Ketamine induces multiple individually distinct whole-brain functional connectivity signatures.

Background: Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine's molecular mechanisms connect to its neural and behavioral effects. Methods: We conducted a single-blind placebo-controlled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified resting-state functional connectivity via data-driven global brain connectivity and related it to individual ketamine-induced symptom variation and cortical gene expression targets. Results: We found that: (i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; (ii) ketamine's data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level. Conclusions: These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry. Funding: This study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1-190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016-0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 - 2056) (FXV). Clinical trial number: NCT03842800.

Ketamine is a widely used anesthetic as well as a popular illegal recreational drug. Recently, it has also gained attention as a potential treatment for depression, particularly in cases that don't respond to conventional therapies. However, individuals can vary in their response to ketamine. For example, the drug can alter some people's perception, such as seeing objects as larger or small than they are, while other individuals are unaffected. Although a single dose of ketamine was shown to improve depression symptoms in approximately 65% of patients, the treatment does not work for a significant portion of patients. Understanding why ketamine does not work for everyone could help to identify which patients would benefit most from the treatment. Previous studies investigating ketamine as a treatment for depression have typically included a group of individuals given ketamine and a group given a placebo drug. Assuming people respond similarly to ketamine, the responses in each group were averaged and compared to one another. However, this averaging of results may have masked any individual differences in response to ketamine. As a result, Moujaes et al. set out to investigate whether individuals show differences in brain activity and behavior in response to ketamine. Moujaes et al. monitored the brain activity and behavior of 40 healthy individuals that were first given a placebo drug and then ketamine. The results showed that brain activity and behavior varied significantly between individuals after ketamine administration. Genetic analysis revealed that different gene expression patterns paired with differences in ketamine response in individuals ­ an effect that was hidden when the results were averaged. Ketamine also caused greater differences in brain activity and behavior between individuals than other drugs, such as psychedelics, suggesting ketamine generates a particularly complex response in people. In the future, extending these findings in healthy individuals to those with depression will be crucial for determining whether differences in response to ketamine align with how effective ketamine treatment is for an individual.

Human brain state dynamics reflect individual neuro-phenotypes.

Neural activity and behavior manifest state and trait dynamics, as well as variation within and between individuals. However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

The spatial extent of anatomical connections within the thalamus varies across the cortical hierarchy in humans and macaques.

Each cortical area has a distinct pattern of anatomical connections within the thalamus, a central subcortical structure composed of functionally and structurally distinct nuclei. Previous studies have suggested that certain cortical areas may have more extensive anatomical connections that target multiple thalamic nuclei, which potentially allows them to modulate distributed information flow. However, there is a lack of quantitative investigations into anatomical connectivity patterns within the thalamus. Consequently, it remains unknown if cortical areas exhibit systematic differences in the extent of their anatomical connections within the thalamus. To address this knowledge gap, we used diffusion magnetic resonance imaging (dMRI) to perform brain-wide probabilistic tractography for 828 healthy adults from the Human Connectome Project. We then developed a framework to quantify the spatial extent of each cortical area's anatomical connections within the thalamus. Additionally, we leveraged resting-state functional MRI, cortical myelin, and human neural gene expression data to test if the extent of anatomical connections within the thalamus varied along the cortical hierarchy. Our results revealed two distinct corticothalamic tractography motifs: 1) a sensorimotor cortical motif characterized by focal thalamic connections targeting posterolateral thalamus, associated with fast, feed-forward information flow; and 2) an associative cortical motif characterized by diffuse thalamic connections targeting anteromedial thalamus, associated with slow, feed-back information flow. These findings were consistent across human subjects and were also observed in macaques, indicating cross-species generalizability. Overall, our study demonstrates that sensorimotor and association cortical areas exhibit differences in the spatial extent of their anatomical connections within the thalamus, which may support functionally-distinct cortico-thalamic information flow.

Illness Phase as a Key Assessment and Intervention Window for Psychosis.

The phenotype of schizophrenia, regardless of etiology, represents the most studied psychotic disorder with respect to neurobiology and distinct phases of illness. The early phase of illness represents a unique opportunity to provide effective and individualized interventions that can alter illness trajectories. Developmental age and illness stage, including temporal variation in neurobiology, can be targeted to develop phase-specific clinical assessment, biomarkers, and interventions. We review an earlier model whereby an initial glutamate signaling deficit progresses through different phases of allostatic adaptation, moving from potentially reversible functional abnormalities associated with early psychosis and working memory dysfunction, and ending with difficult-to-reverse structural changes after chronic illness. We integrate this model with evidence of dopaminergic abnormalities, including cortical D1 dysfunction, which develop during adolescence. We discuss how this model and a focus on a potential critical window of intervention in the early stages of schizophrenia impact the approach to research design and clinical care. This impact includes stage-specific considerations for symptom assessment as well as genetic, cognitive, and neurophysiological biomarkers. We examine how phase-specific biomarkers of illness phase and brain development can be incorporated into current strategies for large-scale research and clinical programs implementing coordinated specialty care. We highlight working memory and D1 dysfunction as early treatment targets that can substantially affect functional outcome.

