Try a new search

Format these results:

Searched for:



Total Results:


A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations

Liu, Anli A; Henin, Simon; Abbaspoor, Saman; Bragin, Anatol; Buffalo, Elizabeth A; Farrell, Jordan S; Foster, David J; Frank, Loren M; Gedankien, Tamara; Gotman, Jean; Guidera, Jennifer A; Hoffman, Kari L; Jacobs, Joshua; Kahana, Michael J; Li, Lin; Liao, Zhenrui; Lin, Jack J; Losonczy, Attila; Malach, Rafael; van der Meer, Matthijs A; McClain, Kathryn; McNaughton, Bruce L; Norman, Yitzhak; Navas-Olive, Andrea; de la Prida, Liset M; Rueckemann, Jon W; Sakon, John J; Skelin, Ivan; Soltesz, Ivan; Staresina, Bernhard P; Weiss, Shennan A; Wilson, Matthew A; Zaghloul, Kareem A; Zugaro, Michaël; Buzsáki, György
Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.
PMID: 36224194
ISSN: 2041-1723
CID: 5352092

Spatiotemporal dynamics between interictal epileptiform discharges and ripples during associative memory processing

Henin, Simon; Shankar, Anita; Borges, Helen; Flinker, Adeen; Doyle, Werner; Friedman, Daniel; Devinsky, Orrin; Buzsáki, György; Liu, Anli
We describe the spatiotemporal course of cortical high-gamma activity, hippocampal ripple activity and interictal epileptiform discharges during an associative memory task in 15 epilepsy patients undergoing invasive EEG. Successful encoding trials manifested significantly greater high-gamma activity in hippocampus and frontal regions. Successful cued recall trials manifested sustained high-gamma activity in hippocampus compared to failed responses. Hippocampal ripple rates were greater during successful encoding and retrieval trials. Interictal epileptiform discharges during encoding were associated with 15% decreased odds of remembering in hippocampus (95% confidence interval 6-23%). Hippocampal interictal epileptiform discharges during retrieval predicted 25% decreased odds of remembering (15-33%). Odds of remembering were reduced by 25-52% if interictal epileptiform discharges occurred during the 500-2000-ms window of encoding or by 41% during retrieval. During encoding and retrieval, hippocampal interictal epileptiform discharges were followed by a transient decrease in ripple rate. We hypothesize that interictal epileptiform discharges impair associative memory in a regionally and temporally specific manner by decreasing physiological hippocampal ripples necessary for effective encoding and recall. Because dynamic memory impairment arises from pathological interictal epileptiform discharge events competing with physiological ripples, interictal epileptiform discharges represent a promising therapeutic target for memory remediation in patients with epilepsy.
PMID: 33889945
ISSN: 1460-2156
CID: 4847522

Learning hierarchical sequence representations across human cortex and hippocampus

Henin, Simon; Turk-Browne, Nicholas B; Friedman, Daniel; Liu, Anli; Dugan, Patricia; Flinker, Adeen; Doyle, Werner; Devinsky, Orrin; Melloni, Lucia
Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain's ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.
PMID: 33608265
ISSN: 2375-2548
CID: 4793972

Machine Learning to Classify Relative Seizure Frequency From Chronic Electrocorticography

Sun, Yueqiu; Friedman, Daniel; Dugan, Patricia; Holmes, Manisha; Wu, Xiaojing; Liu, Anli
PURPOSE/OBJECTIVE:Brain responsive neurostimulation (NeuroPace) treats patients with refractory focal epilepsy and provides chronic electrocorticography (ECoG). We explored how machine learning algorithms applied to interictal ECoG could assess clinical response to changes in neurostimulation parameters. METHODS:We identified five responsive neurostimulation patients each with ≥200 continuous days of stable medication and detection settings (median, 358 days per patient). For each patient, interictal ECoG segments for each month were labeled as "high" or "low" to represent relatively high or low long-episode (i.e., seizure) count compared with the median monthly long-episode count. Power from six conventional frequency bands from four responsive neurostimulation channels were extracted as features. For each patient, five machine learning algorithms were trained on 80% of ECoG, then tested on the remaining 20%. Classifiers were scored by the area-under-the-receiver-operating-characteristic curve. We explored how individual circadian cycles of seizure activity could inform classifier building. RESULTS:Support vector machine or gradient boosting models achieved the best performance, ranging from 0.705 (fair) to 0.892 (excellent) across patients. High gamma power was the most important feature, tending to decrease during low-seizure-frequency epochs. For two subjects, training on ECoG recorded during the circadian ictal peak resulted in comparable model performance, despite less data used. CONCLUSIONS:Machine learning analysis on retrospective background ECoG can classify relative seizure frequency for an individual patient. High gamma power was the most informative, whereas individual circadian patterns of seizure activity can guide model building. Machine learning classifiers built on interictal ECoG may guide stimulation programming.
PMID: 34049367
ISSN: 1537-1603
CID: 5418582

Overlapping and distinct phenotypic profiles in Alzheimer's disease and late onset epilepsy: a biologically-based approach

Liu, Anli A; Barr, William B
Due to shared hippocampal dysfunction, patients with Alzheimer's dementia and late-onset epilepsy (LOE) report memory decline. Multiple studies have described the epidemiological, pathological, neurophysiological, and behavioral overlap between Alzheimer's Disease and LOE, implying a bi-directional relationship. We describe the neurobiological decline occurring at different spatial in AD and LOE patients, which may explain why their phenotypes overlap and differ. We provide suggestions for clinical recognition of dual presentation and novel approaches for behavioral testing that reflect an "inside-out," or biologically-based approach to testing memory. New memory and language assessments could detect-and treat-memory impairment in AD and LOE at an earlier, actionable stage.
PMID: 38545454
ISSN: 1664-2295
CID: 5645052

