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Weiss SA, Sperling MR, Engel J, Liu A, Fried I, Wu C, Doyle W, Mikell C, Mofakham S, Salamon N, Sim MS, Bragin A, Staba R. Simulated resections and RNS placement can optimize post-operative seizure outcomes when guided by fast ripple networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.26.24304802. [PMID: 38585730 PMCID: PMC10996761 DOI: 10.1101/2024.03.26.24304802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure onset zone (SOZ) and incorporation of neuroimaging findings from MRI, PET, SPECT, and MEG modalities. Resecting cortical tissue generating high-frequency oscillations (HFOs) has been investigated as a more efficacious alternative to targeting the SOZ. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio (RR), a spatial FR network measure, and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure free labels of 18 patients with medically refractory focal epilepsy. Leave one out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (p<0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system (RNS) and virtually targeted the RNS stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated SOZ and stimulated FR metrics would trend toward those patients with a better seizure outcome. Our results suggest: 1) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; 2) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; 3) when FR metrics predict the standard of care resection will fail, amending the boundaries of the planned resection with certain FR generating sites may improve outcome; and 4) more work is required to determine if targeting RNS stimulation contact proximal to FR generating sites will improve seizure outcome.
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Affiliation(s)
- Shennan Aibel Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York 11203, USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York 11203, USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, 11203 USA
| | - Michael R. Sperling
- Dept. of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Anli Liu
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, 10016 USA
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, 10016 USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Chengyuan Wu
- Dept. of Neuroradiology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Werner Doyle
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, 10016 USA
| | - Charles Mikell
- Department of Neurosurgery, State University of New York Stony Brook, Stony Brook, New York 11790, USA
| | - Sima Mofakham
- Department of Neurosurgery, State University of New York Stony Brook, Stony Brook, New York 11790, USA
| | - Noriko Salamon
- Dept. of Neuroradiology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Myung Shin Sim
- Dept. of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Anatol Bragin
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Richard Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
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Weiss SA, Fried I, Wu C, Sharan A, Rubinstein D, Engel J, Sperling MR, Staba RJ. Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction. Sci Rep 2023; 13:367. [PMID: 36611059 PMCID: PMC9825369 DOI: 10.1038/s41598-022-27248-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 12/28/2022] [Indexed: 01/09/2023] Open
Abstract
Fast ripples (FR) are a biomarker of epileptogenic brain, but when larger portions of FR generating regions are resected seizure freedom is not always achieved. To evaluate and improve the diagnostic accuracy of FR resection for predicting seizure freedom we compared the FR resection ratio (RR) with FR network graph theoretical measures. In 23 patients FR were semi-automatically detected and quantified in stereo EEG recordings during sleep. MRI normalization and co-registration localized contacts and relation to resection margins. The number of FR, and graph theoretical measures, which were spatial (i.e., FR rate-distance radius) or temporal correlational (i.e., FR mutual information), were compared with the resection margins and with seizure outcome We found that the FR RR did not correlate with seizure-outcome (p > 0.05). In contrast, the FR rate-distance radius resected difference and the FR MI mean characteristic path length RR did correlate with seizure-outcome (p < 0.05). Retesting of positive FR RR patients using either FR rate-distance radius resected difference or the FR MI mean characteristic path length RR reduced seizure-free misclassifications from 44 to 22% and 17%, respectively. These results indicate that graph theoretical measures of FR networks can improve the diagnostic accuracy of the resection of FR events for predicting seizure freedom.
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Affiliation(s)
- Shennan A. Weiss
- grid.262863.b0000 0001 0693 2202Department of Neurology, State University of New York Downstate, Brooklyn, USA ,grid.262863.b0000 0001 0693 2202Department of Physiology and Pharmacology, State University of New York Downstate, 450 Clarkson Avenue, MSC 1213, Brooklyn, NY 11203 USA ,grid.422616.50000 0004 0443 7226Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY USA
| | - Itzhak Fried
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Chengyuan Wu
- grid.265008.90000 0001 2166 5843Department of Neuroradiology, Thomas Jefferson University, Philadelphia, USA ,grid.265008.90000 0001 2166 5843Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107 USA
| | - Ashwini Sharan
- grid.265008.90000 0001 2166 5843Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107 USA
| | - Daniel Rubinstein
- grid.265008.90000 0001 2166 5843Department of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, USA