QuNex-An integrative platform for reproducible neuroimaging analytics.

Introduction: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods: To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results: The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion: Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns.

Importance: Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective: To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment. Design: Double blind randomized controlled trial. Setting: Multicenter. Participants: Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Interventions: Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2). Main Outcomes and Measures: To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group. Results: 192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group. Conclusions and Relevance: Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

Dopamine D1R Receptor Stimulation as a Mechanistic Pro-cognitive Target for Schizophrenia.

Decades of research have highlighted the importance of optimal stimulation of cortical dopaminergic receptors, particularly the D1R receptor (D1R), for prefrontal-mediated cognition. This mechanism is particularly relevant to the cognitive deficits in schizophrenia, given the abnormalities in cortical dopamine (DA) neurotransmission and in the expression of D1R. Despite the critical need for D1R-based therapeutics, many factors have complicated their development and prevented this important therapeutic target from being adequately interrogated. Challenges include determination of the optimal level of D1R stimulation needed to improve cognitive performance, especially when D1R expression levels, affinity states, DA levels, and the resulting D1R occupancy by DA, are not clearly known in schizophrenia, and may display great interindividual and intraindividual variability related to cognitive states and other physiological variables. These directly affect the selection of the level of stimulation necessary to correct the underlying neurobiology. The optimal mechanism for stimulation is also unknown and could include partial or full agonism, biased agonism, or positive allosteric modulation. Furthermore, the development of D1R targeting drugs has been complicated by complexities in extrapolating from in vitro affinity determinations to in vivo use. Prior D1R-targeted drugs have been unsuccessful due to poor bioavailability, pharmacokinetics, and insufficient target engagement at tolerable doses. Newer drugs have recently become available, and these must be tested in the context of carefully designed paradigms that address methodological challenges. In this paper, we discuss how a better understanding of these challenges has shaped our proposed experimental design for testing a new D1R/D5R partial agonist, PF-06412562, renamed CVL-562.

Mapping brain-behavior space relationships along the psychosis spectrum.

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 PSD patients spanning several diagnoses, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results with current sample sizes. However, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and neural gene expression maps derived from the Allen Human Brain Atlas. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.

Advancing clinical response characterization to frontotemporal transcranial direct current stimulation with electric field distribution in patients with schizophrenia and auditory hallucinations: a pilot study.

Transcranial direct current stimulation (tDCS) has been proposed as a therapeutic option for treatment-resistant auditory verbal hallucinations (AVH) in schizophrenia. In such cases, repeated sessions of tDCS are delivered with the anode over the left prefrontal cortex and the cathode over the left temporoparietal junction. Despite promising findings, the clinical response to tDCS is highly heterogeneous among patients. Here, we explored baseline differences between responders and nonresponders to frontotemporal tDCS using electric field modeling. We hypothesized that responders would display different tDCS-induced electric field strength in the brain areas involved in AVH compared to nonresponders.Using baseline structural MRI scans of 17 patients with schizophrenia and daily AVH who received 10 sessions of active frontotemporal tDCS, we constructed individual realistic whole brain models estimating electric field strength. Electric field maps were compared between responders (n = 6) and nonresponders to tDCS (n = 11) using an independent two-sample t test. Clinical response was defined as at least a 50% decrease of AVH 1 month after the last tDCS session.Results from the electric field map comparison showed that responders to tDCS displayed higher electric field strength in the left transverse temporal gyrus at baseline compared to nonresponders (T = 2.37; p = 0.016; 32 voxels).These preliminary findings suggested that the strength of the tDCS-induced electric field reaching the left transverse temporal gyrus could play an important role in the response to frontotemporal tDCS. In addition, this work suggests the interest of using electric field modeling to individualize tDCS and increase response rate.

Bayesian Estimation of the ntPET Model in Single-Scan Competition PET Studies.

