Development of Peptide-Based Probes for Molecular Imaging of the Postsynaptic Density in the Brain.

The postsynaptic density (PSD) comprises numerous scaffolding proteins, receptors, and signaling molecules that coordinate synaptic transmission in the brain. Postsynaptic density protein 95 (PSD-95) is a master scaffold protein within the PSD and one of its most abundant proteins and therefore constitutes a very attractive biomarker of PSD function and its pathological changes. Here, we exploit a high-affinity inhibitor of PSD-95, AVLX-144, as a template for developing probes for molecular imaging of the PSD. AVLX-144-based probes were labeled with the radioisotopes fluorine-18 and tritium, as well as a fluorescent tag. Tracer binding showed saturable, displaceable, and uneven distribution in rat brain slices, proving effective in quantitative autoradiography and cell imaging studies. Notably, we observed diminished tracer binding in human post-mortem Parkinson's disease (PD) brain slices, suggesting postsynaptic impairment in PD. We thus offer a suite of translational probes for visualizing and understanding PSD-related pathologies.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (ß = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Assessment of cerebral drug occupancy in humans using a single PET-scan: A [11C]UCB-J PET study.

PURPOSE: Here, we evaluate a PET displacement model with a Single-step and Numerical solution in healthy individuals using the synaptic vesicle glycoprotein (SV2A) PET-tracer [11C]UCB-J and the anti-seizure medication levetiracetam (LEV). We aimed to (1) validate the displacement model by comparing the brain LEV-SV2A occupancy from a single PET scan with the occupancy derived from two PET scans and the Lassen plot and (2) determine the plasma LEV concentration-SV2A occupancy curve in healthy individuals. METHODS: Eleven healthy individuals (five females, mean age 35.5 [range: 25-47] years) underwent two 120-min [11C]UCB-J PET scans where an LEV dose (5-30 mg/kg) was administered intravenously halfway through the first PET scan to partially displace radioligand binding to SV2A. Five individuals were scanned twice on the same day; the remaining six were scanned once on two separate days, receiving two identical LEV doses. Arterial blood samples were acquired to determine the arterial input function and plasma LEV concentrations. Using the displacement model, the SV2A-LEV target engagement was calculated and compared with the Lassen plot method. The resulting data were fitted with a single-site binding model. RESULTS: SV2A occupancies and VND estimates derived from the displacement model were not significantly different from the Lassen plot (p = 0.55 and 0.13, respectively). The coefficient of variation was 14.6% vs. 17.3% for the Numerical and the Single-step solution in Bland-Altman comparisons with the Lassen plot. The average half maximal inhibitory concentration (IC50), as estimated from the area under the curve of the plasma LEV concentration, was 12.5 µg/mL (95% CI: 5-25) for the Single-Step solution, 11.8 µg/mL (95% CI: 4-25) for the Numerical solution, and 6.3 µg/mL (95% CI: 0.08-21) for the Lassen plot. Constraining Emax to 100% did not significantly improve model fits. CONCLUSION: Plasma LEV concentration vs. SV2A occupancy can be determined in humans using a single PET scan displacement model. The average concentration of the three computed IC50 values ranges between 6.3 and 12.5 µg/mL. The next step is to use the displacement model to evaluate LEV occupancy and corresponding plasma concentrations in relation to treatment efficacy. CLINICAL TRIAL REGISTRATION: NCT05450822. Retrospectively registered 5 July 2022 https://clinicaltrials.gov/ct2/results? term=NCT05450822&Search=Search.

Rumination in patients with major depressive disorder before and after antidepressant treatment.

