Pharmacological stimulation of N-methyl-D-aspartate receptors (NMDAr) could enhance the outcome of cue-exposure therapy for smoking cessation. NMDAr stimulation can be achieved by increasing pharmacologically the synaptic levels of glycine, a necessary co-agonist. Here, we evaluate the effects of SSR504734, a selective inhibitor of glycine type I transporter (GlyT1) in an extinction-reinstatement procedure inducing robust and lasting nicotine-seeking behavior in rats. Male Wistar rats were trained to associate discriminative stimuli (SDs) with the availability of nicotine (0.03 mg/kg/65 μL/2 second/infusion) or sucrose (45-mg pellet) versus non-reward in two-lever operant cages. Reinforced response was followed by cue signaling 20-second time-out (CSs). Once the training criterion was met, rats underwent extinction of lever presses, in the absence of reinforcers, SDs and CSs. Re-exposure to nicotine or sucrose SD+/CS+, but not non-reward SD−/CS−, revived responding at the previously reinforced lever. Acute pre-treatment with SSR504734 (10 mg/kg i.p.) reduced nicotine-seeking but not sucrose-seeking behavior without influencing rats' locomotor activity. Sub-chronic treatment (10 mg/kg i.p. for 5 days) during daily exposure to SD+/CS+ reduced nicotine-seeking; however, this effect was transient, with return to SD+/CS+ responding at 72 hours. Full recovery to SD+/CS+ responding was observed after 1 month suggesting that SSR504734 sub-acute treatment did not engage the long-term plasticity mechanisms probably involved in nicotine-seeking. In conclusion, GlyT1-inhibitors might offer a therapeutic opportunity for acute cue-controlled nicotine-seeking, but the lack of persistent effects of the sub-chronic treatment associated with nicotine cues exposure suggests that short-term administration of GlyT1-inhibitor SSR504734 is not sufficient to promote extinction of nicotine-cue conditioned responding.
Abstract Abnormal tactile response is considered an integral feature of Autism Spectrum Disorders (ASDs), and hypo-responsiveness to tactile stimuli is often associated with the severity of ASDs core symptoms. Patients with Phelan-McDermid syndrome (PMS), caused by mutations in the SHANK3 gene, show ASD-like symptoms associated with aberrant tactile responses. However, the neural underpinnings of these somatosensory abnormalities are still poorly understood. Here we investigated, in Shank3b −/− adult mice, the neural substrates of whisker-guided behaviors, a key component of rodents’ interaction with the surrounding environment. To this aim, we assessed whisker-dependent behaviors in Shank3b −/− adult mice and age-matched controls, using the textured novel object recognition (tNORT) and whisker nuisance (WN) test. Shank3b −/− mice showed deficits in whisker-dependent texture discrimination in tNORT and behavioral hypo-responsiveness to repetitive whisker stimulation in WN. Notably, sensory hypo-responsiveness was accompanied by a significantly reduced activation of the primary somatosensory cortex (S1) and hippocampus, as measured by c- fos mRNA in situ hybridization, a proxy of neuronal activity following whisker stimulation. Moreover, resting-state fMRI showed a significantly reduced S1-hippocampal connectivity in Shank3b mutant mice. Together, these findings suggest that impaired crosstalk between hippocampus and S1 might underlie Shank3b −/− hypo-reactivity to whisker-dependent cues, highlighting a potentially generalizable form of dysfunctional somatosensory processing in ASD. Significance Statement Patients with Phelan-McDermid syndrome, a syndromic form of ASD caused by mutation of the SHANK3 gene, often show aberrant responses to touch. However, the neural basis of atypical sensory responses in ASD remains undetermined. Here we used Shank3 deficient mice to investigate the neural substrates of behavioral responses to repetitive stimulation of the whiskers, a highly developed sensory organ in mice. We found that mice lacking the Shank3 gene are hypo-responsive to repetitive whisker stimulation. This trait was associated with reduced engagement and connectivity between the primary somatosensory cortex and hippocampus. These results suggest that dysfunctional cortico-hippocampal coupling may underlie somatosensory processing deficits in SHANK3 mutation carriers and related syndromic forms of ASD.
