White matter volume and white/gray matter ratio in mammalian species as a consequence of the universal scaling of cortical folding

Evolutionary changes leading to efficient glymphatic circulation in the mammalian brain

The functional significance of the morphological and genetic changes that occurred in the brain during evolution is not fully understood. Here we show the relationships between evolutionary changes of the brain and glymphatic circulation. We establish a mathematical model to simulate glymphatic circulation in the cerebral hemispheres, and our results show that cortical neurons accumulate in areas of the cerebral hemispheres where glymphatic circulation is highly efficient. We also find that cortical folds markedly enhance the efficiency of glymphatic circulation in the cerebral hemispheres. Furthermore, our in vivo study using ferrets reveals sulcus-dominant cerebrospinal fluid (CSF) influx, which enhances the efficiency of glymphatic circulation in the enlarged cerebral hemispheres of gyrencephalic brains. Sulcus-dominant CSF influx is mediated by preferential expression of aquaporin-4 in sulcal regions, and similar expression patterns of aquaporin-4 are also found in human cerebral hemispheres. These results indicate that evolutionary changes in the cerebral hemispheres are related to improved efficiency of glymphatic circulation. It seems plausible that the efficiency of glymphatic circulation is an important factor determining the evolutionary trajectory of the cerebral hemispheres.

Sphingosine 1-phosphate receptor subtype 1 (S1P1) activity in the course of Alzheimer's disease

Some specific lipid molecules in the brain act as signaling molecules, neurotransmitters, or neuromodulators, by binding to specific G protein-coupled receptors (GPCR) for neurolipids. One such receptor, sphingosine 1-phosphate receptor subtype 1 (S1P1), is coupled to Gi/o proteins and is involved in cell proliferation, growth, and neuroprotection. S1P1 constitutes an interesting target for neurodegenerative diseases like multiple sclerosis and Alzheimer's disease (AD), in which changes in the sphingolipid metabolism have been observed. This study analyzes S1P1 receptor-mediated activity in healthy brains and during AD progression using postmortem samples from controls and patients at different Braak's stages. Additionally, the distribution of S1P1 receptor activity in human brains is compared to that in commonly used rodent models, rats and mice, through functional autoradiography, measuring [35S]GTPγS binding stimulated by the S1P1 receptor selective agonist CYM-5442 to obtain the distribution of functional activity of S1P1 receptors. S1P1 receptor-mediated activity, along with that of the cannabinoid CB1 receptor, is one of the highest recorded for any GPCR in many gray matter areas of the brain, reaching maximum values in the cerebellar cortex, specific areas of the hippocampus and the basal forebrain. S1P1 signaling is crucial in areas that regulate learning, memory, motor control, and nociception, such as the basal forebrain and basal ganglia. In AD, S1P1 receptor activity is increased in the inner layers of the frontal cortex and underlying cortical white matter at early stages, but decreases in the hippocampus in advanced stages, indicating ongoing brain impairment. Importantly, we identified significant correlations between S1P1 receptor activity and Braak stages, suggesting that S1P1 receptor dysfunction is associated to disease progression, particularly in memory-related regions. The S1P signaling via S1P1 receptor is a promising neurological target due to its role in key neurophysiological functions and its potential to modify the progression of neurodegenerative diseases. Finally, rats are suggested as a preferred experimental model for studying S1P1 receptor-mediated responses in the human brain.

Deformation-based morphometry: a sensitive imaging approach to detect radiation-induced brain injury?

BACKGROUND

Radiotherapy is a major therapeutic approach in patients with brain tumors. However, it leads to cognitive impairments. To improve the management of radiation-induced brain sequalae, deformation-based morphometry (DBM) could be relevant. Here, we analyzed the significance of DBM using Jacobian determinants (JD) obtained by non-linear registration of MRI images to detect local vulnerability of healthy cerebral tissue in an animal model of brain irradiation.

METHODS

Rats were exposed to fractionated whole-brain irradiation (WBI, 30 Gy). A multiparametric MRI (anatomical, diffusion and vascular) study was conducted longitudinally from 1 month up to 6 months after WBI. From the registration of MRI images, macroscopic changes were analyzed by DBM and microscopic changes at the cellular and vascular levels were evaluated by quantification of cerebral blood volume (CBV) and diffusion metrics including mean diffusivity (MD). Voxel-wise comparisons were performed on the entire brain and in specific brain areas identified by DBM. Immunohistology analyses were undertaken to visualize the vessels and astrocytes.

RESULTS

DBM analysis evidenced time-course of local macrostructural changes; some of which were transient and some were long lasting after WBI. DBM revealed two vulnerable brain areas, namely the corpus callosum and the cortex. DBM changes were spatially associated to microstructural alterations as revealed by both diffusion metrics and CBV changes, and confirmed by immunohistology analyses. Finally, matrix correlations demonstrated correlations between JD/MD in the early phase after WBI and JD/CBV in the late phase both in the corpus callosum and the cortex.

CONCLUSIONS

Brain irradiation induces local macrostructural changes detected by DBM which could be relevant to identify brain structures prone to radiation-induced tissue changes. The translation of these data in patients could represent an added value in imaging studies on brain radiotoxicity.

LncRNA H19 knockdown promotes neuropathologic and functional recovery via the Nrf2/HO-1 axis after traumatic brain injury

AIMS

Traumatic brain injury (TBI) stands as a significant concern in public health, frequently leading to enduring neurological deficits. Long non-coding RNA H19 (lncRNA H19) exerts a potential regulator role in the pathology of brain injury. This study investigates the effects of lncRNA H19 knockdown (H19-KD) on the pathophysiology of TBI and its potential neuroprotective mechanisms.