This paper proposes an innovative method, named b-ntPET, for solving a competition model in PET. The model is built upon the state-of-the-art method called lp-ntPET. It consists in identifying the parameters of the PET kinetic model relative to a reference region that rule the steady state exchanges, together with the identification of four additional parameters defining a displacement curve caused by an endogenous neurotransmitter discharge, or by a competing injected drug targeting the same receptors as the PET tracer. The resolution process of lp-ntPET is however suboptimal due to the use of discretized basis functions, and is very sensitive to noise, limiting its sensitivity and accuracy. Contrary to the original method, our proposed resolution approach first estimates the probability distribution of the unknown parameters using Markov-Chain Monte-Carlo sampling, distributions from which the estimates are then inferred. In addition, and for increased robustness, the noise level is jointly estimated with the parameters of the model. Finally, the resolution is formulated in a Bayesian framework, allowing the introduction of prior knowledge on the parameters to guide the estimation process toward realistic solutions. The performance of our method was first assessed and compared head-to-head with the reference method lp-ntPET using well-controlled realistic simulated data. The results showed that the b-ntPET method is substantially more robust to noise and much more sensitive and accurate than lp-ntPET. We then applied the model to experimental animal data acquired in pharmacological challenge studies and human data with endogenous releases induced by transcranial direct current stimulation. In the drug challenge experiment on cats using [18F]MPPF, a serotoninergic 1A antagonist radioligand, b-ntPET measured a dose response associated with the amount of the challenged injected concurrent 5-HT1A agonist, where lp-ntPET failed. In human [11C]raclopride experiment, contrary to lp-ntPET, b-ntPET successfully detected significant endogenous dopamine releases induced by the stimulation. In conclusion, our results showed that the proposed method b-ntPET has similar performance to lp-ntPET for detecting displacements, but with higher resistance to noise and better robustness to various experimental contexts. These improvements lead to the possibility of detecting and characterizing dynamic drug occupancy from a single PET scan more efficiently.

Sham tDCS: A hidden source of variability? Reflections for further blinded, controlled trials.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique increasingly used to modulate neural activity in the living brain. In order to establish the neurophysiological, cognitive or clinical effects of tDCS, most studies compare the effects of active tDCS to those observed with a sham tDCS intervention. In most cases, sham tDCS consists in delivering an active stimulation for a few seconds to mimic the sensations observed with active tDCS and keep participants blind to the intervention. However, to date, sham-controlled tDCS studies yield inconsistent results, which might arise in part from sham inconsistencies. Indeed, a multiplicity of sham stimulation protocols is being used in the tDCS research field and might have different biological effects beyond the intended transient sensations. Here, we seek to enlighten the scientific community to this possible confounding factor in order to increase reproducibility of neurophysiological, cognitive and clinical tDCS studies.

Frontal Transcranial Direct Current Stimulation Induces Dopamine Release in the Ventral Striatum in Human.

A single transcranial direct current stimulation (tDCS) session applied over the dorsolateral prefrontal cortex (DLFPC) can be associated with procognitive effects. Furthermore, repeated DLPFC tDCS sessions are under investigation as a new therapeutic tool for a range of neuropsychiatric conditions. A possible mechanism explaining such beneficial effects is a modulation of meso-cortico-limbic dopamine transmission. We explored the spatial and temporal neurobiological effects of bifrontal tDCS on subcortical dopamine transmission during and immediately after the stimulation. In a double blind sham-controlled study, 32 healthy subjects randomly received a single session of either active (20 min, 2 mA; n = 14) or sham (n = 18) tDCS during a dynamic positron emission tomography scan using [11C]raclopride binding. During the stimulation period, no significant effect of tDCS was observed. After the stimulation period, compared with sham tDCS, active tDCS induced a significant decrease in [11C]raclopride binding potential ratio in the striatum, suggesting an increase in extracellular dopamine in a part of the striatum involved in the reward-motivation network. The present study provides the first evidence that bifrontal tDCS induces neurotransmitter release in polysynaptic connected subcortical areas. Therefore, levels of dopamine activity and reactivity should be a new element to consider for a general hypothesis of brain modulation by bifrontal tDCS.

N-Acetyl-Aspartate in the dorsolateral prefrontal cortex in men with schizophrenia and auditory verbal hallucinations: A 1.5 T Magnetic Resonance Spectroscopy Study.

