Investigating Associations Between Early Life Stress, Neural Response to Reward, and Depression

The link between exposure to early life stress (ELS), such as child maltreatment, and the development of depression has been well-replicated. However, the mechanisms that underlie this connection remain poorly understood. One potential mechanism may be neural alterations in reward-related brain regions, such as the ventral striatum and sub-regions of the prefrontal cortex. Recent research indicates that exposure to child maltreatment is associated with aberrant reward-related brain activity. A separate body of work implicates similar reward-related neural alterations in the etiology and maintenance of depression. The current study investigated whether altered neural response to reward plays a mechanistic role in explaining the association between ELS and depression. Here, we examined associations between history of child maltreatment, depressive symptoms, and neural response to reward during a reward processing fMRI task in a sample of adult men (N = 165; 30.5% White, 60.6% Black) who were a part of the Pittsburgh Youth Study (PYS), a longitudinal study examining the development of negative mental health outcomes. History of child maltreatment was assessed via referrals prior to age 18 from the Allegheny County’s Office of Children, Youth, and Families. Neuroimaging data and self-reported depressive symptoms were collected in adulthood (M age = 32.64, SD age = 3.62). Child maltreatment significantly predicted greater depressive symptoms. Child maltreatment and depressive symptoms were, however, not significantly associated with altered neural response to reward. Findings from the current study suggest directions for future work probing characteristics of adversity (e.g., chronicity, timing), as well as specific factors likely to moderate neural responses to reward (e.g., reward phase, magnitude of gains)

The words of the body: psychophysiological patterns in dissociative narratives

Trauma has severe consequences on both psychological and somatic levels, even affecting the genetic expression and the cell\u2019s DNA repair ability. A key mechanism in the understanding of clinical disorders deriving from trauma is identified in dissociation, as a primitive defense against the fragmentation of the self originated by overwhelming experiences. The dysregulation of the interpersonal patterns due to the traumatic experience and its detrimental effects on the body are supported by influent neuroscientific models such as Damasio\u2019s somatic markers and Porges\u2019 polyvagal theory. On the basis of these premises, and supported by our previous empirical observations on 40 simulated clinical sessions, we will discuss the longitudinal process of a brief psychodynamic psychotherapy (16 sessions, weekly frequency) with a patient who suffered a relational trauma. The research design consists of the collection of self-report and projective tests, pre-post therapy and after each clinical session, in order to assess personality, empathy, clinical alliance and clinical progress, along with the verbatim analysis of the transcripts trough the Psychotherapy Process Q-Set and the Collaborative Interactions Scale. Furthermore, we collected simultaneous psychophysiological measures of the therapeutic dyad: skin conductance and hearth rate. Lastly, we employed a computerized analysis of non-verbal behaviors to assess synchrony in posture and gestures. These automated measures are able to highlight moments of affective concordance and discordance, allowing for a deep understanding of the mutual regulations between the patient and the therapist. Preliminary results showed that psychophysiological changes in dyadic synchrony, observed in body movements, skin conductance and hearth rate, occurred within sessions during the discussion of traumatic experiences, with levels of attunement that changed in both therapist and the patient depending on the quality of the emotional representation of the experience. These results go in the direction of understanding the relational process in trauma therapy, using an integrative language in which both clinical and neurophysiological knowledge may take advantage of each other

Comparación de técnicas de clasificación de aprendizaje de máquina en el diagnóstico del trastorno depresivo leve

A nivel mundial la depresión lo padece unos 350 millones de seres humamos y el 5% es a nivel de Latinoamérica, es así que, cada veintidós minutos un ser humano intenta hacerse daño, las edades con mayores problemas depresivos son los adolescentes el cual representa el 10%, el 6% adultos mayores de 18 años y 3.5% en niños de 6 a 10 años, en el Perú, el 80% de suicidios es a causa de la depresión, hay un millón setecientos mil personas que presentan cuadro depresivo, pero solo es atendido un 25% con atención especializada y el 65% simplemente no busca ayuda, estudios han demostrado que a nivel del ministerio de salud, el documento técnico llamado “auto escala de Zung”, es el más adecuado para la identificación de este problema analizando la medición de la depresión a través de información de aspectos cognitivos, afectivos y somáticos del paciente, dicho documento tiene una especificidad del 63% y sensibilidad del 97%, aprobando un acierto del 82% para discriminar la depresión. En esta investigación se construyó un método que inicia con la elaboración de un dataset de acuerdo a las variables de ingreso y salida así como el nivel de prioridad basados en el cuestionario de Zung, después se realizó la elección de las técnicas de aprendizaje de máquina, utilizadas para tratar casos de diagnóstico de depresión con mayor precisión, entre ellas lograron destacar, naive bayes, árbol de decisión, redes neuronales y maquinas vectores de soporte, acto seguido se implementó las técnicas mencionadas para ser comparadas y evaluadas según su desempeño, para el desarrollo de las mismas se utilizó la plataforma de google colaboratory con el lenguaje de programación python, según el método propuesto, desarrollado y evaluado se concluye que las redes neuronales tienen una precisión del 100% para el diagnóstico de depresión.TesisInfraestructura, Tecnología y Medio Ambient

