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Resting state functional magnetic resonance imaging (fMRI) has been commonly used to measure functional connectivity between cortical regions, while diffusion tensor imaging (DTI) can be used to characterize structural connectivity of white matter tracts. In principle combining resting state fMRI and DTI data could allow characterization of structure-function relations of distributed neural networks. However, due to differences in the biophysical origins of their signals and in the tissues to which they apply, there has been no direct integration of these techniques to date. We demonstrate that MRI signal variations and power spectra in a resting state are largely comparable between gray matter and white matter, that there are temporal correlations of fMRI signals that persist over long distances within distinct white matter structures, and that neighboring intervoxel correlations of low frequency resting state signals showed distinct anisotropy in many regions. These observations suggest that MRI signal variations from within white matter in a resting state may convey similar information as their corresponding fluctuations of MRI signals in gray matter. We thus derive a local spatio-temporal correlation tensor which captures directional variations of resting-state correlations and which reveals distinct structures in both white and gray matter. This novel concept is illustrated with in vivo experiments in a resting state, which demonstrate the potential of the technique for mapping the functional structure of neural networks and for direct integration of structure-function relations in the human brain.

ObjectiveThe effects of temporal lobe epilepsy (TLE) on subcortical arousal structures remain incompletely understood. Here, we evaluate thalamic arousal network functional connectivity in TLE and examine changes after epilepsy surgery.MethodsWe examined 26 adult patients with TLE and 26 matched control participants and used resting-state functional MRI (fMRI) to measure functional connectivity between the thalamus (entire thalamus and 19 bilateral thalamic nuclei) and both neocortex and brainstem ascending reticular activating system (ARAS) nuclei. Postoperative imaging was completed for 19 patients >1 year after surgery and compared with preoperative baseline.ResultsBefore surgery, patients with TLE demonstrated abnormal thalamo-occipital functional connectivity, losing the normal negative fMRI correlation between the intralaminar central lateral (CL) nucleus and medial occipital lobe seen in controls (p < 0.001, paired t-test). Patients also had abnormal connectivity between ARAS and CL, lower ipsilateral intrathalamic connectivity, and smaller ipsilateral thalamic volume compared with controls (p < 0.05 for each, paired t-tests). Abnormal brainstem–thalamic connectivity was associated with impaired visuospatial attention (ρ = −0.50, p = 0.02, Spearman’s rho) while lower intrathalamic connectivity and volume were related to higher frequency of consciousness-sparing seizures (p < 0.02, Spearman’s rho). After epilepsy surgery, patients with improved seizures showed partial recovery of thalamo-occipital and brainstem–thalamic connectivity, with values more closely resembling controls (p < 0.01 for each, analysis of variance).ConclusionsOverall, patients with TLE demonstrate impaired connectivity in thalamic arousal networks that may be involved in visuospatial attention, but these disturbances may partially recover after successful epilepsy surgery. Thalamic arousal network dysfunction may contribute to morbidity in TLE.

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

•Low-dimensional representations of fMRI data may contain vigilance information.•These representations can be clustered into two or more putative vigilance states.•Low-dimensional fMRI exhibited alignment with behavioral and EEG vigilance.•More frequent anti-correlated cortical activity occurred during higher vigilance. 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.

ObjectiveWe sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality and surgical outcome.Methods151 subjects were included in this analysis: 62 patients (aged 18–68 years, 36 women) and 89 healthy controls (aged 18–71 years, 47 women). We created a supervised machine learning technique that uses diffusion-weighted metrics to classify subject groups. Specifically, we sought to classify patients versus healthy controls, unilateral versus bilateral temporal lobe epilepsy, left versus right temporal lobe epilepsy and seizure-free versus not seizure-free surgical outcome. We then reduced the dimensionality of derived features with community detection for ease of interpretation.ResultsWe classified the subject groups in withheld testing data sets with a cross-fold average testing areas under the receiver operating characteristic curve of 0.745 for patients versus healthy controls, 1.000 for unilateral versus bilateral seizure onset, 0.662 for left versus right seizure onset, 0.800 for left-sided seizure-free vsersu not seizure-free surgical outcome and 0.775 for right-sided seizure-free versus not seizure-free surgical outcome.ConclusionsThis technique classifies important clinical decisions in the presurgical workup of temporal lobe epilepsy by generating discerning white-matter features. We believe that this work augments existing network connectivity findings in the field by further elucidating important white-matter pathology in temporal lobe epilepsy. We hope that this work contributes to recent efforts aimed at using diffusion imaging as an augmentation to the presurgical workup of this devastating neurological disorder.