METHODS

Controlled cortical impact was employed to establish a stable TBI mouse model. The expression levels of various genes in perilesional cortex and striatum tissue after TBI was detected by RT-qPCR. AAV9-shRNA-H19 was injected into the lateral ventricle of mice to knockdown the expression of lncRNA H19. Various behavioral tests were performed to evaluate sensorimotor and cognitive functions after TBI. Immunofluorescence and Nissl staining were performed to assess brain tissue damage and neuroinflammation. The Nrf2 and HO-1 expression was performed by Western blot.

RESULTS

After TBI, the expression of lncRNA H19 was elevated in perilesional tissue and gradually reverted to baseline. Behavioral tests demonstrated that H19-KD significantly promoted the recovery of sensorimotor and cognitive functions after TBI. Besides, H19-KD reduced brain tissue loss, preserved neuronal integrity, and ameliorated white matter damage at the histological level. In addition, H19-KD restrained the pro-inflammatory and facilitated anti-inflammatory phenotypes of microglia/macrophages, attenuating the neuroinflammatory response after TBI. Furthermore, H19-KD promoted activation of the Nrf2/HO-1 axis after TBI, while suppression of Nrf2 partially abolished the neuroprotective effect.

CONCLUSION

H19-KD exerts neuroprotective effects after TBI in mice, partially mediated by the activation of the Nrf2/HO-1 axis.

Volumetric study on sheep brain using stereology technique

Three-dimensional morphometric data better show the structural and functional characteristics of the brain. The objective of this study was to estimate the volume of the cerebral structures of the sheep using design-based stereology. The brains of five sheep were used, fixed in formalin 10% and embedded in agar 6%. An average of 10-12 slab was obtained from each brain. All slabs were stained using Mulligan's method and photographs were recorded. The volume of the brain and its structures were estimated using the Cavalieri's estimator and the point counting system. The total volume was 70604.8 ± 132.45 mm3. The volume fractions of the grey and white matters were calculated as 42.55 ± 0.21% and 24.23 ± 0.51% of the whole brain, respectively. The fractional volume of the caudate nucleus and claustrum were estimated at 2.39 ± 0.08% and at 1.008 ± 0.057% of total brain volume. The volumes of corpus callosum, internal capsule and external capsule were 1.24 ± 0.053%, 3.63 ± 0.22% and 0.698 ± 0.049% of total cerebral volume, respectively. These data could help improve the veterinary comparative neuroanatomy knowledge and development of experimental studies in the field.

Predictability of cortico-cortical connections in the mammalian brain

Despite a five order of magnitude range in size, the brains of mammals share many anatomical and functional characteristics that translate into cortical network commonalities. Here we develop a machine learning framework to quantify the degree of predictability of the weighted interareal cortical matrix. Partial network connectivity data were obtained with retrograde tract-tracing experiments generated with a consistent methodology, supplemented by projection length measurements in a nonhuman primate (macaque) and a rodent (mouse). We show that there is a significant level of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an area under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species. These observations reinforce earlier observations that the formation and evolution of the cortical network at the mesoscale is, to a large extent, rule based. Using the methodology presented here, we performed imputations on all area pairs, generating samples for the complete interareal network in both species. These are necessary for comparative studies of the connectome with minimal bias, both within and across species.

Folding of the cerebellar cortex is clade-specific in form but universal in degree

Like the cerebralcortex, the surface of the cerebellum is repeatedly folded. Unlike the cerebralcortex, however, cerebellar folds are much thinner and more numerous; repeatthemselves largely along a single direction, forming accordion-like folds transverseto the mid-sagittal plane; and occur in all but the smallest cerebella. We haveshown previously that while the location of folds in mammalian cerebral cortex isclade-specific, the overall degree of folding strictly follows a universalpower law relating cortical thickness and the exposed and total surface areas predictedfrom the minimization of the effective free energy of an expanding, self-avoidingsurface of a certain thickness. Here we show that this scaling law extends tothe folding of the mid-sagittal sections of the cerebellum of 53 speciesbelonging to six mammalian clades. Simultaneously, we show that each clade hasa previously unsuspected distinctive spatial pattern of folding evident at themid-sagittal surface of the cerebellum. We note, however, that the mammaliancerebellum folds as a multi-fractal object, because of the difference betweenthe outside-in development of the cerebellar cortex around a preexisting coreof already connected white matter, compared to the inside-out development ofthe cerebral cortex with a white matter volume that develops as the cerebralcortex itself gains neurons. We conclude that repeated folding, one of the mostrecognizable features of biology, can arise simply from the interplay betweenthe universal applicability of the physics of self-organization and biological,phylogenetical clade-specific contingency, without the need for invokingselective pressures in evolution.

Grey matter heterotopia subtypes show specific morpho-electric signatures and network dynamics

Grey matter heterotopia (GMH) are neurodevelopmental disorders associated with abnormal cortical function and epilepsy. Subcortical band heterotopia (SBH) and periventricular nodular heterotopia (PVNH) are two well-recognized GMH subtypes in which neurons are misplaced, either forming nodules lining the ventricles in PVNH, or forming bands in the white matter in SBH. Although both PVNH and SBH are commonly associated with epilepsy, it is unclear whether these two GMH subtypes differ in terms of pathological consequences or, on the contrary, share common altered mechanisms. Here, we studied two robust preclinical models of SBH and PVNH, and performed a systematic comparative assessment of the physiological and morphological diversity of heterotopia neurons, as well as the dynamics of epileptiform activity and input connectivity. We uncovered a complex set of altered properties, including both common and distinct physiological and morphological features across heterotopia subtypes, and associated with specific dynamics of epileptiform activity. Taken together, these results suggest that pro-epileptic circuits in GMH are, at least in part, composed of neurons with distinct, subtype-specific, physiological and morphological properties depending on the heterotopia subtype. Our work supports the notion that GMH represent a complex set of disorders, associating both shared and diverging pathological consequences, and contributing to forming epileptogenic networks with specific properties. A deeper understanding of these properties may help to refine current GMH classification schemes by identifying morpho-electric signatures of GMH subtypes, to potentially inform new treatment strategies.

