Effective Connectivity of Thalamocortical Interactions Following d-Amphetamine, LSD, and MDMA Administration.

BACKGROUND: While the exploration of serotonergic psychedelics as psychiatric medicines deepens, so does the pressure to better understand how these compounds act on the brain. METHODS: We used a double-blind, placebo-controlled, crossover design and administered lysergic acid diethylamide (LSD), 3,4-methylenedioxymethamphetamine (MDMA), and d-amphetamine in 25 healthy participants. By using spectral dynamic causal modeling, we mapped substance-induced changes in effective connectivity between the thalamus and different cortex types (unimodal vs. transmodal) derived from a previous study with resting-state functional magnetic resonance imaging data. Due to the distinct pharmacological modes of action of the 3 substances, we were able to investigate specific effects mainly driven by different neurotransmitter systems on thalamocortical and corticothalamic interactions. RESULTS: Compared with placebo, all 3 substances increased the effective connectivity from the thalamus to specific unimodal cortices, whereas the influence of these cortices on the thalamus was reduced. These results indicate increased bottom-up and decreased top-down information flow between the thalamus and some unimodal cortices. However, for the amphetamines, we found the opposite effects when examining the effective connectivity with transmodal cortices, including parts of the salience network. Intriguingly, LSD increased the effective connectivity from the thalamus to both unimodal and transmodal cortices, indicating a breach in the hierarchical organization of ongoing brain activity. CONCLUSIONS: The results advance our knowledge about the action of psychedelics on the brain and refine current models aiming to explain the underlying neurobiological processes.

Metabolic Fingerprints of Effective Fluoxetine Treatment in the Prefrontal Cortex of Chronically Socially Isolated Rats: Marker Candidates and Predictive Metabolites.

The increasing prevalence of depression requires more effective therapy and the understanding of antidepressants' mode of action. We carried out untargeted metabolomics of the prefrontal cortex of rats exposed to chronic social isolation (CSIS), a rat model of depression, and/or fluoxetine treatment using liquid chromatography-high resolution mass spectrometry. The behavioral phenotype was assessed by the forced swim test. To analyze the metabolomics data, we employed univariate and multivariate analysis and biomarker capacity assessment using the receiver operating characteristic (ROC) curve. We also identified the most predictive biomarkers using a support vector machine with linear kernel (SVM-LK). Upregulated myo-inositol following CSIS may represent a potential marker of depressive phenotype. Effective fluoxetine treatment reversed depressive-like behavior and increased sedoheptulose 7-phosphate, hypotaurine, and acetyl-L-carnitine contents, which were identified as marker candidates for fluoxetine efficacy. ROC analysis revealed 4 significant marker candidates for CSIS group discrimination, and 10 for fluoxetine efficacy. SVM-LK with accuracies of 61.50% or 93.30% identified a panel of 7 or 25 predictive metabolites for depressive-like behavior or fluoxetine effectiveness, respectively. Overall, metabolic fingerprints combined with the ROC curve and SVM-LK may represent a new approach to identifying marker candidates or predictive metabolites for ongoing disease or disease risk and treatment outcome.

Chaos analysis of the cortical boundary for the recognition of psychosis.

BACKGROUND: Structural MRI studies in people with first-episode psychosis (FEP) and those in the clinical high-risk (CHR) state have consistently shown volumetric abnormalities that depict changes in the structural complexity of the cortical boundary. The aim of the present study was to employ chaos analysis in the identification of people with psychosis based on the structural complexity of the cortical boundary and subcortical areas. METHODS: We performed chaos analysis of the grey matter distribution on structural MRIs. First, the outer boundary points for each slice in the axial, coronal and sagittal view were calculated for grey matter maps. Next, the distance of each boundary point from the centre of mass in the grey matter was calculated and stored as spatial series, which was further analyzed by extracting the Largest Lyapunov Exponent (lambda [λ]), a feature depicting the structural complexity of the cortical boundary. RESULTS: Structural MRIs were acquired from 77 FEP, 73 CHR and 44 healthy controls. We compared λ brain maps between groups, which resulted in statistically significant differences in all comparisons. By matching the λ values extracted in axial view with the Morlet wavelet, differences on the surface relief are observed between groups. LIMITATIONS: Parameters were selected after experimentation on the examined sample. Investigation of the effectiveness of the method in a larger data set is needed. CONCLUSION: The proposed framework using spatial series verifies diagnosis-relevant features and may contribute to the identification of structural biomarkers for psychosis.

Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.

Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics texture features describe the interrelationship between voxel intensities across multiple spatial scales capturing the hidden information of underlying disease dynamics in addition to volumetric changes. Structural MR images were acquired from 77 first-episode psychosis (FEP) patients, 58 clinical high-risk subjects with no later transition to psychosis (CHR_NT), 15 clinical high-risk subjects with later transition (CHR_T), and 44 healthy controls (HC). Radiomics texture features were extracted from non-segmented images, and two-classification schemas were performed for the identification of FEP vs. HC and FEP vs. CHR_NT. The group of CHR_T was used as external validation in both schemas. The classification of a subject's clinical status was predicted by importing separately (a) the difference of entropy feature map and (b) the contrast feature map, resulting in classification balanced accuracy above 72% in both analyses. The proposed framework enhances the classification decision for FEP, CHR_NT, and HC subjects, verifies diagnosis-relevant features and may potentially contribute to identification of structural biomarkers for psychosis, beyond and above volumetric brain changes.

Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients.

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis.

Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n = 147), clinical high-risk states of psychosis (CHR, n = 143), recent-onset depression (ROD, n = 151), and healthy controls (HC, n = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p = 0.0001) and CHR (67.38%, p = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrity within ON and FPN driving the VisDys phenomenon and being implicated in core disease mechanisms of early psychosis states.

The Basal Forebrain Cholinergic Nuclei and Their Relevance to Schizophrenia and Other Psychotic Disorders.

The basal forebrain cholinergic nuclei (BFCN) provide the main cholinergic input to prefrontal cortices, the hippocampi, and amygdala. These structures are highly relevant for the regulation and maintenance of many cognitive functions, such as attention and memory. In vivo neuroimaging studies reported alterations of the cholinergic system in psychotic disorders. Particularly, a downregulation of nicotinic and muscarinic acetylcholine receptors has been found. Crucially, such alterations in neurotransmission have been associated with cognitive impairments and positive and negative symptoms. Recent pharmacological studies support these findings, as they demonstrated an association between the manipulation of cholinergic transmission and an attenuation in symptom severity. Targeting acetylcholine receptors has therefore become a focus for the development of novel psychopharmacological drugs. However, many open questions remain. For instance, it remains elusive what causes such alterations in neurotransmission. While evidence supports the idea that BFCN structural integrity is altered in schizophrenia, it remains to be determined whether this is also present in other psychotic disorders. Furthermore, it is unclear when throughout the course of the disorder these alterations make their appearance and whether they reflect changes in the BFCN alone or rather aberrant interactions between the BFCN and other brain areas. In this review, the specific role of the BFCN and their projections are discussed from a neuroimaging perspective and with a focus on psychotic disorders alongside future directions. These directions set the stage for the development of new treatment targets for psychotic disorders.

Characterizing Thalamocortical (Dys)connectivity Following D-Amphetamine, LSD, and MDMA Administration.

