Frontostriatal salience network expansion in individuals in depression.

Decades of neuroimaging studies have shown modest differences in brain structure and connectivity in depression, hindering mechanistic insights or the identification of risk factors for disease onset1. Furthermore, whereas depression is episodic, few longitudinal neuroimaging studies exist, limiting understanding of mechanisms that drive mood-state transitions. The emerging field of precision functional mapping has used densely sampled longitudinal neuroimaging data to show behaviourally meaningful differences in brain network topography and connectivity between and in healthy individuals2-4, but this approach has not been applied in depression. Here, using precision functional mapping and several samples of deeply sampled individuals, we found that the frontostriatal salience network is expanded nearly twofold in the cortex of most individuals with depression. This effect was replicable in several samples and caused primarily by network border shifts, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was stable over time, unaffected by mood state and detectable in children before the onset of depression later in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in frontostriatal circuits that tracked fluctuations in specific symptoms and predicted future anhedonia symptoms. Together, these findings identify a trait-like brain network topology that may confer risk for depression and mood-state-dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Simple and Scalable Algorithms for Cluster-Aware Precision Medicine.

AI-enabled precision medicine promises a transformational improvement in healthcare outcomes. However, training on biomedical data presents significant challenges as they are often high dimensional, clustered, and of limited sample size. To overcome these challenges, we propose a simple and scalable approach for cluster-aware embedding that combines latent factor methods with a convex clustering penalty in a modular way. Our novel approach overcomes the complexity and limitations of current joint embedding and clustering methods and enables hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through numerical experiments and real-world examples, we demonstrate that our approach outperforms fourteen clustering methods on highly underdetermined problems (e.g., with limited sample size) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, yields improved model selection if they do, and yields interpretable hierarchically clustered embedding dendrograms. Thus, our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data and enables scalable and interpretable biomarkers for precision medicine.

Mechanisms of Action of TMS in the Treatment of Depression.

Transcranial magnetic stimulation (TMS) is entering increasingly widespread use in treating depression. The most common stimulation target, in the dorsolateral prefrontal cortex (DLPFC), emerged from early neuroimaging studies in depression. Recently, more rigorous casual methods have revealed whole-brain target networks and anti-networks based on the effects of focal brain lesions and focal brain stimulation on depression symptoms. Symptom improvement during therapeutic DLPFC-TMS appears to involve directional changes in signaling between the DLPFC, subgenual and dorsal anterior cingulate cortex, and salience-network regions. However, different networks may be involved in the therapeutic mechanisms for other TMS targets in depression, such as dorsomedial prefrontal cortex or orbitofrontal cortex. The durability of therapeutic effects for TMS involves synaptic neuroplasticity, and specifically may depend upon dopamine acting at the D1 receptor family, as well as NMDA-receptor-dependent synaptic plasticity mechanisms. Although TMS protocols are classically considered 'excitatory' or 'inhibitory', the actual effects in individuals appear quite variable, and might be better understood at the level of populations of synapses rather than individual synapses. Synaptic meta-plasticity may provide a built-in protective mechanism to avoid runaway facilitation or inhibition during treatment, and may account for the relatively small number of patients who worsen rather than improve with TMS. From an ethological perspective, the antidepressant effects of TMS may involve promoting a whole-brain attractor state associated with foraging/hunting behaviors, centered on the rostrolateral periaqueductal gray and salience network, and suppressing an attractor state associated with passive threat defense, centered on the ventrolateral periaqueductal gray and default-mode network.

Elevating levels of the endocannabinoid 2-arachidonoylglycerol blunts opioid reward but not analgesia.

Converging findings have established that the endocannabinoid (eCB) system serves as a possible target for the development of new treatments for pain as a complement to opioid-based treatments. Here we show in male and female mice that enhancing levels of the eCB, 2-arachidonoylglycerol (2-AG), through pharmacological inhibition of its catabolic enzyme, monoacylglycerol lipase (MAGL), either systemically or in the ventral tegmental area (VTA) with JZL184, leads to a substantial attenuation of the rewarding effects of opioids in male and female mice using conditioned place preference and self-administration paradigms, without altering their analgesic properties. These effects are driven by CB1 receptors (CB1Rs) within the VTA as VTA CB1R conditional knockout, counteracts JZL184's effects. Conversely, pharmacologically enhancing the levels of the other eCB, anandamide (AEA), by inhibition of fatty acid amide hydrolase (FAAH) has no effect on opioid reward or analgesia. Using fiber photometry with fluorescent sensors for calcium and dopamine (DA), we find that enhancing 2-AG levels diminishes opioid reward-related nucleus accumbens (NAc) activity and DA neurotransmission. Together these findings reveal that 2-AG counteracts the rewarding properties of opioids and provides a potential adjunctive therapeutic strategy for opioid-related analgesic treatments.

