and examined the interactions between age and brain dynamics among three neurocognitive networks (midcingulo-insular network, M-CIN; medial frontoparietal network, M-FPN; and lateral frontoparietal network, L-FPN) in relation to behavioral measures of cognitive flexibility. Hierarchical multiple regression analysis revealed brain dynamics among a brain state characterized by co-activation of the L-FPN and M-FPN, and brain state transitions, moderated the relationship between quadratic effects of age and cognitive flexibility as measured by scores on the Delis-Kaplan Executive Function System (D-KEFS) test. Furthermore, simple slope analyses of significant interactions revealed children and older adults were more likely to exhibit brain dynamic patterns associated with poorer cognitive flexibility compared with younger adults. Our findings link changes in cognitive flexibility observed with age with the underlying brain dynamics supporting these changes. Preventative and intervention measures should prioritize targeting these networks with cognitive flexibility training to promote optimal outcomes across the lifespan.

Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity.

Emerging evidence has shown that functional connectivity is dynamic and changes over the course of a scan. Furthermore, connectivity patterns can arise from short periods of co-activation on the order of seconds. Recently, a dynamic co-activation patterns (CAPs) analysis was introduced to examine the co-activation of voxels resulting from individual timepoints. The goal of this study was to apply CAPs analysis on resting state fMRI data collected using an advanced multiband multi-echo (MBME) sequence, in comparison with a multiband (MB) sequence with a single echo. Data from 28 healthy control subjects were examined. Subjects underwent two resting state scans, one MBME and one MB, and 19 subjects returned within two weeks for a repeat scan session. Data preprocessing included advanced denoising namely multi-echo independent component analysis (ME-ICA) for the MBME data and an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) for the MB data. The CAPs analysis was conducted using the newly published TbCAPs toolbox. CAPs were extracted using both seed-based and seed-free approaches. Timepoints were clustered using k-means clustering. The following metrics were compared between MBME and MB datasets: mean activation in each CAP, the spatial correlation and mean squared error (MSE) between each timepoint and the centroid CAP it was assigned to, within-dataset variance across timepoints assigned to the same CAP, and the between-session spatial correlation of each CAP. Co-activation was heightened for MBME data for the majority of CAPs. Spatial correlation and MSE between each timepoint and its assigned centroid CAP were higher and lower respectively for MBME data. The within-dataset variance was also lower for MBME data. Finally, the between-session spatial correlation was higher for MBME data. Overall, our findings suggest that the advanced MBME sequence is a promising avenue for the measurement of dynamic co-activation patterns by increasing the robustness and reproducibility of the CAPs.

State-related neural influences on fMRI connectivity estimation.

The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can co-vary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.

Reconstruction of respiratory variation signals from fMRI data.

Functional MRI signals can be heavily influenced by systemic physiological processes in addition to local neural activity. For example, widespread hemodynamic fluctuations across the brain have been found to correlate with natural, low-frequency variations in the depth and rate of breathing over time. Acquiring peripheral measures of respiration during fMRI scanning not only allows for modeling such effects in fMRI analysis, but also provides valuable information for interrogating brain-body physiology. However, physiological recordings are frequently unavailable or have insufficient quality. Here, we propose a computational technique for reconstructing continuous low-frequency respiration volume (RV) fluctuations from fMRI data alone. We evaluate the performance of this approach across different fMRI preprocessing strategies. Further, we demonstrate that the predicted RV signals can account for similar patterns of temporal variation in resting-state fMRI data compared to measured RV fluctuations. These findings indicate that fluctuations in respiration volume can be extracted from fMRI alone, in the common scenario of missing or corrupted respiration recordings. The results have implications for enriching a large volume of existing fMRI datasets through retrospective addition of respiratory variations information.

Evidence for modulation of EEG microstate sequence by vigilance level.

The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.

Analysis of stimulus-induced brain dynamics during naturalistic paradigms.

