Anatomical substructures of the human brain have characteristic cell-types, connectivity and local circuitry, which are reflected in area-specific transcriptome signatures, but the principles ...governing area-specific transcription and their relation to brain development are still being studied. In adult rodents, areal transcriptome patterns agree with the embryonic origin of brain regions, but the processes and genes that preserve an embryonic signature in regional expression profiles were not quantified. Furthermore, it is not clear how embryonic-origin signatures of adult-brain expression interplay with changes in expression patterns during development. Here we first quantify which genes have regional expression-patterns related to the developmental origin of brain regions, using genome-wide mRNA expression from post-mortem adult human brains. We find that almost all human genes (92%) exhibit an expression pattern that agrees with developmental brain-region ontology, but that this agreement changes at multiple phases during development. Agreement is particularly strong in neuron-specific genes, but also in genes that are not spatially correlated with neuron-specific or glia-specific markers. Surprisingly, agreement is also stronger in early-evolved genes. We further find that pairs of similar genes having high agreement to developmental region ontology tend to be more strongly correlated or anti-correlated, and that the strength of spatial correlation changes more strongly in gene pairs with stronger embryonic signatures. These results suggest that transcription regulation of most genes in the adult human brain is spatially tuned in a way that changes through life, but in agreement with development-determined brain regions.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Synaptic receptors in the human brain consist of multiple protein subunits, many of which have multiple variants, coded by different genes, and are differentially expressed across brain regions and ...developmental stages. The brain can tune the electrophysiological properties of synapses to regulate plasticity and information processing by switching from one protein variant to another. Such condition-dependent variant switch during development has been demonstrated in several neurotransmitter systems including NMDA and GABA. Here we systematically detect pairs of receptor-subunit variants that switch during the lifetime of the human brain by analyzing postmortem expression data collected in a population of donors at various ages and brain regions measured using microarray and RNA-seq. To further detect variant pairs that co-vary across subjects, we present a method to quantify age-corrected expression correlation in face of strong temporal trends. This is achieved by computing the correlations in the residual expression beyond a cubic-spline model of the population temporal trend, and can be seen as a nonlinear version of partial correlations. Using these methods, we detect multiple new pairs of context dependent variants. For instance, we find a switch from GLRA2 to GLRA3 that differs from the known switch in the rat. We also detect an early switch from HTR1A to HTR5A whose trends are negatively correlated and find that their age-corrected expression is strongly positively correlated. Finally, we observe that GRIN2B switch to GRIN2A occurs mostly during embryonic development, presumably earlier than observed in rodents. These results provide a systematic map of developmental switching in the neurotransmitter systems of the human brain.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The auditory system extracts behaviorally relevant information from acoustic stimuli. The average activity in auditory cortex is known to be sensitive to spectro-temporal patterns in sounds. However, ...it is not known whether the auditory cortex also processes more abstract features of sounds, which may be more behaviorally relevant than spectro-temporal patterns. Using recordings from three stations of the auditory pathway, the inferior colliculus (IC), the ventral division of the medial geniculate body (MGB) of the thalamus, and the primary auditory cortex (A1) of the cat in response to natural sounds, we compared the amount of information that spikes contained about two aspects of the stimuli: spectro-temporal patterns, and abstract entities present in the same stimuli such as a bird chirp, its echoes, and the ambient noise. IC spikes conveyed on average approximately the same amount of information about spectro-temporal patterns as they conveyed about abstract auditory entities, but A1 and the MGB neurons conveyed on average three times more information about abstract auditory entities than about spectro-temporal patterns. Thus, the majority of neurons in auditory thalamus and cortex coded well the presence of abstract entities in the sounds without containing much information about their spectro-temporal structure, suggesting that they are sensitive to abstract features in these sounds.
The transcriptome of the brain changes during development, reflecting processes that determine functional specialization of brain regions. We analyzed gene expression, measured using in situ ...hybridization across the full developing mouse brain, to quantify functional specialization of brain regions. Surprisingly, we found that during the time that the brain becomes anatomically regionalized in early development, transcription specialization actually decreases reaching a low, "neurotypic", point around birth. This decrease of specialization is brain-wide, and mainly due to biological processes involved in constructing brain circuitry. Regional specialization rises again during post-natal development. This effect is largely due to specialization of plasticity and neural activity processes. Post-natal specialization is particularly significant in the cerebellum, whose expression signature becomes increasingly different from other brain regions. When comparing mouse and human expression patterns, the cerebellar post-natal specialization is also observed in human, but the regionalization of expression in the human Thalamus and Cortex follows a strikingly different profile than in mouse.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Electrophysiological mass potentials show complex spectral changes upon neuronal activation. However, it is unknown to what extent these complex band-limited changes are interrelated or, ...alternatively, reflect separate neuronal processes. To address this question, intracranial electrocorticograms (ECoG) responses were recorded in patients engaged in visuomotor tasks. We found that in the 10- to 100-Hz frequency range there was a significant reduction in the exponent χ of the 1/f(χ) component of the spectrum associated with neuronal activation. In a minority of electrodes showing particularly high activations the exponent reduction was associated with specific band-limited power modulations: emergence of a high gamma (80-100 Hz) and a decrease in the alpha (9-12 Hz) peaks. Importantly, the peaks' height was correlated with the 1/f(χ) exponent on activation. Control simulation ruled out the possibility that the change in 1/f(χ) exponent was a consequence of the analysis procedure. These results reveal a new global, cross-frequency (10-100 Hz) neuronal process reflected in a significant reduction of the power spectrum slope of the ECoG signal.