BACKGROUND: Patients with psychotic disorders present alterations in thalamocortical intrinsic functional connectivity as measured by resting-state functional magnetic resonance imaging. Specifically, thalamic intrinsic functional connectivity is increased with sensorimotor cortices (hyperconnectivity) and decreased with prefrontal limbic cortices (hypoconnectivity). Psychedelics such as lysergic acid diethlyamide (LSD) elicit similar thalamocortical hyperconnectivity with sensorimotor areas in healthy volunteers. It is unclear whether LSD also induces thalamocortical hypoconnectivity with prefrontal limbic cortices, because current findings are equivocal. Thalamocortical hyperconnectivity was associated with psychotic symptoms in patients and substance-induced altered states of consciousness in healthy volunteers. Thalamocortical dysconnectivity is likely evoked by altered neurotransmission, e.g., via dopaminergic excess in psychotic disorders and serotonergic agonism in psychedelic-induced states. It is unclear whether thalamocortical dysconnectivity is also elicited by amphetamine-type substances, broadly releasing monoamines (i.e., dopamine, norepinephrine) but producing fewer perceptual effects than psychedelics. METHODS: We administrated LSD, d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers and investigated their effects on thalamic intrinsic functional connectivity with 2 brain networks (auditory-sensorimotor and salience networks, corresponding to sensorimotor and prefrontal limbic cortices, respectively), using a double-blind, placebo-controlled, crossover design. RESULTS: All active substances elicited auditory-sensorimotor-thalamic hyperconnectivity compared with placebo, despite predominantly distinct pharmacological actions and subjective effects. LSD-induced effects correlated with subjective changes in perception, indicating a link between hyperconnectivity and psychedelic-type perceptual alterations. Unlike d-amphetamine and MDMA, which induced hypoconnectivity with the salience network, LSD elicited hyperconnectivity. D-amphetamine and MDMA evoked similar thalamocortical dysconnectivity patterns. CONCLUSIONS: Psychedelics, empathogens, and psychostimulants evoke thalamocortical hyperconnectivity with sensorimotor areas, akin to findings in patients with psychotic disorders.

Bridging the Gap? Altered Thalamocortical Connectivity in Psychotic and Psychedelic States.

Psychiatry has a well-established tradition of comparing drug-induced experiences to psychotic symptoms, based on shared phenomena such as altered perceptions. The present review focuses on experiences induced by classic psychedelics, which are substances capable of eliciting powerful psychoactive effects, characterized by distortions/alterations of several neurocognitive processes (e.g., hallucinations). Herein we refer to such experiences as psychedelic states. Psychosis is a clinical syndrome defined by impaired reality testing, also characterized by impaired neurocognitive processes (e.g., hallucinations and delusions). In this review we refer to acute phases of psychotic disorders as psychotic states. Neuropharmacological investigations have begun to characterize the neurobiological mechanisms underpinning the shared and distinct neurophysiological changes observed in psychedelic and psychotic states. Mounting evidence indicates changes in thalamic filtering, along with disturbances in cortico-striato-pallido-thalamo-cortical (CSPTC)-circuitry, in both altered states. Notably, alterations in thalamocortical functional connectivity were reported by functional magnetic resonance imaging (fMRI) studies. Thalamocortical dysconnectivity and its clinical relevance are well-characterized in psychotic states, particularly in schizophrenia research. Specifically, studies report hyperconnectivity between the thalamus and sensorimotor cortices and hypoconnectivity between the thalamus and prefrontal cortices, associated with patients' psychotic symptoms and cognitive disturbances, respectively. Intriguingly, studies also report hyperconnectivity between the thalamus and sensorimotor cortices in psychedelic states, correlating with altered visual and auditory perceptions. Taken together, the two altered states appear to share clinically and functionally relevant dysconnectivity patterns. In this review we discuss recent findings of thalamocortical dysconnectivity, its putative extension to CSPTC circuitry, along with its clinical implications and future directions.

Pattern classification as decision support tool in antipsychotic treatment algorithms.

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.

Effect of gender on transition of normo- to microalbuminuria under angiotensin receptor blocker therapy in diabetes.

Highlights In normoalbuminuric diabetic patients at low cardiovascular risk, the risk of transition from normo- to microalbuminuria is lower in women, despite the nonprotective effects of the angiotensin receptor blocker olmesartan. Additional methods of assessment of albuminuria in clinical studies (eg, measurements of albumin and creatinine excretion rate) should be implemented or the actually accepted higher urine albumin creatinine ratio (UACR) cutoff values for microalbuminuria in women reconsidered.

Influence of pre-treatment blood pressure levels on antihypertensive drug benefits in diabetics: the roadmap experience.

