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Background Overweight and decreased physical fitness are highly prevalent in schizophrenia, represent a major risk factor for cardio-vascular diseases and decrease the patients’ life expectancies. It is thus important to understand the underlying mechanisms that link psychopathology and weight gain. We hypothesize that the dopaminergic reward system plays an important role in this.
Methods: We analyzed the seed-based functional connectivity (FC) of the ventral tegmental area (VTA) in a group of schizophrenic patients (n = 32) and age- as well as gender matched healthy controls (n = 27). We then correlated the resting-state results with physical fitness parameters, obtained in a fitness test, and psychopathology.
Results: The seed-based connectivity analysis revealed decreased functional connections between the VTA and the anterior cingulate cortex (ACC), as well as the dorsolateral prefrontal cortex and increased functional connectivity between the VTA and the middle temporal gyrus in patients compared to healthy controls. The decreased FC between the VTA and the ACC of the patient group could further be associated with increased body fat and negatively correlated with the overall physical fitness. We found no significant correlations with psychopathology.
Conclusion: Although we did not find significant correlations with psychopathology, we could link decreased physical fitness and high body fat with dysconnectivity between the VTA and the ACC in schizophrenia. These findings demonstrate that a dysregulated reward system is not just responsible for symptomatology in schizophrenia but is also involved in comorbidities and could pave the way for future lifestyle therapy interventions.
Beyond well-established difficulties with working memory in individuals with attention deficit hyperactivity disorder (ADHD), evidence is emerging that other memory processes may also be affected. We investigated, first, which memory processes show differences in adults and adolescents with ADHD in comparison to control participants, focusing on working and short-term memory, initial learning, interference, delayed and recognition memory. Second, we investigated whether ADHD severity, co-occurring depressive symptoms, IQ and physical fitness are associated with the memory performance in the individuals with ADHD.
We assessed 205 participants with ADHD (mean age 25.8 years, SD 7.99) and 50 control participants (mean age 21.1 years, SD 5.07) on cognitive tasks including the digit span forward (DSF) and backward (DSB), the Rey Auditory Verbal Learning Test (RAVLT), and the vocabulary and matrix reasoning subtests of the Wechsler Abbreviated Scale of Intelligence. Participants with ADHD were additionally assessed on ADHD severity, depression symptoms and cardiorespiratory fitness. A series of regressions were run, with sensitivity analyses performed when variables were skewed.
ADHD-control comparisons were significant for DSF, DSB, delayed and recognition memory, with people with ADHD performing less well than the control participants. The result for recognition memory was no longer significant in sensitivity analysis. Memory performance was not associated with greater ADHD or depression symptoms severity. IQ was positively associated with all memory variables except DSF. Cardiorespiratory fitness was negatively associated with the majority of RAVLT variables.
Individuals with ADHD showed difficulties with working memory, short-term memory and delayed memory, as well as a potential difficulty with recognition memory, despite preserved initial learning.
Understanding the underlying mechanisms that link psychopathology and physical comorbidities in schizophrenia is crucial since decreased physical fitness and overweight pose major risk factors for cardio-vascular diseases and decrease the patients’ life expectancies. We hypothesize that altered reward anticipation plays an important role in this. We implemented the Monetary Incentive Delay task in a MR scanner and a fitness test battery to compare schizophrenia patients (SZ, n = 43) with sex- and age-matched healthy controls (HC, n = 36) as to reward processing and their physical fitness. We found differences in reward anticipation between SZs and HCs, whereby increased activity in HCs positively correlated with overall physical condition and negatively correlated with psychopathology. On the other handy, SZs revealed stronger activity in the posterior cingulate cortex and in cerebellar regions during reward anticipation, which could be linked to decreased overall physical fitness. These findings demonstrate that a dysregulated reward system is not only responsible for the symptomatology of schizophrenia, but might also be involved in physical comorbidities which could pave the way for future lifestyle therapy interventions.
Structural brain morphometry as classifier and predictor of ADHD and reward-related comorbidities
(2022)
Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, and around two-thirds of affected children report persisting problems in adulthood. This negative trajectory is associated with high comorbidity with disorders like obesity, depression, or substance use disorder (SUD). Decreases in cortical volume and thickness have also been reported in depression, SUD, and obesity, but it is unclear whether structural brain alterations represent unique disorder-specific profiles. A transdiagnostic exploration of ADHD and typical comorbid disorders could help to understand whether specific morphometric brain changes are due to ADHD or, alternatively, to the comorbid disorders. In the current study, we studied the brain morphometry of 136 subjects with ADHD with and without comorbid depression, SUD, and obesity to test whether there are unique or common brain alterations. We employed a machine-learning-algorithm trained to classify subjects with ADHD in the large ENIGMA-ADHD dataset and used it to predict the diagnostic status of subjects with ADHD and/or comorbidities. The parcellation analysis demonstrated decreased cortical thickness in medial prefrontal areas that was associated with presence of any comorbidity. However, these results did not survive correction for multiple comparisons. Similarly, the machine learning analysis indicated that the predictive algorithm grouped most of our ADHD participants as belonging to the ADHD-group, but no systematic differences between comorbidity status came up. In sum, neither a classical comparison of segmented structural brain metrics nor an ML model based on the ADHD ENIGMA data differentiate between ADHD with and without comorbidities. As the ML model is based in part on adolescent brains, this might indicate that comorbid disorders and their brain changes are not captured by the ML model because it represents a different developmental brain trajectory.