Projection-Targeted Photopharmacology Reveals Distinct Anxiolytic Roles for Presynaptic mGluR2 in Prefrontal- and Insula-Amygdala Synapses.

Dissecting how membrane receptors regulate neural circuit function is critical for deciphering basic principles of neuromodulation and mechanisms of therapeutic drug action. Classical pharmacological and genetic approaches are not well-equipped to untangle the roles of specific receptor populations, especially in long-range projections which coordinate communication between brain regions. Here we use viral tracing, electrophysiological, optogenetic, and photopharmacological approaches to determine how presynaptic metabotropic glutamate receptor 2 (mGluR2) activation in the basolateral amygdala (BLA) alters anxiety-related behavior. We find that mGluR2-expressing neurons from the ventromedial prefrontal cortex (vmPFC) and posterior insular cortex (pIC) preferentially target distinct cell types and subregions of the BLA to regulate different forms of avoidant behavior. Using projection-specific photopharmacological activation, we find that mGluR2-mediated presynaptic inhibition of vmPFC-BLA, but not pIC-BLA, connections can produce long-lasting decreases in spatial avoidance. In contrast, presynaptic inhibition of pIC-BLA connections decreased social avoidance, novelty-induced hypophagia, and increased exploratory behavior without impairing working memory, establishing this projection as a novel target for the treatment of anxiety disorders. Overall, this work reveals new aspects of BLA neuromodulation with therapeutic implications while establishing a powerful approach for optical mapping of drug action via photopharmacology.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample.

BACKGROUND: Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample. METHODS: We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes. RESULTS: The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. CONCLUSIONS: Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

Combined small-molecule treatment accelerates maturation of human pluripotent stem cell-derived neurons.

The maturation of human pluripotent stem cell (hPSC)-derived neurons mimics the protracted timing of human brain development, extending over months to years for reaching adult-like function. Prolonged in vitro maturation presents a major challenge to stem cell-based applications in modeling and treating neurological disease. Therefore, we designed a high-content imaging assay based on morphological and functional readouts in hPSC-derived cortical neurons which identified multiple compounds that drive neuronal maturation including inhibitors of lysine-specific demethylase 1 and disruptor of telomerase-like 1 and activators of calcium-dependent transcription. A cocktail of four factors, GSK2879552, EPZ-5676, N-methyl-D-aspartate and Bay K 8644, collectively termed GENtoniK, triggered maturation across all parameters tested, including synaptic density, electrophysiology and transcriptomics. Maturation effects were further validated in cortical organoids, spinal motoneurons and non-neural lineages including melanocytes and pancreatic ß-cells. The effects on maturation observed across a broad range of hPSC-derived cell types indicate that some of the mechanisms controlling the timing of human maturation might be shared across lineages.

A stress-sensitive frontostriatal circuit supporting effortful reward-seeking behavior.

Effort valuation-a process for selecting actions based on the anticipated value of rewarding outcomes and expectations about the work required to obtain them-plays a fundamental role in decision-making. Effort valuation is disrupted in chronic stress states and is supported by the anterior cingulate cortex (ACC), but the circuit-level mechanisms by which the ACC regulates effort-based decision-making are unclear. Here, we show that ACC neurons projecting to the nucleus accumbens (ACC-NAc) play a critical role in effort valuation behavior in mice. Activity in ACC-NAc cells integrates both reward- and effort-related information, encoding a reward-related signal that scales with effort requirements and is necessary for supporting future effortful decisions. Chronic corticosterone exposure reduces motivation, suppresses effortful reward-seeking, and disrupts ACC-NAc signals. Together, our results delineate a stress-sensitive ACC-NAc circuit that supports effortful reward-seeking behavior by integrating reward and effort signals and reinforcing effort allocation in the service of maximizing reward.

Divergent reward cue representations in the prefrontal cortex drive reward motivation in adolescence and adulthood.

Alterations in motivation and reward-seeking are a transdiagnostic feature of numerous psychiatric disorders that commonly emerge in adolescence, including depression, obsessive- compulsive disorder, and attention deficit hyperactivity disorder. During adolescence, reward motivation is naturally heightened, compared to adulthood, but the underlying mechanisms are not well understood. The medial prefrontal cortex (mPFC) is a late developing brain region that regulates reward learning and motivation and is still maturing in adolescence. The mPFC modulates reward-motivated behaviors in adults, and has been hypothesized to be responsible for adolescents' inability to suppress reward-seeking and impulsive behaviors. Using 2-photon imaging of the mPFC and an active reward task, we demonstrate that both the adult and adolescent mPFC encode reward-predictive cues, with distinct neuronal populations encoding rewarded and unrewarded cues. In adolescence the mPFC is hyper-responsive to reward cues and recruits a larger population of neurons to encode reward predictive cues. Furthermore, in the adolescent mPFC, representations of unrewarded cues are attenuated, compared to the adult mPFC, which may tip the balance of action toward reward-seeking. Differences in neuronal responses to rewarded and unrewarded cues were observed in both GABAergic and glutamatergic neurons, with GABAergic inhibition causing disparate effects in adolescents compared to adults. Together our findings identify differences in the functional properties of mPFC microcircuits in adolescents that may underlie differences in reward-seeking behavior and the ability to adaptively suppress reward seeking.

