Cerebral blood flow predicts differential neurotransmitter activity

Application of metabolic magnetic resonance imaging measures such as cerebral blood flow in translational medicine is limited by the unknown link of observed alterations to specific neurophysiological processes. In particular, the sensitivity of cerebral blood flow to activity changes in specific neurotransmitter systems remains unclear. We address this question by probing cerebral blood flow in healthy volunteers using seven established drugs with known dopaminergic, serotonergic, glutamatergic and GABAergic mechanisms of action. We use a novel framework aimed at disentangling the observed effects to contribution from underlying neurotransmitter systems. We find for all evaluated compounds a reliable spatial link of respective cerebral blood flow changes with underlying neurotransmitter receptor densities corresponding to their primary mechanisms of action. The strength of these associations with receptor density is mediated by respective drug affinities. These findings suggest that cerebral blood flow is a sensitive brain-wide in-vivo assay of metabolic demands across a variety of neurotransmitter systems in humans

Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p \u3c 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine

Translating Glutamate: From Pathophysiology to Treatment

The neurotransmitter glutamate is the primary excitatory neurotransmitter in mammalian brain and is responsible for most corticocortical and corticofugal neurotransmission. Disturbances in glutamatergic function have been implicated in the pathophysiology of several neuropsychiatric disorders—including schizophrenia, drug abuse and addiction, autism, and depression—that were until recently poorly understood. Nevertheless, improvements in basic information regarding these disorders have yet to translate into Food and Drug Administration–approved treatments. Barriers to translation include the need not only for improved compounds but also for improved biomarkers sensitive to both structural and functional target engagement and for improved translational models. Overcoming these barriers will require unique collaborative arrangements between pharma, government, and academia. Here, we review a recent Institute of Medicine–sponsored meeting, highlighting advances in glutamatergic theories of neuropsychiatric illness as well as remaining barriers to treatment development.National Institute of Mental Health (U.S.) (grant R37MH49334)National Institute of Mental Health (U.S.) (Intramural Research Program)National Institute of Mental Health (U.S.) (R01DA03383)National Institute of Mental Health (U.S.) (P50MH086385)National Institutes of Health (U.S.)FRAXA Research FoundationHoward Hughes Medical InstituteSimons Foundatio

Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring

Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)–mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia

Monitoring of Serum DNA Methylation as an Early Independent Marker of Response and Survival in Metastatic Breast Cancer: TBCRC 005 Prospective Biomarker Study

Epigenetic alterations measured in blood may help guide breast cancer treatment. The multisite prospective study TBCRC 005 was conducted to examine the ability of a novel panel of cell-free DNA methylation markers to predict survival outcomes in metastatic breast cancer (MBC) using a new quantitative multiplex assay (cMethDNA)

Effects of Metabotropic Glutamate Receptor 3 Genotype on Phonetic Mismatch Negativity

BACKGROUND: The genetic and molecular basis of glutamatergic dysfunction is one key to understand schizophrenia, with the identification of an intermediate phenotype being an essential step. Mismatch negativity (MMN) or its magnetic counterpart, magnetic mismatch field (MMF) is an index of preattentive change detection processes in the auditory cortex and is generated through glutamatergic neurotransmission. We have previously shown that MMN/MMF in response to phoneme change is markedly reduced in schizophrenia. Variations in metabotropic glutamate receptor (GRM3) may be associated with schizophrenia, and has been shown to affect cortical function. Here we investigated the effect of GRM3 genotypes on phonetic MMF in healthy men. METHODS: MMF in response to phoneme change was recorded using magnetoencephalography in 41 right-handed healthy Japanese men. Based on previous genetic association studies in schizophrenia, 4 candidate SNPs (rs6465084, rs2299225, rs1468412, rs274622) were genotyped. RESULTS: GRM3 rs274622 genotype variations significantly predicted MMF strengths (p = 0.009), with C carriers exhibiting significantly larger MMF strengths in both hemispheres compared to the TT subjects. CONCLUSIONS: These results suggest that variations in GRM3 genotype modulate the auditory cortical response to phoneme change in humans. MMN/MMF, particularly those in response to speech sounds, may be a promising and sensitive intermediate phenotype for clarifying glutamatergic dysfunction in schizophrenia

Neurophysiologic Markers of Abnormal Brain Activity in Schizophrenia

Cortical electrophysiologic event-related potentials are multidimensional measures of information processing that are well-suited for efficiently parsing automatic and controlled components of cognition that span the range of deficits evidenced in schizophrenia patients. These information processes are key cognitive measures that are recognized as informative and valid targets for understanding the neurobiology of schizophrenia. These measures may be used in concert with the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) neurocognitive measures in the development of novel treatments for schizophrenia and related neuropsychiatric disorders. The employment of novel event-related potential paradigms designed to carefully characterize the early spectrum of perceptual and cognitive information processing allows investigators to identify the neurophysiologic basis of cognitive dysfunction in schizophrenia and to examine the associated clinical and functional impairments