Dainauskas, Justinas Juozas

Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at hippocampal CA1-CA3 synapses. The goal of the present study is to build computational models that incorporate the effect of AD-related peptides on CA1 pyramidal neuron and hippocampal synaptic plasticity under the AD conditions and investigate the potential pharmacological treatments that could normalize hippocampal synaptic plasticity and learning in AD. We employ a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model that includes the separate receptor contributions on long-term potentiation (LTP) and long-term depression (LTD) and embed it into the a detailed compartmental model of CA1 pyramidal neuron. Modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues LTP in AICD and Aβ conditions. The model provides insight into the complex interactions in AD pathophysiology and suggests the conditions under which the synchronous activation of a cluster of synaptic inputs targeting the dendritic tree of CA1 pyramidal neuron leads to restored synaptic plasticity.

Alzheimer’s Disease (AD) affects millions of individuals, and is one of the main causes of dementia and neurodegeneration worldwide. No significant progress has been made in the treatment of this condition. The molecular mechanisms leading to neurodegeneration in AD are still far from being clear. Alterations in the Amyloid Precursor Protein (APP) processing and clearance have been observed in early stages of AD. Increased levels of specific APP fragments, such as Amyloid β peptide (Aβ) and Amyloid APP Intra-Cellular Domain (AICD), are believed to play an important role in impairment of learning and memory in AD (Opazo et al, Cell Reports 2018; Pousinha et al, Elife 2017). We present a computational study on the effects of the Aβ-induced alterations in synaptic plasticity at a network level. We use a hippocampal CA1-CA3 network which consists of 100 CA1 pyramidal neurons with inhibitory interneurons, medial septum inputs, entorhinal cortex inputs and Schaffer collateral inputs from CA3 neurons. We employ a newly developed NMDAr-dependent voltage-based model of synaptic plasticity that implements the experimentally observed effects of increased levels of AICD and Aβ on long-term potentiation (LTP) and long-term depression (LTD) at CA1- CA3 synapses (Dainauskas et al, Front. Comput. Neurosci. 2023) We illustrate the influence of Aβ-induced alterations in synaptic plasticity on the pattern storage and recall ability of the network. We demonstrate that altered LTP impairs memory storage and recall in hippocampal CA1 network in AD, the process that can be prevented by pharmacological blockage of GluN2B-NMDA receptor. Computational modeling study allows integration of the complex effects of AD related peptides at molecular, synaptic, neuron and network levels, and explains the impaired memory formation and retrieval in the hippocampal networks in AD.

Alzheimer’s disease (AD) has a long preclinical stage and, before any clinical symptoms appear, pathological processes are observed in the hippocampus. Recent experimental evidence supports the fundamental role of AD-related peptides early in the pathology: in particular the most widely studied Amyloid beta (Abeta), and the less investigated Amyloid precursor protein (APP) C-terminal peptide (AICD). The aim of this project is to understand the AD-related peptide-induced mechanisms of impaired learning and memory in hippocampal CA1 region in early pathology of AD by applying the integrated experimental and computational modelling approach. We investigated the effects of Abeta and AICD on intrinsic excitability of hippocampal CA1 pyramidal neurons and synaptic plasticity at hippocampal CA1-CA3 synapses in early pathology of AD. We developed data-driven in silico models of the hippocampal learning in CA1 region under AD conditions, and 1) extended the experimental evidence of Abeta, AICD-related changes in the properties of hippocampal CA1 pyramidal neuron synaptic plasticity, synaptic signal integration and neuronal excitability; 2) incorporated the effects of AD-related peptides into computational models of hippocampal synaptic plasticity to determine and explain the mechanisms of altered hippocampal function that leads to impaired learning in AD; 3) assessed the potential targets for innovative treatment of AD. We used Human Brain Project Brain Simulation Platform to perform computational modeling. The modeling results support the experimental evidence that pathological concentrations of Aβ and AICD cause long-term potentiation (LTP) impairment. Long-term depression (LTD) enhancement was observed in Abeta conditions. Synaptic plasticity was strongly dependent on GluN2B-NMDA receptor subunit functioning, and rescued by its partial blockade in AD. The modeling study provides insight into the complex interactions in AD pathophysiology, and suggests the conditions under which synaptic plasticity is restored. The inter-disciplinary analysis, bringing together experimentalists and modelers, helps to further unravel the neuronal mechanisms most affected by AD, build a biologically-plausible computational models of the hippocampal CA1 area under AD conditions, and suggest potential targets for pharmacological treatment of AD.

