This statistic displays the number of active epilepsy cases in the United States as of 2015, by age. As of this time, it was estimated that around ****** people in Alabama were living with epilepsy, compared to ***** in Delaware. Epilepsy is a brain disorder that causes recurring seizures and can lead to life-long disability.

In 2021, it was estimated that around *** percent of white, non-Hispanic adults in the United States were living with active epilepsy. Epilepsy is a brain disorder that causes recurring seizures and can lead to life-long disability. This statistic displays the percentage of adults with active epilepsy in the United States as of 2021, by ethnicity and race.

In 2023, it was estimated that around ******* children in the United States aged 12 to 17 years currently had epilepsy or a seizure disorder. This statistic shows the number of children in the U.S. with epilepsy or a seizure disorder as of 2023, by age.

Financial overview and grant giving statistics of Epilepsy Coalition of New York State Inc.

In 2021, around *** percent of adults in the United States with less than a high school education or GED were estimated to be living with active epilepsy. Epilepsy is a brain disorder that causes recurring seizures and can lead to life-long disability. This statistic displays the percentage of adults with active epilepsy in the U.S. as of 2021, by education level.

In 2021-2023, it was estimated that almost 1.4 million adults in the United States aged 18 to 44 years were living with active epilepsy. Epilepsy is a brain disorder that causes recurring seizures and can lead to lifelong disability. This statistic displays the number of active epilepsy cases in the United States as of 2021-2023, by age.

Collection description

Statistical maps presented in paper "Altered relationship between heart rate variability and fMRI-based functional connectivity in people with epilepsy" (Kassinopoulos et al. 2021).

Figure 2 : Association of regional BOLD fluctuations with changes in heart rate at the group level. Statistical map of one-sample t-test considering patients and controls (n=44), thresholded with a voxel-wise threshold of family-wise error (FWE) rate p < 0.05 corrected for multiple comparisons using the random-field theory (Worsley et al., 1996).
* Related file: ttest1_HR_all_subjects.nii.gz

Figure 5: Differences in seed-based FC between low and high LF-HRV state with seeds placed in the (top panel) right caudal temporal thalamus and (bottom panel) left posterior parietal thalamus. The first and second rows of each panel show the fisher-transformed correlations average across all healthy controls and epilepsy patients, respectively. Red color indicates higher (towards positive values) connectivity with the seed region in high LF-HRV state whereas blue color indicates lower connectivity. The last row of each panel shows the two-sample t-test thresholded at p < 0.001 (uncorrected). Even though no significant differences were found between the two groups after correcting for multiple comparison (FWE; p < 0.05), the differences observed with p < 0.001 (uncorrected) are consistent with the results obtained with the analysis in the atlas space (Fig. 3-Fig.4) where the spatial autocorrelation between voxels of the same parcel are implicitly taken into account.

* Related files: 1) r_LF_HRV_epilepsy_post_pariet_thal_left.nii.gz; 2) r_LF_HRV_healthy_post_pariet_thal_left.nii.gz; 3) ttest2_diff_LF_HRV_post_pariet_thal_left.nii.gz; 4) r_LF_HRV_epilepsy_caud_temp_thal_left.nii.gz; 5) r_LF_HRV_healthy_caud_temp_thal_left.nii.gz; 6) ttest2_diff_LF_HRV_caud_temp_thal_left.nii.gz.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

rest eyes open

Map type

T

Seizure and medication-related factors of cognitive impairment in patients with epilepsy attending outpatient department of south Gondar zone hospitals Amhara Ethiopia 2021(n = 509).

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In 2021-2023, it was estimated that around 2.16 million white, non-Hispanic adults in the United States were living with active epilepsy. Epilepsy is a brain disorder that causes recurring seizures and can lead to lifelong disability. This statistic displays the number of active epilepsy cases in the United States as of 2021-2023, by ethnicity and race.

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

This statistic displays the number of active epilepsy cases in the United States as of 2015, by region. As of this time, around ******* adults in the Northeast were living with active epilepsy. Epilepsy is a brain disorder that causes recurring seizures and can lead to life-long disability.

1SS: State similarity feature set. These results are reported from previous study.Values are determined by linear interpolation.Seizure prediction performance at the critical false prediction rate.

ObjectiveThe objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators.MethodsThe time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests.ResultsDFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset.ConclusionOur findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.