Macroscale coupling between structural and effective connectivity in the mouse brain

Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be considered: the directionality of the structural connectome and limitations in explaining networks functions through an undirected measure such as FC. Here, we employed an accurate directed SC of the mouse brain acquired through viral tracers and compared it with single-subject effective connectivity (EC) matrices derived from a dynamic causal model (DCM) applied to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their respective couplings by conditioning on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks; only within sensory motor networks did we observe connections that align in terms of both effective and structural strength.

Relation of connectome topology to brain volume across 103 mammalian species

The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains.

A systematic review and meta-analysis on the efficacy of glibenclamide in animal models of intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is a devastating stroke with many mechanisms of injury. Edema worsens outcome and can lead to mortality after ICH. Glibenclamide (GLC), a sulfonylurea 1- transient receptor potential melastatin 4 (Sur1-Trpm4) channel blocker, has been shown to attenuate edema in ischemic stroke models, raising the possibility of benefit in ICH. This meta-analysis synthesizes current pre-clinical (rodent) literature regarding the efficacy of post-ICH GLC administration (vs. vehicle controls) on behaviour (i.e., neurological deficit, motor, and memory outcomes), edema, hematoma volume, and injury volume. Six studies (5 in rats and 1 in mice) were included in our meta-analysis (PROSPERO registration = CRD42021283614). GLC significantly improved behaviour (standardized mean difference (SMD) = -0.63, [-1.16, -0.09], n = 70-74) and reduced edema (SMD = -0.91, [-1.64, -0.18], n = 70), but did not affect hematoma volume (SMD = 0.0788, [-0.5631, 0.7207], n = 18-20), or injury volume (SMD = 0.2892, [-0.4950, 1.0734], n = 24). However, these results should be interpreted cautiously. Findings were conflicted with 2 negative and 4 positive reports, and Egger regressions indicated missing negative edema data (p = 0.0001), and possible missing negative behavioural data (p = 0.0766). Experimental quality assessed via the SYRCLE and CAMARADES checklists was concerning, as most studies demonstrated high risks of bias. Studies were generally low-powered (e.g., average n = 14.4 for behaviour), and future studies should employ sample sizes of 41 to detect our observed effect size in behaviour and 33 to detect our observed effect in edema. Overall, missing negative studies, low study quality, high risk of bias, and incomplete attention to key recommendations (e.g., investigating female, aged, and co-morbid animals) suggest that further high-powered confirmatory studies are needed before conclusive statements about GLC's efficacy in ICH can be made, and before further clinical trials are performed.

Systematic cortical thickness and curvature patterns in primates

Humans are known to have significant and consistent differences in thickness throughout the cortex, with thick outer gyral folds and thin inner sulcal folds. Our previous work has suggested a mechanical basis for this thickness pattern, with the forces generated during cortical folding leading to thick gyri and thin sulci, and shown that cortical thickness varies along a gyral-sulcal spectrum in humans. While other primate species are expected to exhibit similar patterns of cortical thickness, it is currently unknown how these patterns scale across different sizes, forms, and foldedness. Among primates, brains vary enormously from roughly the size of a grape to the size of a grapefruit, and from nearly smooth to dramatically folded; of these, human brains are the largest and most folded. These variations in size and form make comparative neuroanatomy a rich resource for investigating common trends that transcend differences between species. In this study, we examine 12 primate species in order to cover a wide range of sizes and forms, and investigate the scaling of their cortical thickness relative to the surface geometry. The 12 species were selected due to the public availability of either reconstructed surfaces and/or population templates. After obtaining or reconstructing 3D surfaces from publicly available neuroimaging data, we used our surface-based computational pipeline (https://github.com/mholla/curveball) to analyze patterns of cortical thickness and folding with respect to size (total surface area), geometry (i.e. curvature, shape, and sulcal depth), and foldedness (gyrification). In all 12 species, we found consistent cortical thickness variations along a gyral-sulcal spectrum, with convex shapes thicker than concave shapes and saddle shapes in between. Furthermore, we saw an increasing thickness difference between gyri and sulci as brain size increases. Our results suggest a systematic folding mechanism relating local cortical thickness to geometry. Finally, all of our reconstructed surfaces and morphometry data are available for future research in comparative neuroanatomy.

Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity

Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.

Biomechanical models and mechanisms of cellular morphogenesis and cerebral cortical expansion and folding

Morphogenesis of the nervous system involves a highly complex spatio-temporal pattern of physical forces (mainly tension and pressure) acting on cells and tissues that are pliable but have an intricately organized cytoskeletal infrastructure. This review begins by covering basic principles of biomechanics and the core cytoskeletal toolkit used to regulate the shapes of cells and tissues during embryogenesis and neural development. It illustrates how the principle of 'tensegrity' provides a useful conceptual framework for understanding how cells dynamically respond to forces that are generated internally or applied externally. The latter part of the review builds on this foundation in considering the development of mammalian cerebral cortex. The main focus is on cortical expansion and folding - processes that take place over an extended period of prenatal and postnatal development. Cortical expansion and folding are likely to involve many complementary mechanisms, some related to regulating cell proliferation and migration and others related to specific types and patterns of mechanical tension and pressure. Three distinct multi-mechanism models are evaluated in relation to a set of 18 key experimental observations and findings. The Composite Tension Plus (CT+) model is introduced as an updated version of a previous multi-component Differential Expansion Sandwich Plus (DES+) model (Van Essen, 2020); the new CT+ model includes 10 distinct mechanisms and has the greatest explanatory power among published models to date. Much needs to be done in order to validate specific mechanistic components and to assess their relative importance in different species, and important directions for future research are suggested.

White matter BOLD signals at 7 Tesla reveal visual field maps in optic radiation and vertical occipital fasciculus

There is growing evidence that blood-oxygen-level-dependent (BOLD) activity in the white matter (WM) can be detected by functional magnetic resonance imaging (fMRI). However, the functional relevance and significance of WM BOLD signals remain controversial. Here we investigated whether 7T BOLD fMRI can reveal fine-scale functional organizations of a WM bundle. Population receptive field (pRF) analyses of the 7T retinotopy dataset from the Human Connectome Project revealed clear contralateral retinotopic organizations of two visual WM bundles: the optic radiation (OR) and the vertical occipital fasciculus (VOF). The retinotopic maps of OR are highly consistent with post-mortem dissections and diffusion tractographies, while the VOF maps are compatible with the dorsal and ventral visual areas connected by the WM. Similar to the grey matter (GM) visual areas, both WM bundles show over-representations of the central visual field and increasing pRF size with eccentricity. Hemodynamic response functions of visual WM were slower and wider compared with those of GM areas. These findings clearly demonstrate that WM BOLD at 7 Tesla is closely coupled with neural activity related to axons, encoding highly specific information that can be used to characterize fine-scale functional organizations of a WM bundle.

Molecular and cellular mechanisms of human cortical connectivity

Comparative studies of the cerebral cortex have identified various human and primate-specific changes in both local and long-range connectivity, which are thought to underlie our advanced cognitive capabilities. These changes are likely mediated by the divergence of spatiotemporal regulation of gene expression, which is particularly prominent in the prenatal and early postnatal human and non-human primate cerebral cortex. In this review, we describe recent advances in characterizing human and primate genetic and cellular innovations including identification of novel species-specific, especially human-specific, genes, gene expression patterns, and cell types. Finally, we highlight three recent studies linking these molecular changes to reorganization of cortical connectivity.

CHD8 mutations increase gliogenesis to enlarge brain size in the nonhuman primate

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that affects social interaction and behavior. Mutations in the gene encoding chromodomain helicase DNA-binding protein 8 (CHD8) lead to autism symptoms and macrocephaly by a haploinsufficiency mechanism. However, studies of small animal models showed inconsistent findings about the mechanisms for CHD8 deficiency-mediated autism symptoms and macrocephaly. Using the nonhuman primate as a model system, we found that CRISPR/Cas9-mediated CHD8 mutations in the embryos of cynomolgus monkeys led to increased gliogenesis to cause macrocephaly in cynomolgus monkeys. Disrupting CHD8 in the fetal monkey brain prior to gliogenesis increased the number of glial cells in newborn monkeys. Moreover, knocking down CHD8 via CRISPR/Cas9 in organotypic monkey brain slices from newborn monkeys also enhanced the proliferation of glial cells. Our findings suggest that gliogenesis is critical for brain size in primates and that abnormal gliogenesis may contribute to ASD.

Macroscale coupling between structural and effective connectivity in the mouse brain

How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.

Biodistribution of Adeno-Associated Virus Gene Therapy Following Cerebrospinal Fluid-Directed Administration

Adeno-associated virus (AAV)-based gene therapies, exemplified by the approved therapy for spinal muscular atrophy, have the potential to deliver disease-course-altering treatments for central nervous system (CNS) indications. However, several clinical trials have reported severe adverse events, including patient deaths following high-dose systemic administration for muscle-directed gene transfer, highlighting the need to explore approaches utilizing lower doses when targeting the CNS. Animal models of disease provide insight into the response to new AAV therapies. However, translation from small to larger animals and eventually to humans is hampered by anatomical and biological differences across the species and their impact on AAV delivery. We performed a literature review of preclinical studies of AAV gene therapy biodistribution following cerebrospinal fluid (CSF) delivery (intracerebroventricular, intra-cisterna magna, and intrathecal lumbar). The reviewed literature varies greatly in the reported biodistribution of AAV following administration into the CSF. Differences between studies, including animal model, vector serotype used, method used to assess biodistribution, and route of administration, among other variables, contribute to differing outcomes and difficulties in translating these preclinical results. For example, only half of the published AAV-based gene therapy studies report vector copy number, the most direct readout following administration of a vector; none of these studies reported details such as the empty:full capsid ratio and quality of encapsidated genome. Analysis of the last decade's literature focusing on AAV-based gene therapies targeting the CNS underscores limitations of the body of knowledge and room for continued research. In particular, there is a need to understand the biodistribution achieved by different CSF-directed routes of administration and determining if specific cell types/structures of interest will be transduced. Our findings point to a clear need for a more systematic approach across the field to align the assessments and elements reported in preclinical research to enable more reliable translation across animal models and into human studies.

Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny

Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared to existing taxonomies and phylogenies. Here we performed a quantitative comparative analysis using the MaMI database (Tel Aviv University), a collection of brain networks reconstructed from ex vivo diffusion MRI spanning 125 species and 12 taxonomic orders or superorders. We used a broad range of metrics to measure between-mammal distances and compare these estimates to the separation of species as derived from taxonomy and phylogeny. We found that within-taxonomy order network distances are significantly closer than between-taxonomy network distances, and this relation holds for several measures of network distance. Furthermore, to estimate the evolutionary divergence between species, we obtained phylogenetic distances across 10,000 plausible phylogenetic trees. The anatomical network distances were rank-correlated with phylogenetic distances 10,000 times, creating a distribution of coefficients that demonstrate significantly positive correlations between network and phylogenetic distances. Collectively, these analyses demonstrate species-level organization across scales and informational sources: we relate brain networks distances, derived from MRI, with evolutionary distances, derived from genotyping data.

