Neuron Spectrum Software Download WORK
Lida Humbert
An association between autistic spectrum disorder and imitative impairment might result from dysfunction in mirror neurons (MNs) that serve to relate observed actions to motor codings. To explore this hypothesis, we employed a functional magnetic resonance imaging (fMRI) protocol previously used to identify the neural substrate of imitation, and human MN function, to compare 16 adolescent males of normal intelligence with autistic spectrum disorder (ASD) and age, sex and IQ matched controls. In the control group, in accord with previous findings, we identified activity attributable to MNs in areas of the right parietal lobe. Activity in this area was less extensive in the ASD group and was absent during non-imitative action execution. Broca's area was minimally active during imitation in controls. Differential patterns of activity during imitation and action observation in ASD and controls were most evident in an area at the right temporo-parietal junction also associated with a 'theory of mind' (ToM) function. ASD participants also failed to show modulation of left amygdala activity during imitation that was evident in the controls. This may have implications for understanding the imitation of emotional stimuli in ASD. Overall, we suggest that ASD is associated with altered patterns of brain activity during imitation, which could stem from poor integration between areas serving visual, motor, proprioceptive and emotional functions. Such poor integration is likely to adversely affect the development of ToM through imitation as well as other aspects of social cognitive function in ASD.
Alterations in somatosensory (touch and pain) behaviors are highly prevalent among people with autism spectrum disorders (ASDs). However, the neural mechanisms underlying abnormal touch and pain-related behaviors in ASDs and how altered somatosensory reactivity might contribute to ASD pathogenesis has not been well studied. Here, we provide a brief review of somatosensory alterations observed in people with ASDs and recent evidence from animal models that implicates peripheral neurons as a locus of dysfunction for somatosensory abnormalities in ASDs. Lastly, we describe current efforts to understand how altered peripheral sensory neuron dysfunction may impact brain development and complex behaviors in ASD models, and whether targeting peripheral somatosensory neurons to improve their function might also improve related ASD phenotypes.
Autism Spectrum Disorder (ASD) is a complex neuropsychiatric syndrome whose etiology includes genetic and environmental components. Since epigenetic marks are sensitive to environmental insult, they may be involved in the development of ASD. Initial brain studies have suggested a dysregulation of epigenetic marks in ASD. However, due to cellular heterogeneity in the brain, these studies have not determined if there is a true change in the neuronal epigenetic signature. Here, we report a genome-wide methylation study on fluorescence-activated cell sorting-sorted neuronal nuclei from the frontal cortex of 16 male ASD and 15 male control subjects. Using the 450 K BeadArray, we identified 58 differentially methylated regions (DMRs) that included loci associated to GABAergic system genes, particularly ABAT and GABBR1, and brain-specific MicroRNAs. Selected DMRs were validated by targeted Next Generation Bisulfite Sequencing. Weighted gene correlation network analysis detected 3 co-methylation modules which are significantly correlated to ASD that were enriched for genomic regions underlying neuronal, GABAergic, and immune system genes. Finally, we determined an overlap of the 58 ASD-related DMRs with neurodevelopment associated DMRs. This investigation identifies alterations in the DNA methylation pattern in ASD cortical neurons, providing further evidence that epigenetic alterations in disorder-relevant tissues may be involved in the biology of ASD.
According to Kumar, memristor inventor Leon Chua predicted that if you mapped out the possible device parameters there would be regions of chaotic behavior in between regions where behavior is stable. At the edge of some of these chaotic regions, devices can exist that do what the new artificial neuron does.
Here we study the distribution of eigenvalues, or spectrum, of the neuron-to-neuron covariance matrix in recurrently connected neuronal networks. The covariance spectrum is an important global feature of neuron population dynamics that requires simultaneous recordings of neurons. The spectrum is essential to the widely used Principal Component Analysis (PCA) and generalizes the dimensionality measure of population dynamics. We use a simple model to emulate the complex connections between neurons, where all pairs of neurons interact linearly at a strength specified randomly and independently. We derive a closed-form expression of the covariance spectrum, revealing an interesting long tail of large eigenvalues following a power law as the connection strength increases. To incorporate connectivity features important to biological neural circuits, we generalize the result to networks with an additional low-rank connectivity component that could come from learning and networks consisting of sparsely connected excitatory and inhibitory neurons. To facilitate comparing the theoretical results to experimental data, we derive the precise modifications needed to account for the effect of limited time samples and having unobserved neurons. Preliminary applications to large-scale calcium imaging data suggest our model can well capture the high dimensional population activity of neurons.