Predicting the future:Clinical outcome prediction with machine learning in neuropsychiatry

Treatment of psychiatric disorders relies on subjective measures of symptoms to establish diagnoses and lacks an objective way to determine which treatments might work best for an individual patient. To improve the current state-of-the-art and to be able to help a growing number of patients with mental health disorders more efficiently, the discovery of biomarkers predictive of treatment outcome and prognosis is needed. In addition, the application of machine learning methods provides an improvement over the standard group-level analysis approach since it allows for individualized predictions. Machine learning models can also be tested for their generalization capabilities to new patients which would quantify their potential for clinical applicability. In this thesis, these approaches were combined and investigated across a set of different neuropsychiatric disorders. The investigated applications included the prediction of disease course in patients with anxiety disorders, early detection of behavioural frontotemporal dementia in at-risk individuals using structural magnetic resonance imaging (MRI), prediction of deep-brain stimulation treatment-outcome in patients with therapy-resistant obsessive compulsive disorder using structural MRI and prediction of treatment-response for adult and youth patients with posttraumatic stress disorder using resting-state functional MRI scans. Across all studies this thesis showed that machine learning methods combined with neuroimaging data can be utilized to identify biomarkers predictive of future clinical outcomes in neuropsychiatric disorders. Promising as it seems, this can only be the first step for the inclusion of these new approaches into clinical practice as further studies utilizing larger sample sizes are necessary to validate the discovered biomarkers

Structural alterations in functional neurological disorder and related conditions: A software and hardware problem?

Functional neurological (conversion) disorder (FND) is a condition at the interface of neurology and psychiatry. A “software” vs. “hardware” analogy describes abnormal neurobiological mechanisms occurring in the context of intact macroscopic brain structure. While useful for explanatory and treatment models, this framework may require more nuanced considerations in the context of quantitative structural neuroimaging findings in FND. Moreover, high co-occurrence of FND and somatic symptom disorders (SSD) as defined in DSM-IV (somatization disorder, somatoform pain disorder, and undifferentiated somatoform disorder; referred to as SSD for brevity in this article) raises the possibility of a partially overlapping pathophysiology. In this systematic review, we use a transdiagnostic approach to review and appraise the structural neuroimaging literature in FND and SSD. While larger sample size studies are needed for definitive characterization, this article highlights that individuals with FND and SSD may exhibit sensorimotor, prefrontal, striatal-thalamic, paralimbic, and limbic structural alterations. The structural neuroimaging literature is contextualized within the neurobiology of stress-related neuroplasticity, gender differences, psychiatric comorbidities, and the greater spectrum of functional somatic disorders. Future directions that could accelerate the characterization of the pathophysiology of FND and DSM-5 SSD are outlined, including “disease staging” discussions to contextualize subgroups with or without structural changes. Emerging neuroimaging evidence suggests that some individuals with FND and SSD may have a “software” and “hardware” problem, although if structural alterations are present the neural mechanisms of functional disorders remain distinct from lesional neurological conditions. Furthermore, it remains unclear whether structural alterations relate to predisposing vulnerabilities or consequences of the disorder. Keywords: Conversion disorder, Psychogenic, Neuroimaging, MRI, Functional neurological disorder, Somatic symptom disorde

Zero-shot personalization of speech foundation models for depressed mood monitoring

