Neuron-Glia Interactions and Brain Circuits.

Recent evidence suggests that glial cells take an active role in a number of brain functions that were previously attributed solely to neurons. For example, astrocytes, one type of glial cells, have been shown to promote coordinated activation of neuronal networks, modulate sensory-evoked neuronal network activity, and influence brain state transitions during development. This reinforces the idea that astrocytes not only provide the "housekeeping" for the neurons, but that they also play a vital role in supporting and expanding the functions of brain circuits and networks. Despite this accumulated knowledge, the field of computational neuroscience has mostly focused on modeling neuronal functions, ignoring the glial cells and the interactions they have with the neurons. In this chapter, we introduce the biology of neuron-glia interactions, summarize the existing computational models and tools, and emphasize the glial properties that may be important in modeling brain functions in the future.

Glioblastoma Molecular Classification Tool Based on mRNA Analysis: From Wet-Lab to Subtype.

Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7-91% and 85.9-87.7%, respectively. With this work, we propose a cost-efficient three-class (classical, mesenchymal, and proneural) tool for glioblastoma molecular classification based on the mRNA analysis of only 5-20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups.

Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex.

Astrocytes have been shown to modulate synaptic transmission and plasticity in specific cortical synapses, but our understanding of the underlying molecular and cellular mechanisms remains limited. Here we present a new biophysicochemical model of a somatosensory cortical layer 4 to layer 2/3 synapse to study the role of astrocytes in spike-timing-dependent long-term depression (t-LTD) in vivo. By applying the synapse model and electrophysiological data recorded from rodent somatosensory cortex, we show that a signal from a postsynaptic neuron, orchestrated by endocannabinoids, astrocytic calcium signaling, and presynaptic N-methyl-D-aspartate receptors coupled with calcineurin signaling, induces t-LTD which is sensitive to the temporal difference between post- and presynaptic firing. We predict for the first time the dynamics of astrocyte-mediated molecular mechanisms underlying t-LTD and link complex biochemical networks at presynaptic, postsynaptic, and astrocytic sites to the time window of t-LTD induction. During t-LTD a single astrocyte acts as a delay factor for fast neuronal activity and integrates fast neuronal sensory processing with slow non-neuronal processing to modulate synaptic properties in the brain. Our results suggest that astrocytes play a critical role in synaptic computation during postnatal development and are of paramount importance in guiding the development of brain circuit functions, learning and memory.

Does presence of metabolic syndrome impact anxiety and depressive disorder screening results in middle aged and elderly individuals? A population based study.

BACKGROUND: Depressive and anxiety disorders are common in primary care setting but often remain undiagnosed. Metabolic syndrome (MetS) is also prevalent in the general population and can impair recognition of common mental disorders due to significant co-morbidity and overlap with psychiatric symptoms included in self-reported depression/anxiety screening tools. We investigated if MetS has an impact on the accuracy of current major depressive disorder (MDD) and generalized anxiety disorder (GAD) screening results using the Hospital Anxiety and Depression scale (HADS). METHODS: A total of 1115 (562 men; mean age 62.0 ± 9.6 years) individuals of 45+ years of age were randomly selected from the general population and evaluated for current MetS; depressive and anxiety symptoms (HADS); and current MDD and GAD (Mini International Neuropsychiatric Interview [MINI]). RESULTS: The MetS was diagnosed in 34.4% of the study participants. Current MDD and GAD were more common in individuals with MetS relative to individuals without MetS (25.3% vs 14.2%, respectively, p < 0.001; and 30.2% vs 20.9%, respectively, p < 0.001). The ROC analyses demonstrated that optimal thresholds of the HADS-Depression subscale for current MDE were ≥9 in individuals with MetS (sensitivity = 87%, specificity = 73% and PPV = 52%) and ≥8 in individuals without MetS (sensitivity = 81%, specificity = 78% and PPV = 38%). At threshold of ≥9 the HADS-Anxiety subscale demonstrated optimal psychometric properties for current GAD screening in individuals with MetS (sensitivity = 91%, specificity = 85% and PPV = 72%) and without MetS (sensitivity = 84%, specificity = 83% and PPV = 56%). CONCLUSIONS: The HADS is a reliable screening tool for current MDE and GAD in middle aged and elderly population with and without MetS. Optimal thresholds of the HADS-Depression subscale for current MDD is ≥9 for individuals with MetS and ≥8 - without MetS. Optimal threshold of the HADS-Anxiety subscale is ≥9 for current GAD in individuals with and without MetS. The presence of MetS should be considered when interpreting depression screening results.

A computational study on plasticity during theta cycles at Schaffer collateral synapses on CA1 pyramidal cells in the hippocampus.

Cellular activity in the CA1 area of the hippocampus waxes and wanes at theta frequency (4-8 Hz) during exploratory behavior of rats. Perisomatic inhibition onto pyramidal cells tends to be strongest out of phase with pyramidal cell activity, whereas dendritic inhibition is strongest in phase with pyramidal cell activity. Synaptic plasticity also varies across the theta cycle, from strong long-term potentiation (LTP) to long-term depression (LTD), putatively corresponding to encoding and retrieval phases for information patterns encoded by pyramidal cell activity (Hasselmo et al. (2002a) Neural Comput 14:793-817). The mechanisms underpinning the phasic changes in plasticity are not clear, but it is likely that inhibition plays a role by affecting levels of electrical activity and calcium concentration at synapses. We explore the properties of synaptic plasticity during theta at Schaffer collateral synapses on CA1 pyramidal neurons and the influence of spatially and temporally targeted inhibition using a detailed multicompartmental model of the CA1 pyramidal neuron microcircuit and a phenomenological model of synaptic plasticity. The results suggest CA3-CA1 synapses are potentiated on one phase of theta due to high calcium levels provided by paired weak CA3 and layer III entorhinal cortex (EC) inputs even when somatic spiking is inhibited by perisomatic interneuron activity. Weak CA3 inputs alone induce lower calcium transients and result in depression of the CA3-CA1 synapses. These synapses are depressed if activated in phase with dendritic inhibition as strong CA3 inputs alone are not able to cause high calcium in this theta phase even though the CA1 pyramidal neuron shows somatic spiking. Dendritic inhibition acts as a switch that prevents LTP and promotes LTD during the retrieval phases of the theta rhythm in CA1 pyramidal cell. This may be important for not overly reinforcing recalled memories and in forgetting no longer relevant memories.

Spine head calcium as a measure of summed postsynaptic activity for driving synaptic plasticity.

We use a computational model of a hippocampal CA1 pyramidal cell to demonstrate that spine head calcium provides an instantaneous readout at each synapse of the postsynaptic weighted sum of all presynaptic activity impinging on the cell. The form of the readout is equivalent to the functions of weighted, summed inputs used in neural network learning rules. Within a dendritic layer, peak spine head calcium levels are either a linear or sigmoidal function of the number of coactive synapses, with nonlinearity depending on the ability of voltage spread in the dendrites to reach calcium spike threshold. This is strongly controlled by the potassium A-type current, with calcium spikes and the consequent sigmoidal increase in peak spine head calcium present only when the A-channel density is low. Other membrane characteristics influence the gain of the relationship between peak calcium and the number of active synapses. In particular, increasing spine neck resistance increases the gain due to increased voltage responses to synaptic input in spine heads. Colocation of stimulated synapses on a single dendritic branch also increases the gain of the response. Input pathways cooperate: CA3 inputs to the proximal apical dendrites can strongly amplify peak calcium levels due to weak EC input to the distal dendrites, but not so strongly vice versa. CA3 inputs to the basal dendrites can boost calcium levels in the proximal apical dendrites, but the relative electrical compactness of the basal dendrites results in the reverse effect being less significant. These results give pointers as to how to better describe the contributions of pre- and postsynaptic activity in the learning "rules" that apply in these cells. The calcium signal is closer in form to the activity measures used in traditional neural network learning rules than to the spike times used in spike-timing-dependent plasticity.

GluN2B-NMDAR subunit contribution on synaptic plasticity: A phenomenological model for CA3-CA1 synapses.

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.

Altered synaptic plasticity at hippocampal CA1-CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models.

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 (Aß), 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 Aß 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.

Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson's disease patients.

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.

Validation of the patient health questionnaire-9 and the generalized anxiety disorder-7 in Lithuanian student sample.

BACKGROUND: The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder Questionnaire- 7 (GAD-7) are short screening instruments used for detection of depression and anxiety symptoms in various settings, including general and mental health care as well as the general population. The aim of this study is to evaluate psychometric properties and factorial structure of the PHQ-9 and the GAD-7 in a sample of Lithuanian university students. METHODS: 1368 students (mean age 22.5±4.8) completed the PHQ-9 and the GAD-7 questionnaires online; after the completion of the survey, students were asked to provide phone contact for an additional interview. Eligible students were approached later by trained interviewers and completed The Clinical Interview Schedule-Revised for assessment of depressive and anxiety disorders. RESULTS: Results showed that the PHQ-9 and the GAD-7 are reliable screening tools for depression and anxiety (Cronbach alpha 0.86 and 0.91, respectively). The one-factor structure of the PHQ-9 and the GAD-7 was confirmed by the Confirmatory Factor Analysis. A cut-off of ≥10 for the PHQ-9 resulted in 71% sensitivity and 66% specificity recognizing students with increased risk for mood or anxiety disorder. For the GAD-7, a cut-off ≥9 resulted in 73% sensitivity and 70% specificity recognizing students at risk. The PHQ-9 was sensitive but not specific in recognizing students with depressive disorders. The sensitivity and specificity of the GAD-7 in differentiating students with generalized anxiety disorders were low. CONCLUSIONS: The PHQ-9 and the GAD-7 have sufficient formal psychometric properties, but their clinical utility as diagnostic tools for recognition of depressive and anxiety disorders in students is limited. Due to low specificity and high false positive rates, both scales are recommended only as an initial screening tool for recognition of subjects with increased risk of mental disorders, however positive cases should be later assessed using more comprehensive instruments.