The dynamics considered here is simple where the activity fluctuations around the steady-state are described by a linear response [22, 23], which experimentally is related to spontaneous or persistent neural activity in absence of structured spatial-temporal stimuli. Despite the simple dynamics and minimal connectivity model, we find the resulting spectrum has a continuous bulk of nontrivial shape exhibiting interesting features such as a power-law long tail of large eigenvalues (Section 3.2), and strong effects due to the non-normality of the connectivity matrix (Section E.2 in S1 Text). These covariance spectra highlight interesting population-level structures of neuronal variability shaped by recurrent interactions that were previously unexplored.
Using the theory of the covariance spectrum, we derive closed-form expressions for the effective dimensionality (previously known for the simple random i.i.d. Gaussian connectivity [6]) We show that the continuous bulk spectrum has the advantage over low-order statistics such as the dimensionality thanks to its robustness to low rank perturbations (Section 3.3 to 3.5 and 3.8). Our analytically derived eigenvalue distributions can be readily compared to real activity data of recurrent neural circuits or simulations of more sophisticated computational models. We provide ready-to-use code to facilitate such applications (see Data Availability Statement). An example of such an application for a whole-brain calcium imaging data is presented in Section 3.8.
The shape of pC(x) can provide important theoretical insights on interpreting PCA. For example, it can be used to separate outlying eigenvalues corresponding to low dimensional externally driven signals from small eigenvalues corresponding to fluctuations amplified by recurrent connectivity interactions [32] (Section 3.8). the spectrum is also closely related to the effective dimension of the population activity. In many cases, the linear span of the activity fluctuations is full rank, N. Nevertheless, most of the variability is embedded in a much lower dimensional subspace. A useful measure of the effective dimension, known as the participation ratio [8, 33] is given by(4)which can be calculated from the first two moments of pC(x). We will also derive explicit expressions for D in random connectivity models.
Since the general shape and trend depending on g of the spectrum in the simpler symmetric case (Fig 3A) is qualitatively similar to the i.i.d. case (Fig 1C), one can use it to gain intuition, for example, of the thin tail of large eigenvalues as g approaches its critical value.
A. Compare theoretical covariance spectrum for random connectivity with reciprocal motifs and a finite-size network covariance using Eq (2)(g = 0.4, κ = 0.4, N = 400). B. The impact of reciprocal motifs on dimension for various gr = g/gc (Eq (18)). For small gr, the dimension increases sharply with κ. C. The spectra at various κ while fixing g = 0.4. The black dashed line is the i.i.d. random connectivity (κ = 0). D. Same as C. but fixing relative gr = 0.4 to control the main effect (see text). The changes in shape are now smaller and the support narrows with increasing κ.
An important property of the spectrum of C is the robustness of its bulk component to the addition of low rank structured connectivity. Many connectivity structures that are important to the dynamics and function of a recurrent neuronal network can be described by a full rank random component plus a low rank component [39, 40]. For example, such components may arise from Hebbian learning [41] and by training neural networks by gradient decent [42]. A simple case is where we add a rank k structured matrix that is deterministic or independent to the random component [43, 44]. As shown in Section C in S1 Text, in large networks, the bulk covariance spectrum remains unchanged, but the low rank component may give rise to at most 2k outlying eigenvalues. This is illustrated by the example of rank-1 perturbation to J with i.i.d. Gaussian entries in Fig 5C and 5D, where the expected location of the outliers in the covariance spectrum can be predicted analytically (Fig 5E and 5F and Section C.3 in S1 Text). This is in contrast to the spectrum of J, where the same perturbations can lead to an unbounded number of randomly located eigenvalues [43, 45] (Fig 5A and 5B). In sum, the bulk spectrum of covariance is robust against low rank perturbations to the connectivity. Note, however, the relevance of the bulk spectrum for the network dynamics depends on the location of outliers. Outliers to the right of the bulk spectrum may indicate potential instability of the dynamics even for g < 1, as discussed in the example below.
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