The cost-effectiveness of a real-time seizure detection application for people with epilepsy.

OBJECTIVES: Automated seizure detection modalities can increase safety among people with epilepsy (PWE) and reduce seizure-related anxiety. We evaluated the potential cost-effectiveness of a seizure detection mobile application for PWE in Singapore. METHODS: We used a Markov cohort model to estimate the expected changes to total costs and health outcomes from a decision to adopt the seizure detection application versus the current standard of care from the health provider perspective. The time horizon is ten years and cycle duration is one month. Parameter values were updated from national databases and published literature. As we do not know the application efficacy in reducing seizure-related injuries, a conservative estimate of 1% reduction was used. Probabilistic sensitivity analysis, scenario analyses, and value of information analysis were performed. RESULTS: At a willingness-to-pay of $45,000/ quality-adjusted life-years (QALY), the incremental cost-effectiveness ratio was $1,096/QALY, and the incremental net monetary benefit was $13,656. Probabilistic sensitivity analyses reported that the application had a 99.5% chance of being cost-effective. In a scenario analysis in which the reduction in risk of seizure-related injury was 20%, there was a 99.8% chance that the application was cost-effective. Value of information analysis revealed that health utilities was the most important parameter group contributing to model uncertainty. CONCLUSIONS: This early-stage modeling study reveals that the seizure detection application is likely to be cost-effective compared to current standard of care. Future prospective trials will be needed to demonstrate the real-world impact of the application. Changes in health-related quality of life should also be measured in future trials.

Adverse Events and Complications Associated With Vagal Nerve Stimulation: An Analysis of the Manufacturer And User Facility Device Experience Database.

OBJECTIVE: Drug-resistant epilepsy (DRE) can have devastating consequences for patients and families. Vagal nerve stimulation (VNS) is used as a surgical adjunct for treating DRE not amenable to surgical resection. Although VNS is generally safe, it has its inherent complications. With the increasing number of implantations, adequate patient education with discussion of possible complications forms a critical aspect of informed consent and patient counseling. There is a lack of large-scale reviews of device malfunction, patient complaints, and surgically related complications available to date. MATERIALS AND METHODS: Complications associated with VNS implants performed between 2011 and 2021 were identified through a search of the United States Food and Drug Administration Manufacturer And User Facility Device Experience (MAUDE) data base. We found three models on the data base, CYBERONICS, INC pulse gen Demipulse 103, AspireSR 106, and SenTiva 1000. The reports were classified into three main groups, "Device malfunction," "Patient complaints," and "Surgically managed complications." RESULTS: A total of 5888 complications were reported over the ten-year period, of which 501 reports were inconclusive, 610 were unrelated, and 449 were deaths. In summary, there were 2272 reports for VNS 103, 1526 reports for VNS 106, and 530 reports for VNS 1000. Within VNS 103, 33% of reports were related to device malfunction, 33% to patient complaints, and 34% to surgically managed complications. For VNS 106, 35% were related to device malfunction, 24% to patient complaints, and 41% to surgically managed complications. Lastly, for VNS 1000, 8% were device malfunction, 45% patient complaints, and 47% surgically managed complications. CONCLUSION: We present an analysis of the MAUDE data base for adverse events and complications related to VNS. It is hoped that this description of complications and literature review will help promote further improvement in its safety profile, patient education, and management of both patient and clinician expectations.

Patient specific prediction of temporal lobe epilepsy surgical outcomes.

OBJECTIVE: Drug-resistant temporal lobe epilepsy (TLE) is the most common type of epilepsy for which patients undergo surgery. Despite the best clinical judgment and currently available prediction algorithms, surgical outcomes remain variable. We aimed to build and to evaluate the performance of multidimensional Bayesian network classifiers (MBCs), a type of probabilistic graphical model, at predicting probability of seizure freedom after TLE surgery. METHODS: Clinical, neurophysiological, and imaging variables were collected from 231 TLE patients who underwent surgery at the University of California, San Francisco (UCSF) or the Montreal Neurological Institute (MNI) over a 15-year period. Postsurgical Engel outcomes at year 1 (Y1), Y2, and Y5 were analyzed as primary end points. We trained an MBC model on combined data sets from both institutions. Bootstrap bias corrected cross-validation (BBC-CV) was used to evaluate the performance of the models. RESULTS: The MBC was compared with logistic regression and Cox proportional hazards according to the area under the receiver-operating characteristic curve (AUC). The MBC achieved an AUC of 0.67 at Y1, 0.72 at Y2, and 0.67 at Y5, which indicates modest performance yet superior to what has been reported in the state-of-the-art studies to date. SIGNIFICANCE: The MBC can more precisely encode probabilistic relationships between predictors and class variables (Engel outcomes), achieving promising experimental results compared to other well-known statistical methods. Multisite application of the MBC could further optimize its classification accuracy with prospective data sets. Online access to the MBC is provided, paving the way for its use as an adjunct clinical tool in aiding pre-operative TLE surgical counseling.

Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias.

OBJECTIVE: Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized cortical surface sampling of combined MRI and PET features. METHODS: Cortical surfaces of 28 patients with histopathologically-proven FCDs were extracted. Morphology and intensity-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface, and fed to a 2-step (Support Vector Machine and patch-based) classifier. Classifier performance was assessed compared to manual lesion labels. RESULTS: Our classifier using combined feature selections from MRI and PET outperformed both quantitative MRI and multimodal visual analysis in FCD detection (93% vs 82% vs 68%). No false positives were identified in the controls, whereas 3.4% of the vertices outside FCD lesions were also classified to be lesional ("extralesional clusters"). Patients with type I or IIa FCDs displayed a higher prevalence of extralesional clusters at an intermediate distance to the FCD lesions compared to type IIb FCDs (p < 0.05). The former had a correspondingly lower chance of positive surgical outcome (71% vs 91%). CONCLUSIONS: Machine learning with multimodal feature sampling can improve FCD detection. The spread of extralesional clusters characterize different FCD subtypes, and may represent structurally or functionally abnormal tissue on a microscopic scale, with implications for surgical outcomes.

MRI-negative temporal lobe epilepsy-What do we know?

Temporal lobe epilepsy (TLE) is the most common focal epilepsy in adults. TLE has a high chance of becoming medically refractory, and as such, is frequently considered for further evaluation and surgical intervention. Up to 30% of TLE cases, however, can have normal ("nonlesional" or negative) magnetic resonance imaging (MRI) results, which complicates the presurgical workup and has been associated with worse surgical outcomes. Helped by contributions from advanced imaging techniques and electrical source localization, the number of surgeries performed on MRI-negative TLE has increased over the last decade. Thereby new epidemiologic, clinical, electrophysiologic, neuropathologic, and surgical data of MRI-negative TLE has emerged, showing characteristics that are distinct from those of lesional TLE. This review article summarizes what we know today about MRI-negative TLE, and discusses the comprehensive assessment of patients with MRI-negative TLE in a structured and systematic approach. It also includes a concise description of the most recent developments in structural and functional imaging, and highlights postprocessing imaging techniques that have been shown to add localization value in MRI-negative epilepsies. We evaluate surgical outcomes of MRI-negative TLE, identify prognostic makers of postoperative seizure freedom, and discuss strategies for optimizing the selection of surgical candidates in this group.

Psychogenic nonepileptic seizures in patients with surgically treated temporal lobe epilepsy: Presurgical and de novo postsurgical occurrence.

Whether occurring before or after an epilepsy surgery, psychogenic nonepileptic seizures (PNES) impact treatment options and quality of life of patients with epilepsy. We investigated the frequency of pre- and postsurgical PNES, and the postsurgical Engel and psychiatric outcomes in patients with drug-resistant temporal lobe epilepsy (TLE). We reviewed 278 patients with mean age at surgery of 37.1±12.4years. Postsurgical follow-up information was available in 220 patients, with average follow-up of 4years. Nine patients (9/278 or 3.2%) had presurgical documented PNES. Eight patients (8/220 or 3.6%) developed de novo PNES after surgery. Pre- and postsurgery psychiatric comorbidities were similar to the patients without PNES. After surgery, in the group with presurgical PNES, five patients were seizure-free, and three presented persistent PNES. In the group with de novo postsurgery PNES, 62.5% had Engel II-IV, and 37.5% had Engel I. All presented PNES at last follow-up. Presurgical video-EEG monitoring is crucial in the diagnosis of coexisting PNES. Patients presenting presurgical PNES and drug-resistant TLE should not be denied surgery based on this comorbidity, as they can have good postsurgical epilepsy and psychiatric outcomes. Psychogenic nonepileptic seizures may appear after TLE surgery in a low but noteworthy proportion of patients regardless of the Engel outcome.

Evaluation of prognostic scores for status epilepticus in the neurology ICU: A retrospective study.

