Pericyte remodeling is deficient in the aged brain and contributes to impaired capillary flow and structure

Deterioration of brain capillary flow and architecture is a hallmark of aging and dementia. It remains unclear how loss of brain pericytes in these conditions contributes to capillary dysfunction. Here, we conduct cause-and-effect studies by optically ablating pericytes in adult and aged mice in vivo. Focal pericyte loss induces capillary dilation without blood-brain barrier disruption. These abnormal dilations are exacerbated in the aged brain, and result in increased flow heterogeneity in capillary networks. A subset of affected capillaries experience reduced perfusion due to flow steal. Some capillaries stall in flow and regress, leading to loss of capillary connectivity. Remodeling of neighboring pericytes restores endothelial coverage and vascular tone within days. Pericyte remodeling is slower in the aged brain, resulting in regions of persistent capillary dilation. These findings link pericyte loss to disruption of capillary flow and structure. They also identify pericyte remodeling as a therapeutic target to preserve capillary flow dynamics

Trial-unique, delayed nonmatching-to-location (TUNL) touchscreen testing for mice: sensitivity to dorsal hippocampal dysfunction.

RATIONALE: The hippocampus is implicated in many of the cognitive impairments observed in conditions such as Alzheimer's disease (AD) and schizophrenia (SCZ). Often, mice are the species of choice for models of these diseases and the study of the relationship between brain and behaviour more generally. Thus, automated and efficient hippocampal-sensitive cognitive tests for the mouse are important for developing therapeutic targets for these diseases, and understanding brain-behaviour relationships. One promising option is to adapt the touchscreen-based trial-unique nonmatching-to-location (TUNL) task that has been shown to be sensitive to hippocampal dysfunction in the rat. OBJECTIVES: This study aims to adapt the TUNL task for use in mice and to test for hippocampus-dependency of the task. METHODS: TUNL training protocols were altered such that C57BL/6 mice were able to acquire the task. Following acquisition, dysfunction of the dorsal hippocampus (dHp) was induced using a fibre-sparing excitotoxin, and the effects of manipulation of several task parameters were examined. RESULTS: Mice could acquire the TUNL task using training optimised for the mouse (experiments 1). TUNL was found to be sensitive to dHp dysfunction in the mouse (experiments 2, 3 and 4). In addition, we observed that performance of dHp dysfunction group was somewhat consistently lower when sample locations were presented in the centre of the screen. CONCLUSIONS: This study opens up the possibility of testing both mouse and rat models on this flexible and hippocampus-sensitive touchscreen task.CHK received funding from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI11C1183). CJH, LMS and TJB were funded by Medical Research Council/Wellcome Trust grant 089703/Z/09/Z. CR, LMS and TJB were funded by Alzheimer’s Research UK [ART/ESG2010/1]. ACM, MHE, CAO, LMS and TJB also received funding from the Innovative Medicine Initiative Joint Undertaking under grant agreement no 115008 of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013).This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s00213-015-4017-

Pericyte Structural Remodeling in Cerebrovascular Health and Homeostasis

The biology of brain microvascular pericytes is an active area of research and discovery, as their interaction with the endothelium is critical for multiple aspects of cerebrovascular function. There is growing evidence that pericyte loss or dysfunction is involved in the pathogenesis of Alzheimer’s disease, vascular dementia, ischemic stroke and brain injury. However, strategies to mitigate or compensate for this loss remain limited. In this review, we highlight a novel finding that pericytes in the adult brain are structurally dynamic in vivo, and actively compensate for loss of endothelial coverage by extending their far-reaching processes to maintain contact with regions of exposed endothelium. Structural remodeling of pericytes may present an opportunity to foster pericyte-endothelial communication in the adult brain and should be explored as a potential means to counteract pericyte loss in dementia and cerebrovascular disease. We discuss the pathophysiological consequences of pericyte loss on capillary function, and the biochemical pathways that may control pericyte remodeling. We also offer guidance for observing pericytes in vivo, such that pericyte structural remodeling can be more broadly studied in mouse models of cerebrovascular disease

Autism-specific covariation in perceptual performances: "g" or "p" factor?

