Neuroscience

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https://hdl.handle.net/20.500.12213/6536

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    Using Dimensionality Reduction to Decode the Cognitive Correlates of Psychological Trauma in Patients with Post-Traumatic Stress Disorder
    (Al-Quds University, Deanship of Scientific Research, 2022-05-11) Abdallah Khaled Ahmed Ramadan; Ayman A. Salman; Oday M. Abushalbaq; Abdul-Rahman S. Sawalma; Mahmud A. Sehwail; Mohmmad M. Herzallah
    Background: Exposure to psychological trauma usually marks monumental changes in an individual’s clinical features, cognitive function, and underlying neural circuitry. A fraction of exposed individuals will develop subsequent post-raumatic stress disorder (PTSD). To date, there is no clear understanding of the cognitive consequences of exposure to psychological trauma, especially that which is related to PTSD. This could be attributed to the use of generic constructs to describe clinical features and cognitive function.
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    The Interplay of the CRY1 and PER2 Genes in the Modulation of Chronotype
    (Al-Quds University, Deanship of Scientific Research, 2022-05-11) Nadine Assi; Anfal A. AbuHilal; Mohmmad M. Herzallah
    Background: Prior studies investigated the role of the negative feedback loop within the suprachiasmatic nucleus on modulating the circadian rhythm. The regulation of the circadian rhythm is modulated by the synchronization of the endogenous system with the environmental cues including light and temperature. Variations in clock genes between individuals can produce different chronotypes (morningness and eveningness). The cryptochrome gene (CRY1) and period gene (PER2) genes have an inhibitory effect on the negative feedback loop. The c.1657+3A>C CRY1 polymorphism causes a gain of function mutation leading to the lengthening of the chronotype (late chronotype). Meanwhile, PER2 polymorphism (G3853A) has been associated with diurnal performances and the early chronotype.
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    Symptom Variability in Medication-Naïve Patients with Major Depressive Disorder as a Proxy to Predict Response to Treatment
    (Al-Quds University, Deanship of Scientific Research, 2022-05-11) Rosaline Abu Sabbah; Abdul-Rahman S. Sawalma; Mohmmad M. Herzallah
    Background: Major Depressive Disorder (MDD) is characterized by episodes of low mood and loss of interest for two or more consecutive weeks. It is considered to be the leading cause of morbidity and mortality worldwide. Only 30% of patients with MDD achieve full remission after treatment with antidepressants, psychotherapy or neuromodulation. It is unknown whether response to treatment depends on the baseline expression of MDD symptoms among patients.
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    Reinforcement Learning Predicts Response to SSRIs in Medication-Naïve Patients with Major Depressive Disorder
    (Al-Quds University, Deanship of Scientific Research, 2022-05-11) Yasmeen Sultan; Mahmud A. Sehwail; Mohammad M. Herzallah
    Background: Patients with major depressive disorder (MDD) exhibit hyposensitivity to positive reinforcement and hypersensitivity to negative reinforcement. In patients who respond to treatment, selective serotonin reuptake inhibitor (SSRI) antidepressants arguably modulate MDD symptoms by attenuating learning from negative reinforcement. However, only 30% of patients with MDD respond to antidepressants including SSRI. Cognitive differences between responders and non-responders were heretofore not investigated medication-naïve patients with MDD.
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    Quantifiable Structured Clinical Diagnosis for Psychiatry: An Integration of Machine Learning and Cloud Computing Approaches to Achieve Scalability
    (Al-Quds University, Deanship of Scientific Research, 2022-05-11) Laith Azzam Ayasa; Matthew Toegel; Joman Y. Natsheh; Mohmmad M. Herzallah
    Background: Current diagnostic systems for psychiatric disorders suffer many limitations that hinder their applicability. The diagnosis of psychiatric disorders is exclusively conducted by clinicians using lengthy interviews that lack sensitivity and specificity. According to recent clinical trials, only a fraction of patients with psychiatric disorders respond to initial treatment with psychometric medications or psychotherapy. Unfortunately, clinicians cannot predict, a priori, who will or will not respond to treatment. If, however, a simple computer-based system utilizing multidimensional symptom expression could diagnose patients with psychiatric disorders and differentiate those who are, or are not, likely to respond to treatment, this would provide immediate clinical relevance.