Synaptic plasticity is believed to be a key mechanism underlying learning and memory. We developed a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model for synaptic modifications at hippocampal CA3-CA1 synapses on a hippocampal CA1 pyramidal neuron. The model incorporates the GluN2A-NMDA and GluN2B-NMDA receptor subunit-based functions and accounts for the synaptic strength dependence on the postsynaptic NMDA receptor composition and functioning without explicitly modeling the NMDA receptor-mediated intracellular calcium, a local trigger of synaptic plasticity. We embedded the model into a two-compartmental model of a hippocampal CA1 pyramidal cell and validated it against experimental data of spike-timing-dependent synaptic plasticity (STDP), high and low-frequency stimulation. The developed model predicts altered learning rules in synapses formed on the apical dendrites of the detailed compartmental model of CA1 pyramidal neuron in the presence of the GluN2B-NMDA receptor hypofunction and can be used in hippocampal networks to model learning in health and disease.

Background and aim: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by involuntary, uncontrolled movements. Currently one of the best available treatment options is subthalamic nucleus (STN) deep brain stimulation (DBS). However, this invasive procedure may not lead to the substantial improvement in PD symptoms. The goal of the study is to build an individualized brain connectome to later embed it into The Virtual Brain (TVB) platform for human brain activity simulation. The long-term aim is to predict the outcomes of DBS in PD patients to plan successful treatment. Materials and methods: Ten PD patients underwent STN DBS implantation surgery. Preoperative T1 and diffusion MRI images and a postoperative CT image were collected. A structural T1 MRI scan was processed with Freesufers recon-all pipeline to obtain tissue segmentation and reconstruction of the cortical surfaces. Diffusion weighted images were processed using MRtrix3, performing artefact corrections, constrained spherical deconvolution and tractography. The structural T1 image was rigidly registered with the diffusion image in order to project the cortical and subcortical parcellation of the brain onto the reconstructed tracts. The Desikan and DISTAL atlas for cortical and subcortical brain areas were used, respectively, to obtain a structural connectome. The DBS electrode was identified on the postoperative CT image and registered with the T1 image. Results: The structure of the virtual brain model from the patient T1 MRI neuroimaging data was built. The connectome including subcortical areas of globus pallidus internal, globus pallidus external and STN was calculated. The DBS electrode was projected into the virtual brain model to simulate the volume of tissue activated by electric stimulation. Conclusions: The connectivity extracted will be embedded in the TVB platform to model the activity of the patient brain in deep brain stimulation conditions. A dynamical neural mass model will be equipped to every node of the connectome to simulate neural activity. Perturbations of the dynamics will be modelled by a realistic stimulus through the virtualized DBS electrodes. Patient specific virtual brain modelling will improve our understanding in inter-individual outcomes of DBS treatment.