Autistic perception is characterized by atypical and sometimes exceptional performance in several low- (e.g., discrimination) and mid-level (e.g., pattern matching) tasks in both visual and auditory domains. A factor that specifically affects perceptive abilities in autistic individuals should manifest as an autism-specific association between perceptual tasks. The first purpose of this study was to explore how perceptual performances are associated within or across processing levels and/or modalities. The second purpose was to determine if general intelligence, the major factor that accounts for covariation in task performances in non-autistic individuals, equally controls perceptual abilities in autistic individuals.We asked 46 autistic individuals and 46 typically developing controls to perform four tasks measuring low- or mid-level visual or auditory processing. Intelligence was measured with the Wechsler's Intelligence Scale (FSIQ) and Raven Progressive Matrices (RPM). We conducted linear regression models to compare task performances between groups and patterns of covariation between tasks. The addition of either Wechsler's FSIQ or RPM in the regression models controlled for the effects of intelligence.In typically developing individuals, most perceptual tasks were associated with intelligence measured either by RPM or Wechsler FSIQ. The residual covariation between unimodal tasks, i.e. covariation not explained by intelligence, could be explained by a modality-specific factor. In the autistic group, residual covariation revealed the presence of a plurimodal factor specific to autism.Autistic individuals show exceptional performance in some perceptual tasks. Here, we demonstrate the existence of specific, plurimodal covariation that does not dependent on general intelligence (or "g" factor). Instead, this residual covariation is accounted for by a common perceptual process (or "p" factor), which may drive perceptual abilities differently in autistic and non-autistic individuals

Model 1 (Effect of intelligence on performance and between group differences in performances) main results: a. Wechsler's Full Scale IQ (FSIQ) or b. Raven Progressive Matrices (RPM).

<p>Model 1 (Effect of intelligence on performance and between group differences in performances) main results: a. Wechsler's Full Scale IQ (FSIQ) or b. Raven Progressive Matrices (RPM).</p

Descriptive statistics for all participants including age and Wechsler's Intelligence Scale IQ (FSIQ, VIQ, NVIQ) and Raven Progressive Matrices (RPM) scores: mean (standard deviation); range.

<p>Descriptive statistics for all participants including age and Wechsler's Intelligence Scale IQ (FSIQ, VIQ, NVIQ) and Raven Progressive Matrices (RPM) scores: mean (standard deviation); range.</p

Illustration of theoretical models to explain the pattern of covariation between tasks.

<p>Across all figures, experimental tasks are presented in the top row, in grey. Factors are shown in the lower row, in colour. The “Intelligence” factor (purple) includes the effect of RPM, FSIQ or both, depending on the variable and group. These models describe the significant contribution of a given factor (i.e., intelligence or other factor) to the variance of any given perceptual task performance. <b>A.</b> Generic model. Arrows from the same factor (here, intelligence) pointing towards two tasks (here, 1 and 2) indicate that the correlation between these two tasks can be explained by their common relationship with the factor, represented here as intelligence. In the example presented, the intelligence factor does not fully explain the variance of tasks 1 and 2, and a residual covariation attributed to “another factor” (orange), not dependent on intelligence, explains this residual correlation. <b>B.</b> (TD controls) and <b>C.</b> (Autistic individuals). Models that fit the observed patterns of covariation in this study for each group separately (statistics available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103781#pone-0103781-t002" target="_blank">Table 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103781#pone-0103781-t004" target="_blank">4</a>). The factors not dependent on intelligence, that contribute to residual covariations include: the “Unimodal Auditory Aptitude” factor (green), the “Unimodal Visual Aptitude” factor (blue) and the “Plurimodal Perceptual Aptitude” factor (orange). The “Unimodal Auditory Aptitude” factor is a common factor found in both autistic individuals and in the general TD population and explains the relationship between levels of processing within a single perceptual modality. The “Unimodal Visual Aptitude” factor is an analogue to the “Unimodal Auditory Aptitude” factor, but within the visual modality. This factor reaches significance only in the autistics group in the current study. The “Plurimodal Perceptual Aptitude” factor is different from the unimodal aptitude factors and is present only in autistic individuals. This factor is the main finding of the current study and is given the abbreviated “<i>p</i>-factor” label in the discussion. Full Lines: <i>p</i><0.05; Dotted lines: <i>p</i><0.1.</p

Model 2 (Between group differences in residual covariation) main results: a. Wechsler's Full Scale IQ (FSIQ), or b. Raven Progressive Matrices (RPM).

<p>Model 2 (Between group differences in residual covariation) main results: a. Wechsler's Full Scale IQ (FSIQ), or b. Raven Progressive Matrices (RPM).</p

Schematic representation of the study's factorial design and presentation of experimental stimuli and tasks.

<p>The four experimental tasks are presented in each quadrant. Each task is characterized by a sensory modality (visual or auditory) and by a level of cortical processing engaged during task completion (low- or mid-level). <b>A.</b> Luminance-contrast (LC) discrimination: gratings were presented for 753 ms each and separated by an inter-stimulus interval of 271 ms, during which a noise mask was presented to minimize spatial after effects. <b>B.</b> Pitch discrimination: pure tones were presented for 200 ms each and separated by an inter-stimulus interval of 212 ms. <b>C.</b> Block design completion: examples of minimum and maximum perceptual cohesiveness (PC) models. <b>D.</b> Melody discrimination: examples of a standard melody compared to contour modified and contour preserved conditions. Red arrows represent contour direction. Lines represent relationships of interest in the current study. Full lines: unimodal relationships, between levels of processing; Dotted lines: plurimodal relationships, within levels of processing.</p