ALEXANDRE ANDRADE LOCH
Projetos de Pesquisa
Unidades Organizacionais
Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/27 - Laboratório de Neurociências, Hospital das Clínicas, Faculdade de Medicina
LIM/27 - Laboratório de Neurociências, Hospital das Clínicas, Faculdade de Medicina
9 resultados
Resultados de Busca
Agora exibindo 1 - 9 de 9
- Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features(2023) LOCH, Alexandre Andrade; GONDIM, Joao Medrado; ARGOLO, Felipe Coelho; LOPES-ROCHA, Ana Caroline; ANDRADE, Julio Cesar; BILT, Martinus Theodorus van de; JESUS, Leonardo Peroni de; HADDAD, Natalia Mansur; CECCHI, Guillermo A.; MOTA, Natalia Bezerra; GATTAZ, Wagner Farid; CORCORAN, Cheryl Mary; ARA, AndersonAims: Our study aimed to develop a machine learning ensemble to distinguish ""at-risk mental states for psychosis"" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings.Methods: 58 non-help-seeking medication-naive ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and ""Subject's Overview"" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used.Results: Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item).Conclusion: Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.
- Genetic Studies on the Tripartite Glutamate Synapse in the Pathophysiology and Therapeutics of Mood Disorders(2017) SOUSA, Rafael T. de; LOCH, Alexandre A.; CARVALHO, Andre F.; BRUNONI, Andre R.; HADDAD, Marie Reine; HENTER, Ioline D.; ZARATE JR., Carlos A.; MACHADO-VIEIRA, RodrigoBoth bipolar disorder (BD) and major depressive disorder (MDD) have high morbidity and share a genetic background. Treatment options for these mood disorders are currently suboptimal for many patients; however, specific genetic variables may be involved in both pathophysiology and response to treatment. Agents such as the glutamatergic modulator ketamine are effective in treatment-resistant mood disorders, underscoring the potential importance of the glutamatergic system as a target for improved therapeutics. Here we review genetic studies linking the glutamatergic system to the pathophysiology and therapeutics of mood disorders. We screened 763 original genetic studies of BD or MDD that investigated genes encoding targets of the pathway/mediators related to the so-called tripartite glutamate synapse, including pre- and post-synaptic neurons and glial cells; 60 papers were included in this review. The findings suggest the involvement of glutamate-related genes in risk for mood disorders, treatment response, and phenotypic characteristics, although there was no consistent evidence for a specific gene. Target genes of high interest included GRIA3 and GRIK2 (which likely play a role in emergent suicidal ideation after antidepressant treatment), GRIK4 (which may influence treatment response), and GRM7 (which potentially affects risk for mood disorders). There was stronger evidence that glutamate-related genes influence risk for BD compared with MDD. Taken together, the studies show a preliminary relationship between glutamate-related genes and risk for mood disorders, suicide, and treatment response, particularly with regard to targets on metabotropic and ionotropic receptors.
- Motion energy analysis during speech tasks in medication-naive individuals with at-risk mental states for psychosis(2022) LOPES-ROCHA, Ana Caroline; CORCORAN, Cheryl Mary; ANDRADE, Julio Cesar; PERONI, Leonardo; HADDAD, Natalia Mansur; HORTENCIO, Lucas; SERPA, Mauricio Henriques; BILT, Martinus Theodorus van de; GATTAZ, Wagner Farid; LOCH, Alexandre AndradeMovement abnormalities are commonly observed in schizophrenia and at-risk mental states (ARMS) for psychosis. They are usually detected with clinical interviews, such that automated analysis would enhance assessment. Our aim was to use motion energy analysis (MEA) to assess movement during free-speech videos in ARMS and control individuals, and to investigate associations between movement metrics and negative and positive symptoms. Thirty-two medication-naive ARMS and forty-six healthy control individuals were filmed during speech tasks. Footages were analyzed using MEA software, which assesses movement by differences in pixels frame-by-frame. Two regions of interest were defined-head and torso-and mean amplitude, frequency, and coefficient of variability of movements for them were obtained. These metrics were correlated with the Structured Interview for Prodromal Syndromes (SIPS) symptoms, and with the risk of conversion to psychosis-inferred with the SIPS risk calculator. ARMS individuals had significantly lower mean amplitude of head movement and higher coefficients of movement variability for both head and torso, compared to controls. Higher coefficient of variability was related to higher risk of conversion. Negative correlations were seen between frequency of movement and most SIPS negative symptoms. All positive symptoms were correlated with at least one movement variable. Movement abnormalities could be automatically detected in medication-naive ARMS subjects by means of a motion energy analysis software. Significant associations of movement metrics with symptoms were found, supporting the importance of movement analysis in ARMS. This could be a potentially important tool for early diagnosis, intervention, and outcome prediction.