Auditory verbal hallucinations (AVH) in patients with schizophrenia are linked to abnormalities within a large cerebral network including frontal and temporal regions. Whilst abnormalities of frontal speech production and temporal speech perception regions have been extensively studied, alterations of the dorsolateral prefrontal cortex (DLPFC), a region critically involved in the pathophysiology of schizophrenia, have rarely been studied in relation to AVH. Using 1.5 T proton magnetic resonance spectroscopy, this study examined the relationship between right and left DLPFCs N-AcetylAspartate (NAA) levels and the severity of AVH in patients with schizophrenia. Twenty-seven male patients with schizophrenia were enrolled in this study, 15 presented daily treatment-resistant AVH (AVH+) and 12 reported no AVH (no-AVH). AVH+ patients displayed higher NAA levels in the right DLPFC than no-AVH patients (p = 0.033). In AVH+ patients, NAA levels were higher in the right DLPFC than in the left (p = 0.024). No difference between the right and left DLPFC was observed in no-AVH patients. There was a positive correlation between NAA levels in the right DLPFC and the severity of AVH (r = 0.404, p = 0.037). Despite limited by magnetic field strength, these results suggest that AVH may be associated with increased NAA levels in the right DLPFC in schizophrenia.

Integrity of the arcuate fasciculus in patients with schizophrenia with auditory verbal hallucinations: A DTI-tractography study.

Auditory verbal hallucinations (AVH) of schizophrenia are associated with a disrupted connectivity between frontal and temporoparietal language areas. We hypothesized that this dysconnectivity is underpinned by white matter abnormalities in the left arcuate fasciculus, the main fiber bundle connecting speech production and perception areas. We therefore investigated the relationship between AVH severity and the integrity of the arcuate fasciculus measured by diffusion tensor imaging (DTI) tractography in patients with schizophrenia. Thirty-eight patients with treatment-resistant schizophrenia were included: 26 presented with daily severe treatment-resistant AVH, 12 reported prominent negative symptoms and no AVH. Fractional anisotropy (FA) was measured along the length of the left and right anterior arcuate fasciculi and severity of AVH was assessed using P3 PANSS item. FA values were significantly higher in the left arcuate fasciculus in patients with AVH than in no AVH patients (F(1,35) = 3.86; p = 0.05). No difference was observed in the right arcuate fasciculus. There was a significant positive correlation between FA value in the left arcuate fasciculus and the severity of AVH (r = 0.36; p = 0.02). No correlation was observed between FA values and PANSS total score suggesting a specific relationship between AVH severity and the left arcuate fasciculus integrity. These results support the hypothesis of a relationship between left frontotemporal connectivity and AVH in patients with schizophrenia and suggest that whilst a disruption of frontotemporal connectivity might be present to ensure the emergence of AVH, more severe anatomical alterations may prevent the occurrence of AVH in patients with schizophrenia.

[Does Prefrontal Noninvasive Brain Stimulation Alleviating Symptoms in Depression and Schizophrenia Impact Mood and Emotion Processing?]. / Neurostimulation du cortex préfrontal dorsolatéral : quels effets sur la symptomatologie, l'humeur et les émotions dans la dépression et la schizophrénie ?

Repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are noninvasive brain stimulation techniques currently used as therapeutic tools in various psychiatric conditions. Applied over the dorsolateral prefrontal cortex (DLPFC), they showed their efficacy in reducing drug-resistant symptoms in patients with major depression and in patients with schizophrenia with predominantly negative symptoms. The DLPFC is a brain structure involved in the expression of these symptoms as well as in other dysfunctional functions observed in theses conditions such as emotional processes. The goal of this review is to establish whether or not a link exists between clinical improvements and modulation of emotional processes following the stimulation of the DLPFC in both conditions. The data collected show that improved emotional processes is not linked to a clinical improvement neither in patients with depression nor in patients with negative schizophrenia. Our results suggests that although sharing common brain structures, the brain networks involved in both symptoms and in emotional processes would be separate.

Brain dynamics of distractibility: interaction between top-down and bottom-up mechanisms of auditory attention.

Attention improves the processing of specific information while other stimuli are disregarded. A good balance between bottom-up (attentional capture by unexpected salient stimuli) and top-down (selection of relevant information) mechanisms is crucial to be both task-efficient and aware of our environment. Only few studies have explored how an isolated unexpected task-irrelevant stimulus outside the attention focus can disturb the top-down attention mechanisms necessary to the good performance of the ongoing task, and how these top-down mechanisms can modulate the bottom-up mechanisms of attentional capture triggered by an unexpected event. We recorded scalp electroencephalography in 18 young adults performing a new paradigm measuring distractibility and assessing both bottom-up and top-down attention mechanisms, at the same time. Increasing task load in top-down attention was found to reduce early processing of the distracting sound, but not bottom-up attentional capture mechanisms nor the behavioral distraction cost in reaction time. Moreover, the impact of bottom-up attentional capture by distracting sounds on target processing was revealed as a delayed latency of the N100 sensory response to target sounds mirroring increased reaction times. These results provide crucial information into how bottom-up and top-down mechanisms dynamically interact and compete in the human brain, i.e. on the precarious balance between voluntary attention and distraction.