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  • 1.
    Olivo, Gaia
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Schiöth: Functional Pharmacology.
    Zhukovsky, Christina
    Salonen-Ros, Helena
    Larsson, Elna-Marie
    Brooks, Samantha
    Schiöth, Helgi
    Functional connectivity underlying hedonic response to food in female adolescents with atypical AN: The role of somatosensory and salience networksManuscript (preprint) (Other academic)
    Abstract [en]

    Atypical Anorexia Nervosa (AN) usually occurs during adolescence. Patients are often in the normal-weight range at diagnosis, however they often present with signs of medical complications and severe restraint over eating, body dissatisfaction, and low self-esteem. We investigated functional circuitry underlying the hedonic response in 28 female adolescent patients diagnosed with atypical AN and 33 healthy controls. Participants were shown images of food with high (HC) or low (LC) caloric content in alternating blocks during functional MRI. The HC > LC contrast was calculated. Based on previous literature on full-threshold AN, we hypothesized that patients would exhibit increased connectivity in areas involved in sensory processing and bottom-up responses, coupled to increased connectivity from areas related to top-down inhibitory control, compared with controls. Patients showed increased connectivity in pathways related to multimodal somatosensory processing and memory retrieval. The connectivity was on the other hand decreased in patients in salience and attentional networks, and in a wide cerebello-occipital network. Our study was the first investigation of food-related neural response in atypical AN. Our findings support higher somatosensory processing in patients in response to HC food images compared with controls, however HC food was less efficient than LC food in engaging patients’ bottom-up salient responses, and was not associated with connectivity increases in inhibitory control regions. These findings suggest that the psychopathological mechanisms underlying food restriction in atypical AN differ from full-threshold AN. Elucidating the mechanisms underlying the development and maintenance of eating behaviour in atypical AN might help designing specific treatment strategies.

  • 2.
    Pisanu, Claudia
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Schiöth: Functional Pharmacology. Univ Cagliari, Sect Neurosci & Clin Pharmacol, Dept Biomed Sci, Cagliari, Italy.
    Squassina, Alessio
    Univ Cagliari, Sect Neurosci & Clin Pharmacol, Dept Biomed Sci, Cagliari, Italy;Dalhousie Univ, Dept Psychiat, Halifax, NS, Canada.
    Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches2019In: Frontiers in Pharmacology, ISSN 1663-9812, E-ISSN 1663-9812, Vol. 10, article id 617Article, review/survey (Refereed)
    Abstract [en]

    Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the eighth leading cause of disability according to the Wood Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment-resistant and are eligible for clozapine treatment. Treatment-resistant patients show a more severe course of the disease, but it has been suggested that treatment-resistant schizophrenia (TRS) may constitute a distinct phenotype that is more than just a more severe form of SCZ. TRS is heritable, and genetics has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date, findings do not allow the disentanglement of its polygenic nature. More recent studies implemented polygenic risk score, gene-based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.

  • 3.
    Wang, Lu-di
    et al.
    Beijing Univ Posts & Telecommun, Automat Sch, Beijing 100876, Peoples R China.
    Zhou, Wei
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Schiöth: Functional Pharmacology.
    Xing, Ying
    Beijing Univ Posts & Telecommun, Automat Sch, Beijing 100876, Peoples R China.
    Liu, Na
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience.
    Movahedipour, Mahmood
    Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China;ACECR, Tehran 141554364, Iran.
    Zhou, Xiao-guang
    Beijing Univ Posts & Telecommun, Automat Sch, Beijing 100876, Peoples R China.
    A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG2019In: FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, ISSN 2095-9184, Vol. 20, no 3, p. 405-413Article in journal (Refereed)
    Abstract [en]

    Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need for more wearing comfort and ambulatory situations. The accuracy and real-time performance of traditional methods need to be improved. In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis of missing precordial leads. The results show that the proposed method receives better similarity and consumes less time using the PTB database. Particularly, the presented method shows outstanding performance in reconstructing the pathological ECG signal, which is crucial for cardiac diagnosis. Our CNN-based method is shown to be more accurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording. This is promising for real cardiac care.

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