Reattribution to Mind-Brain Processes and Recovery From Chronic Back Pain: A Secondary Analysis of a Randomized Clinical Trial.

Importance: In primary chronic back pain (CBP), the belief that pain indicates tissue damage is both inaccurate and unhelpful. Reattributing pain to mind or brain processes may support recovery. Objectives: To test whether the reattribution of pain to mind or brain processes was associated with pain relief in pain reprocessing therapy (PRT) and to validate natural language-based tools for measuring patients' symptom attributions. Design, Setting, and Participants: This secondary analysis of clinical trial data analyzed natural language data from patients with primary CBP randomized to PRT, placebo injection control, or usual care control groups and treated in a US university research setting. Eligible participants were adults aged 21 to 70 years with CBP recruited from the community. Enrollment extended from 2017 to 2018, with the current analyses conducted from 2020 to 2022. Interventions: PRT included cognitive, behavioral, and somatic techniques to support reattributing pain to nondangerous, reversible mind or brain causes. Subcutaneous placebo injection and usual care were hypothesized not to affect pain attributions. Main Outcomes and Measures: At pretreatment and posttreatment, participants listed their top 3 perceived causes of pain in their own words (eg, football injury, bad posture, stress); pain intensity was measured as last-week average pain (0 to 10 rating, with 0 indicating no pain and 10 indicating greatest pain). The number of attributions categorized by masked coders as reflecting mind or brain processes were summed to yield mind-brain attribution scores (range, 0-3). An automated scoring algorithm was developed and benchmarked against human coder-derived scores. A data-driven natural language processing (NLP) algorithm identified the dimensional structure of pain attributions. Results: We enrolled 151 adults (81 female [54%], 134 White [89%], mean [SD] age, 41.1 [15.6] years) reporting moderate severity CBP (mean [SD] intensity, 4.10 [1.26]; mean [SD] duration, 10.0 [8.9] years). At pretreatment, 41 attributions (10%) were categorized as mind- or brain-related across intervention conditions. PRT led to significant increases in mind- or brain-related attributions, with 71 posttreatment attributions (51%) in the PRT condition categorized as mind- or brain-related, as compared with 22 (8%) in control conditions (mind-brain attribution scores: PRT vs placebo, g = 1.95 [95% CI, 1.45-2.47]; PRT vs usual care, g = 2.06 [95% CI, 1.57-2.60]). Consistent with hypothesized PRT mechanisms, increases in mind-brain attribution score were associated with reductions in pain intensity at posttreatment (standardized ß = -0.25; t127 = -2.06; P = .04) and mediated the effects of PRT vs control on 1-year follow-up pain intensity (ß = -0.35 [95% CI, -0.07 to -0.63]; P = .05). The automated word-counting algorithm and human coder-derived scores achieved moderate and substantial agreement at pretreatment and posttreatment (Cohen κ = 0.42 and 0.68, respectively). The data-driven NLP algorithm identified a principal dimension of mind and brain vs biomechanical attributions, converging with hypothesis-driven analyses. Conclusions and Relevance: In this secondary analysis of a randomized trial, PRT increased attribution of primary CBP to mind- or brain-related causes. Increased mind-brain attribution was associated with reductions in pain intensity.

Expansion of a frontostriatal salience network in individuals with depression.

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Bidirectional Regulation of Motor Circuits Using Magnetogenetic Gene Therapy.

Regulating the activity of discrete neuronal populations in living mammals after delivery of modified ion channels can be used to map functional circuits and potentially treat neurological diseases. Here we report a novel suite of magnetogenetic tools, based on a single anti-ferritin nanobody-TRPV1 receptor fusion protein, which regulated neuronal activity in motor circuits when exposed to magnetic fields. AAV-mediated delivery of a cre-dependent nanobody-TRPV1 calcium channel into the striatum of adenosine 2a (A2a) receptor-cre driver mice led to restricted expression within D2 neurons, resulting in motor freezing when placed in a 3T MRI or adjacent to a transcranial magnetic stimulation (TMS) device. Functional imaging and fiber photometry both confirmed focal activation of the target region in response to the magnetic fields. Expression of the same construct in the striatum of wild-type mice along with a second injection of an AAVretro expressing cre into the globus pallidus led to similar circuit specificity and motor responses. Finally, a mutation was generated to gate chloride and inhibit neuronal activity. Expression of this variant in subthalamic nucleus (STN) projection neurons in PitX2-cre parkinsonian mice resulted in reduced local c-fos expression and a corresponding improvement in motor rotational behavior during magnetic field exposure. These data demonstrate that AAV delivery of magnetogenetic constructs can bidirectionally regulate activity of specific neuronal circuits non-invasively in vivo using clinically available devices for both preclinical analysis of circuit effects on behavior and potential human clinical translation.