BACKGROUND: Rumination is a maladaptive response to distress characteristic of Major Depressive Disorder (MDD). It is unclear to what degree rumination is associated with depression severity prior to treatment and how it responds to antidepressant treatment. Therefore, we evaluated the association between rumination and depression severity in 92 untreated patients with MDD and explored the changes in rumination after initiation of antidepressant medication. METHOD: We measured rumination using the Rumination Response Scale (RRS) and depression severity with the Hamilton Depression Rating Scale (HDRS17 or HDRS6) before and after initiation of 12 weeks of antidepressant treatment. The association between RRS and pre-treatment HDRS17 was evaluated using a linear regression model. RRS at week 4, 8, and 12 across treatment response categories (remission vs. non-response) were evaluated using a mixed effect model. RESULTS: RRS was positively associated with depression severity prior to treatment at a trend level (p = 0.06). After initiation of treatment RRS decreased significantly (p < 0.0001) and remitters exhibited lower rumination compared to non-responders at week 4 (p = 0.03), 8 (p = 0.01), and 12 (p = 0.007). LIMITATIONS: The study had no placebo group. CONCLUSIONS: Although pre-treatment rumination did not significantly associate with depressive symptoms, rumination was closely connected to change in depressive symptoms. Tormented patients could be reassured that rumination symptoms may be alleviated over the course of antidepressant treatment.

Stable neural underpinnings of emotional cognition subgroups in patients newly diagnosed with bipolar disorder: A prospective fMRI study.

OBJECTIVES: This study aimed to investigate the neural underpinnings of emotional cognition subgroups in recently diagnosed patients with bipolar disorder (BD) and change over time over a 15-month follow-up period. METHODS: Patients and healthy controls (HC) underwent emotional and nonemotional cognitive assessments and functional magnetic resonance imaging (fMRI) at the baseline (BD n = 87; HC n = 65) and at 15-month follow-up (BD n = 44; HC n = 38). Neural activity during emotion reactivity and regulation in response to aversive pictures was assessed during fMRI. Patients were clustered into subgroups based on their emotional cognition and, with HC, were compared longitudinally on cognition and neural activity during emotion reactivity and regulation. RESULTS: Patients were optimally clustered into two subgroups: Subgroup 1 (n = 40, 46%) was characterized by heightened emotional reactivity in negative social scenarios, which persisted over time, but were otherwise cognitively intact. This subgroup exhibited stable left amygdala hyper-activity over time during emotion reactivity compared to subgroup 2. Subgroup 2 (n = 47, 54%) was characterized by global emotional cognitive impairments, including stable difficulties with emotion regulation over time. During emotion regulation across both time points, this group exhibited hypo-activity in the left dorsolateral prefrontal cortex. Additionally, patients in subgroup 2 had poorer nonemotional cognition, had more psychiatric hospital admissions and history of psychotic episodes than those in subgroup 1. CONCLUSIONS: Broad impairments in emotional cognition in approximately half of BD patients and associated nonemotional cognitive deficits may originate from insufficient recruitment of prefrontal resources, contributing to poorer clinical outcomes.

Characterizing the binding of TC-5619 and encenicline on the alpha7 nicotinic acetylcholine receptor using PET imaging in the pig.

The alpha7 nicotinic acetylcholine receptor (α7-nAChR) has has long been considered a promising therapeutic target for addressing cognitive impairments associated with a spectrum of neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, despite this potential, clinical trials employing α7-nAChR (partial) agonists such as TC-5619 and encenicline (EVP-6124) have fallen short in demonstrating sufficient efficacy. We here investigate the target engagement of TC-5619 and encenicline in the pig brain by use of the α7-nAChR radioligand 11C-NS14492 to characterize binding both with in vitro autoradiography and in vivo occupancy using positron emission tomography (PET). In vitro autoradiography demonstrates significant concentration-dependent binding of 11C-NS14492, and both TC-5619 and encenicline can block this binding. Of particular significance, our in vivo investigations demonstrate that TC-5619 achieves substantial α7-nAChR occupancy, effectively blocking approximately 40% of α7-nAChR binding, whereas encenicline exhibits more limited α7-nAChR occupancy. This study underscores the importance of preclinical PET imaging and target engagement analysis in informing clinical trial strategies, including dosing decisions.

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features.

To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimer's disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to (1) predict brain age using 5-HT2AR binding outcomes, (2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and (3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean = 38, std = 18) and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE = 6.63 years, std = 0.74 years) and GM volume (mean MAE = 6.95 years, std = 0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE = 5.54 years, std = 0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.

Comorbid physical health burden of serious mental health disorders in 32 European countries.

