Validation of cardiac image-derived input functions for functional PET quantification.

PURPOSE: Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations. METHODS: Twenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard. RESULTS: For both radiotracer cohorts, moderate to high agreement (r: 0.60-0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87-0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975-0.998) with AIF-derived measurements. CONCLUSION: Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.

Serotonergic modulation of effective connectivity in an associative relearning network during task and rest.

An essential core function of one's cognitive flexibility is the use of acquired knowledge and skills to adapt to ongoing environmental changes. Animal models have highlighted the influence serotonin has on neuroplasticity. These effects have been predominantly demonstrated during emotional relearning which is theorized as a possible model for depression. However, translation of these mechanisms is in its infancy. To this end, we assessed changes in effective connectivity at rest and during associative learning as a proxy of neuroplastic changes in healthy volunteers. 76 participants underwent 6 weeks of emotional or non-emotional (re)learning (face-matching or Chinese character-German noun matching). During relearning participants either self-administered 10 mg/day of the selective serotonin reuptake inhibitor (SSRI) escitalopram or placebo in a double-blind design. Associative learning tasks, resting-state and structural images were recorded before and after both learning phases (day 1, 21 and 42). Escitalopram intake modulated relearning changes in a network encompassing the right insula, anterior cingulate cortex and right angular gyrus. Here, the process of relearning during SSRI intake showed a greater decrease in effective connectivity from the right insula to both the anterior cingulate cortex and right angular gyrus, with increases in the opposite direction when compared to placebo. In contrast, intrinsic connections and those at resting-state were only marginally affected by escitalopram. Further investigation of gray matter volume changes in these functionally active regions revealed no significant SSRI-induced structural changes. These findings indicate that the right insula plays a central role in the process of relearning and SSRIs further potentiate this effect. In sum, we demonstrated that SSRIs amplify learning-induced effective connections rather than affecting the intrinsic task connectivity or that of resting-state.

The Role of Relationship Status in Major Depressive Disorder - Results of the European Group for the Study of Resistant Depression.

BACKGROUND: While the association between relationship status and the development of depressive symptoms in the general population were reported previously, its relation to the severity and the course of major depressive disorder (MDD) as well as the treatment patterns and response rates needs to be elucidated. METHODS: The present international multicenter cross-sectional study performed by the European Group for the Study of Resistant Depression (GSRD) investigated socio-demographic and clinical patterns of relationship status in a real-world sample of 1410 adult in- and outpatients with MDD as primary diagnosis. RESULTS: While 49.9% of all MDD patients were partnered, 25.4% were separated, and 24.8% were single. Single relationship status was linked to younger mean age, earlier mean age of onset, and current suicidal risk. Being separated was related to older mean age, unemployment, greater symptom severity, current suicidal risk, and add-on treatment strategies. Partnered relationship status was associated with less frequent current suicidal risk. LIMITATIONS: The retrospective assessment of treatment response that was exclusively based on psychopharmacotherapeutic strategies should be critically considered and weighed while interpreting the present results providing novel insights into the complex interaction of relationship status with the clinical phenotype of MDD. CONCLUSIONS: Although MDD patients living in relationships do not seem to be omitted from the evolution of MDD, they may be spared from chronicity and suicidality. Hence, being aware of the current relationship status might support clinicians in the diagnostic and therapeutic process towards optimized management of such challenging clinical phenomena and their negative consequences.

Sex and the serotonergic underpinnings of depression and migraine.

Most psychiatric disorders demonstrate sex differences in their prevalence and symptomatology, and in their response to treatment. These differences are particularly pronounced in mood disorders. Differences in sex hormone levels are among the most overt distinctions between males and females and are thus an intuitive underpinning for these clinical observations. In fact, treatment with estrogen and testosterone was shown to exert antidepressant effects, which underscores this link. Changes to monoaminergic signaling in general, and serotonergic transmission in particular, are understood as central components of depressive pathophysiology. Thus, modulation of the serotonin system may serve as a mechanism via which sex hormones exert their clinical effects in mental health disorders. Over the past 20 years, various experimental approaches have been applied to identify modes of influence of sex and sex hormones on the serotonin system. This chapter provides an overview of different molecular components of the serotonin system, followed by a review of studies performed in animals and in humans with the purpose of elucidating sex hormone effects. Particular emphasis will be placed on studies performed with positron emission tomography, a method that allows for human in vivo molecular imaging and, therefore, assessment of effects in a clinically representative context. The studies addressed in this chapter provide a wealth of information on the interaction between sex, sex hormones, and serotonin in the brain. In general, they offer evidence for the concept that the influence of sex hormones on various components of the serotonin system may serve as an underpinning for the clinical effects these hormones demonstrate.

Predicting Ventral Striatal Activation During Reward Anticipation From Functional Connectivity at Rest.

Reward anticipation is essential for directing behavior toward positively valenced stimuli, creating motivational salience. Task-related activation of the ventral striatum (VS) has long been used as a target for understanding reward function. However, some subjects may not be able to perform the respective tasks because of their complexity or subjects' physical or mental disabilities. Moreover, task implementations may differ, which results in limited comparability. Hence, developing a task-free method for evaluating neural gain circuits is essential. Research has shown that fluctuations in neuronal activity at rest denoted individual differences in the brain functional networks. Here, we proposed novel models to predict the activation of the VS during gain anticipation, using the functional magnetic resonance imaging data of 45 healthy subjects acquired during a monetary incentive delay task and under rest. In-sample validation and held-out data were used to estimate the generalizability of the models. It was possible to predict three measures of reward activation (sensitivity, average, maximum) from resting-state functional connectivity (Pearson's r = 0.38-0.54 in validation data). Especially high contributions to the models were observed from the default mode network. These findings highlight the potential of using functional connectivity at rest as a task-free alternative for predicting activation in the VS, offering a possibility to estimate reward response in the broader sampling of subject populations.