Biology subjects

Sadeghi, M.

Publications and source records attributed to Sadeghi, M..

4 recordsLinked to original sources

bioRxiv2018

Integrated Cognitive Assessment: Speed and Accuracy of Visual Processing as a Reliable Proxy to Cognitive Performance

Various mental disorders are accompanied by some degree of cognitive impairment. Particularly in neurodegenerative disorders, cognitive impairment is the phenotypical hallmark of the disease. Effective, accurate and timely cognitive assessment is key to early diagnosis of this family of mental disorders. Current standard-of-care techniques for cognitive assessment are primarily paper-based, and need to be administered by a healthcare professional; they are additionally language and education-dependent and typically suffer from a learning bias. These tests are thus not ideal for large-scale pro-active cognitive screening and disease progression monitoring. We developed the Integrated Cognitive Assessment (ICA), a 5-minute computerized cognitive assessment tool based on a rapid visual categorization task, in which a series of carefully selected natural images of varied difficulty are presented to participants. Overall 448 participants, across a wide age-range with different levels of education took the ICA test. We compared participants ICA test results with a variety of standard pen-and-paper tests that are routinely used to assess cognitive performance. ICA had excellent test-retest reliability, and was significantly correlated with all the reference cognitive tests used here, demonstrating ICAs ability as one unified test that can assess various cognitive domains.

neuroscience↗

bioRxiv2018

A Neural Ensemble Correlation Code for Sound Category Identification

Humans and other animals effortlessly identify sounds and categorize them into behaviorally relevant categories. Yet, the acoustic features and neural transformations that enable the formation of perceptual categories are largely unknown. Here we demonstrate that correlation statistics between frequency-organized cochlear sound channels are reflected in the neural ensemble activity of the auditory midbrain and that such activity, in turn, can contribute to discrimination of perceptual categories. Using multi-channel neural recordings in the auditory midbrain of unanesthetized rabbits, we first demonstrate that neuron ensemble correlations are highly structured in both time and frequency and can be decoded to distinguish sounds. Next, we develop a probabilistic framework for measuring the nonstationary spectro-temporal correlation statistics between frequency organized channels in an auditory model. In a 13-category sound identification task, classification accuracy is consistently high (>80%), improving with sound duration and plateauing at ~ 1-3 seconds, mirroring human performance trends. Nonstationary short-term correlation statistics are more informative about the sound category than the time-average correlation statistics (84% vs. 73% accuracy). When tested independently, the spectral and temporal correlations between the model outputs achieved a similar level of performance and appear to contribute equally. These results outline a plausible neural code in which correlation statistics between neuron ensembles of different frequencies can be read-out to identify and distinguish acoustic categories.

neuroscience↗

bioRxiv2017

Noise-driven Cell Differentiation and the Emergence of Organization

One of the major transitions in evolution is the step from unicellularity into the brave new world of multicellularity. To understand this feat, one has to fathom two main characteristics of multicellular organisms: differentiation and self-organization. Any explanation concerning this major transition should involve mechanisms that can simultaneously explain the marvellous intricacies manifest in the aforementioned characteristics, and an account of the evolution of such traits. Here we propose a noise-driven differentiation (NDD) model. The reliance on noise, in place of a more mechanistic approach, makes the NDD model a more suitable approach to explain differentiation and self-organization. Furthermore, our model sheds some light on the possible evolutionary origins of these biological innovations. To test the NDD model, we utilize a model of cell aggregation. The behavior of this model of cell aggregation is in concert with the NDD model.

cell biology↗

bioRxiv2017

Elucidation of the 14-3-3ζ interactome reveals critical roles of RNA splicing factors during adipogenesis

Adipogenesis is facilitated by a complex signaling network requiring strict temporal and spatial organization of effector molecules. Molecular scaffolds, such as 14-3-3 proteins, coordinate such events, and we have previously identified 14-3-3{zeta} as an essential scaffold in adipocyte differentiation. The interactome of 14-3-3{zeta} is large and diverse, and it is possible that novel adipogenic factors may be present within it. Mouse embryonic fibroblasts from mice over-expressing a TAP-epitope-tagged 14-3-3{zeta} molecule were generated, and following the induction of adipogenesis, TAP-14-3-3{zeta} complexes were purified, followed by mass spectrometry analysis to determine the 14-3-3{zeta} interactome. Over 100 proteins were identified as being unique to adipocyte differentiation, of which 56 were novel interacting partners. Previously established regulators of adipogenesis (ie, Ptrf/Cavin1 and Phb2) were found within the 14-3-3{zeta} interactome, confirming the ability of this approach to identify regulators of adipocyte differentiation. An enrichment of proteins in the interactome related to RNA metabolism, processing, and splicing was identified, and analysis of transcriptomic data revealed that 14-3-3{zeta} depletion in 3T3-L1 cells affected the alternative splicing of mRNA during adipocyte differentiation. Of the RNA splicing factors within the 14-3-3{zeta} interactome, depletion of Hnrnpf, Hnrnpk, Ddx6, and Sfpq by siRNA revealed essential roles of these proteins in adipogenesis and their roles in the alternative splicing of Lpin1. In summary, novel adipogenic factors can be detected within the 14-3-3{zeta} interactome, and further characterization of additional proteins within the 14-3-3{zeta} interactome has the potential of identifying novel targets to block the expansion of adipose tissue mass that occurs in obesity.

physiology↗