Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology.

Alzheimer's Disease (AD) is characterized by a range of behavioral alterations, including memory loss and psychiatric symptoms. While there is evidence that molecular pathologies, such as amyloid beta (Aβ), contribute to AD, it remains unclear how this histopathology gives rise to such disparate behavioral deficits. One hypothesis is that Aβ exerts differential effects on neuronal circuits across brain regions, depending on the neurophysiology and connectivity of different areas.

From synapses to circuits and back: Bridging levels of understanding in animal models of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by behavioural changes that include memory loss and cognitive decline and is associated with the appearance of amyloid-β plaques and neurofibrillary tangles throughout the brain. Although aspects of the disease percolate across multiple levels of neuronal organization, from the cellular to the behavioural, it is increasingly clear that circuits are a critical junction between the cellular pathology and the behavioural phenotypes that bookend these levels of analyses. In this review, we discuss critical aspects of neural circuit research, beginning with synapses and progressing to network activity and how they influence our understanding of disease processed in AD.

Divergence in Population Coding for Space between Dorsal and Ventral CA1.

Molecular, anatomic, and behavioral studies show that the hippocampus is structurally and functionally heterogeneous, with dorsal hippocampus implicated in mnemonic processes and spatial navigation and ventral hippocampus involved in affective processes. By performing electrophysiological recordings of large neuronal populations in dorsal and ventral CA1 in head-fixed mice navigating a virtual environment, we found that this diversity resulted in different strategies for population coding of space. Populations of neurons in dorsal CA1 showed more complex patterns of activity, which resulted in a higher dimensionality of neural representations that translated to more information being encoded, as compared ensembles in vCA1.

Changes in pairwise correlations during running reshape global network state in the main olfactory bulb.

Neural codes for sensory inputs have been hypothesized to reside in a broader space defined by ongoing patterns of spontaneous activity. To understand the structure of this spontaneous activity in the olfactory system, we performed high-density recordings of neural populations in the main olfactory bulb of awake mice. We observed changes in pairwise correlations of spontaneous activity between mitral and tufted (M/T) cells when animals were running, which resulted in an increase in the entropy of the population.

Altered dorsal CA1 neuronal population coding in the APP/PS1 mouse model of Alzheimer's disease.

While the link between amyloid β (Aβ) accumulation and synaptic degradation in Alzheimer's disease (AD) is known, the consequences of this pathology on population coding remain unknown. We found that the entropy, a measure of the diversity of network firing patterns, was lower in the dorsal CA1 region in the APP/PS1 mouse model of Aβ pathology, relative to controls, thereby reducing the population's coding capacity. Our results reveal a network level signature of the deficits Aβ accumulation causes to the computations performed by neural circuits.

Automated diagnosis of HIV-associated neurocognitive disorders using large-scale Granger causality analysis of resting-state functional MRI.

HIV-associated neurocognitive disorders (HAND) represent an important source of neurologic complications in individuals with HIV. The dynamic, often subclinical, course of HAND has rendered diagnosis, which currently depends on neuropsychometric (NP) evaluation, a challenge for clinicians. Here, we present evidence that functional brain connectivity, derived by large-scale Granger causality (lsGC) analysis of resting-state functional MRI (rs-fMRI) time-series, represents a potential biomarker to address this critical diagnostic need.

Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI.

Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND).

Mutual connectivity analysis of resting-state functional MRI data with local models.

Functional connectivity analysis of functional MRI (fMRI) can represent brain networks and reveal insights into interactions amongst different brain regions. However, most connectivity analysis approaches adopted in practice are linear and non-directional. In this paper, we demonstrate the advantage of a data-driven, directed connectivity analysis approach called Mutual Connectivity Analysis using Local Models (MCA-LM) that approximates connectivity by modeling nonlinear dependencies of signal interaction, over more conventionally used approaches, such as Pearson's and partial correlation, Patel's conditional dependence measures, etcetera.

Using Large-Scale Granger Causality to Study Changes in Brain Network Properties in the Clinically Isolated Syndrome (CIS) Stage of Multiple Sclerosis.

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques.

Mixtures of opposing phosphorylations within hexamers precisely time feedback in the cyanobacterial circadian clock.

Circadian oscillations are generated by the purified cyanobacterial clock proteins, KaiA, KaiB, and KaiC, through rhythmic interactions that depend on multisite phosphorylation of KaiC. However, the mechanisms that allow these phosphorylation reactions to robustly control the timing of oscillations over a range of protein stoichiometries are not clear. We show that when KaiC hexamers consist of a mixture of differentially phosphorylated subunits, the two phosphorylation sites have opposing effects on the ability of each hexamer to bind to the negative regulator KaiB.