Neuroprotection and neuroregeneration: roles for the white matter

Efficient strategies for neuroprotection and repair are still an unmet medical need for neurodegenerative diseases and lesions of the central nervous system. Over the last few decades, a great deal of attention has been focused on white matter as a potential therapeutic target, mainly due to the discovery of the oligodendrocyte precursor cells in the adult central nervous system, a cell type able to fully repair myelin damage, and to the development of advanced imaging techniques to visualize and measure white matter lesions. The combination of these two events has greatly increased the body of research into white matter alterations in central nervous system lesions and neurodegenerative diseases and has identified the oligodendrocyte precursor cell as a putative target for white matter lesion repair, thus indirectly contributing to neuroprotection. This review aims to discuss the potential of white matter as a therapeutic target for neuroprotection in lesions and diseases of the central nervous system. Pivot conditions are discussed, specifically multiple sclerosis as a white matter disease; spinal cord injury, the acute lesion of a central nervous system component where white matter prevails over the gray matter, and Alzheimer's disease, where the white matter was considered an ancillary component until recently. We first describe oligodendrocyte precursor cell biology and developmental myelination, and its regulation by thyroid hormones, then briefly describe white matter imaging techniques, which are providing information on white matter involvement in central nervous system lesions and degenerative diseases. Finally, we discuss pathological mechanisms which interfere with myelin repair in adulthood.

Data-driven learning to identify biomarkers in bipolar disorder

BACKGROUND AND OBJECTIVE

Bipolar disorder (BD) is one of the primary causes of disability globally and can be easily misdiagnosed as schizophrenia or major depression due to their similar symptoms. Hence, it is of great significance to explore the pathogenesis of BD. Statistical analysis is currently the most common method for exploring the neuropathological mechanisms of psychiatric disorders. However, this method only considers the relationship between groups and does not reflect the individual-level diagnosis. Therefore, we developed machine learning algorithms to measure pathological brain changes in psychiatric disorders.

METHODS

An autoencoder and a feature selection method are proposed to identify the abnormal structural patterns of BD in this study. The autoencoder was constructed using structural imaging data from 1113 healthy controls, which aims to define the normal range of anatomical deviations to distinguish healthy individuals from BD patients. The biomarkers of BD were identified by the reconstruction errors in each brain region. The proposed feature selection (FS)-select framework aimed to determine the optimal FS method and identify the most reproducible feature associated with BD.

RESULTS

We found that the left orbital region of the middle frontal gyrus had the greatest difference between healthy controls and BD patients using a trained autoencoder. The most reproducible feature was the left orbital region of the middle frontal gyrus by FS-select framework when using the different cross-validation strategies.

CONCLUSIONS

A consistent result was obtained from the above two proposed methods wherein a significant difference between healthy controls and BD patients was identified in the left orbital region of the middle frontal gyrus.

Behavior is movement only but how to interpret it? Problems and pitfalls in translational neuroscience-a 40-year experience

Translational research in behavioral neuroscience seeks causes and remedies for human mental health problems in animals, following leads imposed by clinical research in psychiatry. This endeavor faces several problems because scientists must read and interpret animal movements to represent human perceptions, mood, and memory processes. Yet, it is still not known how mammalian brains bundle all these processes into a highly compressed motor output in the brain stem and spinal cord, but without that knowledge, translational research remains aimless. Based on some four decades of experience in the field, the article identifies sources of interpretation problems and illustrates typical translational pitfalls. (1) The sensory world of mice is different. Smell, hearing, and tactile whisker sensations dominate in rodents, while visual input is comparatively small. In humans, the relations are reversed. (2) Mouse and human brains are equated inappropriately: the association cortex makes up a large portion of the human neocortex, while it is relatively small in rodents. The predominant associative cortex in rodents is the hippocampus itself, orchestrating chiefly inputs from secondary sensorimotor areas and generating species-typical motor patterns that are not easily reconciled with putative human hippocampal functions. (3) Translational interpretation of studies of memory or emotionality often neglects the ecology of mice, an extremely small species surviving by freezing or flight reactions that do not need much cognitive processing. (4) Further misinterpretations arise from confounding neuronal properties with system properties, and from rigid mechanistic thinking unaware that many experimentally induced changes in the brain do partially reflect unpredictable compensatory plasticity. (5) Based on observing hippocampal lesion effects in mice indoors and outdoors, the article offers a simplistic general model of hippocampal functions in relation to hypothalamic input and output, placing hypothalamus and the supraspinal motor system at the top of a cerebral hierarchy. (6) Many translational problems could be avoided by inclusion of simple species-typical behaviors as end-points comparable to human cognitive or executive processing, and to rely more on artificial intelligence for recognizing patterns not classifiable by traditional psychological concepts.

Deuterium metabolic imaging and hyperpolarized 13C-MRI of the normal human brain at clinical field strength reveals differential cerebral metabolism

Deuterium metabolic imaging (DMI) and hyperpolarized 13C-pyruvate MRI (13C-HPMRI) are two emerging methods for non-invasive and non-ionizing imaging of tissue metabolism. Imaging cerebral metabolism has potential applications in cancer, neurodegeneration, multiple sclerosis, traumatic brain injury, stroke, and inborn errors of metabolism. Here we directly compare these two non-invasive methods at 3 T for the first time in humans and show how they simultaneously probe both oxidative and non-oxidative metabolism. DMI was undertaken 1-2 h after oral administration of [6,6'-2H2]glucose, and 13C-MRI was performed immediately following intravenous injection of hyperpolarized [1-13C]pyruvate in ten and nine normal volunteers within each arm respectively. DMI was used to generate maps of deuterium-labelled water, glucose, lactate, and glutamate/glutamine (Glx) and the spectral separation demonstrated that DMI is feasible at 3 T. 13C-HPMRI generated maps of hyperpolarized carbon-13 labelled pyruvate, lactate, and bicarbonate. The ratio of 13C-lactate/13C-bicarbonate (mean 3.7 ± 1.2) acquired with 13C-HPMRI was higher than the equivalent 2H-lactate/2H-Glx ratio (mean 0.18 ± 0.09) acquired using DMI. These differences can be explained by the route of administering each probe, the timing of imaging after ingestion or injection, as well as the biological differences in cerebral uptake and cellular physiology between the two molecules. The results demonstrate these two metabolic imaging methods provide different yet complementary readouts of oxidative and reductive metabolism within a clinically feasible timescale. Furthermore, as DMI was undertaken at a clinical field strength within a ten-minute scan time, it demonstrates its potential as a routine clinical tool in the future.

