Biology subjects

Dehghani, N.

Publications and source records attributed to Dehghani, N..

4 recordsLinked to original sources

bioRxiv2026

Morphoelectric properties of inhibitory neurons shift gradually and regardless of cell type along the depth of the cerebral cortex

How can we understand the enormous diversity of the GABAergic inhibitory neurons in the cerebral cortex? To address this question, we quantify the electrophysiological and morphological properties of inhibitory neurons across the depth of an entire cortical column in the rat barrel cortex. We find properties that shift gradually with the cortical depth of the cell bodies across all inhibitory neurons, regardless of their cell types. By isolating morphoelectric variations from their shifts along the cortical depth, we find that the same simple relationships between morphoelectric properties distinguish between the four main molecular cell types of inhibitory neurons at any cortical depth, and in both the rat barrel cortex and mouse primary visual cortex. We provide converging evidence from dense electron-microscopic reconstructions of inhibitory neurons in the mouse visual cortex, and observe comparable depth-dependent shifts in additional datasets from the mouse primary motor cortex and the middle temporal gyrus of the human cortex. Our findings indicate that two different sources of morphoelectric variations can account for the diversity of cortical inhibitory neurons. The first source is molecular cell type-specific, but cortical depth-independent. The second source is cortical depth-dependent, but affects inhibitory neurons similarly across all cell types. We propose that intrinsic developmental specification vs. extrinsic environmental modulation leads to such a decoupling of inhibitory type-specific properties from gradual shifts of these properties with cortical depth.

neuroscience↗

bioRxiv2020

How understudied populations have contributed to our understanding of Alzheimer's disease genetics

The majority of genome-wide association studies have been conducted using samples with a European genetic background. As a field, we acknowledge this limitation and the need to increase the diversity of populations studied. A major challenge when designing and conducting such studies is to assimilate large samples sizes so that we attain enough statistical power to detect variants associated with disease, particularly when trying to identify variants with low and rare minor allele frequencies. In this study, we aimed to illustrate the benefits, to genetic characterization of Alzheimers disease (AD), in researching currently understudied populations. This is important for both fair representation of world populations and the translatability of findings. To that end, we have conducted a literature search to understand the contributions of studies, on different populations, to AD genetics. We systematically quantified the number of studies identifying mutations in known disease-causing genes, in a world-wide manner, and discussed the contributions of research in understudied populations to the identification of novel genetic factors in this disease. Additionally, we compared the effects of genome-wide significant SNPs across populations by focusing on loci that show different association profiles between populations (a key example being APOE). This work functions to both highlight how understudied populations have furthered our understanding of AD genetics, and to help us gage our progress in understanding the genetic architecture of this disease in all populations.

genetics↗

bioRxiv2018

Heritability and genetic variance of dementia with Lewy bodies

Recent large-scale genetic studies have allowed for the first glimpse of the effects of common genetic variability in dementia with Lewy bodies (DLB), identifying risk variants with appreciable effect sizes. However, it is currently well established that a substantial portion of the genetic heritable component of complex traits is not captured by genome-wide significant SNPs. To overcome this issue, we have estimated the proportion of phenotypic variance explained by genetic variability (SNP heritability) in DLB using a method that is unbiased by allele frequency or linkage disequilibrium properties of the underlying variants. This shows that the heritability of DLB is nearly twice as high as previous estimates based on common variants only (31% vs 59.9%). We also determine the amount of phenotypic variance in DLB that can be explained by recent polygenic risk scores from either Parkinsons disease (PD) or Alzheimers disease (AD), and show that, despite being highly significant, they explain a low amount of variance. Additionally, to identify pleiotropic events that might improve our understanding of the disease, we performed genetic correlation analyses of DLB with over 200 diseases and biomedically relevant traits. Our data shows that DLB has a positive correlation with education phenotypes, which is opposite to what occurs in AD. Overall, our data suggests that novel genetic risk factors for DLB should be identified by larger GWAS and these are likely to be independent from known AD and PD risk variants.

neuroscience↗

bioRxiv2017

Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective

Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales. In doing so, we draw attention to the importance of formalizing the goals of stimulation in terms of neural computations so that the possible implementations are matched in scale to the underlying circuit modules.

neuroscience↗