In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia (Sherif et al 2020)

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"Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability"
1 . Sherif MA, Neymotin SA, Lytton WW (2020) In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia. NPJ Schizophr 6:25 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron;
Channel(s): I h;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s): NR2A GRIN2A;
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Schizophrenia;
Implementer(s): Sherif, Mohamed [ at];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; AMPA; NMDA; I h; Gaba; Glutamate;
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#!/usr/bin/env python

"""Estimate power spectral density characteristcs using Welch's method."""

from __future__ import division, print_function
import numpy as np
from scipy import signal, integrate

__author__ = 'Marcos Duarte,'
__version__ = ' v.1 2013/09/16'

def calc_psd(x, fs=1.0, window='hanning', nperseg=None, noverlap=None, nfft=None,
        detrend='constant', calcExtraData=False): #, show=True, ax=None, scales='linear', xlim=None, units='V', plotExtraData=False, *args, **kwargs):
    """Estimate power spectral density characteristcs using Welch's method.

    This function is just a wrap of the scipy.signal.welch function with
    estimation of some frequency characteristcs and a plot. For completeness,
    most of the help from scipy.signal.welch function is pasted here.

    Welch's method [1]_ computes an estimate of the power spectral density
    by dividing the data into overlapping segments, computing a modified
    periodogram for each segment and averaging the periodograms.

    x : array_like
        Time series of measurement values
    fs : float, optional
        Sampling frequency of the `x` time series in units of Hz. Defaults
        to 1.0.
    window : str or tuple or array_like, optional
        Desired window to use. See `get_window` for a list of windows and
        required parameters. If `window` is array_like it will be used
        directly as the window and its length will be used for nperseg.
        Defaults to 'hanning'.
    nperseg : int, optional
        Length of each segment.  Defaults to half of `x` length.
    noverlap: int, optional
        Number of points to overlap between segments. If None,
        ``noverlap = nperseg / 2``.  Defaults to None.
    nfft : int, optional
        Length of the FFT used, if a zero padded FFT is desired.  If None,
        the FFT length is `nperseg`. Defaults to None.
    detrend : str or function, optional
        Specifies how to detrend each segment. If `detrend` is a string,
        it is passed as the ``type`` argument to `detrend`. If it is a
        function, it takes a segment and returns a detrended segment.
        Defaults to 'constant'.
    show : bool, optional (default = False)
        True (1) plots data in a matplotlib figure.
        False (0) to not plot.
    ax : a matplotlib.axes.Axes instance (default = None)
    scales : str, optional
        Specifies the type of scale for the plot; default is 'linear' which
        makes a plot with linear scaling on both the x and y axis.
        Use 'semilogy' to plot with log scaling only on the y axis, 'semilogx'
        to plot with log scaling only on the x axis, and 'loglog' to plot with
        log scaling on both the x and y axis.
    xlim : float, optional
        Specifies the limit for the `x` axis; use as [xmin, xmax].
        The defaukt is `None` which sets xlim to [0, Fniquist].
    units : str, optional
        Specifies the units of `x`; default is 'V'.

    Fpcntile : 1D array
        frequency percentiles of the power spectral density
        For example, Fpcntile[50] gives the median power frequency in Hz.
    mpf : float
        Mean power frequency in Hz.
    fmax : float
        Maximum power frequency in Hz.
    Ptotal : float
        Total power in `units` squared.
    f : 1D array
        Array of sample frequencies in Hz.
    P : 1D array
        Power spectral density or power spectrum of x.

    See Also

    An appropriate amount of overlap will depend on the choice of window
    and on your requirements.  For the default 'hanning' window an
    overlap of 50% is a reasonable trade off between accurately estimating
    the signal power, while not over counting any of the data.  Narrower
    windows may require a larger overlap.
    If `noverlap` is 0, this method is equivalent to Bartlett's method [2]_.

    .. [1] P. Welch, "The use of the fast Fourier transform for the
           estimation of power spectra: A method based on time averaging
           over short, modified periodograms", IEEE Trans. Audio
           Electroacoust. vol. 15, pp. 70-73, 1967.
    .. [2] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra",
           Biometrika, vol. 37, pp. 1-16, 1950.

    Examples (also from scipy.signal.welch)
    >>> import numpy as np
    >>> from psd import psd
    #Generate a test signal, a 2 Vrms sine wave at 1234 Hz, corrupted by
    # 0.001 V**2/Hz of white noise sampled at 10 kHz and calculate the PSD:
    >>> fs = 10e3
    >>> N = 1e5
    >>> amp = 2*np.sqrt(2)
    >>> freq = 1234.0
    >>> noise_power = 0.001 * fs / 2
    >>> time = np.arange(N) / fs
    >>> x = amp*np.sin(2*np.pi*freq*time)
    >>> x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
    >>> psd(x, fs=freq);

    if not nperseg:
        nperseg = np.ceil(len(x) / 2)
    f, P = signal.welch(x, fs, window, nperseg, noverlap, nfft, detrend)
    Area = integrate.cumtrapz(P, f, initial=0)
    Ptotal = Area[-1]
    mpf = integrate.trapz(f * P, f) / Ptotal  # mean power frequency
    fmax = f[np.argmax(P)]
    # frequency percentiles
    inds = [0]
    Area = 100 * Area / Ptotal  # + 10 * np.finfo(np.float).eps
    for i in range(1, 101):
        inds.append(np.argmax(Area[inds[-1]:] >= i) + inds[-1])
    fpcntile = f[inds]

    # if show:
    #     _plot(x, fs, f, P, mpf, fmax, fpcntile, scales, xlim, units, ax, plotExtraData, *args, **kwargs)
    if calcExtraData:
        return fpcntile, mpf, fmax, Ptotal, f, P
        return f, P

def plot_psd(f, P, scales, xlim, units, ax, plotExtraData=False, mpf=None, fmax=None, fpcntile=None, *args, **kwargs):
    """Plot results of the ellipse function, see its help."""
        import matplotlib.pyplot as plt
    except ImportError:
        print('matplotlib is not available.')
        if ax is None:
            fig, ax = plt.subplots(1, 1, figsize=(7, 5))
        if scales.lower() == 'semilogy' or scales.lower() == 'loglog':
        if scales.lower() == 'semilogx' or scales.lower() == 'loglog':
        plt.plot(f, P, linewidth=2, *args, **kwargs)
        ylim = ax.get_ylim()
        if plotExtraData:
          plt.plot([fmax, fmax], [np.max(P), np.max(P)], 'ro',
                   label='Fpeak  = %.2f' % fmax, *args, **kwargs)
          plt.plot([fpcntile[50], fpcntile[50]], ylim, 'r', lw=1.5,
                   label='F50%%   = %.2f' % fpcntile[50], *args, **kwargs)
          plt.plot([mpf, mpf], ylim, 'r--', lw=1.5,
                   label='Fmean = %.2f' % mpf, *args, **kwargs)
          plt.plot([fpcntile[95], fpcntile[95]], ylim, 'r-.', lw=2,
                   label='F95%%   = %.2f' % fpcntile[95], *args, **kwargs)
          leg = ax.legend(loc='best', numpoints=1, framealpha=.5,
                          title='Frequencies [Hz]')
          plt.setp(leg.get_title(), fontsize=12)
        plt.xlabel('Frequency [$Hz$]', fontsize=12)
        plt.ylabel('Magnitude [%s$^2/Hz$]' % units, fontsize=12)
        plt.title('Power spectral density', fontsize=12)
        if xlim: