Understanding how the brain perceives and responds to sensory information to generate behavior is a fundamental question in neuroscience. One of the most powerful sensory stimuli is light, which has been shown to affect mood and is often dysregulated in mood disorders involving corticotropin-releasing factor (CRF) signaling. Despite these observations, the precise relationships between light, CRF, and their effects on the brain are still not well understood.

To address this gap in knowledge, our research team developed a novel algorithm that employs larval zebrafish as a model organism. These fish have analogous CRF systems to mammals, making them an ideal subject for investigating the role of CRF signaling in the perception of light.

Our research revealed that chemo-genetic ablation of hypothalamic CRF (CRFHy) neurons or blocking CRF signaling ameliorates dark avoidance in free-swimming zebrafish, whereas optogenetic activation of these neurons promotes avoidance. Moreover, we found that light suppresses the overall activity of CRFHy neurons, which are connected with sensorimotor and decision-making brain areas.

To investigate how photic stimuli are perceived and processed in the brain, we used brain-wide calcium imaging in head-restrained/tail-free zebrafish under alternating light/dark conditions. Our results uncovered distributed photic coding in the brain, revealing brain-wide photic perception. Furthermore, our findings suggest a previously unidentified role of CRFHy neurons in regulating photic valence and altering photic perception and functional connectivity toward light- and exploration-associated brain states.

Our newly developed algorithm and the use of larval zebrafish as a model organism have provided valuable insights into the complex mechanisms underlying photic perception and CRF signaling in the brain. Our findings not only shed light on how light affects mood and behavior but also offer new directions for developing novel therapies for mood disorders.

By identifying the role of CRFHy neurons in regulating photic valence and altering functional connectivity, our research suggests that targeting these neurons or CRF signaling may be a promising strategy for treating mood disorders. Moreover, our discovery of brain-wide photic coding provides a foundation for future studies exploring the neural circuitry involved in photic perception.

We believe that our research has significant implications for understanding the fundamental mechanisms of brain function and has the potential to inspire new approaches to treating mood disorders that are urgently needed.

+ Wagle, Mahendra, Mahdi Zarei, Matthew Lovett-Barron, Kristina Tyler Poston, Jin Xu, Vince Ramey, Katherine S. Pollard et al. "Brain-wide perception of the emotional valence of light is regulated by distinct hypothalamic neurons." Molecular psychiatry (2022): 1-17. .

 Mahendra Wagle, Mahdi Zarei, Jin Xu, David Prober, Jay Schulkin, Su Guo, "Hypothalamic CRF neurons integrate visual stimuli to program a functional circuitry for avoidance behavior," The 16th International Zebrafish Conference (IZFC), June 16-22, 2021 

Movement disorders are among the most prevalent neurological conditions, affecting an estimated 1% of the population in the United States, or about 3 million individuals. These disorders encompass a wide range of conditions, including epilepsy, Parkinson's disease, brain tumors, and stroke, and can cause significant impairment of motor function, impacting a person's quality of life. Therefore, there is a pressing need to develop effective methods for diagnosing and managing these conditions, and neuroscience research has focused on identifying key aspects of cell activities that contribute to movement disorders.

One such key aspect is spike discharge, a phenomenon in which neurons fire in a rapid and synchronized manner. Spike discharge has been linked to movement disorders, and detecting this activity in electroencephalogram (EEG) signals can be an important diagnostic tool. Moreover, accurate analysis of neuron activity during motor tasks can lead to the development of improved systems for managing movement disorders.

To investigate the relationship between neuron activity and movement disorders, we focused on the study of arm-related neurons and their relationship to premotor cortical cell activity and the direction of arm movement. Specifically, we used artificial intelligence algorithms to predict key neuronal behaviors, such as firing rates and timing, in response to different motor tasks. Our findings have important implications for the diagnosis and treatment of movement disorders, as well as for the development of brain-computer interface platforms that can enable individuals with movement disorders to control devices using their neural activity.

The findings of this study provide valuable insights into the functional organization of the brain and the complex network of neural connections involved in schizophrenia. By identifying gender-specific differences in dysconnectivity involving the left superior temporal gyrus, our study contributes to a more nuanced understanding of the heterogeneity of schizophrenia, and has important implications for the development of more personalized treatment approaches that take into account the distinct male and female phenotypes of the disorder. In addition, the data-driven approach used in this study demonstrates the potential of complex network analysis and artificial intelligence algorithms for investigating the intricate interplay between brain function and psychiatric disorders, and highlights the importance of considering gender as a key factor in future studies of schizophrenia and other neurological disorders. 

Zarei M. Spike discharge prediction based on neuro-fuzzy system. J Unexplored Med Data 2017;2:88-101. http://dx.doi.org/10.20517/2572-8180.2017.16 

Schizophrenia is a debilitating psychiatric condition that affects approximately 1% of the population worldwide. The neurobiological underpinnings of this disorder have been extensively investigated, and recent research has pointed to the involvement of the precentral gyrus (PreCG) in the impairments of voluntary movement associated with schizophrenia. Studies have shown that patients with schizophrenia exhibit reduced functional activity and volume deficits in the PreCG. Additionally, lower activation in the left PreCG and decreased regional homogeneity in the right precentral gyrus have been reported in patients with schizophrenia.

To further investigate PreCG functional connectivity impairment in schizophrenia, we conducted a study using ROI-based analysis on the Center for Biomedical Research Excellence data set (COBRE). Our results support previous findings that suggest abnormal connectivity between PreCG and brain regions such as the thalamus and hippocampus in schizophrenia. Interestingly, we found that these impairments were not similar in the two hemispheres. Furthermore, we analyzed the functional connectivity differences between male and female patients with schizophrenia and observed that regions like the thalamus are more affected in female patients.