Purpose: Most guidelines for treatment of hypertension in the setting of diabetes recommend a blood pressure (BP) target of <130/80 mmHg. However, uncertainty exists about the extent, effectiveness and safety of lowering BP in diabetics. To expand the evidence on this issue, we analysed data from the Randomised Olmesartan and Diabetes MicroAlbuminuria Prevention (ROADMAP) study population.Material: Substudy with blood pressure readings.Methods: The response after initiation of therapy and adequacy of BP control across patients with different BP levels at baseline were analysed.Results: BP at randomisation was 136.2(15.3)/80.6(9.5) [mean (SD)] mmHg with a range of 87-213/37-123 mmHg. At 1 year, mean BP was 127 (11.9)/75 (8.1) mmHg and the overall control rate (<130/80 mmHg) exceeded 61% in this population. The mean reductions in systolic [-9.4 (15.4) mmHg] and diastolic BP [-5.4 (9.5) mmHg] were highly dependent on the BP stage at Visit 1. At 1 year, treatment decreased the prevalence of patients with baseline BP levels of >160/100 from 9 to 2%[[mean BP change -31 (15.7)/ -14 (9.8) mmHg]] and of 140-159/90-99 mmHg from 32 to 11% [[mean BP change -16(12.7)/ -8.9 (8.7) mmHg]], with corresponding increases in the prevalence of patients with baseline BP levels of 120-139/80-99 from 48 to 65% [[mean BP change -4.1 (10.6)/ -3.1 (7.8) mmHg]]and of <120/80 from 11 to 22% [[mean BP change +5.9 (11.8)/+2.5 (8.6) mmHg]]. These effects did not change significantly thereafter and were maintained throughout follow-up.Conclusion: Blood pressure control is feasible in patients with diabetes without nephropathy, independent of baseline BP values. Asymmetric BP-lowering in the first year after starting therapy represents a true antihypertensive effect with sustainable shifts in BP severity.

Discrete states of attention during active visual fixation revealed by Markovian analysis of the time series of intrusive saccades.

The frequency of intrusive saccades during maintenance of active visual fixation has been used as a measure of sustained visual attention in studies of healthy subjects as well as of neuropsychiatric patient populations. In this study, the mechanism that generates intrusive saccades during active visual fixation was investigated in a population of young healthy men performing three sustained fixation tasks (fixation to a visual target, fixation to a visual target with visual distracters, and fixation straight ahead in the dark). Markov Chain modeling of inter-saccade intervals (ISIs) was utilized. First- and second-order Markov modeling provided indications for the existence of a non-random pattern in the production of intrusive saccades. Accordingly, the system of intrusive saccade generation may operate in two "attractor" states, one in which intrusive saccades occur at short consecutive ISIs and another in which intrusive saccades occur at long consecutive ISIs. These states might correspond to two distinct states of the attention system, one of low focused - high distractibility and another of high focused - low distractibility, such as those proposed in the adaptive gain theory for the control of attention by the noradrenergic system in the brain. To the authors knowledge, this is the first time that Markov Chain modeling has been applied to the analysis of the ISIs of intrusive saccades.

Automatic identification of oculomotor behavior using pattern recognition techniques.

In this paper, a methodological scheme for identifying distinct patterns of oculomotor behavior such as saccades, microsaccades, blinks and fixations from time series of eye's angular displacement is presented. The first step of the proposed methodology involves signal detrending for artifacts removal and estimation of eye's angular velocity. Then, feature vectors from fourteen first-order statistical features are formed from each angular displacement and velocity signal using sliding, fixed-length time windows. The obtained feature vectors are used for training and testing three artificial neural network classifiers, connected in cascade. The three classifiers discriminate between blinks and non-blinks, fixations and non-fixations and saccades and microsaccades, respectively. The proposed methodology was tested on a dataset from 1392 subjects, each performing three oculomotor fixation conditions. The average overall accuracy of the three classifiers, with respect to the manual identification of eye movements by experts, was 95.9%. The proposed methodological scheme provided better results than the well-known Velocity Threshold algorithm, which was used for comparison. The findings of the present study indicate that the utilization of pattern recognition techniques in the task of identifying the various eye movements may provide accurate and robust results.