Challenges in epilepsy diagnosis and management in a low-resource setting: An experience from Bhutan

Dorji, Thinley; Yangchen,; Wangmo, Shacha; Tenzin, Karma; Jamtsho, Sonam; Pema, Dechen; Chhetri, Bikram; Nirola, Damber Kumar; Dhakal, Guru Prasad

doi:10.1016/j.eplepsyres.2023.107126

Cited by 11 publications

(2 citation statements)

References 25 publications

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“…In fact, intensive work has been dedicated in literature in the definition and characterization of easy-to-use methods as AI in IEDs and epilepsy diagnosis 9 , 10 , 17 , 18 On the contrary, this workflow should be considered as a potential user-friendly pipeline in support of clinicians in the diagnosis process, leveraging the time resolution and non-invasiveness of the EEG. The present pipeline might be of important help also in the low-income countries where patients have difficulty accessing specialist care 39 . The manageability and cost-effectiveness of the EEG, together with the present workflow, may represent a suitable instrument for helping clinicians in their daily practice.…”

Section: Discussionmentioning

confidence: 99%

Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics

Corsi,

Troisi Lopez,

Sorrentino

et al. 2024

Sci Rep

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The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86 ± 0.08 (SD) and an area under the curve of 0.93 ± 0.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5 min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.

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Section: Discussionmentioning

confidence: 99%

Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics

Corsi,

Troisi Lopez,

Sorrentino

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…On the contrary, this workflow should be considered as a potential user-friendly pipeline in support of clinicians in the diagnosis process, leveraging the time resolution and non-invasiveness of the EEG. The present pipeline might be of important help also in the low-income countries where patients have difficulty accessing specialist care (Dorji et al, 2023; Espinosa-Jovel et al, 2018). The manageability and cost-effectiveness of the EEG, together with the present workflow, may represent a suitable instrument for helping clinicians in their daily practice.…”

Section: Discussionmentioning

confidence: 99%

Neuronal avalanches in temporal lobe epilepsy as a diagnostic tool: a noninvasive investigation of intrinsic resting state dynamics

Corsi,

Lopez,

Sorrentino

et al. 2023

Preprint

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Background and ObjectivesThe epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. Here, we aimed at building an automatable workflow, to help the clinicians in the diagnostic process, differentiating between controls and a population of patients with temporal lobe epilepsy (TLE). While primarily interested in correctly classifying the participants, we used data features providing hints on the underlying pathophysiological processes. Specifically, we hypothesized that neuronal avalanches (NA) may represent a feature that encapsulates the rich brain dynamics better than the classically used functional connectivity measures (Imaginary Coherence; ImCoh).MethodsWe recorded 10 minutes of resting state activity with high-density scalp electroencephalography (hdEEG; 128 channels). We analyzed large-scale activation bursts (NA) from source activation, to capture altered dynamics. Then, we used machine-learning algorithms to classify epilepsy patients vs. controls, and we described the goodness of the classification as well as the effect of the durations of the data segments on the performance.ResultsUsing a support vector machine (SVM), we reached a classification accuracy of 0.87 ± 0.10 (SD) and an area under the curve (AUC) of 0.94 ± 0.06. The use of avalanches-derived features, generated a mean increase of 16% in the accuracy of diagnosis prediction, compared to ImCoh. Investigating the main features informing the model, we observed that the dynamics of the entorhinal cortex, superior and inferior temporal gyri, cingulate cortex and prefrontal dorsolateral cortex were informing the model with NA. Finally, we studied the time-dependent accuracy in the classification. While the classification performance grows with the duration of the data length, there are specific lengths, at 30s and 180s at which the classification performance becomes steady, with intermediate lengths showing greater variability. Classification accuracy reached a plateau at 5 minutes of recording.DiscussionWe showed that NA represents a better EEG feature for an automated epilepsy identification, being related with neuronal dynamics of pathology-relevant brain areas. Furthermore, the presence of specific durations and the performance plateau might be interpreted as the manifestation of the specific intrinsic neuronal timescales altered in epilepsy. The study represents a potentially automatable and noninvasive workflow aiding the clinicians in the diagnosis.

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Access to Mental Health Care in Bhutan: Current Status, Potential Challenges, and Ways Out

Sharma,

Joshi

2024

Access to Mental Health Care in South Asia

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Challenges in epilepsy diagnosis and management in a low-resource setting: An experience from Bhutan

Cited by 11 publications

References 25 publications

Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics

Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics

Neuronal avalanches in temporal lobe epilepsy as a diagnostic tool: a noninvasive investigation of intrinsic resting state dynamics

Access to Mental Health Care in Bhutan: Current Status, Potential Challenges, and Ways Out

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