Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease.

INTRODUCTION: Sleep disturbances are common in Alzheimer's disease (AD) and may reflect pathologic changes in brain networks. To date, no studies have examined changes in sleep functional connectivity (FC) in AD or their relationship with network hyperexcitability and cognition. METHODS: We assessed electroencephalogram (EEG) sleep FC in 33 healthy controls, 36 individuals with AD without epilepsy, and 14 individuals with AD and epilepsy. RESULTS: AD participants showed increased gamma connectivity in stage 2 sleep (N2), which was associated with longitudinal cognitive decline. Network hyperexcitability in AD was associated with a distinct sleep connectivity signature, characterized by decreased N2 delta connectivity and reversal of several connectivity changes associated with AD. Machine learning algorithms using sleep connectivity features accurately distinguished diagnostic groups and identified "fast cognitive decliners" among study participants who had AD. DISCUSSION: Our findings reveal changes in sleep functional networks associated with cognitive decline in AD and may have implications for disease monitoring and therapeutic development. HIGHLIGHTS: Brain functional connectivity (FC) in Alzheimer's disease is altered during sleep. Sleep FC measures correlate with cognitive decline in AD. Network hyperexcitability in AD has a distinct sleep connectivity signature.

Altered Sleep Microarchitecture and Cognitive Impairment in Patients With Temporal Lobe Epilepsy.

BACKGROUND AND OBJECTIVES: To investigate the spatiotemporal characteristics of sleep waveforms in temporal lobe epilepsy (TLE) and examine their association with cognition. METHODS: In this retrospective, cross-sectional study, we examined overnight EEG data from adult patients with TLE and nonepilepsy comparisons (NECs) admitted to the epilepsy monitoring unit at Mass General Brigham hospitals. Automated algorithms were used to characterize sleep macroarchitecture (sleep stages) and microarchitecture (spindles, slow oscillations [SOs]) on scalp EEG and to detect hippocampal interictal epileptiform discharges (hIEDs) from foramen ovale electrodes simultaneously recorded in a subset of patients with TLE. We examined the association of sleep features and hIEDs with memory and executive function from clinical neuropsychological evaluations. RESULTS: A total of 81 adult patients with TLE and 28 NEC adult patients were included with similar mean ages. There were no significant differences in sleep macroarchitecture between groups, including relative time spent in each sleep stage, sleep efficiency, and sleep fragmentation. By contrast, the spatiotemporal characteristics of sleep microarchitecture were altered in TLE compared with NEC and were associated with cognitive impairments. Specifically, we observed a ∼30% reduction in spindle density in patients with TLE compared with NEC, which was significantly associated with worse memory performance. Spindle-SO coupling strength was also reduced in TLE and, in contrast to spindles, was associated with diminished executive function. We found no significant association between sleep macroarchitectural and microarchitectural parameters and hIEDs. DISCUSSION: There is a fundamental alteration of sleep microarchitecture in TLE, characterized by a reduction in spindle density and spindle-SO coupling, and these changes may contribute to neurocognitive comorbidity in this disorder.

Noninvasive Detection of Hippocampal Epileptiform Activity on Scalp Electroencephalogram.

Importance: The hippocampus is a highly epileptogenic brain region, yet over 90% of hippocampal epileptiform activity (HEA) cannot be identified on scalp electroencephalogram (EEG) by human experts. Currently, detection of HEA requires intracranial electrodes, which limits our understanding of the role of HEA in brain diseases. Objective: To develop and validate a machine learning algorithm that accurately detects HEA from a standard scalp EEG, without the need for intracranial electrodes. Design, Setting, and Participants: In this diagnostic study, conducted from 2008 to 2021, EEG data were used from patients with temporal lobe epilepsy (TLE) and healthy controls (HCs) to train and validate a deep neural network, HEAnet, to detect HEA on scalp EEG. Participants were evaluated at tertiary-level epilepsy centers at 2 academic hospitals: Massachusetts General Hospital (MGH) or Brigham and Women's Hospital (BWH). Included in the study were patients aged 12 to 78 years with a clinical diagnosis of TLE and HCs without epilepsy. Patients with TLE and HCs with a history of intracranial surgery were excluded from the study. Exposures: Simultaneous intracranial EEG and/or scalp EEG. Main Outcomes and Measures: Performance was assessed using cross-validated areas under the receiver operating characteristic curve (AUC ROC) and precision-recall curve (AUC PR) and additional clinically relevant metrics. Results: HEAnet was trained and validated using data sets that were derived from a convenience sample of 141 eligible participants (97 with TLE and 44 HCs without epilepsy) whose retrospective EEG data were readily available. Data set 1 included the simultaneous scalp EEG and intracranial electrode recordings of 51 patients with TLE (mean [SD] age, 40.7 [15.9] years; 30 men [59%]) at MGH. An automatically generated training data set with 972 095 positive HEA examples was created, in addition to a held-out expert-annotated testing data set with 22 762 positive HEA examples. HEAnet's performance was validated on 2 independent scalp EEG data sets: (1) data set 2 (at MGH; 24 patients with TLE and 20 HCs; mean [SD] age, 42.3 [16.2] years; 17 men [39%]) and (2) data set 3 (at BWH; 22 patients with TLE and 24 HCs; mean [SD] age, 43.0 [14.4] years; 20 men [43%]). For single-event detection of HEA on data set 1, HEAnet achieved a mean (SD) AUC ROC of 0.89 (0.01) and a mean (SD) AUC PR of 0.39 (0.03). On external validation with data sets 2 and 3, HEAnet accurately distinguished TLE from HC (AUC ROC of 0.88 and 0.95, respectively) and predicted epilepsy lateralization with 100% and 92% accuracy, respectively. HEAnet tracked dynamic changes in HEA in response to seizure medication adjustments and performed comparably with human experts in diagnosing TLE from 1-hour scalp EEG recordings, diagnosing TLE in several individuals that experts missed. Without reducing specificity, addition of HEAnet to human expert EEG review increased sensitivity for diagnosing TLE in humans from 50% to 58% to 63% to 67%. Conclusions and Relevance: Results of this diagnostic study suggest that HEAnet provides a novel, noninvasive, quantitative, and clinically relevant biomarker of hippocampal hyperexcitability in humans.

Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.

STUDY OBJECTIVES: Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. METHODS: Using a clinical dataset of polysomnograms from 6,431 patients (MGH-PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm's inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH-PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24-72 h) scalp EEG recordings from 112 patients (scalpEEG dataset). RESULTS: The algorithm achieved a Cohen's kappa of 0.74 on the MGH-PSG holdout testing set and cross-validated Cohen's kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen's kappa ~ 0.75 ± 0.11). The algorithm's performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities. CONCLUSION: We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.

Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

OBJECTIVE: Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings. METHODS: An epileptologist annotated 13,959 epileptiform discharges from a dataset of intracranial EEG recordings from 46 epilepsy patients. Using this dataset, we trained a convolutional neural network (CNN) to recognize mTL epileptiform discharges from a single intracranial bipolar channel. The CNN outputs from multiple bipolar channel inputs were averaged to generate the final detector output. Algorithm performance was estimated using a nested 5-fold cross-validation. RESULTS: On the receiver-operating characteristic curve, our algorithm achieved an area under the curve (AUC) of 0.996 and a partial AUC (for specificity > 0.9) of 0.981. AUC on a precision-recall curve was 0.807. A sensitivity of 84% was attained at a false positive rate of 1 per minute. 35.9% of the false positive detections corresponded to epileptiform discharges that were missed during expert annotation. CONCLUSIONS: Using deep learning, we developed a high-performing, patient non-specific algorithm for detection of mTL epileptiform discharges on intracranial electrodes. SIGNIFICANCE: Our algorithm has many potential applications for understanding the impact of mTL epileptiform discharges in epilepsy and on cognition, and for developing therapies to specifically reduce mTL epileptiform activity.

Association of epileptiform abnormalities and seizures in Alzheimer disease.

OBJECTIVE: To examine the relationship between scalp EEG biomarkers of hyperexcitability in Alzheimer disease (AD) and to determine how these electric biomarkers relate to the clinical expression of seizures in AD. METHODS: In this cross-sectional study, we performed 24-hour ambulatory scalp EEGs on 43 cognitively normal elderly healthy controls (HC), 41 participants with early-stage AD with no history or risk factors for epilepsy (AD-NoEp), and 15 participants with early-stage AD with late-onset epilepsy related to AD (AD-Ep). Two epileptologists blinded to diagnosis visually reviewed all EEGs and annotated all potential epileptiform abnormalities. A panel of 9 epileptologists blinded to diagnosis was then surveyed to generate a consensus interpretation of epileptiform abnormalities in each EEG. RESULTS: Epileptiform abnormalities were seen in 53% of AD-Ep, 22% of AD-NoEp, and 4.7% of HC. Specific features of epileptiform discharges, including high frequency, robust morphology, right temporal location, and occurrence during wakefulness and REM, were associated with clinical seizures in AD. Multiple EEG biomarkers concordantly demonstrated a pattern of left temporal lobe hyperexcitability in early stages of AD, whereas clinical seizures in AD were often associated with bitemporal hyperexcitability. Frequent small sharp spikes were specifically associated with epileptiform EEGs and thus identified as a potential biomarker of hyperexcitability in AD. CONCLUSION: Epileptiform abnormalities are common in AD but not all equivalent. Specific features of epileptiform discharges are associated with clinical seizures in AD. Given the difficulty recognizing clinical seizures in AD, these EEG features could provide guidance on which patients with AD are at high risk for clinical seizures.