BACKGROUND: Mental health disorders (MHDs) are associated with physical health disparities, but underlying excess risk and health burden have not yet been comprehensively assessed. OBJECTIVE: To assess the burden of comorbid physical health conditions (PHCs) across serious MHDs in Europe. METHODS: We estimated the relative prevalence risk of PHCs associated with alcohol use disorders (AUD), bipolar disorder (BD), depressive disorders (DD) and schizophrenia (SZ) across working-age populations of 32 European countries in 2019 based on a targeted literature review. Excess physical health burden was modelled using population-attributable fractions and country-level prevalence data. FINDINGS: We screened 10 960 studies, of which 41 were deemed eligible, with a total sample size of over 18 million persons. Relative prevalence of PHCs was reported in 54%, 20%, 15%, 5% and 7% of studies, respectively, for SZ, DD, BD, AUD or mixed. Significant relative risk estimates ranged from 1.44 to 3.66 for BD, from 1.43 to 2.21 for DD, from 0.81 to 1.97 for SZ and 3.31 for AUD. Excess physical health burden ranged between 27% and 67% of the total, corresponding to 84 million (AUD), 67 million (BD), 66 million (DD) and 5 million (SZ) PHC diagnoses in Europe. A 1% reduction in excess risk assuming causal inference could result in two million fewer PHCs across investigated MHDs. CONCLUSIONS: This is the first comprehensive study of the physical health burden of serious MHDs in Europe. The methods allow for updates, refinement and extension to other MHDs or geographical areas. CLINICAL IMPLICATIONS: The results indicate potential population health benefits achievable through more integrated mental and physical healthcare and prevention approaches.

Partial volume correction of PET image data using geometric transfer matrices based on uniform B-splines.

Objective. Most methods for partial volume correction (PVC) of positron emission tomography (PET) data employ anatomical segmentation of images into regions of interest. This approach is not optimal for exploratory functional imaging beyond regional hypotheses. Here, we describe a novel method for unbiased voxel-wise PVC.Approach.B-spline basis functions were combined with geometric transfer matrices to enable a method (bsGTM) that provides PVC or alternatively provides smoothing with minimal regional crosstalk. The efficacy of the proposed method was evaluated using Monte Carlo simulations, human PET data, and murine functional PET data.Main results.In simulations, bsGTM provided recovery of partial volume signal loss comparable to iterative deconvolution, while demonstrating superior resilience to noise. In a real murine PET dataset, bsGTM yielded much higher sensitivity for detecting amphetamine-induced reduction of [11C]raclopride binding potential. In human PET data, bsGTM smoothing enabled increased signal-to-noise ratios with less degradation of binding potentials relative to Gaussian convolution or non-local means.Significance.bsGTM offers improved performance for PVC relative to iterative deconvolution, the current method of choice for voxel-wise PVC, especially in the common PET regime of low signal-to-noise ratio. The new method provides an anatomically unbiased way to compensate partial volume errors in cases where anatomical segmentation is unavailable or of questionable relevance or accuracy.

CCH attack frequency reduction after psilocybin correlates with hypothalamic functional connectivity.

OBJECTIVE: To evaluate the feasibility and prophylactic effect of psilocybin as well as its effects on hypothalamic functional connectivity (FC) in patients with chronic cluster headache (CCH). BACKGROUND: CCH is an excruciating and difficult-to-treat disorder with incompletely understood pathophysiology, although hypothalamic dysfunction has been implicated. Psilocybin may have beneficial prophylactic effects, but clinical evidence is limited. METHODS: In this small open-label clinical trial, 10 patients with CCH were included and maintained headache diaries for 10 weeks. Patients received three doses of peroral psilocybin (0.14 mg/kg) on the first day of weeks five, six, and seven. The first 4 weeks served as baseline and the last 4 weeks as follow-up. Hypothalamic FC was determined using functional magnetic resonance imaging the day before the first psilocybin dose and 1 week after the last dose. RESULTS: The treatment was well tolerated. Attack frequency was reduced by mean (standard deviation) 31% (31) from baseline to follow-up (pFWER = 0.008). One patient experienced 21 weeks of complete remission. Changes in hypothalamic-diencephalic FC correlated negatively with a percent change in attack frequency (pFWER = 0.03, R = -0.81), implicating this neural pathway in treatment response. CONCLUSION: Our results indicate that psilocybin may have prophylactic potential and implicates the hypothalamus in possible treatment response. Further clinical studies are warranted.