- A Brazilian bottom-up strategy to address mental health in a diverse population over a large territorial area - an inspiration for the use of digital mental health(2022) MOTA, Natalia Bezerra; PIMENTA, Juliana; TAVARES, Maria; PALMEIRA, Leonardo; LOCH, Alexandre Andrade; HEDIN-PEREIRA, Cecilia; DIAS, Elisa C.Brazil is a continental country with a history of massive immigration waves from around the world. Consequently, the Brazilian population is rich in ethnic, cultural, and religious diversity, but suffers from tremendous socioeconomic inequality. Brazil has a documented history of categorizing individuals with culturally specific behaviors as mentally ill, which has led to psychiatric institutionalization for reasons that were more social than clinical. To address this, a ""network for psychosocial care"" was created in Brazil, that included mental health clinics and community services distributed throughout the country. This generates local support for mental health rehabilitation, integrating psychiatric care, family support and education/work opportunities. These clinics and community services are tailored to provide care for each specific area, and are more attuned to regional culture, values and neighborhood infrastructure. Here we review existing reports about the Brazilian experience, including advances in public policy on mental health, and challenges posed by the large diversity to the psychosocial rehabilitation. In addition, we show how new digital technologies in general, and computational speech analysis in particular, can contribute to unbiased assessments, resulting in decreased stigma and more effective diagnosis of the mental diseases, with methods that are free of gender, ethnic, or socioeconomic biases.
- Gesticulation in individuals with at risk mental states for psychosis(2023) LOPES-ROCHA, Ana Caroline; RAMOS, Willian Henrique de Paula; ARGOLO, Felipe; GONDIM, Joao Medrado; MOTA, Natalia Bezerra; ANDRADE, Julio Cesar; JAFET, Andrea Fontes; MEDEIROS, Matheus Wanderley de; SERPA, Mauricio Henriques; CECCHI, Guillermo; ARA, Anderson; GATTAZ, Wagner Farid; CORCORAN, Cheryl Mary; LOCH, Alexandre AndradeNonverbal communication (NVC) is a complex behavior that involves different modalities that are impaired in the schizophrenia spectrum, including gesticulation. However, there are few studies that evaluate it in individuals with at-risk mental states (ARMS) for psychosis, mostly in developed countries. Given our prior findings of reduced movement during speech seen in Brazilian individuals with ARMS, we now aim to determine if this can be accounted for by reduced gesticulation behavior. Fifty-six medication-naive ARMS and 64 healthy controls were filmed during speech tasks. The frequency of specifically coded gestures across four categories (and self-stimulatory behaviors) were compared between groups and tested for correlations with prodromal symptoms of the Structured Interview for Prodromal Syndromes (SIPS) and with the variables previously published. ARMS individuals showed a reduction in one gesture category, but it did not survive Bonferroni's correction. Gesture frequency was negatively correlated with prodromal symptoms and positively correlated with the variables of the amount of movement previously analyzed. The lack of significant differences between ARMS and control contradicts literature findings in other cultural context, in which a reduction is usually seen in at-risk individuals. However, gesture frequency might be a visual proxy of prodromal symptoms, and of other movement abnormalities. Results show the importance of analyzing NVC in ARMS and of considering different cultural and sociodemographic contexts in the search for markers of these states.
- Editorial: Stigma's Impact on People With Mental Illness: Advances in Understanding, Management, and Prevention(2021) LOCH, Alexandre Andrade; DIAZ, Alexandre Paim; PACHECO-PALHA, Antonio; WAINBERG, Milton L.; SILVA, Antonio Geraldo da; MALLOY-DINIZ, Leandro Fernandes
- Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders(2022) LOCH, Alexandre Andrade; LOPES-ROCHA, Ana Caroline; ARA, Anderson; GONDIM, Joao Medrado; CECCHI, Guillermo A.; CORCORAN, Cheryl Mary; MOTA, Natalia Bezerra; ARGOLO, Felipe C.Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.
