It is generally assumed that human intelligence relies on efficient processing by neurons in our brain. Although grey matter thickness and activity of temporal and frontal cortical areas correlate with IQ scores, no direct evidence exists that links structural and physiological properties of neurons to human intelligence. Here, we find that high IQ scores and large temporal cortical thickness associate with larger, more complex dendrites of human pyramidal neurons. We show in silico that larger dendritic trees enable pyramidal neurons to track activity of synaptic inputs with higher temporal precision, due to fast action potential kinetics. Indeed, we find that human pyramidal neurons of individuals with higher IQ scores sustain fast action potential kinetics during repeated firing. These findings provide the first evidence that human intelligence is associated with neuronal complexity, action potential kinetics and efficient information transfer from inputs to output within cortical neurons.

It is generally assumed that human intelligence relies on efficient processing by neurons in our brain. Although gray matter thickness and activity of temporal and frontal cortical areas correlate with IQ scores, no direct evidence exists that links structural and physiological properties of neurons to human intelligence. Here, we find that high IQ scores and large temporal cortical thickness associate with larger, more complex dendrites of human pyramidal neurons. We show in silico that larger dendritic trees enable pyramidal neurons to track activity of synaptic inputs with higher temporal precision, due to fast action potential kinetics. Indeed, we find that human pyramidal neurons of individuals with higher IQ scores sustain fast action potential kinetics during repeated firing. These findings provide the first evidence that human intelligence is associated with neuronal complexity, action potential kinetics and efficient information transfer from inputs to output within cortical neurons.All rights reserved. No reuse allowed without permission.was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Axonal Charcot-Marie-Tooth neuropathies (CMT type 2) are caused by inherited mutations in various genes functioning in different pathways. The type of genes and multiplicity of mutations reflect the clinical and genetic heterogeneity in CMT2 disease, which complicates the diagnosis and has halted therapy development. Here, we used CMT2 patient-derived pluripotent stem cells (iPSCs) to identify common hallmarks of axonal degeneration shared by different CMT2 subtypes. We compared the cellular phenotypes of neurons differentiated from CMT2 patient iPSCs with those from healthy controls and a CRISPR/Cas9-corrected isogenic line. Our results demonstrate neurite network alterations along with extracellular electrophysiological abnormalities in the differentiated motor neurons. Progressive deficits in mitochondrial and lysosomal trafficking, as well as in mitochondrial morphology, were observed in all CMT2 patient lines. Differentiation of the same CMT2 iPSC-lines into peripheral sensory neurons, only gave rise to cellular phenotypes in subtypes with sensory involvement, supporting the notion that some gene mutations predominantly affect motor neurons. We revealed a common mitochondrial dysfunction in CMT2-derived motor neurons, supported by alterations in the expression pattern and oxidative phosphorylation, which could be recapitulated in the sciatic nerve tissue of a symptomatic mouse model. Inhibition of a dual leucine zipper kinase (DLK) could partially ameliorate the mitochondrial disease phenotypes in CMT2 subtypes. Altogether, our data reveals shared cellular phenotypes across different CMT2 subtypes and suggests that targeting such common pathomechanisms could allow the development of a uniform treatment for CMT2.

This work presents a detailed analysis of the microstructure and the composition of our record Cu 2 ZnSnSe 4 (CZTSe)-CdS-ZnO solar cell with a total area efficiency of 9.7%. The average composition of the CZTSe crystallites is Cu 1.94 Zn 1.12 Sn 0.95 Se 3.99. Large crystals of ZnSe secondary phase (up to 400 nm diameter) are observed at the voids between the absorber and the back contact, while smaller ZnSe domains are segregated at the grain boundaries and close to the surface of the CZTSe grains. An underlying layer and some particles of Cu x Se are observed at the Mo-MoSe 2-Cu 2 ZnSnSe 4 interface. The free surface of the voids at the back interface is covered by an amorphous layer containing Cu, S, O, and C, while the presence of Cd, Na, and K is also observed in this region. V

The dynamics and the sharp onset of action potential (AP) generation have recently been the subject of intense experimental and theoretical investigations. According to the resistive coupling theory, an electrotonic interplay between the site of AP initiation in the axon and the somato-dendritic load determines the AP waveform. This phenomenon not only alters the shape of APs recorded at the soma, but also determines the dynamics of excitability across a variety of time scales. Supporting this statement, here we generalize a previous numerical study and extend it to the quantification of the input-output gain of the neuronal dynamical response. We consider three classes of multicompartmental mathematical models, ranging from ball-and-stick simplified descriptions of neuronal excitability to 3D-reconstructed biophysical models of excitatory neurons of rodent and human cortical tissue. For each model, we demonstrate that increasing the distance between the axonal site of AP initiation and the soma markedly increases the bandwidth of neuronal response properties. We finally consider the Liquid State Machine paradigm, exploring the impact of altering the site of AP initiation at the level of a neuronal population, and demonstrate that an optimal distance exists to boost the computational performance of the network in a simple classification task.