Effect of immersive virtual reality-based cognitive remediation in patients with mood or psychosis spectrum disorders: study protocol for a randomized, controlled, double-blinded trial.

BACKGROUND: Cognitive impairments are prevalent across mood disorders and psychosis spectrum disorders, but there is a lack of real-life-like cognitive training programmes. Fully immersive virtual reality has the potential to ensure motivating and engaging cognitive training directly relevant to patients' daily lives. We will examine the effect of a 4-week, intensive virtual reality-based cognitive remediation programme involving daily life challenges on cognition and daily life functioning in patients with mood disorders or psychosis spectrum disorders and explore the neuronal underpinnings of potential treatment efficacy. METHODS: The trial has a randomized, controlled, double-blinded, parallel-group design. We will include 66 symptomatically stable outpatients with mood disorders or psychosis spectrum disorders aged 18-55 years with objective and subjective cognitive impairment. Assessments encompassing a virtual reality test of daily life cognitive skills, neuropsychological testing, measures of daily life functioning, symptom ratings, questionnaires on subjective cognitive complaints, and quality of life are carried out at baseline, after the end of 4 weeks of treatment and at a 3-month follow-up after treatment completion. Functional magnetic resonance imaging scans are performed at baseline and at the end of treatment. The primary outcome is a broad cognitive composite score comprising five subtasks on a novel ecologically valid virtual reality test of daily life cognitive functions. Two complete data sets for 54 patients will provide a power of 80% to detect a clinically relevant between-group difference in the primary outcome. Behavioural data will be analysed using linear mixed models in SPSS, while MRI data will be analysed with the FMRIB Expert Analysis Tool (FEAT). Treatment-related changes in neural activity from baseline to end of treatment will be investigated for the dorsal prefrontal cortex and hippocampus as the regions of interest. DISCUSSION: The results will provide insight into whether virtual reality-based cognitive remediation has beneficial effects on cognition and functioning in symptomatically stable patients with mood disorders or psychosis spectrum disorders, which can aid future treatment development. TRIAL REGISTRATION: ClinicalTrials.gov NCT06038955. Registered on September 15, 2023.

EEG abnormalities are not associated with poor antidepressant treatment outcome - A NeuroPharm study.

EEG brain abnormalities, such as slowing and isolated epileptiform discharges (IEDs), has previously been associated with non-response to antidepressant treatment with escitalopram and venlafaxine, suggesting a potential need for treatment with anticonvulsant property in some patients. The current study aims to replicate the reported association of EEG abnormality and treatment outcomes in an open-label trial of escitalopram for major depressive disorder (MDD) and explore its relationship to mood and cognition. Pretreatment, 6 min eyes-closed resting-state 256-channel EEG was recorded in 91 patients with MDD (age 18-57) who were treated with 10-20 mg escitalopram for 12 weeks; patients could switch to duloxetine after four weeks. A certified clinical neurophysiologist rated the EEGs. IED and EEG slowing was seen in 13.2%, and in 6.6% there were findings with unclear significance (i.e., Wicket spikes and theta activity). We saw no group-difference in remission or response rates after 8 and 12 weeks of treatment or switching to duloxetine. Patients with EEG abnormalities had higher pretreatment mood disturbances driven by greater anger (p=.039) and poorer verbal memory (p=.012). However, EEG abnormality was not associated with improved mood or verbal memory after treatment. Our findings should be interpreted in light of the rarity of EEG abnormalities and the sample size. While we cannot confirm that EEG abnormalities are associated with non-response to treatment, including escitalopram, abnormal EEG activity is associated with poor mood and verbal memory. The clinical utility of EEG abnormality in antidepressant treatment selection needs careful evaluation before deciding if useful for clinical implementation.

Effects of escitalopram on synaptic density in the healthy human brain: a randomized controlled trial.

