Cognitive therapy | EPFL Graph Search

Cognitive therapy (CT) is a type of psychotherapy developed by American psychiatrist Aaron T. Beck. CT is one therapeutic approach within the larger group of cognitive behavioral therapies (CBT) and was first expounded by Beck in the 1960s. Cognitive therapy is based on the cognitive model, which states that thoughts, feelings and behavior are all connected, and that individuals can move toward overcoming difficulties and meeting their goals by identifying and changing unhelpful or inaccurate thinking, problematic behavior, and distressing emotional responses. This involves the individual working with the therapist to develop skills for testing and changing beliefs, identifying distorted thinking, relating to others in different ways, and changing behaviors. A cognitive case conceptualization is developed by the cognitive therapist as a guide to understand the individual's internal reality, select appropriate interventions and identify areas of distress. Precursors of certain aspects of cognitive therapy have been identified in various ancient philosophical traditions, particularly Stoicism. For example, Beck's original treatment manual for depression states, "The philosophical origins of cognitive therapy can be traced back to the Stoic philosophers". Albert Ellis worked on cognitive treatment methods from the 1950s (Ellis, 1956). He called his approach Rational Therapy (RT) at first, then Rational Emotive Therapy (RET) and later Rational Emotive Behavior Therapy (REBT). Becoming disillusioned with long-term psychodynamic approaches based on gaining insight into unconscious emotions, in the late 1950s Aaron T. Beck came to the conclusion that the way in which his patients perceived and attributed meaning in their daily lives—a process known as cognition—was a key to therapy. Beck outlined his approach in Depression: Causes and Treatment in 1967. He later expanded his focus to include anxiety disorders, in Cognitive Therapy and the Emotional Disorders in 1976, and other disorders later on.

Determining patterns of post-stroke motor recovery through longitudinal multimodal MRI: A step towards patient stratification

Julia Brügger

Motivated by the need for a better understanding of post-stroke recovery and new biomarkers to improve stroke patient stratification and outcomes, this thesis investigated structure-function coupling and its role in post-stroke recovery. Furthermore, in order to increase data comparability between sessions and centers (a critical challenge in contemporary clinical research), this thesis assessed the reproducibility of microstructure-informed structural connectivity measures in a multi-center dataset.Stroke is one of the major sources of permanent impairment, frequently of motor origin. However the clinical picture is very heterogeneous: patients show divergent courses of recovery and the underlying mechanisms are still unclear. In addition, current treatments still have limited success and are restricted to a 'one-fits-all' approach, not considering the individual patient's phenotype. As efforts to stratify patients based on structural or functional biomarkers are still needed, we chose to investigate the potential of a multimodal biomarker, the Structural Decoupling Index (SDI) (Preti & Van De Ville, 2019b), a new metric assessing the structure-function coupling strength in the brain. The first part (Studies 1 and 2) of this thesis investigates the potential of the SDI as a biomarker. In Study 1, the goals were to evaluate the feasibility of applying the SDI on an individual level in healthy older adults and to investigate the effect of an acute stroke on it. Consistent with the literature, we found a network gradient of SDI in healthy older adults, from high coupling in lower-level sensory areas, to low coupling in higher-level cognitive areas. This confirmed the applicability of SDI on an individual level. Furthermore, we showed that stroke impacts the SDI, with a higher decoupling on the ipsi- compared to the contralesional side, and with network-specific effects in RH stroke patients. In Study 2, the goal was to see whether the SDI evolved over time post-stroke and whether it links to behavior. The longitudinal analysis revealed differential network effects of stroke at T1 and T3. Furthermore, we showed that impairment in cognitive and psychological behavioral domains significantly correlates with variations in SDI in a number of key areas including motor regions (e.g., primary motor cortex) and that the brain pattern associated to behavior changes between T1 and T2. Surprisingly, motor performance did not explain variability in key motor areas (e.g., primary motor cortex). The link between post-stroke behavioral performance and SDI underlines its potential clinical relevance.Driven by the quest for more reproducible analysis pipelines in the context of longitudinal and clinical studies, the second part of this thesis (Study 3) addressed the subject-specific reproducibility and repeatability of microstructure-informed tractography. Through a multi-center study, we demonstrated its high reproducibility and subject-specificity, and we found evidence for increased biological accuracy. My thesis made a contribution by showing that the SDI is sensitive to pathophysiological changes that occur following a stroke and that it links to clinically relevant behavioral measures. In addition, it confirms the reproducibility and subject-specificity of microstructure-informed tractography. Together, my findings pave the way towards patient stratification and more personalized treatments in stroke rehabilitation.

EPFL2022

Determining patterns of post-stroke motor recovery through longitudinal multimodal MRI: A step towards patient stratification

Julia Brügger

EPFL2022