OBJECTIVE: Status epilepticus (SE) in the neurology intensive care unit (ICU) is associated with significant morbidity. We aimed to evaluate the utility of existing prognostic scores, namely the Status Epilepticus Severity Score (STESS), Epidemiology Based Mortality Score in Status Epilepticus (EMSE)-EACE and Encephalitis-Nonconvulsive Status Epilepticus-Diazepam Resistance-Image Abnormalities-Tracheal Intubation (END-IT), among SE patients in the neurology ICU. METHODS: Neurology ICU patients with SE requiring continuous electroencephalography (cEEG) monitoring over a 10 year period were included. The STESS, EMSE-EACE and END-IT scores were applied retrospectively. Receiver operating characteristic (ROC) analysis was performed to assess the discriminatory value of the scores for inpatient mortality and functional decline, as measured by increase in the modified Rankin Scale (mRS) on discharge. RESULTS: Eighty-five patients were included in the study, of which 71 (83.5%) had refractory SE. Inpatient mortality was 36.5%. Sixty - seven (78.8%) of patients suffered functional decline, with a median mRS of 5 upon hospital discharge. The AUCs of the STESS, EMSE-EACE and END-IT scores associated with inpatient mortality were 0.723 (95% CI 0.613-0.833), 0.722 (95% CI 0.609-0.834) and 0.560 (95% CI 0.436-0.684) respectively. The AUCs of the STESS, EMSE-EACE and END-IT scores associated with functional decline were 0.604 (95% CI 0.468-0.741), 0.596 (95% CI 0.439-0.754) and 0.477 (95% CI 0.331-0.623). SIGNIFICANCE: SE was associated with high mortality and morbidity in this cohort of neurology ICU patients requiring cEEG monitoring. The STESS and EMSE-EACE scores had acceptable AUCs for prediction of inpatient mortality. However, the STESS, EMSE-EACE and END-IT were poorly-correlated with discharge functional outcomes. Further refinements of the scores may be necessary among neurology ICU patients for predicting discharge functional outcomes.

Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG.

Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures' start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617-1.00, PRE of 0.534-1.00, aFPR/h of 0.425-2.002, and mFPR/h of 0-1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.

Ictal EEG Source Imaging With Supplemental Electrodes.

INTRODUCTION: Noninvasive brain imaging tests play a major role in guiding decision-making and the usage of invasive, costly intracranial electroencephalogram (ICEEG) in the presurgical epilepsy evaluation. This study prospectively examined the concordance in localization between ictal EEG source imaging (ESI) and ICEEG as a reference standard. METHODS: Between August 2014 and April 2019, patients during video monitoring with scalp EEG were screened for those with intractable focal epilepsy believed to be amenable to surgical treatment. Additional 10-10 electrodes (total = 31-38 per patient, "31+") were placed over suspected regions of seizure onset in 104 patients. Of 42 patients requiring ICEEG, 30 (mean age 30, range 19-59) had sufficiently localized subsequent intracranial studies to allow comparison of localization between tests. ESI was performed using realistic forward boundary element models used in dipole and distributed source analyses. RESULTS: At least partial sublobar concordance between ESI and ICEEG solutions was obtained in 97% of cases, with 73% achieving complete agreement. Median Euclidean distances between ESI and ICEEG solutions ranged from 25 to 30 mm (dipole) and 23 to 38 mm (distributed source). The latter was significantly more accurate with 31+ compared with 21 electrodes (P < 0.01). A difference of ≤25 mm was present in two thirds of the cases. No significant difference was found between dipole and distributed source analyses. CONCLUSIONS: A practical method of ictal ESI (nonuniform placement of 31-38 electrodes) yields high accuracy for seizure localization in epilepsy surgery candidates. These results support routine clinical application of ESI in the presurgical evaluation.

A framework for evaluating predictive models.

Predictive models provide estimates on an individual's probability of having a disease or developing a disease/disease outcome. Clinicians often use them to support clinical decision-making. Many prediction models are published annually; online versions of models (such as MDCalc and QxMD) facilitate their use at the point of care. However, before using a model, the clinician should first establish that the model has undergone external validation demonstrating satisfactory predictive performance. Ideally, the model should also demonstrate improved outcomes from an impact analysis. This article summarizes the basic steps of predictive model evaluation, and is followed by an application example.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs.

Objective.Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs.Approach.In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India.Main results.In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs.Significance.These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study.

