Prefrontal cortex activation during neuropsychological tasks might predict response to pharmacotherapy in patients with obsessive&amp;ndash;compulsive disorder

Takeda, Tomoya; Sumitani, Satsuki; Hamatani, Sayo; Yokose, Yosuke; M, Shikata; Ohmori, Tetsuro

doi:10.2147/ndt.s127752

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…Nest-building therefore encompasses sensorimotor, goaldirected and reward based neural mechanisms involving several neurotransmitter systems. Since, aberration of the abovementioned regions and neurotransmitter mechanisms has been documented in several studies investigating clinical populations (253)(254)(255)(256)(257)(258)(259), nest-building can be considered as a relevant and suitable behavioral phenotype to address some aspects of compulsivity in humans.…”

Section: Nest Buildingmentioning

confidence: 99%

Bidirectional Behavioral Selection in Mice: A Novel Pre-clinical Approach to Examining Compulsivity

Mitra

Bult‐Ito

2021

Front. Psychiatry

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Obsessive-compulsive disorder (OCD) and related disorders (OCRD) is one of the most prevalent neuropsychiatric disorders with no definitive etiology. The pathophysiological attributes of OCD are driven by a multitude of factors that involve polygenic mechanisms, gender, neurochemistry, physiological status, environmental exposures and complex interactions among these factors. Such complex intertwining of contributing factors imparts clinical heterogeneity to the disorder making it challenging for therapeutic intervention. Mouse strains selected for excessive levels of nest- building behavior exhibit a spontaneous, stable and predictable compulsive-like behavioral phenotype. These compulsive-like mice exhibit heterogeneity in expression of compulsive-like and other adjunct behaviors that might serve as a valuable animal equivalent for examining the interactions of genetics, sex and environmental factors in influencing the pathophysiology of OCD. The current review summarizes the existing findings on the compulsive-like mice that bolster their face, construct and predictive validity for studying various dimensions of compulsive and associated behaviors often reported in clinical OCD and OCRD.

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Section: Nest Buildingmentioning

confidence: 99%

Bidirectional Behavioral Selection in Mice: A Novel Pre-clinical Approach to Examining Compulsivity

Mitra

Bult‐Ito

2021

Front. Psychiatry

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“…Nearinfrared spectroscopy: NIRS Fukuda, 2012NIRS Hirosawa et al, 2013Nakanishi et al, 2014;Okada et al, 2013;Ota et al, 2013;Takeda et al, 2017 Table 1 16…”

Section: Nirsmentioning

confidence: 99%

“…Zung Selfrating Depression Scale SDS; Zung, 1965 Beck Depression Inventory BDI;Beck et al, 2003 State-Trait Anxiety Inventory STAI; Spielberger et al, 1970 the Autism-spectrum Quotient questionnaire AQ; Baron-Cohen et al, 2001 Wechsler Adult Intelligence Scale-3 rd ed. WAIS-III Scale HAM-A; Hamilton, 1960 Hamilton Rating Scale for Depression HAM-D; Hamilton, 1959 Yale-Brown Obsessive-Compulsive Scale Y-BOCS; Goodman et al, 1989a;1989b 3.…”

Section: Nirsmentioning

confidence: 99%

Hemodynamic Changes Using Near-Infrared Spectroscopy (NIRS) in Patients with Obsessive Compulsive Disorder

2017

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“…Treatment is found to be effective where the response appears to be better [12]. In the region of the prefrontal cortex of the brain, the change in oxyhemoglobin appears for those who get effected by DOCD while observation is performed using infrared spectroscopy [13]. SSRI responders show good improvement while applying treatment compared to the others.…”

Section: Introductionmentioning

confidence: 99%

Designing a Deep Learning Hybrid Using CNN and Inception V3 Transfer Learning to Detect the Aggression Level of Deep Obsessive Compulsive Disorder in Children

Madanan

Sayed

2022

International Journal of Biology and Biomedical Engineering

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The usage of Artificial intelligence in medical arena has proved to be a game changer in the detection and diagnosis of several medical conditions. In the current digital era, children with stressful medical issues are suffering from Deep Obsessive-Compulsive Disorder (DOCD). This kind of mental stress occurs in children because of the continuous usage of gadgets such as mobile phone, playing games using play stations, watching videos on tablets, etc. In most of the possibilities, single children are the ones affected with several obsessions such as stubborn activities, fighting for selfish priorities and so on. In medical terms, these kinds of complex behavioral changes are identified as DOCD. Genetic behaviors sometimes in a few group of children are also noticed as a modality difference. As symptoms are psychiatric impairment, such a child remains isolated, abnormal silence, being obsessive and repeating irrelevant words, high stress or anxiety. All medical challenges could be treated as healthcare research metrics and the gradual increase in DOCD disorder among children of this generation can be considered too. Early detection of DOCD is essential as it can help in early diagnosis but techniques to do so is unavailable currently. Deep learning-an artificial intelligence method can be utilized to detect DOCD, diagnose and treat it and bring about a positive character in children. Behavior changes in children can be classified and detected using transfer learning algorithms. In COVID-19 pandemic situation, 3% of DOCD has increased to 10-15% as a disorder. This information is retrieved from children by monitoring negative activities, unusual behavior such as nail biting, removing spectacles and placing them in the wrong place, watching tablets, mobile phones and television for more hours. Using Convolutional Neural Networks (CNN), input such as MRI (Magnetic resonance Imaging) is used for experimenting the variations in behavior with the high dimension that are analyzed from the image dataset. Using Transfer Learning with Inception V3-, CNN generalization of misophonia level can be statistically analyzed to avoid overfitting problems. By employing AI techniques, the aggression level can be predicted using data augmentation method with better accuracy and a low error rate than the existing systems. In the research it is observed that using the model employing Inception-V3 transfer learning CNN a better prediction of aggression levels can be achieved in comparison to the existing CNN model used.

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Prefrontal cortex activation during neuropsychological tasks might predict response to pharmacotherapy in patients with obsessive–compulsive disorder

Cited by 6 publications

References 27 publications

Bidirectional Behavioral Selection in Mice: A Novel Pre-clinical Approach to Examining Compulsivity

Bidirectional Behavioral Selection in Mice: A Novel Pre-clinical Approach to Examining Compulsivity

Hemodynamic Changes Using Near-Infrared Spectroscopy (NIRS) in Patients with Obsessive Compulsive Disorder

Designing a Deep Learning Hybrid Using CNN and Inception V3 Transfer Learning to Detect the Aggression Level of Deep Obsessive Compulsive Disorder in Children

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