Religious conversion in an older male with longstanding epilepsy [Case Report]

Barr, William B; Liu, Anli; Laduke, Casey; Nadkarni, Siddhartha; Devinsky, Orrin
Religious experiences in epilepsy patients have provoked much interest with suggestions that hyperreligiosity is associated with temporal lobe seizures. Extreme varieties of religious behavior may be more frequent in epilepsy patients during ictal activity or during post-ictal psychotic episodes. We report a 75 year-old man with epilepsy who developed a progressive decline in cognition and behavior following a religious conversion 15 years earlier. He subsequently developed religious delusions of increasing severity and symptoms of Capgras syndrome. Brain imaging revealed bilateral posterior cortical atrophy, chronic right parieto-occipital encephalomalacia, and right mesial temporal sclerosis. Electroencephalograms and neuropsychological testing revealed initial right temporal lobe abnormalities followed by progressive frontal and bilateral dysfunction. The case highlights how a history of seizures, superimposed on sensory deprivation and a progressive impairment of right posterior and bilateral anterior brain function, may have contributed to religious conversion, which was followed by dementia and delusions involving religious content.
PMID: 35528136
ISSN: 2589-9864
CID: 5214052

Time-dependent transformations of memory representations differ along the long axis of the hippocampus

Cowan, Emily T; Liu, Anli A; Henin, Simon; Kothare, Sanjeev; Devinsky, Orrin; Davachi, Lila
Research has shown that sleep is beneficial for the long-term retention of memories. According to theories of memory consolidation, memories are gradually reorganized, becoming supported by widespread, distributed cortical networks, particularly during postencoding periods of sleep. However, the effects of sleep on the organization of memories in the hippocampus itself remains less clear. In a 3-d study, participants encoded separate lists of word-image pairs differing in their opportunity for sleep-dependent consolidation. Pairs were initially studied either before or after an overnight sleep period, and were then restudied in a functional magnetic resonance imaging (fMRI) scan session. We used multivariate pattern similarity analyses to examine fine-grained effects of consolidation on memory representations in the hippocampus. We provide evidence for a dissociation along the long axis of the hippocampus that emerges with consolidation, such that representational patterns for object-word memories initially formed prior to sleep become differentiated in anterior hippocampus and more similar, or overlapping, in posterior hippocampus. Differentiation in anterior hippocampal representations correlated with subsequent behavioral performance. Furthermore, representational overlap in posterior hippocampus correlated with the duration of intervening slow wave sleep. Together, these results demonstrate that sleep-dependent consolidation promotes the reorganization of memory traces along the long axis of the hippocampus.
PMID: 34400534
ISSN: 1549-5485
CID: 5010952

Effects of hippocampal interictal discharge timing, duration, and spatial extent on list learning

Leeman-Markowski, Beth; Hardstone, Richard; Lohnas, Lynn; Cowen, Benjamin; Davachi, Lila; Doyle, Werner; Dugan, Patricia; Friedman, Daniel; Liu, Anli; Melloni, Lucia; Selesnick, Ivan; Wang, Binhuan; Meador, Kimford; Devinsky, Orrin
Interictal epileptiform discharges (IEDs) can impair memory. The properties of IEDs most detrimental to memory, however, are undefined. We studied the impact of temporal and spatial characteristics of IEDs on list learning. Subjects completed a memory task during intracranial EEG recordings including hippocampal depth and temporal neocortical subdural electrodes. Subjects viewed a series of objects, and after a distracting task, recalled the objects from the list. The impacts of IED presence, duration, and propagation to neocortex during encoding of individual stimuli were assessed. The effects of IED total number and duration during maintenance and recall periods on delayed recall performance were also determined. The influence of IEDs during recall was further investigated by comparing the likelihood of IEDs preceding correctly recalled items vs. periods of no verbal response. Across 6 subjects, we analyzed 28 hippocampal and 139 lateral temporal contacts. Recall performance was poor, with a median of 17.2% correct responses (range 10.4-21.9%). Interictal epileptiform discharges during encoding, maintenance, and recall did not significantly impact task performance, and there was no significant difference between the likelihood of IEDs during correct recall vs. periods of no response. No significant effects of discharge duration during encoding, maintenance, or recall were observed. Interictal epileptiform discharges with spread to lateral temporal cortex during encoding did not adversely impact recall. A post hoc analysis refining model assumptions indicated a negative impact of IED count during the maintenance period, but otherwise confirmed the above results. Our findings suggest no major effect of hippocampal IEDs on list learning, but study limitations, such as baseline hippocampal dysfunction, should be considered. The impact of IEDs during the maintenance period may be a focus of future research.
PMID: 34416521
ISSN: 1525-5069
CID: 4988992

Is formal scoring better than just looking? A comparison of subjective and objective scoring methods of the Rey Complex Figure Test for lateralizing temporal lobe epilepsy

LeMonda, Brittany C; MacAllister, William; Morrison, Chris; Vaurio, Linnea; Blackmon, Karen; Maiman, Moshe; Liu, Anli; Liberta, Taylor; Bar, William B
OBJECTIVE/UNASSIGNED:Findings highlight concerns regarding the usefulness of the RCFT in TLE lateralization, regardless of scoring approach.
PMID: 33356888
ISSN: 1744-4144
CID: 4954292

Sounds of seizures

Shum, Jennifer; Fogarty, Adam; Dugan, Patricia; Holmes, Manisha G; Leeman-Markowski, Beth A; Liu, Anli A; Fisher, Robert S; Friedman, Daniel
PURPOSE/OBJECTIVE:A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings. METHODS:We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated. RESULTS:The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97). CONCLUSION/CONCLUSIONS:This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected.
PMID: 32276233
ISSN: 1532-2688
CID: 4374322