Selective serotonin reuptake inhibitors (SSRIs) are widely used for treating neuropsychiatric disorders. However, the exact mechanism of action and why effects can take several weeks to manifest is not clear. The hypothesis of neuroplasticity is supported by preclinical studies, but the evidence in humans is limited. Here, we investigate the effects of the SSRI escitalopram on presynaptic density as a proxy for synaptic plasticity. In a double-blind placebo-controlled study (NCT04239339), 32 healthy participants with no history of psychiatric or cognitive disorders were randomized to receive daily oral dosing of either 20 mg escitalopram (n = 17) or a placebo (n = 15). After an intervention period of 3-5 weeks, participants underwent a [11C]UCB-J PET scan (29 with full arterial input function) to quantify synaptic vesicle glycoprotein 2A (SV2A) density in the hippocampus and the neocortex. Whereas we find no statistically significant group difference in SV2A binding after an average of 29 (range: 24-38) days of intervention, our secondary analyses show a time-dependent effect of escitalopram on cerebral SV2A binding with positive associations between [11C]UCB-J binding and duration of escitalopram intervention. Our findings suggest that brain synaptic plasticity evolves over 3-5 weeks in healthy humans following daily intake of escitalopram. This is the first in vivo evidence to support the hypothesis of neuroplasticity as a mechanism of action for SSRIs in humans and it offers a plausible biological explanation for the delayed treatment response commonly observed in patients treated with SSRIs. While replication is warranted, these results have important implications for the design of future clinical studies investigating the neurobiological effects of SSRIs.

Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages.

Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a person's chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower or accelerated biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a comparison of such packages with respect to (1) predictive accuracy, (2) test-retest reliability, and (3) the ability to track age progression over time. We evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 ± 17.5 years). All packages showed significant correlations between predicted brain age and chronological age (r = 0.66-0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94-0.98), as well as predicting age progression over a longer time span. Of the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.

Aberrant resting-state functional connectivity underlies cognitive and functional impairments in remitted patients with bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is commonly associated with cognitive impairments, that directly contribute to patients' functional disability. However, there is no effective treatment targeting cognition in BD. A key reason for the lack of pro-cognitive interventions is the limited insight into the brain correlates of cognitive impairments in these patients. This is the first study investigating the resting-state neural underpinnings of cognitive impairments in different neurocognitive subgroups of patients with BD. METHOD: Patients with BD in full or partial remission and healthy controls (final sample of n = 144 and n = 50, respectively) underwent neuropsychological assessment and resting-state functional magnetic resonance imaging. We classified the patients into cognitively impaired (n = 83) and cognitively normal (n = 61) subgroups using hierarchical cluster analysis of the four cognitive domains. We used independent component analysis (ICA) to investigate the differences between the neurocognitive subgroups and healthy controls in resting-state functional connectivity (rsFC) in the default mode network (DMN), executive central network (ECN), and frontoparietal network (FPN). RESULTS: Cognitively impaired patients displayed greater positive rsFC within the DMN and less negative rsFC within the ECN than healthy controls. Across cognitively impaired patients, lower positive connectivity within DMN and lower negative rsFC within ECN correlated with worse global cognitive performance. CONCLUSION: Cognitive impairments in BD seem to be associated with a hyper-connectivity within the DMN, which may explain the failure to suppress task-irrelevant DMN activity during the cognitive performance, and blunted anticorrelation in the ECN. Thus, aberrant connectivity within the DMN and ECN may serve as brain targets for pro-cognitive interventions.

Text mining of electronic health records can validate a register-based diagnosis of epilepsy and subgroup into focal and generalized epilepsy.

