Brain size scales as different functions of its number of neurons across mammalian orders such as rodents, primates, and insectivores. In rodents, we have previously shown that, across a sample of 6 species, from mouse to capybara, the cerebral cortex, cerebellum and the remaining brain structures increase in size faster than they gain neurons, with an accompanying decrease in neuronal density in these structures [Herculano-Houzel et al.: Proc Natl Acad Sci USA 2006;103:12138–12143]. Important remaining questions are whether such neuronal scaling rules within an order apply equally to all pertaining species, and whether they extend to closely related taxa. Here, we examine whether 4 other species of Rodentia, as well as the closely related rabbit (Lagomorpha), conform to the scaling rules identified previously for rodents. We report the updated neuronal scaling rules obtained for the average values of each species in a way that is directly comparable to the scaling rules that apply to primates [Gabi et al.: Brain Behav Evol 2010;76:32–44], and examine whether the scaling relationships are affected when phylogenetic relatedness in the dataset is accounted for. We have found that the brains of the spiny rat, squirrel, prairie dog and rabbit conform to the neuronal scaling rules that apply to the previous sample of rodents. The conformity to the previous rules of the new set of species, which includes the rabbit, suggests that the cellular scaling rules we have identified apply to rodents in general, and probably to Glires as a whole (rodents/lagomorphs), with one notable exception: the naked mole-rat brain is apparently an outlier, with only about half of the neurons expected from its brain size in its cerebral cortex and cerebellum.

Light touch contact of a fingertip with a stationary surface can provide orientation information that enhances control of upright stance. Slight changes in contact force at the fingertip provide sensory cues about the direction of body sway, allowing attenuation of sway. In the present study, we asked to which extent somatosensory cues are part of the postural control system, that is, which sensory signal supports this coupling? We investigated postural control not only when the contact surface was stationary, but also when it was moving rhythmically (from 0.1 to 0.5 Hz). In doing so, we brought somatosensory cues from the hand into conflict with other parts of the postural control system. Our focus was the temporal relationship between body sway and the contact surface. Postural sway was highly coherent with contact surface motion. Head and body sway assumed the frequency of the moving contact surface at all test frequencies. To account for these results, a simple model was formulated by approximating the postural control system as a second-order linear dynamical system. The influence of the touch stimulus was captured as the difference between the velocity of the contact surface and the velocity of body sway, multiplied by a coupling constant. Comparison of empirical results (relative phase, coherence, and gain) with model predictions supports the hypothesis of coupling between body sway and touch cues through the velocity of the somatosensory stimulus at the fingertip. One subject, who perceived movement of the touch surface, demonstrated weaker coupling than other subjects, suggesting that cognitive mechanisms introduce flexibility into the postural control scheme.

The risk factors for coronavirus disease 2019 (COVID-19) severity are still poorly understood. Considering the pivotal role of the gut microbiota on host immune and inflammatory functions, we investigated the association between changes in the gut microbiota composition and the clinical severity of COVID-19. We conducted a multicenter cross-sectional study prospectively enrolling 115 COVID-19 patients categorized according to: (1) the WHO Clinical Progression Scale—mild, 19 (16.5%); moderate, 37 (32.2%); or severe, 59 (51.3%), and (2) the location of recovery from COVID-19—ambulatory, 14 (household isolation, 12.2%); hospitalized in ward, 40 (34.8%); or hospitalized in the intensive care unit, 61 (53.0%). Gut microbiota analysis was performed through 16S rRNA gene sequencing, and the data obtained were further related to the clinical parameters of COVID-19 patients. The risk factors for COVID-19 severity were identified by univariate and multivariable logistic regression models. In comparison to mild COVID-19 patients, the gut microbiota of moderate and severe patients have: (a) lower Firmicutes/Bacteroidetes ratio; (b) higher abundance of Proteobacteria; and (c) lower abundance of beneficial butyrate-producing bacteria such as the genera Roseburia and Lachnospira. Multivariable regression analysis showed that the Shannon diversity index [odds ratio (OR) = 2.85, 95% CI = 1.09–7.41, p = 0.032) and C-reactive protein (OR = 3.45, 95% CI = 1.33–8.91, p = 0.011) are risk factors for severe COVID-19 (a score of 6 or higher in the WHO Clinical Progression Scale). In conclusion, our results demonstrated that hospitalized patients with moderate and severe COVID-19 have microbial signatures of gut dysbiosis; for the first time, the gut microbiota diversity is pointed out as a prognostic biomarker of COVID-19 severity.

The human prefrontal cortex has been considered different in several aspects and relatively enlarged compared to the rest of the cortical areas. Here we determine whether the white and gray matter of the prefrontal portion of the human cerebral cortex have similar or different cellular compositions relative to the rest of the cortical regions by applying the Isotropic Fractionator to analyze the distribution of neurons along the entire anteroposterior axis of the cortex, and its relationship with the degree of gyrification, number of neurons under the cortical surface, and other parameters. The prefrontal region shares with the remainder of the cerebral cortex (except for occipital cortex) the same relationship between cortical volume and number of neurons. In contrast, both occipital and prefrontal areas vary from other cortical areas in their connectivity through the white matter, with a systematic reduction of cortical connectivity through the white matter and an increase of the mean axon caliber along the anteroposterior axis. These two parameters explain local differences in the distribution of neurons underneath the cortical surface. We also show that local variations in cortical folding are neither a function of local numbers of neurons nor of cortical thickness, but correlate with properties of the white matter, and are best explained by the folding of the white matter surface. Our results suggest that the human cerebral cortex is divided in two zones (occipital and non-occipital) that differ in how neurons are distributed across their gray matter volume and in three zones (prefrontal, occipital, and non-occipital) that differ in how neurons are connected through the white matter. Thus, the human prefrontal cortex has the largest fraction of neuronal connectivity through the white matter and the smallest average axonal caliber in the white matter within the cortex, although its neuronal composition fits the pattern found for other, non-occipital areas.

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