| | - Jerome Engel
- grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718David Geffen School of Medicine at UCLA, Brain Research Institute, Los Angeles, CA 90095 USA
| | - Michael R. Sperling
- grid.265008.90000 0001 2166 5843Department of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, USA
| | - Richard J. Staba
- grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, USA
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Weiss SA, Sheybani L, Seenarine N, Fried I, Wu C, Sharan A, Engel J, Sperling MR, Nir Y, Staba RJ. Delta oscillation coupled propagating fast ripples precede epileptiform discharges in patients with focal epilepsy. Neurobiol Dis 2022; 175:105928. [DOI: 10.1016/j.nbd.2022.105928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022] Open
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Batista Tsukahara VH, de Oliveira Júnior JN, de Oliveira Barth VB, de Oliveira JC, Rosa Cota V, Maciel CD. Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis. Front Neural Circuits 2022; 16:747910. [PMID: 36034337 PMCID: PMC9399918 DOI: 10.3389/fncir.2022.747910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 06/08/2022] [Indexed: 11/16/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders worldwide. Recent findings suggest that the brain is a complex system composed of a network of neurons, and seizure is considered an emergent property resulting from its interactions. Based on this perspective, network physiology has emerged as a promising approach to explore how brain areas coordinate, synchronize and integrate their dynamics, both under perfect health and critical illness conditions. Therefore, the objective of this paper is to present an application of (Dynamic) Bayesian Networks (DBN) to model Local Field Potentials (LFP) data on rats induced to epileptic seizures based on the number of arcs found using threshold analytics. Results showed that DBN analysis captured the dynamic nature of brain connectivity across ictogenesis and a significant correlation with neurobiology derived from pioneering studies employing techniques of pharmacological manipulation, lesion, and modern optogenetics. The arcs evaluated under the proposed approach achieved consistent results based on previous literature, in addition to demonstrating robustness regarding functional connectivity analysis. Moreover, it provided fascinating and novel insights, such as discontinuity between forelimb clonus and generalized tonic-clonic seizure (GTCS) dynamics. Thus, DBN coupled with threshold analytics may be an excellent tool for investigating brain circuitry and their dynamical interplay, both in homeostasis and dysfunction conditions.
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Affiliation(s)
- Victor Hugo Batista Tsukahara
- Signal Processing Laboratory, School of Engineering of São Carlos, Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil
| | - Jordão Natal de Oliveira Júnior
- Signal Processing Laboratory, School of Engineering of São Carlos, Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil
| | - Vitor Bruno de Oliveira Barth
- Signal Processing Laboratory, School of Engineering of São Carlos, Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil
| | - Jasiara Carla de Oliveira
- Laboratory of Neuroengineering and Neuroscience, Department of Electrical Engineering, Federal University of São João Del-Rei, São João Del Rei, Brazil
| | - Vinicius Rosa Cota
- Laboratory of Neuroengineering and Neuroscience, Department of Electrical Engineering, Federal University of São João Del-Rei, São João Del Rei, Brazil
| | - Carlos Dias Maciel
- Signal Processing Laboratory, School of Engineering of São Carlos, Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil
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Weiss SA, Pastore T, Orosz I, Rubinstein D, Gorniak R, Waldman Z, Fried I, Wu C, Sharan A, Slezak D, Worrell G, Engel J, Sperling MR, Staba RJ. Graph theoretical measures of fast ripples support the epileptic network hypothesis. Brain Commun 2022; 4:fcac101. [PMID: 35620169 PMCID: PMC9128387 DOI: 10.1093/braincomms/fcac101] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 02/10/2022] [Accepted: 04/12/2022] [Indexed: 11/16/2022] Open
Abstract
The epileptic network hypothesis and epileptogenic zone hypothesis are two
theories of ictogenesis. The network hypothesis posits that coordinated activity
among interconnected nodes produces seizures. The epileptogenic zone hypothesis
posits that distinct regions are necessary and sufficient for seizure
generation. High-frequency oscillations, and particularly fast ripples, are
thought to be biomarkers of the epileptogenic zone. We sought to test these
theories by comparing high-frequency oscillation rates and networks in surgical
responders and non-responders, with no appreciable change in seizure frequency
or severity, within a retrospective cohort of 48 patients implanted with
stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye
movement sleep and semi-automatically detected and quantified high-frequency
oscillations. Each electrode contact was localized in normalized coordinates. We
found that the accuracy of seizure onset zone electrode contact classification
using high-frequency oscillation rates was not significantly different in
surgical responders and non-responders, suggesting that in non-responders the
epileptogenic zone partially encompassed the seizure onset zone(s)
(P > 0.05). We also found that in the
responders, fast ripple on oscillations exhibited a higher spectral content in
the seizure onset zone compared with the non-seizure onset zone
(P < 1 × 10−5).
By contrast, in the non-responders, fast ripple had a lower spectral content in
the seizure onset zone
(P < 1 × 10−5).
