BrainImageR: spatiotemporal gene set analysis referencing the human brain.

Motivation: Neuronal analyses such as transcriptomics, epigenetics and genome-wide association studies must be assessed in the context of the human brain to generate biologically meaningful inferences. It is often difficult to access primary human brain tissue; therefore, approximations are made using alternative sources such as peripheral tissues or in vitro-derived neurons. Gene sets from these studies are then assessed for their association with the post-mortem human brain. However, most analyses of post-mortem datasets are achieved by building new computational tools each time in-house, which can cause discrepancies from study to study. The field is in need of a user-friendly tool to examine spatiotemporal expression with respect to the postmortem brain. Such a tool will be of use in the molecular interrogation of neurological and psychiatric disorders, with direct advantages for the disease-modeling and human genetics communities. Results: We have developed brainImageR, an R package that calculates both the spatial and temporal association of a dataset with post-mortem human brain. BrainImageR identifies anatomical regions enriched for candidate gene set expression. It further predicts the developmental time point of the sample, a task that has become increasingly important in the field of in vitro neuronal modeling. These functionalities of brainImageR enable a quick and efficient characterization of a given dataset across normal human brain development. Availability and implementation: BrainImageR is released under the Creative Commons CC BY-SA 4.0 license and can be accessed directly at brainimager.salk.edu or the R code can be downloaded through github at https://github.com/saralinker/brainImageR.

Catatonia in autism and other neurodevelopmental disabilities: a state-of-the-art review.

Individuals with neurodevelopmental disabilities (NDDs) may be at increased risk for catatonia, which can be an especially challenging condition to diagnose and treat. There may be symptom overlap between catatonia and NDD-associated behaviors, such as stereotypies. The diagnosis of catatonia should perhaps be adjusted to address symptom overlap and to include extreme behaviors observed in patients with NDDs, such as severe self-injury. Risk factors for catatonia in individuals with NDDs may include trauma and certain genetic variants, such as those that disrupt SHANK3. Common etiologic features between neurodevelopmental disabilities and catatonia, such as excitatory/inhibitory imbalance and neuroimmune dysfunction, may partially account for comorbidity. New approaches leveraging genetic testing and neuroimmunologic evaluation may allow for more precise diagnoses and effective treatments.

Combinatorial processing of bacterial and host-derived innate immune stimuli at the single-cell level.

During the course of a bacterial infection, cells are exposed simultaneously to a range of bacterial and host factors, which converge on the central transcription factor nuclear factor (NF)-κB. How do single cells integrate and process these converging stimuli? Here we tackle the question of how cells process combinatorial signals by making quantitative single-cell measurements of the NF-κB response to combinations of bacterial lipopolysaccharide and the stress cytokine tumor necrosis factor. We found that cells encode the presence of both stimuli via the dynamics of NF-κB nuclear translocation in individual cells, suggesting the integration of NF-κB activity for these stimuli occurs at the molecular and pathway level. However, the gene expression and cytokine secretion response to combinatorial stimuli were more complex, suggesting that other factors in addition to NF-κB contribute to signal integration at downstream layers of the response. Taken together, our results support the theory that during innate immune threat assessment, a pathogen recognized as both foreign and harmful will recruit an enhanced immune response. Our work highlights the remarkable capacity of individual cells to process multiple input signals and suggests that a deeper understanding of signal integration mechanisms will facilitate efforts to control dysregulated immune responses.

Dynamical Electrical Complexity Is Reduced during Neuronal Differentiation in Autism Spectrum Disorder.

Neuronal activity can be modeled as a nonlinear dynamical system to yield measures of neuronal state and dysfunction. The electrical recordings of stem cell-derived neurons from individuals with autism spectrum disorder (ASD) and controls were analyzed using minimum embedding dimension (MED) analysis to characterize their dynamical complexity. MED analysis revealed a significant reduction in dynamical complexity in ASD neurons during differentiation, which was correlated to bursting and spike interval measures. MED was associated with clinical endpoints, such as nonverbal intelligence, and was correlated with 53 differentially expressed genes, which were overrepresented with ASD risk genes related to neurodevelopment, cell morphology, and cell migration. Spatiotemporal analysis also showed a prenatal temporal enrichment in cortical and deep brain structures. Together, we present dynamical analysis as a paradigm that can be used to distinguish disease-associated cellular electrophysiological and transcriptional signatures, while taking into account patient variability in neuropsychiatric disorders.

Modeling psychiatric disorders using patient stem cell-derived neurons: a way forward.

Our understanding of the neurobiology of psychiatric disorders remains limited, and biomarker-based clinical management is yet to be developed. Induced pluripotent stem cell (iPSC) technology has revolutionized our capacity to generate patient-derived neurons to model psychiatric disorders. Here, we highlight advantages and caveats of iPSC disease modeling and outline strategies for addressing current challenges.

Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior.

Motivation for reward drives adaptive behaviors, whereas impairment of reward perception and experience (anhedonia) can contribute to psychiatric diseases, including depression and schizophrenia. We sought to test the hypothesis that the medial prefrontal cortex (mPFC) controls interactions among specific subcortical regions that govern hedonic responses. By using optogenetic functional magnetic resonance imaging to locally manipulate but globally visualize neural activity in rats, we found that dopamine neuron stimulation drives striatal activity, whereas locally increased mPFC excitability reduces this striatal response and inhibits the behavioral drive for dopaminergic stimulation. This chronic mPFC overactivity also stably suppresses natural reward-motivated behaviors and induces specific new brainwide functional interactions, which predict the degree of anhedonia in individuals. These findings describe a mechanism by which mPFC modulates expression of reward-seeking behavior, by regulating the dynamical interactions between specific distant subcortical regions.

Anomalous gray matter structural networks in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is characterized by abnormalities in structure, function, and connectivity in several brain regions. Few studies have examined how these regions are organized in the brain or investigated network-level structural aberrations that might be associated with depression. METHODS: We used graph analysis to examine the gray matter structural networks of individuals diagnosed with MDD (n = 93) and a demographically similar healthy comparison group (n = 151) with no history of psychopathology. The efficiency of structural networks for processing information was determined by quantifying local interconnectivity (clustering) and global integration (path length). We also compared the groups on the contributions of high-degree nodes (i.e., hubs) and regional network measures, including degree (number of connections in a node) and betweenness (fraction of short path connections in a node). RESULTS: Depressed participants had significantly decreased clustering in their brain networks across a range of network densities. Compared with control subjects, depressed participants had fewer hubs primarily in medial frontal and medial temporal areas, had higher degree in the left supramarginal gyrus and right gyrus rectus, and had higher betweenness in the right amygdala and left medial orbitofrontal gyrus. CONCLUSIONS: Networks of depressed individuals are characterized by a less efficient organization involving decreased regional connectivity compared with control subjects. Regional connections in the amygdala and medial prefrontal cortex may play a role in maintaining or adapting to depressive pathology. This is the first report of anomalous large-scale gray matter structural networks in MDD and provides new insights concerning the neurobiological mechanisms associated with this disorder.