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification. Specifically, we investigate the IED detector based on convolutional neural network (ConvNet) with different input features (temporal, spectral, and wavelet features). We explore different ConvNet architectures and types, including 1D (one-dimensional) ConvNet, 2D (two-dimensional) ConvNet, and noise injection at various layers. We evaluate the EEG classification performance on five independent datasets. The 1D ConvNet with preprocessed full-frequency EEG signal and frequency bands (delta, theta, alpha, beta) with Gaussian additive noise at the output layer achieved the best IED detection results with a false detection rate of 0.23/min at 90% sensitivity. The EEG classification system obtained a mean EEG classification Leave-One-Institution-Out (LOIO) cross-validation (CV) balanced accuracy (BAC) of 78.1% (area under the curve (AUC) of 0.839) and Leave-One-Subject-Out (LOSO) CV BAC of 79.5% (AUC of 0.856). Since the proposed classification system only takes a few seconds to analyze a 30-min routine EEG, it may help in reducing the human effort required for epilepsy diagnosis.

Postsurgical seizure outcome in temporal lobe epilepsy patients with normal or subtle, nonspecific MRI findings.

OBJECTIVE: The authors' objective was to report postsurgical seizure outcome of temporal lobe epilepsy (TLE) patients with normal or subtle, nonspecific MRI findings and to identify prognostic factors related to seizure control after surgery. METHODS: This was a retrospective study of patients who underwent surgery from 1999 to 2014 at two comprehensive epilepsy centers. Patients with a clear MRI lesion according to team discussion and consensus were excluded. Presurgical information, surgery details, pathological data, and postsurgical outcomes were retrospectively collected from medical charts. Multiple logistic regression analysis was used to assess the effect of clinical, surgical, and neuroimaging factors on the probability of Engel class I (favorable) versus class II-IV (unfavorable) outcome at last follow-up. RESULTS: The authors included 73 patients (59% were female; median age at surgery 35.9 years) who underwent operations after a median duration of epilepsy of 13 years. The median follow-up after surgery was 30.6 months. At latest follow-up, 44% of patients had Engel class I outcome. Favorable prognostic factors were focal nonmotor aware seizures and unilateral or no spikes on interictal scalp EEG. CONCLUSIONS: Favorable outcome can be achieved in a good proportion of TLE patients with normal or subtle, nonspecific MRI findings, particularly when presurgical investigation suggests a rather circumscribed generator. Presurgical factors such as the presence of focal nonmotor aware seizures and unilateral or no spikes on interictal EEG may indicate a higher probability of seizure freedom.

Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features.

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.

BACKGROUND: Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but is still superior to current rule-based approaches in most automated algorithms. OBJECTIVE: To develop an unsupervised machine-learning algorithm for the improved, automated detection and localization of interictal epileptiform discharges based on spatiotemporal pattern recognition. METHODS: We decomposed 24 h of intracranial electroencephalography signals into basis functions and activation vectors using non-negative matrix factorization (NNMF). Thresholding the activation vector and the basis function of interest detected interictal epileptiform discharges in time and space (specific electrodes), respectively. We used convolutive NNMF, a refined algorithm, to add a temporal dimension to basis functions. RESULTS: The receiver operating characteristics for NNMF-based detection are close to the gold standard of human visual-based detection and superior to currently available alternative automated approaches (93% sensitivity and 97% specificity). The algorithm successfully identified thousands of interictal epileptiform discharges across a full day of neurophysiological recording and accurately summarized their localization into a single map. Adding a temporal window allowed for visualization of the archetypal propagation network of these epileptiform discharges. CONCLUSION: Unsupervised learning offers a powerful approach towards automated identification of recurrent pathological neurophysiological signals, which may have important implications for precise, quantitative, and individualized evaluation of focal epilepsy.

Frontotemporal dementia in southeast Asia: a comparative study.

BACKGROUND: The clinical profile of frontotemporal dementia (FTD) in Southeast Asia is not known. We characterized and compared the demographic and clinical characteristics of FTD patients in Southeast Asia with North Asian and Western patients. METHODS: The study included Southeast Asian FTD patients presenting to a tertiary neurology institute. Behavioral variant (bv-FTD) and language variant (lv-FTD) subtypes of FTD were diagnosed based on the Lund-Manchester criteria. The patients were characterized according to demographics, clinical, neuroimaging and longitudinal profiles. RESULTS: Twenty-five bv-FTD and 19 lv-FTD patients were identified, with a female predominance ratio of 2:1 and a mean age of 56 years. The mean MMSE score was 16.2, and 88.4% of patients had memory symptoms. Over 5.1 ± 2.4 years of follow-up, 60% of bv-FTD and 36.8% of lv-FTD patients developed a second FTD syndrome. bv-FTD was the predominant type of FTD among Southeast Asians. CONCLUSION: FTD represents an important cause of young-onset dementia in Southeast Asia. Greater awareness of FTD is required to ensure early diagnosis and management.