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation.

The space of possible neural models is enormous and under-explored. Single cell computational neuroscience models account for a range of dynamical properties of membrane potential, but typically do not address network function. In contrast, most models focused on network function address the dimensions of excitatory weight matrices and firing thresholds without addressing the complexities of metabotropic receptor effects on intrinsic properties. There are many under-explored dimensions of neural parameter space, and the field needs a framework for representing what has been explored and what has not. Possible frameworks include maps of parameter spaces, or efforts to categorize the fundamental elements and molecules of neural circuit function. Here we review dimensions that are under-explored in network models that include the metabotropic modulation of synaptic plasticity and presynaptic inhibition, spike frequency adaptation due to calcium-dependent potassium currents, and afterdepolarization due to calcium-sensitive non-specific cation currents and hyperpolarization activated cation currents. Neuroscience research should more effectively explore possible functional models incorporating under-explored dimensions of neural function.

Impulsivity Mediates Associations Between Problematic Internet Use, Anxiety, and Depressive Symptoms in Students: A Cross-Sectional COVID-19 Study.

Background: Problematic internet use (PIU) is a serious global mental health issue that especially manifested during the Coronavirus disease (COVID-19) pandemic. Engagement in PIU as an impulsive coping with mental distress may pose a long-lasting threat to develop anxiety and depressive disorders. The first aim of our study was to investigate the prevalence of PIU and mental distress symptoms during the COVID-19 pandemic among university students in Lithuania. The second aim was to test the hypothesis that PIU affects anxiety and depressive symptoms through the mediating role of impulsivity. Methods: The cross-sectional study was comprised of 619 university students (92.9% females and 7.1% males) with a mean age of 22 ± 3 years who participated in an online survey from May to November, 2020. Participants completed the following scales: the Problematic Internet Use Questionnaire-9, the Generalized Anxiety Disorder Questionnaire-7, the Patient Health Questionnaire-9, and the Barratt Impulsiveness Scale-11. K-means cluster analysis and one-way multivariate analysis of variance were used for group comparison in terms of internet use time and habit change during COVID-19 pandemic. Structural equation modeling was applied to examine the mediating effect of impulsivity in association between PIU and mental distress, while controlling for age. Results: In sum, 45.1% of the participants reported PIU and 38.1% had markedly expressed symptoms of anxiety while 43.6% of the students reported moderate to severe depressive symptoms. During the COVID-19 pandemic 76% of the students reported at least moderate increase in their internet use time. Anxiety and depressive symptoms were significantly higher in the group of frequent internet users. The results of the structural equational modeling analysis showed a statistically significant effect of PIU on subjective anxiety symptoms and the statistically significant effect of PIU on subjective depression symptoms, both mediated via impulsivity. Conclusions: During COVID-19 pandemic, PIU, anxiety and depression symptoms are highly prevalent among students. Findings also suggest that relationships between PIU, anxiety and depressive symptoms are mediated via impulsivity. These results underscore the importance of the inclusion of impulsivity factor in the studies analyzing longitudinal effects of PIU on mental distress during COVID-19 pandemic.

Common genetic variations of deiodinase genes and prognosis of brain tumor patients.

BACKGROUND: Thyroid hormone (TH) metabolism can have prognostic significance in brain tumors. We studied the association of common variations in three deiodinase gene single-nucleotide polymorphisms (SNPs) with circulating TH concentrations and prognosis of brain tumor patients. METHODS: Patients admitted for glioma and meningioma surgery between January, 2010 and September, 2011 were evaluated for functional status (Barthel Index or BI) and circulating free tri-iodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH) concentrations. Ten common SNPs in the DIO1 gene; five SNPs in the DIO2 gene; and one SNP in the DIO3 gene were genotyped. Follow-up continued until November, 2017. RESULTS: In glioblastoma patients, the DIO1 SNP rs2235544 CC genotype was associated with significantly lower risk of death at 2 years when compared to AA + CA genotypes after adjusting for patient gender, age, pre-operative functional status, adjuvant therapy, and extent of resection (HR = 0.34, 95% CI: 0.13-0.84, p = 0.019). The TT genotype vs. CC + TC genotypes of the DI02 SNP rs12885300 was associated with increased mortality risk after adjusting for patient gender, age, pre-operative functional status, adjuvant therapy, extent of resection, and FT3/FT4 (HR = 3.13, 95% CI: 1.20-8.16, p < 0.019). The C-allele of the DI01 SNP rs2235544 was related to increased circulating free T3/ free T4 ratio in glioma and meningioma patients, indicating greater T4 to T3 conversion. CONCLUSIONS: SNPs of DIO1 gene (rs2235544) and DIO2 gene (rs12885300) have independent prognostic significance in glioblastoma patients. The C-allele of the DIO1 (rs2235544) is associated with greater T4 to T3 conversion.