Regulation of social interaction in mice by a frontostriatal circuit modulated by established hierarchical relationships.

Social hierarchies exert a powerful influence on behavior, but the neurobiological mechanisms that detect and regulate hierarchical interactions are not well understood, especially at the level of neural circuits. Here, we use fiber photometry and chemogenetic tools to record and manipulate the activity of nucleus accumbens-projecting cells in the ventromedial prefrontal cortex (vmPFC-NAcSh) during tube test social competitions. We show that vmPFC-NAcSh projections signal learned hierarchical relationships, and are selectively recruited by subordinate mice when they initiate effortful social dominance behavior during encounters with a dominant competitor from an established hierarchy. After repeated bouts of social defeat stress, this circuit is preferentially activated during social interactions initiated by stress resilient individuals, and plays a necessary role in supporting social approach behavior in subordinated mice. These results define a necessary role for vmPFC-NAcSh cells in the adaptive regulation of social interaction behavior based on prior hierarchical interactions.

A somato-cognitive action network alternates with effector regions in motor cortex.

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

Functional Connectivity Mapping for rTMS Target Selection in Depression.

OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) protocols increasingly use subgenual anterior cingulate cortex (sgACC) functional connectivity to individualize treatment targets. However, the efficacy of this approach is unclear, with conflicting findings and varying effect sizes across studies. Here, the authors investigated the effect of the stimulation site's functional connectivity with the sgACC (sgACC-StimFC) on treatment outcome to rTMS in 295 patients with major depression. METHODS: The reliability and accuracy of estimating sgACC functional connectivity were validated with data from individuals who underwent extensive functional MRI testing. Electric field modeling was used to analyze associations between sgACC-StimFC and clinical improvement using standardized assessments and to evaluate sources of heterogeneity. RESULTS: An imputation-based method provided reliable and accurate sgACC functional connectivity estimates. Treatment responses weakly but robustly correlated with sgACC-StimFC (r=-0.16), but only when the stimulated cortex was identified using electric field modeling. Surprisingly, this association was driven by patients with strong global signal fluctuations stemming from a specific periodic respiratory pattern (r=-0.49). CONCLUSIONS: Functional connectivity between the sgACC and the stimulated cortex was correlated with individual differences in treatment outcomes, but the association was weaker than those observed in previous studies and was accentuated in a subgroup of patients with distinct, respiration-related signal patterns in their scans. These findings indicate that in a large representative sample of patients with major depressive disorder, individual differences in sgACC-StimFC explained only ∼3% of the variance in outcomes, which may limit the utility of existing sgACC-based targeting protocols. However, these data also provide strong evidence for a true-albeit small-effect and highlight opportunities for incorporating additional functional connectivity measures to generate models of rTMS response with enhanced predictive power.

An automated platform for Assessing Working Memory and prefrontal circuit function.

Working memory is a process for actively maintaining and updating task-relevant information, despite interference from competing inputs, and is supported in part by sustained activity in prefrontal cortical pyramidal neurons and coordinated interactions with inhibitory interneurons, which may serve to regulate interference. Chronic stress has potent effects on working memory performance, possibly by interfering with these interactions or by disrupting long-range inputs from key upstream brain regions. Still, the mechanisms by which chronic stress disrupts working memory are not well understood, due in part to a need for scalable, easy-to-implement behavioral assays that are compatible with two-photon calcium imaging and other tools for recording from large populations of neurons. Here, we describe the development and validation of a platform that was designed specifically for automated, high-throughput assessments of working memory and simultaneous two-photon imaging in chronic stress studies. This platform is relatively inexpensive and easy to build; fully automated and scalable such that one investigator can test relatively large cohorts of animals concurrently; fully compatible with two-photon imaging, yet also designed to mitigate head-fixation stress; and can be easily adapted for other behavioral paradigms. Our validation data confirm that mice could be trained to perform a delayed response working memory task with relatively high-fidelity over the course of ∼15 days. Two-photon imaging data validate the feasibility of recording from large populations of cells during working memory tasks performance and characterizing their functional properties. Activity patterns in >70% of medial prefrontal cortical neurons were modulated by at least one task feature, and a majority of cells were engaged by multiple task features. We conclude with a brief literature review of the circuit mechanisms supporting working memory and their disruption in chronic stress states-highlighting directions for future research enabled by this platform.

Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder.

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.