• Natural Sciences and Engineering Re... close
• US close
• Frontiers in Neurology close
• Neuroinformatics close

It is increasingly recognized that deep understanding of epileptic seizures requires both localizing and characterizing the functional network of the region where they are initiated, i. e., the epileptic focus. Previous investigations of the epileptogenic focus' functional connectivity have yielded contrasting results, reporting both pathological increases and decreases during resting periods and seizures. In this study, we shifted paradigm to investigate the time course of connectivity in relation to interictal epileptiform discharges. We recruited 35 epileptic patients undergoing intracranial EEG (iEEG) investigation as part of their presurgical evaluation. For each patient, 50 interictal epileptic discharges (IEDs) were marked and iEEG signals were epoched around those markers. Signals were narrow-band filtered and time resolved phase-locking values were computed to track the dynamics of functional connectivity during IEDs. Results show that IEDs are associated with a transient decrease in global functional connectivity, time-locked to the peak of the discharge and specific to the high range of the gamma frequency band. Disruption of the long-range connectivity between the epileptic focus and other brain areas might be an important process for the generation of epileptic activity. Transient desynchronization could be a potential biomarker of the epileptogenic focus since 1) the functional connectivity involving the focus decreases significantly more than the connectivity outside the focus and 2) patients with good surgical outcome appear to have a significantly more disconnected focus than patients with bad outcomes.

IntroductionPrevious case-control studies of sudden unexpected death in epilepsy (SUDEP) patients failed to identify ECG features (peri-ictal heart rate, heart rate variability, corrected QT interval, postictal heart rate recovery, and cardiac rhythm) predictive of SUDEP risk. This implied a need to derive novel metrics to assess SUDEP risk from ECG.MethodsWe applied Single Spectrum Analysis and Independent Component Analysis (SSA-ICA) to remove artifact from ECG recordings. Then cross-frequency phase-phase coupling (PPC) was applied to a 20-s mid-seizure window and a contour of −3 dB coupling strength was determined. The contour centroid polar coordinates, amplitude (alpha) and angle (theta), were calculated. Association of alpha and theta with SUDEP was assessed and a logistic classifier for alpha was constructed.ResultsAlpha was higher in SUDEP patients, compared to non-SUDEP patients (p < 0.001). Theta showed no significant difference between patient populations. The receiver operating characteristic (ROC) of a logistic classifier for alpha resulted in an area under the ROC curve (AUC) of 94% and correctly classified two test SUDEP patients.DiscussionThis study develops a novel metric alpha, which highlights non-linear interactions between two rhythms in the ECG, and is predictive of SUDEP risk.

BackgroundFifty-one percent of individuals with multiple sclerosis (MS) develop cognitive impairment (CI) in information processing speed (IPS). Although IPS scores are associated with health and well-being, neural changes that underlie IPS impairments in MS are not understood. Resting state fMRI can provide insight into brain function changes underlying impairment in persons with MS.ObjectivesWe aimed to assess functional connectivity (FC) differences in (i) persons with MS compared to healthy controls (HC), (ii) persons with both MS and CI (MS-CI) compared to HC, (iii) persons with MS that are cognitively preserved (MS-CP) compared to HC, (iv) MS-CI compared to MS-CP, and (v) in relation to cognition within the MS group.MethodsWe included 107 participants with MS (age 49.5 ± 12.9, 82% women), and 94 controls (age 37.9 ± 15.4, 66% women). Each participant was administered the Symbol Digit Modalities Test (SDMT) and underwent a resting state fMRI scan. The MS-CI group was created by applying a z-score cut-off of ≤−1.5 to locally normalized SDMT scores. The MS-CP group was created by applying a z-score of ≥0. Control groups (HCMS-CI and HCMS-CP) were based on the nearest age-matched HC participants. A whole-brain ROI-to-ROI analysis was performed followed by specific contrasts and a regression analysis.ResultsIndividuals with MS showed FC differences compared to HC that involved the cerebellum, visual and language-associated brain regions, and the thalamus, hippocampus, and basal ganglia. The MS-CI showed FC differences compared to HCMS-CI that involved the cerebellum, visual and language-associated areas, thalamus, and caudate. SDMT scores were correlated with FC between the cerebellum and lateral occipital cortex in MS. No differences were observed between the MS-CP and HCMS-CP or MS-CI and MS-CP groups.ConclusionOur findings emphasize FC changes of cerebellar, visual, and language-associated areas in persons with MS. These differences were apparent for (i) all MS participants compared to HC, (ii) MS-CI subgroup and their matched controls, and (iii) the association between FC and SDMT scores within the MS group. Our findings strongly suggest that future work that examines the associations between FC and IPS impairments in MS should focus on the involvement of these regions.