In summary, our study provides further evidence of functional connectivity impairment in the PreCG region of the brain in patients with schizophrenia. Our ROI-based analysis on the COBRE dataset revealed abnormal connectivity patterns between the PreCG and brain regions such as the Thalamus and Hippocampus, with hemisphere-specific differences. Our findings are consistent with previous research on this topic, highlighting the importance of PreCG in the pathophysiology of schizophrenia. Interestingly, our analysis also suggests that female patients with schizophrenia exhibit greater impairment in connectivity with the Thalamus, suggesting gender-specific differences in the disorder. These results have important implications for future research on the neural mechanisms underlying schizophrenia and the development of targeted interventions for improving functional connectivity in affected individuals.

Zarei M. Precentral gyrus abnormal connectivity in male and female patients with schizophrenia. Neuroimmunol Neuroinflammation 2018;5:13. http://dx.doi.org/10.20517/2347-8659.2018.02 

The analysis of complex data sets such as gene expression liver infection data requires the development of robust theoretical and methodological frameworks. Recently, a new strain of MHV (MHV-MI) was identified from the liver tissue of a Severe Combined Immune Deficient (SCID) mouse liver, and a microarray data set for differential gene expression between MHV-MI infected and non-infected SCID mice liver was generated to identify important genes and their involvement in the disease process. This gene expression liver cancer dataset contains 28853 genes with five features, making it a complex data set to analyze.

To address this complexity, we evaluate two algorithms in this study: a gene selection algorithm and a modified global k-means clustering algorithm. Compared with traditional statistical techniques, optimization-based methods have the potential to detect nonlinearity and are more effective analysis tools for complex data sets. Feature selection and cluster analysis are logical approaches to identify important genes and their involvement in disease process pathways. Moreover, classification algorithms can predict significant discrete features based on the other attributes in the dataset, allowing consideration of datasets with an arbitrary number of classes.

It should be noted that the gene selection algorithm is expected to perform better than the modified global k-means clustering algorithm in identifying the nature of the collection of genes in the data set. However, a formal comparison of different approaches has not been previously done. The modified global k-means clustering algorithm is another method for analyzing nonlinear data sets such as the MHV data. This algorithm computes clusters incrementally and computes as many clusters as a data set contains, concerning a given tolerance. These clusters correspond to groups of genes with similar nature, and the identification of these clusters may allow us to give recommendations on modification of existing gene control systems and the design of more efficient future microarray data for differential gene expression.

In conclusion, the development of sophisticated algorithms and data mining techniques has revolutionized the field of gene expression analysis and paved the way for a more comprehensive understanding of complex diseases such as liver cancer. The gene selection algorithm and modified global k-means clustering algorithm evaluated in this study have shown significant promise in identifying crucial genes and their involvement in disease progression pathways. The use of optimization-based methods has also shown considerable potential in detecting nonlinearity and complex relationships in gene expression data sets.

Moreover, the results of this research could have significant implications for the development of new diagnostic and therapeutic strategies for liver cancer and other diseases associated with gene expression dysregulation. By identifying critical genes and their interactions, we can develop a better understanding of the underlying mechanisms driving disease progression, and thus develop more targeted and effective interventions.

Future research could build on the findings of this study by exploring additional data mining and optimization techniques to further refine our understanding of gene expression dysregulation in liver cancer. Additionally, further investigation into the identified clusters of genes could shed light on the specific pathways and mechanisms involved in disease progression, potentially leading to the development of novel therapeutic targets. Overall, the application of advanced data analysis techniques in gene expression research holds significant promise for improving our understanding and management of complex diseases.

Electrocardiography is a crucial tool for identifying ventricular tachycardia (VT) from supraventricular tachycardia with aberrant conduction, especially when fusion and capture beats are present. However, in cases where these features are absent, other clues from the ECG may be necessary to make a definitive differentiation. Therefore, it is necessary to develop machine learning algorithms that can help in the identification of these features in the ECG.

One approach to this problem is to use deep learning techniques, such as convolutional neural networks (CNNs), to automatically identify the characteristic features of VT and supraventricular tachycardia with aberrancy in the ECG signal. By training a CNN on a large dataset of annotated ECG signals, it is possible to create a highly accurate and robust classifier that can accurately differentiate between these two types of arrhythmias.

Another approach is to use decision trees or support vector machines (SVMs) to identify the key features of the ECG that are indicative of VT or supraventricular tachycardia with aberrancy. By analyzing the ECG waveform and extracting relevant features, such as the duration of the QRS complex, the morphology of the P wave, and the RR interval, it is possible to create a model that can accurately differentiate between these two types of arrhythmias.

Additionally, ensemble learning algorithms, such as random forests and gradient boosting machines, can also be used to improve the accuracy and robustness of the classification model. By combining multiple decision trees or SVMs, it is possible to create a more accurate and robust model that can accurately differentiate between VT and supraventricular tachycardia with aberrancy in a wider range of scenarios.

In conclusion, machine learning algorithms can aid in the differentiation of VT from supraventricular tachycardia with aberrant conduction when electrocardiographic features such as fusion and capture beats are not present. Other ECG characteristics can be used as clues to distinguish between the two arrhythmias, including the presence of a premature P wave at the onset of tachycardia, a very short RP interval, a QRS configuration consistent with supraventricular conduction, a P wave and QRS rate and rhythm linked to suggest ventricular activation depending on atrial discharge, and the ability of vagal maneuvers to slow or terminate the tachycardia. The development of machine learning models that incorporate these features may improve the accuracy and efficiency of differentiating VT from supraventricular tachycardia with aberrant conduction, ultimately leading to better patient outcomes.