Genomic decoding of neuronal depolarization by stimulus-specific NPAS4 heterodimers, bioRxiv, 2019-03-04

Cells regulate gene expression in response to salient external stimuli. In neurons, depolarization leads to the expression of inducible transcription factors (ITFs) that direct subsequent gene regulation. Depolarization encodes both neuronal action potential (AP) output and synaptic inputs, via excitatory postsynaptic potentials (EPSPs). However, it is unclear if different types of depolarizing signals can be transformed by an ITF into distinct modes of genomic regulation. Here, we show that APs and EPSPs in the murine hippocampus trigger two spatially segregated and molecularly distinct mechanisms that lead to the expression of the ITF NPAS4. These two pathways culminate in the assembly of unique, stimulus-specific NPAS4 heterodimers that exhibit distinctive DNA binding patterns. Thus, NPAS4 independently communicates increases in neuronal spiking output and synaptic inputs to the nucleus, enabling gene regulation to be tailored to the type of depolarizing activity experienced by a neuron.

biorxiv neuroscience 0-100-users 2019

Brain Aging in Major Depressive Disorder Results from the ENIGMA Major Depressive Disorder working group, bioRxiv, 2019-02-26

Background Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset. Methods We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted brain age and chronological age was calculated to indicate brain predicted age difference (brain-PAD). Findings On average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohen's d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004). Interpretation This highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates.

biorxiv neuroscience 100-200-users 2019

Task-evoked activity quenches neural correlations and variability in large-scale brain systems, bioRxiv, 2019-02-26

Many studies of large-scale neural systems have emphasized the importance of communication through increased inter-region correlations (functional connectivity) during task states relative to resting state. In contrast, local circuit studies have demonstrated that task states reduce correlations among local neural populations, likely enhancing their information coding. Here we sought to adjudicate between these conflicting perspectives, assessing whether large-scale system correlations tend to increase or decrease during task states. To establish a mechanistic framework for interpreting changes in neural correlations, we conceptualized neural populations as having a sigmoidal neural transfer function. In a computational model we found that this straightforward assumption predicts reductions in neural populations' dynamic output range as task-evoked activity levels increase, reducing responsiveness to inputs from other regions (i.e., reduced correlations). We demonstrated this empirically in large-scale neural populations across two highly distinct data sets human functional magnetic resonance imaging data and non-human primate spiking data. We found that task states increased global neural activity, while globally quenching neural variability and correlations. Further, this global reduction of neural correlations led to an overall increase in dimensionality (reflecting less information redundancy) during task states, providing an information-theoretic explanation for task-induced correlation reductions. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

biorxiv neuroscience 0-100-users 2019

Self-inactivating rabies viruses are just first-generation, ΔG rabies viruses, bioRxiv, 2019-02-19

A recent article in Cell reported a new form of modified rabies virus that was apparently capable of labeling neurons without adverse effects on neuronal physiology and circuit function. These self-inactivating rabies (SiR) viruses differed from the widely-used first-generation deletion-mutant (ΔG) rabies viruses only by the addition of a destabilization domain to the viral nucleoprotein. However, we observed that the transsynaptic tracing results from that article were inconsistent with the logic described in it, and we hypothesized that the viruses used were actually mutants that had lost the intended modification to the nucleoprotein. We obtained samples of two SiR viruses from the authors and show here that, in both SiR-CRE and SiR-FLPo, the great majority of viral particles were indeed mutants that had lost the intended modification and were therefore just first-generation, ΔG rabies viruses. We also found that SiR-CRE killed 70% of infected neurons in vivo within two weeks. We have shown elsewhere that a ΔG rabies virus encoding Cre can leave a large percentage of labeled neurons alive; we presume that Ciabatti et al. found such remaining neurons at long survival times and mistakenly concluded that they had developed a nontoxic version of rabies virus. Here we have analyzed only the two samples that were sent to MIT by Ciabatti et al., and these may not be from the same batches that were used for their paper. However, 1) both of the two viruses that we analyzed had independently lost the intended modification, 2) the mutations in the two samples were genetically quite distinct from each other yet in both cases caused the same result total or near-total loss of the C-terminal modification, and 3) the mutations that we found in these two virus samples perfectly explain the otherwise-paradoxical transsynaptic tracing results from Ciabatti et al.'s paper. We suggest that the SiR strategy, or any other such attempt to attenuate a virus by addition rather than deletion, is an inherently unstable approach that can easily be evaded by mutation, as it was in this case.

biorxiv neuroscience 100-200-users 2019

“Self-inactivating” rabies viruses are just first-generation, ΔG rabies viruses, bioRxiv, 2019-02-19

SUMMARYA recent article in Cell reported a new form of modified rabies virus that was apparently capable of labeling neurons “without adverse effects on neuronal physiology and circuit function”. These “self-inactivating” rabies (“SiR”) viruses differed from the widely-used first-generation deletion-mutant (ΔG) rabies viruses only by the addition of a destabilization domain to the viral nucleoprotein. However, we observed that the transsynaptic tracing results from that article were inconsistent with the logic described in it, and we hypothesized that the viruses used were actually mutants that had lost the intended modification to the nucleoprotein. We obtained samples of two SiR viruses from the authors and show here that, in both “SiR-CRE” and “SiR-FLPo”, the great majority of viral particles were indeed mutants that had lost the intended modification and were therefore just first-generation, ΔG rabies viruses. We also found that SiR-CRE killed 70% of infected neurons in vivo within two weeks. We have shown elsewhere that a ΔG rabies virus encoding Cre can leave a large percentage of labeled neurons alive; we presume that Ciabatti et al. found such remaining neurons at long survival times and mistakenly concluded that they had developed a nontoxic version of rabies virus. Here we have analyzed only the two samples that were sent to MIT by Ciabatti et al., and these may not be from the same batches that were used for their paper. However, 1) both of the two viruses that we analyzed had independently lost the intended modification, 2) the mutations in the two samples were genetically quite distinct from each other yet in both cases caused the same result total or near-total loss of the C-terminal modification, and 3) the mutations that we found in these two virus samples perfectly explain the otherwise-paradoxical transsynaptic tracing results from Ciabatti et al.’s paper. We suggest that the SiR strategy, or any other such attempt to attenuate a virus by addition rather than deletion, is an inherently unstable approach that can easily be evaded by mutation, as it was in this case.

biorxiv neuroscience 100-200-users 2019

Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior, bioRxiv, 2019-02-14

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here httpsgithub.comThomasYeoLabCBIGtreemasterstable_projectspreprocessingLi2019_GSR.

biorxiv neuroscience 100-200-users 2019