ObjectiveVascular comorbidities are associated with reduced cognitive performance and with changes in brain structure in people with multiple sclerosis (MS). Understanding causal pathways is necessary to support the design of interventions to mitigate the impacts of comorbidities, and to monitor their effectiveness. We assessed the inter-relationships among vascular comorbidity, cognition and brain structure in people with MS.MethodsAdults with neurologist-confirmed MS reported comorbidities, and underwent assessment of their blood pressure, HbA1c, and cognitive functioning (i.e., Symbol Digit Modalities Test, California Verbal Learning Test, Brief Visuospatial Memory Test-Revised, and verbal fluency). Test scores were converted to age-, sex-, and education-adjusted z-scores. Whole brain magnetic resonance imaging (MRI) was completed, from which measures of thalamic and hippocampal volumes, and mean diffusivity of gray matter and normal-appearing white matter were converted to age and sex-adjusted z-scores. Canonical correlation analysis was used to identify linear combinations of cognitive measures (cognitive variate) and MRI measures (MRI variate) that accounted for the most correlation between the cognitive and MRI measures. Regression analyses were used to test whether MRI measures mediated the relationships between the number of vascular comorbidities and cognition measures.ResultsOf 105 participants, most were women (84.8%) with a mean (SD) age of 51.8 (12.8) years and age of symptom onset of 29.4 (10.5) years. Vascular comorbidity was common, with 35.2% of participants reporting one, 15.2% reporting two, and 8.6% reporting three or more. Canonical correlation analysis of the cognitive and MRI variables identified one pair of variates (Pillai's trace = 0.45, p = 0.0035). The biggest contributors to the cognitive variate were the SDMT and CVLT-II, and to the MRI variate were gray matter MD and thalamic volume. The correlation between cognitive and MRI variates was 0.50; these variates were used in regression analyses. On regression analysis, vascular comorbidity was associated with the MRI variate, and with the cognitive variate. After adjusting for the MRI variate, vascular comorbidity was not associated with the cognitive variate.ConclusionVascular comorbidity is associated with lower cognitive function in people with MS and this association is partially mediated via changes in brain macrostructure and microstructure.

Objective: Magnetoencephalography source imaging (MSI) of interictal epileptiform discharges (IED) is a useful presurgical tool in the evaluation of drug-resistant frontal lobe epilepsy (FLE) patients. Yet, failures in MSI can arise related to artifacts and to interference of background activity. Independent component analysis (ICA) is a popular denoising procedure but its clinical application remains challenging, as the selection of multiple independent components (IC) is controversial, operator dependent, and time consuming. We evaluated whether selecting only one IC of interest based on its similarity with the average IED field improves MSI in FLE. Methods: MSI was performed with the equivalent current dipole (ECD) technique and two distributed magnetic source imaging (dMSI) approaches: minimum norm estimate (MNE) and coherent Maximum Entropy on the Mean (cMEM). MSI accuracy was evaluated under three conditions: (1) ICA of continuous data (Cont_ICA), (2) ICA at the time of IED (IED_ICA), and (3) without ICA (No_ICA). Localization performance was quantitatively measured as actual distance of the source maximum in relation to the focus (Dmin), and spatial dispersion (SD) for dMSI. Results: After ICA, ECD Dmin did not change significantly (p > 0.200). For both dMSI techniques, ICA application worsened the source localization accuracy. We observed a worsening of both MNE Dmin (p < 0.05, consistently) and MNE SD (p < 0.001, consistently) for both ICA approaches. A similar behaviour was observed for cMEM, for which, however, Cont_ICA seemed less detrimental. Conclusion: We demonstrated that a simplified ICA approach selecting one IC of interest in combination with distributed magnetic source imaging can be detrimental. More complex approaches may provide better results but would be rather difficult to apply in real-world clinical setting. In a broader perspective, caution should be taken in applying ICA for source localization of interictal activity. To ensure optimal and useful results, effort should focus on acquiring good quality data, minimizing artifacts, and determining optimal candidacy for MEG, rather than counting on data cleaning techniques.

Biomarkers derived from brain magnetic resonance (MR) imaging have promise in being able to assist in the clinical diagnosis of brain pathologies. These have been used in many studies in which the goal has been to distinguish between pathologies such as Alzheimer's disease and healthy aging. However, other dementias, in particular, frontotemporal dementia, also present overlapping pathological brain morphometry patterns. Hence, a classifier that can discriminate morphometric features from a brain MRI from the three classes of normal aging, Alzheimer's disease (AD), and frontotemporal dementia (FTD) would offer considerable utility in aiding in correct group identification. Compared to the conventional use of multiple pair-wise binary classifiers that learn to discriminate between two classes at each stage, we propose a single three-way classification system that can discriminate between three classes at the same time. We present a novel classifier that is able to perform a three-class discrimination test for discriminating among AD, FTD, and normal controls (NC) using volumes, shape invariants, and local displacements (three features) of hippocampi and lateral ventricles (two structures times two hemispheres individually) obtained from brain MR images. In order to quantify its utility in correct discrimination, we optimize the three-class classifier on a training set and evaluate its performance using a separate test set. This is a novel, first-of-its-kind comparative study of multiple individual biomarkers in a three-class setting. Our results demonstrate that local atrophy features in lateral ventricles offer the potential to be a biomarker in discriminating among AD, FTD, and NC in a three-class setting for individual patient classification.

Despite changes in guideline-based management of moderate/severe traumatic brain injury (TBI) over the preceding decades, little impact on mortality and morbidity have been seen. This argues against the “one-treatment fits all” approach to such management strategies. With this, some preliminary advances in the area of personalized medicine in TBI care have displayed promising results. However, to continue transitioning toward individually-tailored care, we require integration of complex “-omics” data sets. The past few decades have seen dramatic increases in the volume of complex multi-modal data in moderate and severe TBI care. Such data includes serial high-fidelity multi-modal characterization of the cerebral physiome, serum/cerebrospinal fluid proteomics, admission genetic profiles, and serial advanced neuroimaging modalities. Integrating these complex and serially obtained data sets, with patient baseline demographics, treatment information and clinical outcomes over time, can be a daunting task for the treating clinician. Within this review, we highlight the current status of such multi-modal omics data sets in moderate/severe TBI, current limitations to the utilization of such data, and a potential path forward through employing integrative neuroinformatic approaches, which are applied in other neuropathologies. Such advances are positioned to facilitate the transition to precision prognostication and inform a top-down approach to the development of personalized therapeutics in moderate/severe TBI.