Cells respond to environmental perturbations with changes in their gene expression that are coordinated in magnitude and time. Timing information about individual genes, rather than clusters, ...provides a refined way to view and analyze responses, but it is hard to estimate accurately. To analyze response timing of individual genes, we developed a parametric model that captures the typical temporal responses: an abrupt early response followed by a second transition to a steady state. This impulse model explicitly represents natural temporal properties such as the onset and the offset time, and can be estimated robustly, as demonstrated by its superior ability to impute missing values in gene expression data. Using response time of individual genes, we identify relations between gene function and their response timing, showing, for example, how cytosolic ribosomal genes are only repressed after the mitochondrial ribosome is activated. We further demonstrate a strong relation between the binding affinity of a transcription factor and the activation timing of its targets, suggesting that graded binding affinities could be a widely used mechanism for controlling expression timing. See online Supplementary Material at (www.liebertonline.com).
Information processing by a sensory system is reflected in the changes in stimulus representation along its successive processing stages. We measured information content and stimulus-induced ...redundancy in the neural responses to a set of natural sounds in three successive stations of the auditory pathway—inferior colliculus (IC), auditory thalamus (MGB), and primary auditory cortex (A1). Information about stimulus identity was somewhat reduced in single A1 and MGB neurons relative to single IC neurons, when information is measured using spike counts, latency, or temporal spiking patterns. However, most of this difference was due to differences in firing rates. On the other hand, IC neurons were substantially more redundant than A1 and MGB neurons. IC redundancy was largely related to frequency selectivity. Redundancy reduction may be a generic organization principle of neural systems, allowing for easier readout of the identity of complex stimuli in A1 relative to IC.
Gene expression controls how the brain develops and functions. Understanding control processes in the brain is particularly hard since they involve numerous types of neurons and glia, and very little ...is known about which genes are expressed in which cells and brain layers. Here we describe an approach to detect genes whose expression is primarily localized to a specific brain layer and apply it to the mouse cerebellum. We learn typical spatial patterns of expression from a few markers that are known to be localized to specific layers, and use these patterns to predict localization for new genes. We analyze images of in-situ hybridization (ISH) experiments, which we represent using histograms of local binary patterns (LBP) and train image classifiers and gene classifiers for four layers of the cerebellum: the Purkinje, granular, molecular and white matter layer. On held-out data, the layer classifiers achieve accuracy above 94% (AUC) by representing each image at multiple scales and by combining multiple image scores into a single gene-level decision. When applied to the full mouse genome, the classifiers predict specific layer localization for hundreds of new genes in the Purkinje and granular layers. Many genes localized to the Purkinje layer are likely to be expressed in astrocytes, and many others are involved in lipid metabolism, possibly due to the unusual size of Purkinje cells.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Capturing the interesting components of an image is a key aspect of image understanding. When a speaker annotates an image, selecting labels that are informative greatly depends on the prior ...knowledge of a prospective listener. Motivated by cognitive theories of categorization and communication, we present a new unsupervised approach to model this prior knowledge and quantify the informativeness of a description. Specifically, we compute how knowledge of a label reduces uncertainty over the space of labels and use this uncertainty reduction to rank candidate labels for describing an image. While the full estimation problem is intractable, we describe an efficient algorithm to approximate entropy reduction using a tree-structured graphical model. We evaluate our approach on the open-images dataset using a new evaluation set of 10K ground-truth ratings and find that it achieves over 65% agreement with human raters, close to the upper bound of inter-rater agreement and largely outperforming other unsupervised baseline approaches.
Real-world data is often unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. To address unbalanced data, most studies try balancing the ...data, the loss, or the classifier to reduce classification bias towards head classes. Far less attention has been given to the latent representations learned with unbalanced data. We show that the feature extractor part of deep networks suffers greatly from this bias. We propose a new loss based on robustness theory, which encourages the model to learn high-quality representations for both head and tail classes. While the general form of the robustness loss may be hard to compute, we further derive an easy-to-compute upper bound that can be minimized efficiently. This procedure reduces representation bias towards head classes in the feature space and achieves new SOTA results on CIFAR100-LT, ImageNet-LT, and iNaturalist long-tail benchmarks. We find that training with robustness increases recognition accuracy of tail classes while largely maintaining the accuracy of head classes. The new robustness loss can be combined with various classifier balancing techniques and can be applied to representations at several layers of the deep model.
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