Establishing a Baseline for Human Cortical Folding Morphological Variables: A Multisite Study

Differences in the way human cerebral cortices fold have been correlated to health, disease, development, and aging. However, to obtain a deeper understanding of the mechanisms that generate such differences, it is useful to derive one's morphometric variables from the first principles. This study explores one such set of variables that arise naturally from a model for universal self-similar cortical folding that was validated on comparative neuroanatomical data. We aim to establish a baseline for these variables across the human lifespan using a heterogeneous compilation of cross-sectional datasets as the first step to extending the model to incorporate the time evolution of brain morphology. We extracted the morphological features from structural MRI of 3,650 subjects: 3,095 healthy controls (CTL) and 555 patients with Alzheimer's Disease (AD) from 9 datasets, which were harmonized with a straightforward procedure to reduce the uncertainty due to heterogeneous acquisition and processing. The unprecedented possibility of analyzing such a large number of subjects in this framework allowed us to compare CTL and AD subjects' lifespan trajectories, testing if AD is a form of accelerated aging at the brain structural level. After validating this baseline from development to aging, we estimate the variables' uncertainties and show that Alzheimer's Disease is similar to premature aging when measuring global and local degeneration. This new methodology may allow future studies to explore the structural transition between healthy and pathological aging and may be essential to generate data for the cortical folding process simulations.

Short- and Long-Range Connections Differentially Modulate the Dynamics and State of Small-World Networks

The human brain contains billions of neurons that flexibly interconnect to support local and global computational spans. As neuronal activity propagates through the neural medium, it approaches a critical state hedged between ordered and disordered system regimes. Recent work demonstrates that this criticality coincides with the small-world topology, a network arrangement that accommodates both local (subcritical) and global (supercritical) system properties. On one hand, operating near criticality is thought to offer several neurocomputational advantages, e.g., high-dynamic range, efficient information capacity, and information transfer fidelity. On the other hand, aberrations from the critical state have been linked to diverse pathologies of the brain, such as post-traumatic epileptiform seizures and disorders of consciousness. Modulation of brain activity, through neuromodulation, presents an attractive mode of treatment to alleviate such neurological disorders, but a tractable neural framework is needed to facilitate clinical progress. Using a variation on the generative small-world model of Watts and Strogatz and Kuramoto's model of coupled oscillators, we show that the topological and dynamical properties of the small-world network are divided into two functional domains based on the range of connectivity, and that these domains play distinct roles in shaping the behavior of the critical state. We demonstrate that short-range network connections shape the dynamics of the system, e.g., its volatility and metastability, whereas long-range connections drive the system state, e.g., a seizure. Together, these findings lend support to combinatorial neuromodulation approaches that synergistically normalize the system dynamic while mobilizing the system state.

Segmentation of Cerebral Small Vessel Diseases-White Matter Hyperintensities Based on a Deep Learning System

Objective: Reliable quantification of white matter hyperintensities (WHMs) resulting from cerebral small vessel diseases (CSVD) is essential for understanding their clinical impact. We aim to develop and clinically validate a deep learning system for automatic segmentation of CSVD-WMH from fluid-attenuated inversion recovery (FLAIR) imaging using large multicenter data.

Method: A FLAIR imaging dataset of 1,156 patients diagnosed with CSVD associated WMH (median age, 54 years; 653 males) obtained between September 2018 and September 2019 from Beijing Tiantan Hospital was retrospectively analyzed in this study. Locations of CSVD-WMH on the FLAIR scans were manually marked by two experienced neurologists. Using the manually labeled data of 996 patients (development set), a U-shaped novel 2D convolutional neural network (CNN) architecture was trained for automatic segmentation of CSVD-WMH. The segmentation performance of the network was evaluated with per pixel and lesion level dice scores using an independent internal test set (n = 160) and a multi-center external test set (n = 90, three medical centers). The clinical suitability of the segmentation results, classified as acceptable, acceptable with minor revision, acceptable with major revision, and not acceptable, was analyzed by three independent neuroradiologists. The inter-neuroradiologists agreement rate was assessed by the Kendall-W test.

Results: On the internal and external test sets, the proposed CNN architecture achieved per pixel and lesion level dice scores of 0.72 (external test set), and they were significantly better than the state-of-the-art deep learning architectures proposed for WMH segmentation. In the clinical evaluation, neuroradiologists observed the segmentation results for 95% of the patients were acceptable or acceptable with a minor revision.

Conclusions: A deep learning system can be used for automated, objective, and clinically meaningful segmentation of CSVD-WMH with high accuracy.

Brain research challenges supercomputing

[Figure: see text].

Scaling Principles of White Matter Connectivity in the Human and Nonhuman Primate Brain

Brains come in many shapes and sizes. Nature has endowed big-brained primate species like humans with a proportionally large cerebral cortex. Comparative studies have suggested, however, that the total volume allocated to white matter connectivity-the brain's infrastructure for long-range interregional communication-does not keep pace with the cortex. We investigated the consequences of this allometric scaling on brain connectivity and network organization. We collated structural and diffusion magnetic resonance imaging data across 14 primate species, describing a comprehensive 350-fold range in brain size across species. We show volumetric scaling relationships that indeed point toward a restriction of macroscale connectivity in bigger brains. We report cortical surface area to outpace white matter volume, with larger brains showing lower levels of overall connectedness particularly through sparser long-range connectivity. We show that these constraints on white matter connectivity are associated with longer communication paths, higher local network clustering, and higher levels of asymmetry in connectivity patterns between homologous areas across the left and right hemispheres. Our findings reveal conserved scaling relationships of major brain components and show consequences for macroscale brain circuitry, providing insights into the connectome architecture that could be expected in larger brains such as the human brain.

