Microglial Density Alters Measures of Axonal Integrity and Structural Connectivity.
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ABSTRACT:
SUBMITTER: Yi SY
PROVIDER: S-EPMC7709542 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Neurons innervate space by extending axonal and dendritic arborizations. When axons and dendrites come in close proximity of each other, synapses between neurons can be formed. Neurons vary greatly in their morphologies and synaptic connections with other neurons. The size and shape of the arborizations determine the way neurons innervate space. A neuron may therefore be characterized by the spatial distribution of its axonal and dendritic "mass." A population mean "mass" density field of a particular neuron type can be obtained by averaging over the individual variations in neuron geometries. Connectivity in terms of candidate synaptic contacts between neurons can be determined directly on the basis of their arborizations but also indirectly on the basis of their density fields. To decide when a candidate synapse can be formed, we previously developed a criterion defining that axonal and dendritic line pieces should cross in 3D and have an orthogonal distance less than a threshold value. In this paper, we developed new methodology for applying this criterion to density fields. We show that estimates of the number of contacts between neuron pairs calculated from their density fields are fully consistent with the number of contacts calculated from the actual arborizations. However, the estimation of the connection probability and the expected number of contacts per connection cannot be calculated directly from density fields, because density fields do not carry anymore the correlative structure in the spatial distribution of synaptic contacts. Alternatively, these two connectivity measures can be estimated from the expected number of contacts by using empirical mapping functions. The neurons used for the validation studies were generated by our neuron simulator NETMORPH. An example is given of the estimation of average connectivity and Euclidean pre- and postsynaptic distance distributions in a network of neurons represented by their population mean density fields.
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Project description:ObjectivesTo identify structural connectivity change occurring during the first 6 months after traumatic brain injury and to evaluate the utility of diffusion tensor tractography for predicting long-term outcome.MethodsThe participants were 28 patients with mild to severe traumatic axonal injury and 20 age- and sex-matched healthy control subjects. Neuroimaging was obtained 0-9 days postinjury for acute scans and 6-14 months postinjury for chronic scans. Long-term outcome was evaluated on the day of the chronic scan. Twenty-eight fiber regions of 9 major white matter structures were reconstructed, and reliable tractography measurements were determined and used.ResultsAlthough most (23 of 28) patients had severe brain injury, their long-term outcome ranged from good recovery (16 patients) to moderately (5 patients) and severely disabled (7 patients). In concordance with the diverse outcome, the white matter change in patients was heterogeneous, ranging from improved structural connectivity, through no change, to deteriorated connectivity. At the group level, all 9 fiber tracts deteriorated significantly with 7 (corpus callosum, cingulum, angular bundle, cerebral peduncular fibers, uncinate fasciculus, and inferior longitudinal and fronto-occipital fasciculi) showing structural damage acutely and 2 (fornix body and left arcuate fasciculus) chronically. Importantly, the amount of change in tractography measurements correlated with patients' long-term outcome. Acute tractography measurements were able to predict patients' learning and memory performance; chronic measurements also determined performance on processing speed and executive function.ConclusionsDiffusion tensor tractography is a valuable tool for identifying structural connectivity changes occurring between the acute and chronic stages of traumatic brain injury and for predicting patients' long-term outcome.
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Project description:Fatigue is a common and disabling symptom in Multiple Sclerosis (MS). However, consistent neuroimaging correlates of its severity are not fully elucidated. In this article, we study the neuronal correlates of fatigue severity in MS. Forty-three Relapsing Remitting MS (RRMS) patients with MS-related fatigue (Fatigue Severity Scale (FSS) range: 1-7) and Expanded Disability Status Scale (EDSS) ? 4, were divided into high fatigue (HF, FSS ? 5.1) and low fatigue groups (LF, FSS ? 3). We measured T2 lesion load using a semi-automated technique. Cortical thickness, volume of sub-cortical nuclei, and brainstem structures were measured using Freesurfer. Cortical Diffusion Tensor Imaging (DTI) parameters were extracted using a cross modality technique. A correlation analysis was performed between FSS, volumetric, and DTI indices across all patients. HF patients showed significantly lower volume of thalamus, (p = 0.02), pallidum (p = 0.01), and superior cerebellar peduncle ((SCP), p = 0.002). The inverse correlation between the FSS score and the above volumes was significant in the total study population. In the right temporal cortex (RTC), the Radial Diffusivity ((RD), p = 0.01) and Fractional Anisotropy ((FA), p = 0.01) was significantly higher and lower, respectively, in the HF group. After Bonferroni correction, thalamic volume, FA-RTC, and RD-RTC remained statistically significant. Multivariate regression analysis identified FA-RTC as the best predictor of fatigue severity. Our data suggest an association between fatigue severity and volumetric changes of thalamus, pallidum, and SCP. Early neuronal injury in the RTC is implicated in the pathogenesis of MS-related fatigue.
