Authors: Sadibolova, R.; DiMarco, E. K.; Jiang, A.; Maas, B.; Tatter, S. B.; Laxton, A.; Kishida, K. T.; Terhune, D. B.

Score: 8.5, Published: 2024-02-09

DOI: 10.1101/2024.02.09.24302276

Timing behaviour and the perception of time are fundamental to cognitive and emotional processes in humans. In non-human model organisms, the neuromodulator dopamine has been associated with variations in timing behaviour, but the connection between variations in dopamine levels and the human experience of time has not been directly assessed. Here, we report how dopamine levels in human striatum, measured with sub-second temporal resolution during awake deep brain stimulation surgery, relate to participants perceptual judgements of time intervals. Fast, phasic, dopaminergic signals were associated with underestimation of temporal intervals, whereas slower, tonic, decreases in dopamine were associated with poorer temporal precision. Our findings suggest a delicate and complex role for the dynamics and tone of dopaminergic signals in the conscious experience of time in humans.

Authors: Montanari, S.; Jansen, R.; Schranner, D.; Kastenmuller, G.; Arnold, M.; Janiri, D.; Sani, G.; Bhattacharyya, S.; Dehkordi, S.; Dunlop, B.; Rush, J. A.; Penninx, B.; Kaddurah-Daouk, R.; Milaneschi, Y.

Score: 1.2, Published: 2024-02-15

DOI: 10.1101/2024.02.14.24302813

BackgroundAcylcarnitines (ACs) are involved in bioenergetics processes that may play a role in the pathophysiology of depression. Studies linking AC levels to depression are few and provide mixed findings. We examined the association of circulating ACs levels with Major Depressive Disorder (MDD) diagnosis, overall depression severity and specific symptom profiles. MethodsThe sample from the Netherlands Study of Depression and Anxiety included participants with current (n=1035) or remitted (n=739) MDD and healthy controls (n=800). Plasma levels of four ACs (short-chain: acetylcarnitine C2 and propionylcarnitine C3; medium-chain: octanoylcarnitine C8 and decanoylcarnitine C10) were measured. Overall depression severity as well as atypical/energy-related (AES), anhedonic and melancholic symptom profiles were derived from the Inventory of Depressive Symptomatology. ResultsAs compared to healthy controls, subjects with current or remitted MDD presented similarly lower mean C2 levels (Cohens d=0.2, p[≤]1e-4). Higher overall depression severity was significantly associated with higher C3 levels ({beta}=0.06, SE=0.02, p=1.21e-3). No associations were found for C8 and C10. Focusing on symptom profiles, only higher AES scores were linked to lower C2 ({beta}=-0.05, SE=0.02, p=1.85e-2) and higher C3 ({beta}=0.08, SE=0.02, p=3.41e-5) levels. Results were confirmed in analyses pooling data with an additional internal replication sample from the same subjects measured at 6-year follow-up (totaling 4195 observations). ConclusionsSmall alterations in levels of short-chain acylcarnitine levels were related to the presence and severity of depression, especially for symptoms reflecting altered energy homeostasis. Cellular metabolic dysfunctions may represent a key pathway in depression pathophysiology potentially accessible through AC metabolism.

Authors: Peyrot, W. J.; Panagiotaropoulou, G.; Olde Loohuis, L. M.; Adams, M.; Awasthi, S.; Ge, T.; McIntosh, A. M.; Mitchell, B. L.; Mullins, N.; O'Connell, K. S.; Penninx, B. W. J. H.; Posthuma, D.; Ripke, S.; Ruderfer, D. M.; Uffelmann, E.; Vilhjalmsson, B. J.; Zhu, Z.; Schizophrenia Working Group of the Psychiatric Genomics Consortium, ; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, ; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, ; Smoller, J. W.; Price, A. L.

Score: 22.5, Published: 2024-02-04

DOI: 10.1101/2024.02.02.24302228

Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS (N=41,917-173,140 cases; total N=1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N=11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.