- Stigma toward individuals with mental disorders among Brazilian psychiatrists: a latent class analysis(2021) SILVA, Antonio G. da; LOCH, Alexandre A.; LEAL, Vanessa P.; SILVA, Paulo R. da; ROSA, Monike M.; BOMFIM, Ozeias da C.; MALLOY-DINIZ, Leandro F.; SCHWARZBOLD, Marcelo L.; DIAZ, Alexandre P.; PALHA, Antonio P.Objective: The stigma toward individuals with mental disorders is highly prevalent, not only in the general population but among health care providers as well. The aim of this study was to identify subgroups based on stigmatizing beliefs related to psychiatric disorders among Brazilian psychiatrists, as well as to investigate their association with clinical and personality characteristics. Methods: Latent cluster analysis was used to find subgroups of cases in multivariate data according to a psychotic (schizophrenia) and a nonpsychotic disorder (attention-deficit hyperactivity disorder). The clusters for each psychiatric disorder were compared according to sociodemographic, emotional traits, and personality characteristics. Results: A total of 779 psychiatrists answered the questionnaire. Three different subgroups of stigma levels were identified regarding schizophrenia: the highest (n=202 [51.7%]), intermediate (108 [27.6%]), and the lowest (81 [20.7%]). Participants from the highest stigma group had a significantly longer time since graduation, higher anxiety-state scores, and lower positive affect. Two subgroups were identified with respect to attention-deficit hyperactivity disorder, although there were no differences between them in sociodemographic or clinical variables. Conclusion: There were more subgroups of stigmatizing beliefs regarding psychotic disorders. Individual characteristics, such as those related to trait anxiety and affect, can be associated with high stigma toward schizophrenia.
- ChatGPT is not ready yet for use in providing mental health assessment and interventions(2024) DERGAA, Ismail; FEKIH-ROMDHANE, Feten; HALLIT, Souheil; LOCH, Alexandre Andrade; GLENN, Jordan M.; FESSI, Mohamed Saifeddin; AISSA, Mohamed Ben; SOUISSI, Nizar; GUELMAMI, Noomen; SWED, Sarya; OMRI, Abdelfatteh El; BRAGAZZI, Nicola Luigi; SAAD, Helmi BenBackground: Psychiatry is a specialized field of medicine that focuses on the diagnosis, treatment, and prevention of mental health disorders. With advancements in technology and the rise of artificial intelligence (AI), there has been a growing interest in exploring the potential of AI language models systems, such as Chat Generative Pre-training Transformer (ChatGPT), to assist in the field of psychiatry. Objective: Our study aimed to evaluates the effectiveness, reliability and safeness of ChatGPT in assisting patients with mental health problems, and to assess its potential as a collaborative tool for mental health professionals through a simulated interaction with three distinct imaginary patients. Methods: Three imaginary patient scenarios (cases A, B, and C) were created, representing different mental health problems. All three patients present with, and seek to eliminate, the same chief complaint (i.e., difficulty falling asleep and waking up frequently during the night in the last 2 degrees weeks). ChatGPT was engaged as a virtual psychiatric assistant to provide responses and treatment recommendations. Results: In case A, the recommendations were relatively appropriate (albeit non-specific), and could potentially be beneficial for both users and clinicians. However, as complexity of clinical cases increased (cases B and C), the information and recommendations generated by ChatGPT became inappropriate, even dangerous; and the limitations of the program became more glaring. The main strengths of ChatGPT lie in its ability to provide quick responses to user queries and to simulate empathy. One notable limitation is ChatGPT inability to interact with users to collect further information relevant to the diagnosis and management of a patient's clinical condition. Another serious limitation is ChatGPT inability to use critical thinking and clinical judgment to drive patient's management. Conclusion: As for July 2023, ChatGPT failed to give the simple medical advice given certain clinical scenarios. This supports that the quality of ChatGPT-generated content is still far from being a guide for users and professionals to provide accurate mental health information. It remains, therefore, premature to conclude on the usefulness and safety of ChatGPT in mental health practice.