Structure-function similarities in deep brain stimulation targets cross-species

Deep Brain Stimulation (DBS) is an effective neurosurgical treatment to alleviate motor symptoms of advanced Parkinson's disease. Due to its potential, DBS usage is rapidly expanding to target a large number of brain regions to treat a wide range of diseases and neuropsychiatric disorders. The identification and validation of new target regions heavily rely on the insights gained from rodent and primate models. Here we present a large-scale automatic meta-analysis in which the structure-function associations within and between species are compared for 21 DBS targets in humans. The results indicate that the structure-function association for the majority of the 21 included subcortical areas were conserved cross-species. A subset of structures showed overlapping functional association. This can potentially be attributed to shared brain networks and might explain why multiple brain areas are targeted for the same disease or neuropsychiatric disorder.

Regional White Matter Scaling in the Human Brain

Anatomical organization of the primate cortex varies as a function of total brain size, where possession of a larger brain is accompanied by disproportionate expansion of associative cortices alongside a relative contraction of sensorimotor systems. However, equivalent scaling maps are not yet available for regional white matter anatomy. Here, we use three large-scale neuroimaging datasets to examine how regional white matter volume (WMV) scales with interindividual variation in brain volume among typically developing humans (combined N = 2391: 1247 females, 1144 males). We show that WMV scaling is regionally heterogeneous: larger brains have relatively greater WMV in anterior and posterior regions of cortical white matter, as well as the genu and splenium of the corpus callosum, but relatively less WMV in most subcortical regions. Furthermore, regions of positive WMV scaling tend to connect previously-defined regions of positive gray matter scaling in the cortex, revealing a coordinated coupling of regional gray and white matter organization with naturally occurring variations in human brain size. However, we also show that two commonly studied measures of white matter microstructure, fractional anisotropy (FA) and magnetization transfer (MT), scale negatively with brain size, and do so in a manner that is spatially unlike WMV scaling. Collectively, these findings provide a more complete view of anatomic scaling in the human brain, and offer new contexts for the interpretation of regional white matter variation in health and disease.SIGNIFICANCE STATEMENT Recent work has shown that, in humans, regional cortical and subcortical anatomy show systematic changes as a function of brain size variation. Here, we show that regional white matter structures also show brain-size related changes in humans. Specifically, white matter regions connecting higher-order cortical systems are relatively expanded in larger human brains, while subcortical and cerebellar white matter tracts responsible for unimodal sensory or motor functions are relatively contracted. This regional scaling of white matter volume (WMV) is coordinated with regional scaling of cortical anatomy, but is distinct from scaling of white matter microstructure. These findings provide a more complete view of anatomic scaling of the human brain, with relevance for evolutionary, basic, and clinical neuroscience.

Structural Attributes and Principles of the Neocortical Connectome in the Marmoset Monkey

The marmoset monkey has become an important primate model in Neuroscience. Here, we characterize salient statistical properties of interareal connections of the marmoset cerebral cortex, using data from retrograde tracer injections. We found that the connectivity weights are highly heterogeneous, spanning 5 orders of magnitude, and are log-normally distributed. The cortico-cortical network is dense, heterogeneous and has high specificity. The reciprocal connections are the most prominent and the probability of connection between 2 areas decays with their functional dissimilarity. The laminar dependence of connections defines a hierarchical network correlated with microstructural properties of each area. The marmoset connectome reveals parallel streams associated with different sensory systems. Finally, the connectome is spatially embedded with a characteristic length that obeys a power law as a function of brain volume across rodent and primate species. These findings provide a connectomic basis for investigations of multiple interacting areas in a complex large-scale cortical system underlying cognitive processes.

The distribution, number, and certain neurochemical identities of infracortical white matter neurons in the brains of a southern lesser galago, a black-capped squirrel monkey, and a crested macaque

In the current study, we examined the number, distribution, and aspects of the neurochemical identities of infracortical white matter neurons, also termed white matter interstitial cells (WMICs), in the brains of a southern lesser galago (Galago moholi), a black-capped squirrel monkey (Saimiri boliviensis boliviensis), and a crested macaque (Macaca nigra). Staining for neuronal nuclear marker (NeuN) revealed WMICs throughout the infracortical white matter, these cells being most dense close to inner cortical border, decreasing in density with depth in the white matter. Stereological analysis of NeuN-immunopositive cells revealed estimates of approximately 1.1, 10.8, and 37.7 million WMICs within the infracortical white matter of the galago, squirrel monkey, and crested macaque, respectively. The total numbers of WMICs form a distinct negative allometric relationship with brain mass and white matter volume when examined in a larger sample of primates where similar measures have been obtained. In all three primates studied, the highest densities of WMICs were in the white matter of the frontal lobe, with the occipital lobe having the lowest. Immunostaining revealed significant subpopulations of WMICs containing neuronal nitric oxide synthase (nNOS) and calretinin, with very few WMICs containing parvalbumin, and none containing calbindin. The nNOS and calretinin immunopositive WMICs represent approximately 21% of the total WMIC population; however, variances in the proportions of these neurochemical phenotypes were noted. Our results indicate that both the squirrel monkey and crested macaque might be informative animal models for the study of WMICs in neurodegenerative and psychiatric disorders in humans.

Toward three-dimensional in vitro models to study neurovascular unit functions in health and disease

The high metabolic demands of the brain require an efficient vascular system to be coupled with neural activity to supply adequate nutrients and oxygen. This supply is coordinated by the action of neurons, glial and vascular cells, known collectively as the neurovascular unit, which temporally and spatially regulate local cerebral blood flow through a process known as neurovascular coupling. In many neurodegenerative diseases, changes in functions of the neurovascular unit not only impair neurovascular coupling but also permeability of the blood-brain barrier, cerebral blood flow and clearance of waste from the brain. In order to study disease mechanisms, we need improved physiologically-relevant human models of the neurovascular unit. Advances towards modeling the cellular complexity of the neurovascular unit in vitro have been made using stem-cell derived organoids and more recently, vascularized organoids, enabling intricate studies of non-cell autonomous processes. Engineering and design innovations in microfluidic devices and tissue engineering are progressing our ability to interrogate the cerebrovasculature. These advanced models are being used to gain a better understanding of neurodegenerative disease processes and potential therapeutics. Continued innovation is required to build more physiologically-relevant models of the neurovascular unit encompassing both the cellular complexity and designed features to interrogate neurovascular unit functionality.