The most common form of dementia in the world is Alzheimer’s disease (AD), a degenerative and irreversible brain illness. Despite the fact that the number of AD patients is rising, no ground-breaking treatments have been suggested recently. In order to understand the intricate molecular, synaptic, cellular, neuronal, and network level causes of impaired learning and memory in the AD, a new multidisciplinary approach is required. We use an integrated experimental and computational modelling approach to explore and better understand the impairment in synaptic plasticity caused by AD-related peptides at hippocampal CA1-CA3 synapses in early AD disease. Amyloid beta (Aβ), Amyloid eta (Aβ), and the Amyloid APP intracellular domain are among the AD-related peptides that are produced as a result of altered amyloid precursor protein (APP) processing and clearance in the early stages of the disease (AICD). While high concentrations of AICD cause LTP disruption and leave LTD intact at glutamatergic synapses, Aβ inhibits long-term potentiation (LTP) and promotes long-term depression (LTD) in hippocampal CA1 pyramidal neurons. The aim of this study is to investigate the joint effect of AICD and Aβ on LTP and LTD at hippocampal CA1-CA3 synapses applying computational modeling approach. We used a newly developed NMDAr-dependent voltage-based model of synaptic plasticity along with a compartmental model of a CA1 pyramidal neuron. The increased AICD levels were modeled, by adjusting the conductances of SK channels, L-type calcium channels, and the contribution of GluN2Bcontaining NMDA receptor. The heightened levels of Aβ were modeled as increased extracellular glutamate concentration, endocytosis of synaptic AMPA receptors, decreased synaptic density, and altered GluN2B-containing NMDA receptor-mediated activation of calcium/calmodulin-dependent kinase II (CaMKII). Our modeling results show that increased AICD levels disrupt LTP while LTD is unaffected, while increased Aβ levels disrupt LTP and enhance LTD, mirroring the experimental results. Simulation with both AICD and Aβ having pathological concentrations disrupt synapse ability to potentiate weight. Computational modeling study sheds light on the AICD- and Aβ-induced complex processes and their interactions in shaping synaptic plasticity at the hippocampal synapses.

Background and purpose: The aim of the study is to predict the subthalamic nucleus (STN) deep brain stimulation (DBS) outcomes for Parkinson’s disease (PD) patients using the radiomic features extracted from pre-operative magnetic resonance images (MRI). Methods: The study included 34 PD patients who underwent DBS implantation in the STN. Five patients (15%) showed poor DBS motor outcome. All together 9 amygdalar nuclei and 12 hippocampus subfields were segmented using Freesurfer 7.0 pipeline from pre-operative MRI images. Furthermore, PyRadiomics platform was used to extract 120 radiomic features for each nuclei and subfield resulting in 5,040 features. Minimum Redundancy Maximum Relevance (mRMR) feature selection method was employed to reduce the number of features to 20, and 8 machine learning methods (regularized binary logistic regression (LR), decision tree classifier (DT), linear discriminant analysis (LDA), naive Bayes classifier (NB), kernel support vector machine (SVM), deep feed-forward neural network (DNN), one-class support vector machine (OC-SVM), feed-forward neural network-based autoencoder for anomaly detection (DNN-A)) were applied to build the models for poor vs. good and very good STN-DBS motor outcome prediction. Results: The highest mean prediction accuracy was obtained using regularized LR (96.65 ± 7.24%, AUC 0.98 ± 0.06) and DNN (87.25 ± 14.80%, AUC 0.87 ± 0.18). Conclusion: The results show the potential power of the radiomic features extracted from hippocampus and amygdala MRI in the prediction of STN-DBS motor outcomes for PD patients.

Synaptic plasticity is believed to be a biological basis of learning and memory. Long-term potentiation (LTP) and long-term depression (LTD) are the most common forms of synaptic plasticity, induced by a pre- and postsynaptic neuronal activity, and refer to the strengthening and weakening of synaptic weight. We develop a NMDA receptor-based voltage dependent synaptic plasticity model for synaptic modifications at Schaffer-collateral synapses onto hippocampal CA1 pyramidal neuron. The model incorporates the NMDA receptor-based function and is able to capture dynamics of the NMDA NR2A and NR2B receptor subunits without explicitly modeling dendritic spine intracellular calcium dynamics, a local trigger of synaptic plasticity. The model was embedded into a detailed compartmental model of a hippocampal CA1 pyramidal cell to investigate the dependence of synaptic modifications on the NMDA receptor functioning for spatially and temporally specific inputs. The present study reproduces experimentally observed outcomes of LTP and LTD induction, as well as standard STDP protocol, analyzes the sensitivity of the model and predicts the influence of synaptic location on synaptic modifications and explains the impaired learning during theta cycles in the presence of the NMDA receptor hypofunction. During experiments it was observed that high frequency stimulation leads to LTP induction and EPSPs increase up to 180%, while low frequency stimulation induces LTD which leads to EPSP decrease up to 54%. The developed NMDA receptor-dependent synaptic plasticity model can be used for experimentally testable predictions and be applied in large scale simulations for modeling hippocampal networks in health and disease.