Inhibitory control of site-specific synaptic plasticity in a model CA1 pyramidal neuron.

A computational model of a biochemical network underlying synaptic plasticity is combined with simulated on-going electrical activity in a model of a hippocampal pyramidal neuron to study the impact of synapse location and inhibition on synaptic plasticity. The simulated pyramidal neuron is activated by the realistic stimulation protocol of causal and anticausal spike pairings of presynaptic and postsynaptic action potentials in the presence and absence of spatially targeted inhibition provided by basket, bistratified and oriens-lacunosum moleculare (OLM) interneurons. The resulting Spike-timing-dependent plasticity (STDP) curves depend strongly on the number of pairing repetitions, the synapse location and the timing and strength of inhibition.

Metabolic syndrome, major depression, generalized anxiety disorder, and ten-year all-cause and cardiovascular mortality in middle aged and elderly patients.

BACKGROUND: Studies investigating specifically whether metabolic syndrome (MetS) and common psychiatric disorders are independently associated with mortality are lacking. In a middle-aged general population, we investigated the association of the MetS, current major depressive episode (MDE), lifetime MDE, and generalized anxiety disorder (GAD) with ten-year all-cause and cardiovascular disease mortality. METHODS: From February 2003 until January 2004, 1115 individuals aged 45 years and older were randomly selected from a primary care practice and prospectively evaluated for: (1) MetS (The World Health Organization [WHO], National Cholesterol Education Program/Adult Treatment Panel III and International Diabetes Federation [IDF] definitions); (2) current MDE and GAD, and lifetime MDE (Mini International Neuropsychiatric Interview); and (3) conventional cardiovascular risk factors. Follow-up continued through January, 2013. RESULTS: During the 9.32 ± 0.47 years of follow-up, there were 248 deaths, of which 148 deaths were attributed to cardiovascular causes. In women, WHO-MetS and IDF-MetS were associated with greater all-cause (HR-values range from 1.77 to 1.91; p-values ≤ 0.012) and cardiovascular (HR-values range from 1.83 to 2.77; p-values ≤ 0.013) mortality independent of cardiovascular risk factors and MDE/GAD. Current GAD predicted greater cardiovascular mortality (HR-values range from 1.86 to 1.99; p-values ≤ 0.025) independently from MetS and cardiovascular risk factors. In men, the MetS and MDE/GAD were not associated with mortality. CONCLUSIONS: In middle aged women, the MetS and GAD predicted greater 10-year cardiovascular mortality independently from each other; 10-year all-cause mortality was independently predicted by the MetS. MetS and GAD should be considered important and independent mortality risk factors in women.

Local learning rules: predicted influence of dendritic location on synaptic modification in spike-timing-dependent plasticity.

Recent indirect experimental evidence suggests that synaptic plasticity changes along the dendrites of a neuron. Here we present a synaptic plasticity rule which is controlled by the properties of the pre- and postsynaptic signals. Using recorded membrane traces of back-propagating and dendritic spikes we demonstrate that LTP and LTD will depend specifically on the shape of the postsynaptic depolarization at a given dendritic site. We find that asymmetrical spike-timing-dependent plasticity (STDP) can be replaced by temporally symmetrical plasticity within physiologically relevant time windows if the postsynaptic depolarization rises shallow. Presynaptically the rule depends on the NMDA channel characteristic, and the model predicts that an increase in Mg(2+) will attenuate the STDP curve without changing its shape. Furthermore, the model suggests that the profile of LTD should be governed by the postsynaptic signal while that of LTP mainly depends on the presynaptic signal shape.

How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model.

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We find that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse. (2) The typical antisymmetrical STDP curve (LTD and LTP) can become similar to a standard Hebbian characteristic (LTP only) without having to change the learning rule. This occurs if the membrane depolarization has a shallow onset and is long lasting. (3) It is known that the membrane potential varies along the dendrite as a result of the active or passive backpropagation of somatic spikes or because of local dendritic processes. As a consequence, our model predicts that learning properties will be different at different locations on the dendritic tree. In conclusion, such site-specific synaptic plasticity would provide a neuron with powerful learning capabilities.