OBJECTIVE: Combining population-based health registries and electronic health records offers the opportunity to create large, phenotypically detailed patient cohorts of high quality. In this study, we used text mining of clinical notes to confirm International Classification of Diseases, 10th Revision (ICD-10)-registered epilepsy diagnoses and classify patients according to focal and generalized epilepsy types. METHODS: Using the Danish National Patient Registry, we identified patients who between 2006 and 2016 received an ICD-10 diagnosis of epilepsy. To validate the epilepsy diagnosis and stratify patients into focal and generalized epilepsy types, we constructed dictionaries for text mining-based extraction of clinical notes. Two physicians manually reviewed the clinical notes for a total of 527 patients and assigned epilepsy diagnoses, which were compared with the text-mined diagnoses. RESULTS: We identified 23 632 patients with an ICD-10 diagnosis of epilepsy, of whom 50% were registered with an unspecified epilepsy diagnosis. In total, 11 211 patients were considered likely to have epilepsy by text mining, with an F1 measure ranging from 82% to 90%. Manual review of the electronic health records for 310 patients revealed a false discovery rate of 29%. This rate was decreased to 4% by the text mining algorithm. The weighted average F1 measure for text mining-assigned epilepsy types was 79% (82% for focal and 76% for generalized epilepsy). Text mining successfully assigned a focal or generalized epilepsy type to 92% of the text mining-eligible patients registered with unspecified epilepsy. SIGNIFICANCE: Text mining of electronic health records can be used to establish a patient cohort with much higher likelihood of having a diagnosis of epilepsy and a focal or generalized epilepsy type compared to the cohort created from ICD-10 epilepsy codes alone. We believe the concept will be essential for future genome-wide and phenome-wide association studies and subsequently the development of precision medicine for epilepsy patients.

Stress Hormone Dynamics Are Coupled to Brain Serotonin 4 Receptor Availability in Unmedicated Patients With Major Depressive Disorder: A NeuroPharm Study.

BACKGROUND: A prominent finding in major depressive disorder (MDD) is distorted stress hormone dynamics, which is regulated by serotonergic brain signaling. An interesting feature of the cerebral serotonin system is the serotonin 4 receptor (5-HT4R), which is lower in depressed relative to healthy individuals and also has been highlighted as a promising novel antidepressant target. Here, we test the novel hypothesis that brain 5-HT4R availability in untreated patients with MDD is correlated with cortisol dynamics, indexed by the cortisol awakening response (CAR). Further, we evaluate if CAR changes with antidepressant treatment, including a selective serotonin reuptake inhibitor, and if pretreatment CAR can predict treatment outcome. METHODS: Sixty-six patients (76% women) with a moderate to severe depressive episode underwent positron emission tomography imaging with [11C]SB207145 for quantification of brain 5-HT4R binding using BPND as outcome. Serial home sampling of saliva in the first hour from awakening was performed to assess CAR before and after 8 weeks of antidepressant treatment. Treatment outcome was measured by change in Hamilton Depression Rating Scale 6 items. RESULTS: In the unmedicated depressed state, prefrontal and anterior cingulate cortices 5-HT4R binding was positively associated with CAR. CAR remained unaltered after 8 weeks of antidepressant treatment, and pretreatment CAR did not significantly predict treatment outcome. CONCLUSIONS: Our findings highlight a link between serotonergic disturbances in MDD and cortisol dynamics, which likely is involved in disease and treatment mechanisms. Further, our data support 5-HT4R agonism as a promising precision target in patients with MDD and disturbed stress hormone dynamics.

Synaptic density in aging mice measured by [18F]SynVesT-1 PET.

Synaptic alterations in certain brain structures are related to cognitive decline in neurodegeneration and in aging. Synaptic loss in many neurodegenerative diseases can be visualized by positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A (SV2A). However, the use of SV2A PET for studying synaptic changes during aging is not particularly explored. Thus, in the present study, PET ligand [18F]SynVesT-1, which binds to SV2A, was used to investigate synaptic density at different ages in healthy mice. Wild type C57BL/6 mice divided into three age groups (4-5 months (n = 7), 12-14 months (n = 11), 17-19 months (n = 7)) were PET scanned with [18F]SynVesT-1. Brain retention of [18F]SynVesT-1 expressed as the volume of distribution (VIDIF) was calculated using an image-derived input function. Estimates of VIDIF were derived using either a one-tissue compartment model (1TCM), a two-tissue compartment model (2TCM), or the Logan plot with blood input to find the best-fit model for [18F]SynVesT-1. After the PET scans, tissue sections were immunostained for the detection of SV2A and neuronal markers. We found that [18F]SynVesT-1 data acquired 60 min post intravenously injection and analyzed with 1TCM described the brain pharmacokinetics of the radioligand in mice well. [18F]SynVesT-1 brain retention was lower in the oldest group of mice, indicating a decrease in synaptic density in this age group. However, no gradual age-dependent decrease in synaptic density at a region-specific level was observed. Immunostaining indicated that SV2A expression and neuron numbers were similar across all three age groups. In general, these data obtained in healthy aging mice are consistent with previous findings in humans where synaptic density appeared stable during aging up to a certain age, after which a small decrease is observed.