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Project description:Neurons in the brains of those with Alzheimer's disease (AD) and many frontotemporal dementias (FTDs) contain neurofibrillary tangles comprised of hyperphosphorylated tau protein. Tau normally stabilizes microtubules (MTs), and tau misfolding could lead to a loss of this function with consequent MT destabilization and neuronal dysfunction. Accordingly, a possible therapeutic strategy for AD and related "tauopathies" is treatment with a MT-stabilizing anti-cancer drug such as paclitaxel. However, paclitaxel and related taxanes have poor blood-brain barrier permeability and thus are unsuitable for diseases of the brain. We demonstrate here that the MT-stabilizing agent, epothilone D (EpoD), is brain-penetrant and we subsequently evaluated whether EpoD can compensate for tau loss-of-function in PS19 tau transgenic mice that develop forebrain tau inclusions, axonal degeneration and MT deficits. Treatment of 3-month-old male PS19 mice with low doses of EpoD once weekly for a 3 month period significantly improved CNS MT density and axonal integrity without inducing notable side-effects. Moreover, EpoD treatment reduced cognitive deficits that were observed in the PS19 mice. These results suggest that certain brain-penetrant MT-stabilizing agents might provide a viable therapeutic strategy for the treatment of AD and FTDs.
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Project description:OBJECTIVES:Recovery from coma might critically depend on the structural and functional integrity of frontoparietal networks. We aimed to measure this integrity in traumatic brain injury and anoxo-ischemic (cardiac arrest) coma patients by using an original multimodal MRI protocol. DESIGN:Prospective cohort study. SETTING:Three Intensive Critical Care Units affiliated to the University in Toulouse (France). PATIENTS:We longitudinally recruited 43 coma patients (Glasgow Coma Scale at the admission < 8; 29 cardiac arrest and 14 traumatic brain injury) and 34 age-matched healthy volunteers. Exclusion criteria were disorders of consciousness lasting more than 30 days and focal brain damage within the explored brain regions. Patient assessments were conducted at least 2 days (5?±?2 d) after complete withdrawal of sedation. All patients were followed up (Coma Recovery Scale-Revised) 3 months after acute brain injury. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Functional and structural MRI data were recorded, and the analysis was targeted on the posteromedial cortex, the medial prefrontal cortex, and the cingulum. Univariate analyses and machine learning techniques were used to assess diagnostic and predictive values. Coma patients displayed significantly lower medial prefrontal cortex-posteromedial cortex functional connectivity (area under the curve, 0.94; 95% CI, 0.93-0.95). Cardiac arrest patients showed specific structural disturbances within posteromedial cortex. Significant cingulum architectural disturbances were observed in traumatic brain injury patients. The machine learning medial prefrontal cortex-posteromedial cortex multimodal classifier had a significant predictive value (area under the curve, 0.96; 95% CI, 0.95-0.97), best combination of subregions that discriminates a binary outcome based on Coma Recovery Scale-Revised). CONCLUSIONS:This exploratory study suggests that frontoparietal functional disconnections are specifically observed in coma and their structural counterpart provides information about brain injury mechanisms. Multimodal MRI biomarkers of frontoparietal disconnection predict 3-month outcome in our sample. These findings suggest that fronto-parietal disconnection might be particularly relevant for coma outcome prediction and could inspire innovative precision medicine approaches.
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Project description:BACKGROUND:Schizophrenia is believed to result from abnormal functional integration of neural processes thought to arise from aberrant brain connectivity. However, evidence for anatomical dysconnectivity has been equivocal, and few studies have examined axonal fiber connectivity in schizophrenia at the level of whole-brain networks. METHODS:Cortico-cortical anatomical connectivity at the scale of axonal fiber bundles was modeled as a network. Eighty-two network nodes demarcated functionally specific cortical regions. Sixty-four direction diffusion tensor-imaging coupled with whole-brain tractography was performed to map the architecture via which network nodes were interconnected in each of 74 patients with schizophrenia and 32 age- and gender-matched control subjects. Testing was performed to identify pairs of nodes between which connectivity was impaired in the patient group. The connectional architecture of patients was tested for changes in five network attributes: nodal degree, small-worldness, efficiency, path length, and clustering. RESULTS:Impaired connectivity in the patient group was found to involve a distributed network of nodes comprising medial frontal, parietal/occipital, and the left temporal lobe. Although small-world attributes were conserved in schizophrenia, the cortex was interconnected more sparsely and up to 20% less efficiently in patients. Intellectual performance was found to be associated with brain efficiency in control subjects but not in patients. CONCLUSIONS:This study presents evidence of widespread dysconnectivity in white-matter connectional architecture in a large sample of patients with schizophrenia. When considered from the perspective of recent evidence for impaired synaptic plasticity, this study points to a multifaceted pathophysiology in schizophrenia encompassing axonal as well as putative synaptic mechanisms.
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