A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.

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SummaryParkinson's disease (PD) is associated with the degeneration of ventral midbrain dopaminergic neurons (vmDAns) and the accumulation of toxic α-synuclein. A non-cell-autonomous contribution, in particular of astrocytes, during PD pathogenesis has been suggested by observational studies, but remains to be experimentally tested. Here, we generated induced pluripotent stem cell-derived astrocytes and neurons from familial mutant LRRK2 G2019S PD patients and healthy individuals. Upon co-culture on top of PD astrocytes, control vmDAns displayed morphological signs of neurodegeneration and abnormal, astrocyte-derived α-synuclein accumulation. Conversely, control astrocytes partially prevented the appearance of disease-related phenotypes in PD vmDAns. We additionally identified dysfunctional chaperone-mediated autophagy (CMA), impaired macroautophagy, and progressive α-synuclein accumulation in PD astrocytes. Finally, chemical enhancement of CMA protected PD astrocytes and vmDAns via the clearance of α-synuclein accumulation. Our findings unveil a crucial non-cell-autonomous contribution of astrocytes during PD pathogenesis, and open the path to exploring novel therapeutic strategies aimed at blocking the pathogenic cross talk between neurons and glial cells.

We introduce an approach for the quantitative assessment of the connectivity in neuronal cultures, based on the statistical mechanics of percolation on a graph. This allows us to monitor the development of the culture and to see the emergence of connectivity in the network. The culture becomes fully connected at a time equivalent to the expected time of birth. The spontaneous bursting activity that characterizes cultures develops in parallel with the connectivity. The average number of inputs per neuron can be quantitatively determined in units of m0, the number of activated inputs needed to excite the neuron. For m0 Ӎ 15 we find that hippocampal neurons have on average Ϸ60 -120 inputs, whereas cortical neurons have Ϸ75-150, depending on neuronal density. The ratio of excitatory to inhibitory neurons is determined by using the GABAA antagonist bicuculine. This ratio changes during development and reaches the final value at day 7-8, coinciding with the expected time of the GABA switch. For hippocampal cultures the inhibitory cells comprise Ϸ30% of the neurons in the culture whereas for cortical cultures they are Ϸ20%. Such detailed global information on the connectivity of networks in neuronal cultures is at present inaccessible by any electrophysiological or other technique.neural network ͉ network connectivity ͉ inhibition ͉ graph theory ͉ percolation T he formation of the brain is one of the most complicated processes during development. The neural connectivity that initially emerges is organized but imprecise, and further refinement is needed for the accurate formation of the neural circuits. This requires the presence of neural activity (1), first in the form of large-scale spontaneous activity (2), and later driven by sensory experience (3, 4). The connectivity must be flexible enough to allow complex refinement, yet robust enough to sustain synchronous patterns of activity across hundreds of neurons. The formation of connectivity during maturation of the nervous system thus naturally arises as an intriguing issue. Neural cultures have been very useful as model systems to study such spontaneous activity mechanisms (5-7), persistent activity (8) and connectivity in neural networks (9, 10).Unraveling neural connectivity, however, is a daunting task; even a small culture with Ϸ10 5 neurons has several million connections. Electrophysiological approaches combined with microscopy and three-dimensional reconstructions (11, 12) have an enormous capability for identifying the connections between any two neurons, and even all of the connections of a single neuron. However, the identification of the statistical properties of the full connection distribution is beyond current capabilities. Monitoring the development of these connections in embryonic stages is even more ambitious, because it serves to link the stages of growth in the culture with those in the brain (13).We have recently developed a global bath excitation protocol coupled with graph and percolation concepts (14) to extract many properties of the network...

The epigenomic landscape of Parkinson's disease (PD) remains unknown. We performed a genomewide DNA methylation and a transcriptome studies in induced pluripotent stem cell (iPSC)‐derived dopaminergic neurons (DAn) generated by cell reprogramming of somatic skin cells from patients with monogenic LRRK2‐associated PD (L2PD) or sporadic PD (sPD), and healthy subjects. We observed extensive DNA methylation changes in PD DAn, and of RNA expression, which were common in L2PD and sPD. No significant methylation differences were present in parental skin cells, undifferentiated iPSCs nor iPSC‐derived neural cultures not‐enriched‐in‐DAn. These findings suggest the presence of molecular defects in PD somatic cells which manifest only upon differentiation into the DAn cells targeted in PD. The methylation profile from PD DAn, but not from controls, resembled that of neural cultures not‐enriched‐in‐DAn indicating a failure to fully acquire the epigenetic identity own to healthy DAn in PD. The PD‐associated hypermethylation was prominent in gene regulatory regions such as enhancers and was related to the RNA and/or protein downregulation of a network of transcription factors relevant to PD (FOXA1, NR3C1, HNF4A, and FOSL2). Using a patient‐specific iPSC‐based DAn model, our study provides the first evidence that epigenetic deregulation is associated with monogenic and sporadic PD.