Association between brain serotonin 4 receptor binding and reactivity to emotional faces in depressed and healthy individuals.

Brain serotonergic (5-HT) signaling is posited to modulate neural responses to emotional stimuli. Dysfunction in 5-HT signaling is implicated in major depressive disorder (MDD), a disorder associated with significant disturbances in emotion processing. In MDD, recent evidence points to altered 5-HT4 receptor (5-HT4R) levels, a promising target for antidepressant treatment. However, how these alterations influence neural processing of emotions in MDD remains poorly understood. This is the first study to examine the association between 5-HT4R binding and neural responses to emotions in patients with MDD and healthy controls. The study included one hundred and thirty-eight participants, comprising 88 outpatients with MDD from the NeuroPharm clinical trial (ClinicalTrials.gov identifier: NCT02869035) and 50 healthy controls. Participants underwent an [11C]SB207145 positron emission tomography (PET) scan to quantify 5-HT4R binding (BPND) and a functional magnetic resonance imaging (fMRI) scan during which they performed an emotional face matching task. We examined the association between regional 5-HT4R binding and corticolimbic responses to emotional faces using a linear latent variable model, including whether this association was moderated by depression status. We observed a positive correlation between 5-HT4R BPND and the corticolimbic response to emotional faces across participants (r = 0.20, p = 0.03). This association did not differ between groups (parameter estimate difference = 0.002, 95% CI = -0.008: 0.013, p = 0.72). Thus, in the largest PET/fMRI study of associations between serotonergic signaling and brain function, we found a positive association between 5-HT4R binding and neural responses to emotions that appear unaltered in MDD. Future clinical trials with novel pharmacological agents targeting 5-HT4R are needed to confirm whether they ameliorate emotion processing biases in MDD.

Association of neurocognitive function with psychiatric hospitalization and socio-demographic conditions in individuals with bipolar and major depressive disorders.

Background: Neurocognitive impairments are associated with poor clinical and employment outcomes in individuals with affective disorders. However, little is known about their associations with long-term clinical outcomes such as psychiatric hospitalizations, and with socio-demographic indicators other than employment. In the largest longitudinal study of neurocognition in affective disorders to date, we investigate the role of neurocognitive impairments on psychiatric hospitalizations and socio-demographic conditions. Methods: The study included 518 individuals with bipolar or major depressive disorder. Neurocognitive assessments covered executive function and verbal memory domains. Longitudinal data on psychiatric hospitalization and socio-demographic conditions (employment, cohabitation, and marital status) for up to 11 years were obtained using National population-based registers. The primary and secondary outcomes were psychiatric hospitalizations (n = 398) and worsening of socio-demographic conditions (n = 518), in the follow-up period since study inclusion, respectively. Cox regression models were used to examine the association of neurocognition with future psychiatric hospitalizations and the worsening of socio-demographic conditions. Findings: Clinically significant impairment in verbal memory (z-score ≤ -1; defined by the ISBD Cognition Task Force), but not in executive function, was associated with a higher risk of future hospitalization, when adjusted for age, sex, hospitalization in the year preceding inclusion, depression severity, diagnosis, and type of clinical trial (HR = 1.84, 95% CI:1.05-3.25, p = 0.034; n = 398). The results remained significant even after accounting for illness duration. Neurocognitive impairments were not associated with the worsening of socio-demographic conditions (p ≥ 0.17; n = 518). Interpretation: Promoting neurocognitive function, especially verbal memory, may mitigate the risk of future psychiatric hospitalization in individuals with affective disorders. Funding: Lundbeckfonden (R279-2018-1145).