We constructed two different networks of fast ripple with a spectral content
>350 Hz. The first was a rate–distance network that
multiplied the Euclidian distance between fast ripple-generating contacts by the
average rate of fast ripple in the two contacts. The radius of the
rate–distance network, which excluded seizure onset zone nodes,
discriminated non-responders, including patients not offered resection or
responsive neurostimulation due to diffuse multifocal onsets, with an accuracy
of 0.77 [95% confidence interval (CI) 0.56–0.98]. The second fast
ripple network was constructed using the mutual information between the timing
of the events to measure functional connectivity. For most non-responders, this
network had a longer characteristic path length, lower mean local efficiency in
the non-seizure onset zone, and a higher nodal strength among non-seizure onset
zone nodes relative to seizure onset zone nodes. The graphical theoretical
measures from the rate–distance and mutual information networks of 22
non- responsive neurostimulation treated patients was used to train a support
vector machine, which when tested on 13 distinct patients classified
non-responders with an accuracy of 0.92 (95% CI 0.75–1). These
results indicate patients who do not respond to surgery or those not selected
for resection or responsive neurostimulation can be explained by the epileptic
network hypothesis that is a decentralized network consisting of widely
distributed, hyperexcitable fast ripple-generating nodes.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Tomas Pastore
- Dept. of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Iren Orosz
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Daniel Rubinstein
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Richard Gorniak
- Dept. of Neuroradiology, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Zachary Waldman
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Chengyuan Wu
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Ashwini Sharan
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Diego Slezak
- Dept. of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Gregory Worrell
- Dept. of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA
- Dept. of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Michael R. Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Richard J Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
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7
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Role of NMDAR plasticity in a computational model of synaptic memory. Sci Rep 2021; 11:21182. [PMID: 34707139 PMCID: PMC8551337 DOI: 10.1038/s41598-021-00516-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 10/12/2021] [Indexed: 11/08/2022] Open
Abstract
A largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N-methyl-d-aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs' glutamate and voltage gate conductances induce modifications of the synapse's NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.
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Aizenberg M, Rolón-Martínez S, Pham T, Rao W, Haas JS, Geffen MN. Projection from the Amygdala to the Thalamic Reticular Nucleus Amplifies Cortical Sound Responses. Cell Rep 2019; 28:605-615.e4. [PMID: 31315041 PMCID: PMC7081884 DOI: 10.1016/j.celrep.2019.06.050] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 04/29/2019] [Accepted: 06/13/2019] [Indexed: 12/16/2022] Open
Abstract
Many forms of behavior require selective amplification of neuronal representations of relevant environmental signals. Emotional learning enhances sensory responses in the sensory cortex, yet the underlying circuits remain poorly understood. We identify a pathway between the basolateral amygdala (BLA), an emotional learning center in the mouse brain, and the inhibitory reticular nucleus of the thalamus (TRN). Optogenetic activation of BLA suppressed spontaneous, but not tone-evoked, activity in the auditory cortex (AC), amplifying tone-evoked responses. Viral tracing identified BLA projections terminating at TRN. Optogenetic activation of amygdala-TRN projections further amplified tone-evoked responses in the auditory thalamus and cortex. The results are explained by a computational model of the thalamocortical circuitry, in which activation of TRN by BLA primes thalamocortical neurons to relay relevant sensory input. This circuit mechanism shines a neural spotlight on behaviorally relevant signals and provides a potential target for the treatment of neuropsychological disorders.
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Affiliation(s)
- Mark Aizenberg
- Department of Otorhinolaryngology: HNS, Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Solymar Rolón-Martínez
- Department of Otorhinolaryngology: HNS, Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Tuan Pham
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Winnie Rao
- Department of Otorhinolaryngology: HNS, Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Julie S Haas
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Maria N Geffen
- Department of Otorhinolaryngology: HNS, Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA.
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Esmaeeli S, Murphy K, Swords GM, Ibrahim BA, Brown JW, Llano DA. Visual hallucinations, thalamocortical physiology and Lewy body disease: A review. Neurosci Biobehav Rev 2019; 103:337-351. [PMID: 31195000 DOI: 10.1016/j.neubiorev.2019.06.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 06/03/2019] [Accepted: 06/08/2019] [Indexed: 12/22/2022]
Abstract
One of the core diagnostic criteria for Dementia with Lewy Bodies (DLB) is the presence of visual hallucinations. The presence of hallucinations, along with fluctuations in the level of arousal and sleep disturbance, point to potential pathological mechanisms at the level of the thalamus. However, the potential role of thalamic dysfunction in DLB, particularly as it relates to the presence of formed visual hallucinations is not known. Here, we review the literature on the pathophysiology of DLB with respect to modern theories of thalamocortical function and attempt to derive an understanding of how such hallucinations arise. Based on the available literature, we propose that combined thalamic-thalamic reticular nucleus and thalamocortical pathology may explain the phenomenology of visual hallucinations in DLB. In particular, diminished α7 cholinergic activity in the thalamic reticular nucleus may critically disinhibit thalamocortical activity. Further, concentrated pathological changes within the posterior regions of the thalamus may explain the predilection for the hallucinations to be visual in nature.
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Affiliation(s)
- Shooka Esmaeeli
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Kathleen Murphy
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Gabriel M Swords
- University of Illinois at Chicago College of Medicine, Chicago, IL, United States
| | - Baher A Ibrahim
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Jeffrey W Brown
- University of Illinois at Chicago College of Medicine, Chicago, IL, United States
| | - Daniel A Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Carle Neuroscience Institute, Urbana, IL, United States.
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