Abstract BACKGROUND Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

ObjectiveSeizures in temporal lobe epilepsy (TLE) disturb brain networks and lead to connectivity disturbances. We previously hypothesised that recurrent seizures in TLE may lead to abnormal connections involving subcortical activating structures including the ascending reticular activating system (ARAS), contributing to neocortical dysfunction and neurocognitive impairments. However, no studies of ARAS connectivity have been previously reported in patients with epilepsy.MethodsWe used resting-state functional MRI recordings in 27 patients with TLE (67% right sided) and 27 matched controls to examine functional connectivity (partial correlation) between eight brainstem ARAS structures and 105 cortical/subcortical regions. ARAS nuclei included: cuneiform/subcuneiform, dorsal raphe, locus coeruleus, median raphe, parabrachial complex, pontine oralis, pedunculopontine and ventral tegmental area. Connectivity patterns were related to disease and neuropsychological parameters.ResultsIn control subjects, regions showing highest connectivity to ARAS structures included limbic structures, thalamus and certain neocortical areas, which is consistent with prior studies of ARAS projections. Overall, ARAS connectivity was significantly lower in patients with TLE than controls (p<0.05, paired t-test), particularly to neocortical regions including insular, lateral frontal, posterior temporal and opercular cortex. Diminished ARAS connectivity to these regions was related to increased frequency of consciousness-impairing seizures (p<0.01, Pearson’s correlation) and was associated with impairments in verbal IQ, attention, executive function, language and visuospatial memory on neuropsychological evaluation (p<0.05, Spearman’s rho or Kendell’s tau-b).ConclusionsRecurrent seizures in TLE are associated with disturbances in ARAS connectivity, which are part of the widespread network dysfunction that may be related to neurocognitive problems in this devastating disorder.

This study presents a cross-sectional investigation of functional networks in temporal lobe epilepsy (TLE) as they evolve over years of disease. Networks of interest were identified based on a priori hypotheses: the network of seizure propagation ipsilateral to the seizure focus, the same regions contralateral to seizure focus, the cross hemisphere network of the same regions, and a cingulate midline network. Resting functional magnetic resonance imaging data were acquired for 20 min in 12 unilateral TLE patients, and 12 age- and gender-matched healthy controls. Functional changes within and between the four networks as they evolve over years of disease were quantified by standard measures of static functional connectivity and novel measures of dynamic functional connectivity. The results suggest an initial disruption of cross-hemispheric networks and an increase in static functional connectivity in the ipsilateral temporal network accompanying the onset of TLE seizures. As seizures progress over years, the static functional connectivity across the ipsilateral network diminishes, while dynamic functional connectivity measures show the functional independence of this ipsilateral network from the network of midline regions of the cingulate declines. This implies a gradual breakdown of the seizure onset and early propagation network involving the ipsilateral hippocampus and temporal lobe as it becomes more synchronous with the network of regions responsible for secondary generalization of the seizures, a process that may facilitate the spread of seizures across the brain. Ultimately, the significance of this evolution may be realized in relating it to symptoms and treatment outcomes of TLE.

The capabilities of magnetic resonance imaging (MRI) to measure structural and functional connectivity in the human brain have motivated growing interest in characterizing the relationship between these measures in the distributed neural networks of the brain. In this study, we attempted an integration of structural and functional analyses of the human language circuits, including Wernicke's (WA), Broca's (BA) and supplementary motor area (SMA), using a combination of blood oxygen level dependent (BOLD) and diffusion tensor MRI. Functional connectivity was measured by low frequency inter-regional correlations of BOLD MRI signals acquired in a resting steady-state, and structural connectivity was measured by using adaptive fiber tracking with diffusion tensor MRI data. The results showed that different language pathways exhibited different structural and functional connectivity, indicating varying levels of inter-dependence in processing across regions. Along the path between BA and SMA, the fibers tracked generally formed a single bundle and the mean radius of the bundle was positively correlated with functional connectivity. However, fractional anisotropy was found not to be correlated with functional connectivity along paths connecting either BA and SMA or BA and WA. These findings suggest that structure-function relations in the human language circuits may involve a number of confounding factors that need to be addressed. Nevertheless, the insights gained from this work offers a useful guidance for continued studies that may provide a non-invasive means to evaluate brain network integrity in vivo for use in diagnosing and determining disease progression and recovery.

Abstract BACKGROUND Focal seizures in temporal lobe epilepsy (TLE) are associated with widespread brain network perturbations and neurocognitive problems. OBJECTIVE To determine whether brainstem connectivity disturbances improve with successful epilepsy surgery, as recent work has demonstrated decreased brainstem connectivity in TLE that is related to disease severity and neurocognitive profile. METHODS We evaluated 15 adult TLE patients before and after (>1 yr; mean, 3.4 yr) surgery, and 15 matched control subjects using magnetic resonance imaging to measure functional and structural connectivity of ascending reticular activating system (ARAS) structures, including cuneiform/subcuneiform nuclei (CSC), pedunculopontine nucleus (PPN), and ventral tegmental area (VTA). RESULTS TLE patients who achieved long-term postoperative seizure freedom (10 of 15) demonstrated increases in functional connectivity between ARAS structures and fronto-parietal-insular neocortex compared to preoperative baseline (P = .01, Kruskal–Wallis), with postoperative connectivity patterns resembling controls’ connectivity. No functional connectivity changes were detected in 5 patients with persistent seizures after surgery (P = .9, Kruskal–Wallis). Among seizure-free postoperative patients, larger increases in CSC, PPN, and VTA functional connectivity were observed in individuals with more frequent seizures before surgery (P < .05 for each, Spearman's rho). Larger postoperative increases in PPN functional connectivity were seen in patients with lower baseline verbal IQ (P = .03, Spearman's rho) or verbal memory (P = .04, Mann–Whitney U). No changes in ARAS structural connectivity were detected after successful surgery. CONCLUSION ARAS functional connectivity disturbances are present in TLE but may recover after successful epilepsy surgery. Larger increases in postoperative connectivity may be seen in individuals with more severe disease at baseline.