Alzheimer’s disease (AD) is an irreversible and incurable brain disorder, characterized by progressive memory loss and cognitive dysfunction. In early AD, the alterations in amyloid precursor protein (APP) processing and clearance of APP peptides are observed. Increased APP levels lead to the production of AD related peptides such as amyloid beta, and the amyloid APP intracellular domain (AICD) at higher concentrations [1]. It was recently shown that AICD modifies intrinsic excitability of hippocampal CA1 pyramidal neuron and impairs synaptic plasticity in AD [1]. The aim of this study is to investigate the effect of the pathological AICD levels on long-term potentiation (LTP) and long-term depression (LTD) in a detailed computational model of a CA1 pyramidal neuron. We used a detailed compartmental model [2] and included the influence of the elevated AICD levels by increasing the conductances of SK channels and L-type calcium channels. At a synaptic level, the contribution of the GluN2B-containing NMDA receptor (NMDAr) was also increased. A modified NMDAr dependent voltage-based model of synaptic plasticity [3] was used to analyse synaptic strengths at clustered Shaffer collateral synapses. The results support the experimental indication that pathological concentration of AICD leads to LTP disruption and leaves LTD intact in AD. The model provides insight into the complex interactions in AD pathophysiology and suggest the conditions under which the synchronous activation of a cluster of synaptic inputs targeting the dendritic tree can concur in generating the observed signal at the soma after a LTP conditioning period.

Long-term potentiation (LTP) and long-term depression(LTD), the ability of a synapse to enhance or weaken itsstrength, is believed to be a biological basis of learning andmemory. Hippocampal synaptic plasticity is modulated bythe alterations in neuronal intrinsic excitability. Intrinsicexcitability and synaptic plasticity are affected in Alzheimer’sdisease (AD), a neurodegenerative disorder, characterizedby progressive memory loss and cognitive dysfunction.In the early stage of AD, hippocampal learning impairmentis observed due to the accumulation of amyloid precursorprotein (APP) metabolite APP intracellular fragment(AICD) that modifies intrinsic excitability of hippocampalCA1 pyramidal neuron and disrupts synaptic plasticity (Sajikumaret al., 2014 Aug 19).In this study, we investigated the effect of altered intrinsicexcitability on synaptic plasticity in a hippocampal CA1pyramidal cell affected by AD using a computational modelingapproach. We used a detailed compartmental modelof a hippocampal CA1 pyramidal neuron (Cichon & Gan,2015 Apr) and included the influence of AICD by alteringthe small-conductance calcium-activated potassium channels(SK), L-type calcium channels, and contribution of theGluN2B-containing NMDA receptor (NMDAr). A modifiedNMDAr dependent voltage-based synaptic plasticity model(Sezener et al., 2021) was used to analyse synaptic plasticitychanges at clustered Schaffer collateral synapses. Eachcluster contained 50 synapses distributed along the dendriticbranches with densities in a range of 0.05 to 1.0 synapse/μm. The synapses were stimulated with 1 Hz for 900 s toinduce LTD and 2 bursts of 100 Hz for 1 s, separated by2 s window for LTP (Sajikumar et al., 2014 Aug 19). Theresults show that altered neuronal intrinsic excitability dueto the increased AICD production disrupts LTP leaving LTD intact. Elevated AICD levels enhance NMDAr expressionand lead to SK channel overactivation, thus reducing neuronsensitivity to the incoming presynaptic inputs for high frequencyLTP induction protocol. Contrary, neuron adequatelyresponds to low frequency stimulation and maintains LTD.Partial blockade of NMDAr restores normal SK channelfunction and rescues LTP. These findings provide insightsinto the pathological dynamical effects of AICD on NMDAr,SK channel properties, the resulting neuronal intrinsic excitabilityand impaired synaptic plasticity.