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In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.

PURPOSE The EEG rhythms demonstrate changes in frequency and power with spontaneous changes in behavioral state that do not have well-understood metabolic correlates within the brain. To investigate this question and compare the temporal lobe theta and delta rhythms, resting-state functional MRI was obtained with simultaneous EEG. METHODS Simultaneous EEG-functional MRI was recorded from 14 healthy sleep-deprived subjects in awake and drowsy states. Scalp electrodes corresponding to bilateral temporal lobes were used to calculate delta and theta band power. The resulting time series was used as input in a general linear model, and the final power curves were convolved with the standard hemodynamic response function. Resulting images were thresholded at Z > 2.0. RESULTS Positive and negative correlations for unilateral theta and delta rhythms were present bilaterally in different structures and with differing correlation signs. Theta rhythm positive correlation was present in hindbrain, peri-opercular, and frontoparietal regions and subcortical gray structures, whereas negative correlation was present in parietooccipital cortex. Delta rhythm positive correlation was present in parietooccipital cortex, and negative correlation roughly resembled positive correlations for the theta rhythm. CONCLUSIONS Temporal lobe theta and delta rhythms are correlated with functional MRI signal in an almost mutually exclusive distribution. The different distributions indicate different corresponding networks. These normal findings supplement the understanding of theta and delta rhythm significance.

The default mode network (DMN) is composed of cerebral regions involved in conscious, resting state cognition. The hippocampus is an essential component of this network. Here, the DMN in TLE is compared to control subjects to better understand its involvement in TLE. We performed resting state connectivity analysis using regions of interest (ROIs) in the retrosplenium/precuneus (Rsp/PCUN) and the ventro-medial pre-frontal cortex (vmPFC) in 36 subjects (11 with right TLE, 12 with left TLE, 13 controls) to delineate the posterior and anterior DMN regions respectively. We found reduced connectivity of the posterior to the anterior DMN in patients with both right and left TLE. However, the posterior and anterior networks were found to be individually preserved. Lateralization of TLE affects the DMN with left TLE demonstrating more extensive networks. These DMN changes may be relevant to altered cognition and memory in TLE and may be relevant to right vs. left TLE differences in cognitive involvement.

© 2015. Temporal lobe epilepsy (TLE) is often associated with progressive changes to seizures, memory, and mood during its clinical course. However, the cerebral changes related to this progression are not well understood. Because the changes may be related to changes in brain networks, we used functional connectivity MRI (fcMRI) to determine whether brain network parameters relate to the duration of TLE. Graph theory-based analysis of the sites of reported regions of TLE abnormality was performed on resting-state fMRI data in 48 subjects: 24 controls, 13 patients with left TLE, and 11 patients with right TLE. Various network parameters were analyzed including betweenness centrality (BC), clustering coefficient (CC), path length (PL), small-world index (SWI), global efficiency (GE), connectivity strength (CS), and connectivity diversity (CD). These were compared for patients with TLE as a group, compared to controls, and for patients with left and right TLE separately. The association of changes in network parameters with epilepsy duration was also evaluated. We found that CC, CS, and CD decreased in subjects with TLE compared to control subjects. Analyzed according to epilepsy duration, patients with TLE showed a progressive reduction in CD. In conclusion, we found that several network parameters decreased in patients with TLE compared to controls, which suggested reduced connectivity in TLE. Reduction in CD associated with epilepsy duration suggests a homogenization of connections over time in TLE, indicating a reduction of the normal repertoire of stronger and weaker connections to other brain regions.

Objective Temporal lobe epilepsy (TLE) affects brain areas beyond the temporal lobes due to connections of the hippocampi and other temporal lobe structures. Using functional connectivity magnetic resonance imaging (MRI), we determined the changes of hippocampal networks in TLE to assess for a more complete distribution of abnormality. Methods Regions of interest (ROIs) were defined in the right and left hippocampi in three groups of participants: left TLE (n = 13), right TLE (n = 11), and healthy controls (n = 16). Brain regions functionally connected to these ROIs were identified by correlating resting-state low-frequency functional MRI (fMRI) blood oxygenation level-dependent (BOLD) signal fluctuations. The grouped results were compared using independent sample t-test. Results TLE was associated with increased hippocampal connectivity involving several key areas of the limbic network (temporal lobe, insula, thalamus), frontal lobes, angular gyrus, basal ganglia, brainstem, and cerebellum, along with reduced connectivity involving areas of the sensorimotor cortex (visual, somatosensory, auditory, primary motor) and the default mode network (precuneus). Left TLE had more marked connectivity changes than right TLE. Significance The observed connectivity changes in TLE indicate dysfunctional networks that underlie widespread brain involvement in TLE. There are identifiable differences in the connectivity of the hippocampi between left and right TLE. © 2013 International League Against Epilepsy.

Sleep spindles and K-complexes are EEG hallmarks of non-REM sleep. However, the brain regions generating these discharges and the functional connections of their generators to other regions are not fully known. We investigated the neuroanatomical correlates of spindles and K-complexes using simultaneous EEG and fMRI.EEGs recorded during EEG-fMRI studies of 7 individuals were used for fMRI analysis. Higher-level group analyses were performed, and images were thresholded at Z ≥ 2.3.fMRI of 106 spindles and 60 K-complexes was analyzed. Spindles corresponded to increased signal in thalami and posterior cingulate, and right precuneus, putamen, paracentral cortex, and temporal lobe. K-complexes corresponded to increased signal in thalami, superior temporal lobes, paracentral gyri, and medial regions of the occipital, parietal and frontal lobes. Neither corresponded to regions of decreased signal.fMRI of both spindles and K-complexes depicts signal subjacent to the vertex, which likely indicates each discharges' source. The thalamic signal is consistent with thalamic involvement in sleep homeostasis. The limbic region's signal is consistent with roles in memory consolidation. Unlike the spindle, the K-complex corresponds to extensive signal in primary sensory cortices.Identification of these active regions contributes to the understanding of sleep networks and the physiology of awareness and memory during sleep.

The vertex sharp transient (VST) is an electroencephalographic (EEG) discharge that is an early marker of non-REM sleep. It has been recognized since the beginning of sleep physiology research, but its source and function remain mostly unexplained. We investigated VST generation using functional MRI (fMRI).Simultaneous EEG and fMRI were recorded from seven individuals in drowsiness and light sleep. VST occurrences on EEG were modeled with fMRI using an impulse function convolved with a hemodynamic response function to identify cerebral regions correlating to the VSTs. A resulting statistical image was thresholded at Z2.3.Two hundred VSTs were identified. Significantly increased signal was present bilaterally in medial central, lateral precentral, posterior superior temporal, and medial occipital cortex. No regions of decreased signal were present.The regions are consistent with electrophysiologic evidence from animal models and functional imaging of human sleep, but the results are specific to VSTs. The regions principally encompass the primary sensorimotor cortical regions for vision, hearing, and touch.The results depict a network comprising the presumed VST generator and its associated regions. The associated regions functional similarity for primary sensation suggests a role for VSTs in sensory experience during sleep.