Background: Disruptions in central autonomic processes in people with epilepsy have been studied through evaluation of heart rate variability (HRV). Decreased HRV appears in epilepsy compared to healthy controls, suggesting a shift in autonomic balance toward sympathetic dominance; recent studies have associated HRV changes with seizure severity and outcome of interventions. However, the processes underlying these autonomic changes remain unclear. We examined the nature of these changes by assessing alterations in whole-brain functional connectivity, and relating those alterations to HRV.Methods: We examined regional brain activity and functional organization in 28 drug-resistant epilepsy patients and 16 healthy controls using resting-state functional magnetic resonance imaging (fMRI). We employed an HRV state-dependent functional connectivity (FC) framework with low and high HRV states derived from the following four cardiac-related variables: 1. RR interval, 2. root mean square of successive differences (RMSSD), 4. low-frequency HRV (0.04–0.15 Hz; LF-HRV) and high-frequency HRV (0.15–0.40 Hz; HF-HRV). The effect of group (epilepsy vs. controls), HRV state (low vs. high) and the interactions of group and state were assessed using a mixed analysis of variance (ANOVA). We assessed FC within and between 7 large-scale functional networks consisting of cortical regions and 4 subcortical networks, the amygdala, hippocampus, basal ganglia and thalamus networks.Results: Consistent with previous studies, decreased RR interval (increased heart rate) and decreased HF-HRV appeared in people with epilepsy compared to healthy controls. For both groups, fluctuations in heart rate were positively correlated with BOLD activity in bilateral thalamus and regions of the cerebellum, and negatively correlated with BOLD activity in the insula, putamen, superior temporal gyrus and inferior frontal gyrus. Connectivity strength in patients between right thalamus and ventral attention network (mainly insula) increased in the high LF-HRV state compared to low LF-HRV; the opposite trend appeared in healthy controls. A similar pattern emerged for connectivity between the thalamus and basal ganglia.Conclusion: The findings suggest that resting connectivity patterns between the thalamus and other structures underlying HRV expression are modified in people with drug-resistant epilepsy compared to healthy controls.

Ripple oscillations (80–200 Hz) in the normal hippocampus are involved in memory consolidation during rest and sleep. In the epileptic brain, increased ripple and fast ripple (200–600 Hz) rates serve as a biomarker of epileptogenic brain. We report that both ripples and fast ripples exhibit a preferred phase angle of coupling with the trough-peak (or On-Off) state transition of the sleep slow wave in the hippocampal seizure onset zone (SOZ). Ripples on slow waves in the hippocampal SOZ also had a lower power, greater spectral frequency, and shorter duration than those in the non-SOZ. Slow waves in the mesial temporal lobe modulated the baseline firing rate of excitatory neurons, but did not significantly influence the increased firing rate associated with ripples. In summary, pathological ripples and fast ripples occur preferentially during the On-Off state transition of the slow wave in the epileptogenic hippocampus, and ripples do not require the increased recruitment of excitatory neurons.

In 2023, it was estimated that around ** percent of non-Hispanic Black children in the United States aged 0 to 17 years currently had epilepsy or a seizure disorder. This statistic shows the percentage of children in the U.S. with epilepsy or a seizure disorder as of 2023, by race/ethnicity.

From the period 2016 to 2019, around 7.7 percent of children in the United States aged 0 to 17 years with epilepsy needed healthcare but did not receive it. In comparison, only three percent of children without epilepsy did not receive the healthcare that they needed during that period. This statistic shows the percentage of children in the United States with epilepsy who needed healthcare but did not receive it from 2016 to 2019.

Tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) affect ≈90% of individuals with TSC and significantly reduce their quality of life (QoL). However, there are limited studies assessing pharmacotherapy for TAND. A plant-derived highly purified pharmaceutical formulation of cannabidiol (CBD; Epidiolex®/Epidyolex® oral solution) is approved for seizures associated with TSC. Anecdotal evidence also suggests psychiatric, neuropsychological, and behavioral benefits of CBD. EpiCom (Epilepsy Comorbidities; NCT05864846; EU-CT, 2023-507426-17), a multicenter, open-label, phase 3b/4 study, with hybrid decentralized approach, was designed in collaboration with patient advisory groups and healthcare professionals to evaluate behavioral and other outcomes following adjunctive CBD treatment in individuals with TSC-associated seizures. EpiCom will enroll participants, aged 1–65 years (United States [US]) or 2–65 years (United Kingdom [UK], Canada, and Poland), who are starting CBD for seizures and have moderate/severe behavioral challenges according to the Caregiver Global Impression of Severity scale at screening. Participants will receive CBD (up to 25 mg/kg/d based on individual response and tolerability) in addition to their standard of care (SoC) for 26 weeks, after which participants may choose to continue CBD with SoC or SoC alone for an additional 26 weeks. Key efficacy endpoints include change from baseline on the Aberrant Behavior Checklist (e.g., irritability subscale) and the most problematic behavior on the TAND-Self-Report, Quantified Checklist. Changes in executive function, sleep, QoL, family functioning, seizure outcomes (severity, responder rates, seizure-free days), retention rate, and safety will be evaluated. The trial will enroll ≈75 participants at ≈20 sites across the US, the UK, Canada, and Poland. EpiCom will assess the changes in behavioral and other outcomes associated with TAND and seizure outcomes, including overall symptom severity and treatment retention, following adjunctive CBD treatment in individuals with TSC-associated seizures. The results will inform future studies evaluating pharmacotherapy for behavioral outcomes in similar populations.

From the period 2016 to 2019, around 93 percent of children in the United States aged 0 to 17 years with epilepsy saw a healthcare professional for medical care over the last 12 months. In comparison, around 83 percent of children without epilepsy saw a healthcare professional in the past year during that period. This statistic shows the percentage of children in the United States with epilepsy who saw a healthcare professional for medical care over the last 12 months from 2016 to 2019.