Constant neuropilar ratio in the insect brain

Revealing scaling rules is necessary for understanding the morphology, physiology and evolution of living systems. Studies of animal brains have revealed both general patterns, such as Haller's rule, and patterns specific for certain animal taxa. However, large-scale studies aimed at studying the ratio of the entire neuropil and the cell body rind in the insect brain have never been performed. Here we performed morphometric study of the adult brain in 37 insect species of 26 families and ten orders, ranging in volume from the smallest to the largest by a factor of more than 4,000,000, and show that all studied insects display a similar ratio of the volume of the neuropil to the cell body rind, 3:2. Allometric analysis for all insects shows that the ratio of the volume of the neuropil to the volume of the brain changes strictly isometrically. Analyses within particular taxa, size groups, and metamorphosis types also reveal no significant differences in the relative volume of the neuropil; isometry is observed in all cases. Thus, we establish a new scaling rule, according to which the relative volume of the entire neuropil in insect brain averages 60% and remains constant.

Network structure of the mouse brain connectome with voxel resolution

Fine-grained descriptions of brain connectivity are required to understand how neural information is processed and relayed across spatial scales. Previous investigations of the mouse brain connectome have used discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to connectome organization. Here, we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We report a number of previously unappreciated organizational principles in the mammalian brain, including a directional segregation of hub regions into neural sink and sources, and a strategic wiring of neuromodulatory nuclei as connector hubs and critical orchestrators of network communication. We also find that the mouse cortical connectome is hierarchically organized along two superimposed cortical gradients reflecting unimodal-transmodal functional processing and a modality-specific sensorimotor axis, recapitulating a phylogenetically conserved feature of higher mammals. These findings advance our understanding of the foundational wiring principles of the mammalian connectome.

Model-based correction algorithm for Fourier Transform infrared microscopy measurements of complex tissue-substrate systems

Model-based algorithms have recently attracted much attention for data pre-processing in tissue mapping and imaging by Fourier transform infrared micro-spectroscopy (FTIR). Their versatility, robustness and computational performance enabled the improvement of spectral quality by mitigating the impact of scattering and fringing in FTIR spectra of chemically homogeneous biological systems. However, to date, no comprehensive algorithm has been optimized and automated for large-area FTIR imaging of histologically complex tissue samples. Herein, for the first time, we propose a unique, integrated and fully-automated Multiple Linear Regression Multi-Reference (MLR-MR) method for correcting linear baseline effects due to diffuse scattering, for compensating substrate thickness inhomogeneity and accounting for sample chemical heterogeneity in FTIR images. In particular, the algorithm uses multiple-reference spectra for histologically heterogeneous biological samples. The performance of the procedure was demonstrated for FTIR imaging of chemically complex rat brain frontal cortex tissue samples, mounted onto Ultralene® films. The proposed MLR-MR correction algorithm allows the efficient retrieval of "pure" absorbance spectra and greatly improves the histological fidelity of FTIR imaging data, as compared with the one-reference approach. In addition, the MLR-MR algorithm here presented opens up the possibility for extracting information on substrate thickness variability, thus enabling the indirect evaluation of its topography. As a whole, the MLR-MR procedure can be easily extended to more complex systems for which Mie scattering effects must also be eliminated.

Cerebral cortical folding, parcellation, and connectivity in humans, nonhuman primates, and mice

Advances in neuroimaging and neuroanatomy have yielded major insights concerning fundamental principles of cortical organization and evolution, thus speaking to how well different species serve as models for human brain function in health and disease. Here, we focus on cortical folding, parcellation, and connectivity in mice, marmosets, macaques, and humans. Cortical folding patterns vary dramatically across species, and individual variability in cortical folding increases with cortical surface area. Such issues are best analyzed using surface-based approaches that respect the topology of the cortical sheet. Many aspects of cortical organization can be revealed using 1 type of information (modality) at a time, such as maps of cortical myelin content. However, accurate delineation of the entire mosaic of cortical areas requires a multimodal approach using information about function, architecture, connectivity, and topographic organization. Comparisons across the 4 aforementioned species reveal dramatic differences in the total number and arrangement of cortical areas, particularly between rodents and primates. Hemispheric variability and bilateral asymmetry are most pronounced in humans, which we evaluated using a high-quality multimodal parcellation of hundreds of individuals. Asymmetries include modest differences in areal size but not in areal identity. Analyses of cortical connectivity using anatomical tracers reveal highly distributed connectivity and a wide range of connection weights in monkeys and mice; indirect measures using functional MRI suggest a similar pattern in humans. Altogether, a multifaceted but integrated approach to exploring cortical organization in primate and nonprimate species provides complementary advantages and perspectives.

Life history changes accompany increased numbers of cortical neurons: A new framework for understanding human brain evolution

Narratives of human evolution have focused on cortical expansion and increases in brain size relative to body size, but considered that changes in life history, such as in age at sexual maturity and thus the extent of childhood and maternal dependence, or maximal longevity, are evolved features that appeared as consequences of selection for increased brain size, or increased cognitive abilities that decrease mortality rates, or due to selection for grandmotherly contribution to feeding the young. Here I build on my recent finding that slower life histories universally accompany increased numbers of cortical neurons across warm-blooded species to propose a simpler framework for human evolution: that slower development to sexual maturity and increased post-maturity longevity are features that do not require selection, but rather inevitably and immediately accompany evolutionary increases in numbers of cortical neurons, thus fostering human social interactions and cultural and technological evolution as generational overlap increases.