Article

Diminished Perisomatic GABAergic Terminals on Cortical Neurons Adjacent to Amyloid Plaques

Laboratorio de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid Madrid, Spain.
Frontiers in Neuroanatomy (Impact Factor: 3.54). 11/2009; 3:28. DOI: 10.3389/neuro.05.028.2009
Source: PubMed

ABSTRACT

One of the main pathological hallmarks of Alzheimer's disease (AD) is the accumulation of plaques in the cerebral cortex, which may appear either in the neuropil or in direct association with neuronal somata. Since different axonal systems innervate the dendritic (mostly glutamatergic) and perisomatic (mostly GABAergic) regions of neurons, the accumulation of plaques in the neuropil or associated with the soma might produce different alterations to synaptic circuits. We have used a variety of conventional light, confocal and electron microscopy techniques to study their relationship with neuronal somata in the cerebral cortex from AD patients and APP/PS1 transgenic mice. The main finding was that the membrane surfaces of neurons (mainly pyramidal cells) in contact with plaques lack GABAergic perisomatic synapses. Since these perisomatic synapses are thought to exert a strong influence on the output of pyramidal cells, their loss may lead to the hyperactivity of the neurons in contact with plaques. These results suggest that plaques modify circuits in a more selective manner than previously thought.

Download full-text

Full-text

Available from: José-Rodrigo Rodríguez
  • Source
    • "Dementia of the Alzheimer3s type (DAT) is the major cause of clinical dementia in the elderly (Qiu et al., 2009), and is characterized by the accumulation of the Beta amyloid protein, the phosphorylation of the Tau protein, and the loss of synapses. Amyloid deposition impairs normal inter-neuronal connectivity (Garcia-Marin et al., 2009), whereas Tau results in disruption of axonal microtubule organization (Taniguchi et al., 2001). The progressive loss of the number and efficiency of synapses disrupts inter-and intra-regional communication, leading to the idea that the DAT is a disconnection syndrome (Delbeuck et al., 2003; Selkoe, 2002; Morrison et al., 1991). "

    Full-text · Article · Sep 2015
  • Source
    • "Dementia of the Alzheimer3s type (DAT) is the major cause of clinical dementia in the elderly (Qiu et al., 2009), and is characterized by the accumulation of the Beta amyloid protein, the phosphorylation of the Tau protein, and the loss of synapses. Amyloid deposition impairs normal inter-neuronal connectivity (Garcia-Marin et al., 2009), whereas Tau results in disruption of axonal microtubule organization (Taniguchi et al., 2001). The progressive loss of the number and efficiency of synapses disrupts inter-and intra-regional communication, leading to the idea that the DAT is a disconnection syndrome (Delbeuck et al., 2003; Selkoe, 2002; Morrison et al., 1991). "

    Full-text · Dataset · Sep 2015
  • Source
    • "From those that suffer from MCI there is a rate of progression to dementia of about 10% (Petersen, 2011). The pathophysiology of the disease lead to a progressive loss of synapsis efficacy (Selkoe, 2002) and loss of neurons as well as damage in the white matter, due to the phosphorylation of the Tau protein affecting axon transmission, and the accumulation of the beta amyloid protein, which impairs gabaergic transmission (Garcia-Marin et al., 2009). All these lead to the view of AD as a " disconnection syndrome " (Bajo et al., 2010; Delbeuck et al., 2003; Stam et al., 2009) in which a progressive damage of global functional and structural connections are potentially the cause of the insidious cognitive impairment. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corresponding functional connections. We applied beamformer source reconstruction to the resting state MEG recordings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was obtained for each subject, and time series were assigned to each of the regions. The fiber densities between the regions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introducing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.
    Full-text · Article · Aug 2014 · NeuroImage
Show more