Biology subjects

Sanacora, G.

Publications and source records attributed to Sanacora, G..

6 recordsLinked to original sources

bioRxiv2026

Disordered brain circuits linked to diagnostic specificity and comorbidity revealed by multivariate symptom modeling

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

neuroscience↗

bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

neuroscience↗

bioRxiv2024

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

BackgroundSubstantial strides have been made in developing connectome-based predictive models that establish connections between external measures of cognition and/or symptoms obtained through testing performed in a clinical setting, and the human functional connectome. Often referred to as brain-behavior modeling such models offer insights into the functional brain organization supporting the test scores of these external measures1-7. Here, we depart from the conventional feed-forward approach and introduce a feed-back approach that provides new insight into the systems the external measures are reflecting and provides a framework for developing new test instruments that better target specific brain systems. MethodsIn fMRI data from 227 demographically and clinically diverse subjects (healthy participants and patients), we a priori define connectivity networks for the six cognitive constructs and employ kernel ridge regression in a predictive modeling framework to quantify each networks contribution to performance across a spectrum of standardized tests. ResultsThis approach provides a ranking of test scores according to the predictive power of each cognitive network, allowing one to choose the best test to probe a specific brain network. It yields a brain-driven process for forming new tests through selection of combinations of measures that probe the same brain systems. These new composite tests yield better external measures, as reflected by higher predictive power in brain-behavior modeling. We also evaluate the inclusion of specific subtests within a composite score, revealing instances where composite scores are reinforced or weakened by subtest inclusion regarding the specificity of the brain network they interrogate. ConclusionsThe brain-behavior modeling problem can be reconfigured to provide a biologically driven approach to the selection of external measures directed at specific brain systems. It opens new avenues of research by providing a framework for the development of measures, both cognitive and clinical, guided by quantitative brain metrics.

neuroscience↗

bioRxiv2023

Prefrontal Cortex Astroglia Modulate Anhedonia-like Behavior

Reductions of astroglia expressing glial fibrillary acidic protein (GFAP) are consistently found in the prefrontal cortex (PFC) of patients with depression and in rodent chronic stress models. Here, we examine the consequences of PFC GFAP+ cell depletion and cell activity enhancement on depressive-like behaviors in rodents. Using viral expression of diphtheria toxin receptor in PFC GFAP+ cells, which allows experimental depletion of these cells following diphtheria toxin administration, we demonstrated that PFC GFAP+ cell depletion induced anhedonia-like behavior within 2 days and lasting up to 8 days, but no anxiety-like deficits. Conversely, activating PFC GFAP+ cell activity for 3 weeks using designer receptor exclusively activated by designer drugs (DREADDs) reversed chronic restraint stress-induced anhedonia-like deficits, but not anxiety-like deficits. Our results highlight a critical role of cortical astroglia in the development of anhedonia and further support the idea of targeting astroglia for the treatment of depression.

neuroscience↗

bioRxiv2020

Prophylactic efficacy of riluzole against anxiety- and depressive-like behaviors in two rodent stress models

BackgroundChronic stress-related illnesses, such as major depressive disorder and post-traumatic stress disorder share symptomatology, including anxiety, anhedonia, and helplessness. Across disorders, neurotoxic dysregulated glutamate (Glu) signaling may underlie symptom emergence. Current first-line antidepressant drugs, which do not directly target Glu signaling, fail to provide adequate benefit for many patients and are associated with high relapse rates. Riluzole modulates glutamatergic neurotransmission by increasing metabolic cycling and modulating signal transduction. Clinical studies exploring riluzoles efficacy in stress-related disorders have provided varied results. However, the utility of riluzole for treating specific symptom dimensions or as a prophylactic treatment has not been comprehensively assessed. MethodsWe investigated whether chronic prophylactic riluzole (~12-15mg/kg/day p.o.) could prevent the emergence of behavioral deficits induced by unpredictable chronic mild stress (UCMS) in mice. We assessed: i) anxiety-like behavior using the elevated-plus maze, open field test, and novelty-suppressed feeding, ii) mixed anxiety/anhedonia-like behavior in the novelty-induced hypophagia test and, iii) anhedonia-like behavior using the sucrose consumption test. Z-scoring summarized changes across tests measuring similar dimensions. In a separate learned helplessness (LH) cohort, we investigated whether chronic prophylactic riluzole treatment could block the development of helplessness-like behavior. ResultsUCMS induced an elevation in anhedonia-like behavior, and overall behavioral emotionality that was blocked by prophylactic riluzole. In the LH cohort, prophylactic riluzole blocked the development of helplessness-like behavior. ConclusionThis study supports the utility of riluzole as a prophylactic medication for preventing anhedonia, and helplessness symptoms associated with stress-related disorders.

pharmacology and toxicology↗

bioRxiv2018

Rapamycin, an Immunosuppressant and mTORC1 Inhibitor, Triples the antidepressant Response Rate of Ketamine at 2 Weeks Following Treatment: A double-blind, placebo-controlled, cross-over, randomized clinical trial

BACKGROUNDKetamine exerts rapid and robust antidepressant effects thought to be mediated by activation of the mechanistic target of rapamycin complex 1 (mTORC1). To test this hypothesis, depressed patients were pretreated with rapamycin, an mTORC1 inhibitor, prior to receiving ketamine.\n\nMETHODSTwenty-three patients suffering a major depressive episode were randomized to oral rapamycin (6 mg) or placebo, each was followed 2 hours later by ketamine 0.5 mg/kg in a double-blind cross-over design with treatment days separated by at least 2 weeks. Depression severity was assessed using Montgomery-[A]sberg Depression Rating Scale (MADRS). Antidepressant response was defined as a MADRS improvement of 50% or more.\n\nRESULTSOver the two-week follow-up, we found a significant treatment by time interaction (F(8,245) = 2.02, p = 0.04), reflecting prolonged antidepressant effects post rapamycin+ketamine treatment. At 2 weeks, we found a significantly higher response rate following rapamycin+ketamine (41%) compared to placebo+ketamine (13%, p = 0.04). However, rapamycin pretreatment did not alter the acute effects of ketamine.\n\nCONCLUSIONUnexpectedly, pretreatment with rapamycin prolonged rather than blocked the acute antidepressant effects of ketamine. This observation raises questions about the role of mTORC1 in the antidepressant effects of keta-mine, raises the possibility that rapamycin may extend the benefits of ketamine, and thereby potentially sheds light on mechanisms that limit the duration of ketamine effects. The supplementing of ketamine with rapamycin may be a treat-ment strategy for reducing the frequency of ketamine infusions during maintenance treatment.

neuroscience↗