The monitoring of depressed mood plays an important role as a diagnostic tool in psychotherapy. An automated analysis of speech can provide a non-invasive measurement of a patient’s affective state. While speech has been shown to be a useful biomarker for depression, existing approaches mostly build population-level models that aim to predict each individual’s diagnosis as a (mostly) static property. Because of inter-individual differences in symptomatology and mood regulation behaviors, these approaches are ill-suited to detect smaller temporal variations in depressed mood. We address this issue by introducing a zero-shot personalization of large speech foundation models. Compared with other personalization strategies, our work does not require labeled speech samples for enrollment. Instead, the approach makes use of adapters conditioned on subject-specific metadata. On a longitudinal dataset, we show that the method improves performance compared with a set of suitable baselines. Finally, applying our personalization strategy improves individual-level fairness

Gray matter volume correlates of Comorbid Depression in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) involves diverse neurodevelopmental syndromes with significant deficits in communication, motor behaviours, emotional and social comprehension. Often, individuals with ASD exhibit comorbid conditions, one of the most prevalent being depression characterized by a persistent change in mood and diminished interest in previously enjoyable activities. Due to communicative challenges and lack of appropriate assessments in individuals with ASD, comorbid depression can often go undiagnosed during routine clinical examinations, which may aggravate their problems. The current literature on comorbid depression in adults with ASD is limited. Therefore, understanding the neural basis of the comorbid psychopathology of depression in ASD is crucial for identifying objective brain-based markers for its timely and effective management. Towards this end, using structural MRI and phenotypic data from the Autism Brain Imaging Data Exchange II (ABIDE II) repository, we specifically examined the pattern of relationship regional grey matter volume (rGMV) has with comorbid depression and autism severity within regions of a priori interest in adults with ASD (n = 44). The severity of comorbid depression correlated negatively with the rGMV of the right thalamus. Additionally, a significant interaction was evident between the severity of comorbid depression and core ASD symptoms towards explaining the rGMV in the left cerebellum crus II. The whole-brain regional rGMV differences between ASD and typically developed (TD, n = 39) adults remained inconclusive. The results further the understanding of the neurobiological underpinnings of comorbid depression in adults with ASD and are relevant in exploring structural neuroimaging-based biomarkers in the same cohort.Comment: 33 pages, 3 figures, 3 tables, journal submissio

The Multi-Dimensional Contributions of Prefrontal Circuits to Emotion Regulation during Adulthood and Critical Stages of Development

The prefrontal cortex (PFC) plays a pivotal role in regulating our emotions. The importance of ventromedial regions in emotion regulation, including the ventral sector of the medial PFC, the medial sector of the orbital cortex and subgenual cingulate cortex, have been recognized for a long time. However, it is increasingly apparent that lateral and dorsal regions of the PFC, as well as neighbouring dorsal anterior cingulate cortex, also play a role. Defining the underlying psychological mechanisms by which these functionally distinct regions modulate emotions and the nature and extent of their interactions is a critical step towards better stratification of the symptoms of mood and anxiety disorders. It is also important to extend our understanding of these prefrontal circuits in development. Specifically, it is important to determine whether they exhibit differential sensitivity to perturbations by known risk factors such as stress and inflammation at distinct developmental epochs. This Special Issue brings together the most recent research in humans and other animals that addresses these important issues, and in doing so, highlights the value of the translational approach

The neurobiology of functional neurological disorders characterised by impaired awareness

We review the neurobiology of Functional Neurological Disorders (FND), i.e., neurological disorders not explained by currently identifiable histopathological processes, in order to focus on those characterised by impaired awareness (functionally impaired awareness disorders, FIAD), and especially, on the paradigmatic case of Resignation Syndrome (RS). We thus provide an improved more integrated theory of FIAD, able to guide both research priorities and the diagnostic formulation of FIAD. We systematically address the diverse spectrum of clinical presentations of FND with impaired awareness, and offer a new framework for understanding FIAD. We find that unraveling the historical development of neurobiological theory of FIAD is of paramount importance for its current understanding. Then, we integrate contemporary clinical material in order to contextualise the neurobiology of FIAD within social, cultural, and psychological perspectives. We thus review neuro-computational insights in FND in general, to arrive at a more coherent account of FIAD. FIAD may be based on maladaptive predictive coding, shaped by stress, attention, uncertainty, and, ultimately, neurally encoded beliefs and their updates. We also critically appraise arguments in support of and against such Bayesian models. Finally, we discuss implications of our theoretical account and provide pointers towards an improved clinical diagnostic formulation of FIAD. We suggest directions for future research towards a more unified theory on which future interventions and management strategies could be based, as effective treatments and clinical trial evidence remain limited