Naturalistic stimuli offer promising avenues for investigating brain function across the rich, realistic spectrum of human experiences. Functional magnetic resonance imaging (fMRI) studies of brain activity during naturalistic paradigms have provided new information about dynamic neural processing in ecologically valid contexts. Yet, the complex, uncontrolled nature of such stimuli -- and the resulting mixture of neuronal and physiological responses embedded within the fMRI signals -- present challenges with respect to data analysis and interpretation. In this brief commentary, we discuss methods and open challenges in naturalistic fMRI investigations, with a focus on extracting and interpreting stimulus-induced fMRI signals.

Resting-state "physiological networks".

Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which manifest as structured spatial patterns of temporal correlations between distant brain regions. Here, we investigated whether such "physiological networks"-sets of segregated brain regions that exhibit similar responses following slow changes in systemic physiology-resemble patterns associated with large-scale networks typically attributed to remotely synchronized neuronal activity. By analyzing a large group of subjects from the 3T Human Connectome Project (HCP) database, we demonstrate brain-wide and noticeably heterogenous dynamics tightly coupled to either respiratory variation or heart rate changes. We show, using synthesized data generated from physiological recordings across subjects, that these physiologically-coupled fluctuations alone can produce networks that strongly resemble previously reported resting-state networks, suggesting that, in some cases, the "physiological networks" seem to mimic the neuronal networks. Further, we show that such physiologically-relevant connectivity estimates appear to dominate the overall connectivity observations in multiple HCP subjects, and that this apparent "physiological connectivity" cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiologically-coupled responses. Our results challenge previous notions that physiological confounds are either localized to large veins or globally coherent across the cortex, therefore emphasizing the necessity to consider potential physiological contributions in fMRI-based functional connectivity studies. The rich spatiotemporal patterns carried by such "physiological" dynamics also suggest great potential for clinical biomarkers that are complementary to large-scale neuronal networks.

Impaired vigilance networks in temporal lobe epilepsy: Mechanisms and clinical implications.

Mesial temporal lobe epilepsy (mTLE) is a neurological disorder in which patients suffer from frequent consciousness-impairing seizures, broad neurocognitive deficits, and diminished quality of life. Although seizures in mTLE originate focally in the hippocampus or amygdala, mTLE patients demonstrate cognitive deficits that extend beyond temporal lobe function-such as decline in executive function, cognitive processing speed, and attention-as well as diffuse decreases in neocortical metabolism and functional connectivity. Given prior observations that mTLE patients exhibit impairments in vigilance, and that seizures may disrupt the activity and long-range connectivity of subcortical brain structures involved in vigilance regulation, we propose that subcortical activating networks underlying vigilance play a critical role in mediating the widespread neural and cognitive effects of focal mTLE. Here, we review evidence for impaired vigilance in mTLE, examine clinical implications and potential network underpinnings, and suggest neuroimaging strategies for determining the relationship between vigilance, brain connectivity, and neurocognition in patients and healthy controls.

Default mode network connectivity change corresponds to ketamine's delayed glutamatergic effects.

Ketamine exerts rapid antidepressant effects peaking 24 h after a single infusion, which have been suggested to be reflected by both reduced functional connectivity (FC) within default mode network (DMN) and altered glutamatergic levels in the perigenual anterior cingulate cortex (pgACC) at 24 h. Understanding the interrelation and time point specificity of ketamine-induced changes of brain circuitry and metabolism is thus key to future therapeutic developments. We investigated the correlation of late glutamatergic changes with FC changes seeded from the posterior cingulate cortex (PCC) and tested the prediction of the latter by acute fractional amplitude of low-frequency fluctuations (fALFF). In a double-blind, randomized, placebo-controlled study of 61 healthy subjects, we compared effects of subanesthetic ketamine infusion (0.5 mg/kg over 40 min) on resting-state fMRI and MR-Spectroscopy at 7 T 1 h and 24 h post-infusion. FC decrease between PCC and dorsomedial prefrontal cortex (dmPFC) was found at 24 h post-infusion (but not 1 h) and this FC decrease correlated with glutamatergic changes at 24 h in pgACC. Acute increase in fALFF was found in ventral PCC at 1 h which was not observed at 24 h and inversely correlated with the reduced dPCC FC towards the dmPFC at 24 h. The correlation of metabolic and functional markers of delayed ketamine effects and their temporal specificity suggest a potential mechanistic relationship between glutamatergic modulation and reconfiguration of brain regions belonging to the DMN.