Abstract Postmortem studies have revealed increased density of excitatory synapses in the brains of individuals with autism, with a putative link to aberrant mTOR-dependent synaptic pruning. Autism is also characterized by atypical macroscale functional connectivity as measured with resting-state fMRI (rsfMRI). These observations raise the question of whether excess of synapses cause aberrant functional connectivity in autism. Using rsfMRI, electrophysiology and in silico modelling in Tsc2 haploinsufficient mice, we show that mTOR-dependent increased spine density is associated with autism-like stereotypies and cortico-striatal hyperconnectivity. These deficits are completely rescued by pharmacological inhibition of mTOR. Notably, we further demonstrate that children with idiopathic autism exhibit analogous cortical-striatal hyperconnectivity, and document that this connectivity fingerprint is enriched for autism-dysregulated genes interacting with mTOR or TSC2. Finally, we show that the identified transcriptomic signature is predominantly expressed in a subset of children with autism, thereby defining a segregable autism subtype. Our findings causally link mTOR-related synaptic pathology to large-scale network aberrations, revealing a unifying multi-scale framework that mechanistically reconciles developmental synaptopathy and functional hyperconnectivity in autism. Significance Aberrant brain functional connectivity is a hallmark of autism, but the neural basis of this phenomenon remains unclear. We show that a mouse line recapitulating mTOR-dependent synaptic pruning deficits observed in postmortem autistic brains exhibits widespread functional hyperconnectivity. Importantly, pharmacological normalization of mTOR signalling completely rescues synaptic, behavioral and functional connectivity deficits. We also show that a similar connectivity fingerprint can be isolated in human fMRI scans of people with autism, where it is linked to over-expression of mTOR-related genes. Our results reveal a unifying multi-scale translational framework that mechanistically links aberrations in synaptic pruning with functional hyperconnectivity in autism.
A bstract Both macroscale connectome miswiring and microcircuit anomalies have been suggested to play a role in the pathophysiology of autism. However, an overarching framework that consolidates these macro and microscale perspectives of the condition is lacking. Here, we combined connectome-wide manifold learning and biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. Our analysis established that autism showed significant differences in structural connectome organization relative to neurotypical controls, with strong effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models revealed that the degree of macroscale anomalies was related to atypical increases of subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic decoding and developmental gene enrichment analyses provided biological context and pointed to genes expressed in cortical and thalamic areas during childhood and adolescence. Supervised machine learning showed the macroscale perturbations predicted socio-cognitive symptoms and repetitive behaviors. Our analyses provide convergent support that atypical subcortico-cortical interactions may contribute to both microcircuit and macroscale connectome anomalies in autism.
The peopling of the Mediterranean basin in the first millennium AD can be determined by exploring a combination of cultural, economic and biological factors that influence the structure of populations and determine particular situations of gene frequencies. Quantitative characters from 2,487 adult crania of both sexes from central and southern Europe and the Italian peninsula were analyzed using multivariate statistical analyses. Biological distances representing phenotypic variation between these populations were not found. An analysis of Mahalanobis D2 distances established a great homogeneity. This scarce morphological variability is shown also by principal component analysis. The application of cluster analysis shows the formation of two clusters: one distributed along the Adriatic coast; the other along the Tyrrhenian. This model is similar to the first millennium BC pattern. The difference is that in this case there are no significant biological divergences but only geographic ones, perhaps attributable to micro-adaptive factors. These two lines flow together in the populations of central and southern Italy. The phenomenon of the Migration of People (or Barbarian Invasions) in Italy during the first millennium AD does not seem to produce a significant variation in the Italian genetic substratum. This could be associated with the great gene flow that the Roman Empire opened, not only in the Mediterranean basin but also in Europe, following the conquest wars. Over time this produced a new general genetic model common to southern Europe and Italy that explains the low variability found in this study.
The kinetics of global changes in transcription patterns during competence development in Streptococcus pneumoniae was analysed with high-density arrays. Four thousand three hundred and one clones of a S. pneumoniae library, covering almost the entire genome, were amplified by PCR and gridded at high density onto nylon membranes. Competence was induced by the addition of CSP (competence stimulating peptide) to S. pneumoniae cultures grown to the early exponential phase. RNA was extracted from samples at 5 min intervals (for a period of 30 min) after the addition of CSP. Radiolabelled cDNA was generated from isolated total RNA by random priming and the probes were hybridized to identical high density arrays. Genes whose transcription was induced or repressed during competence were identified. Most of the genes previously known to be competence induced were detected together with several novel genes that all displayed the characteristic transient kinetics of competence-induced genes. Among the newly identified genes many have suggested functions compatible with roles in genetic transformation. Some of them may represent new members of the early or late competence regulons showing competence specific consensus sequences in their promoter regions. Northern experiments and mutational analysis were used to confirm some of the results.
Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Traditionally, research unraveling seasonal neuroplasticity in songbirds has focused on the male song control system and testosterone. We longitudinally monitored the song behavior and neuroplasticity in male and female starlings during multiple photoperiods using Diffusion Tensor and Fixel-Based techniques. These exploratory data-driven whole-brain methods resulted in a population-based tractogram confirming microstructural sexual dimorphisms in the song control system. Furthermore, male brains showed hemispheric asymmetries in the pallium, whereas females had higher interhemispheric connectivity, which could not be attributed to brain size differences. Only females with large brains sing but differ from males in their song behavior by showing involvement of the hippocampus. Both sexes experienced multisensory neuroplasticity in the song control, auditory and visual system, and cerebellum, mainly during the photosensitive period. This period with low gonadal hormone levels might represent a ‘sensitive window’ during which different sensory and motor systems in the cerebrum and cerebellum can be seasonally re-shaped in both sexes. Introduction Although various songbird species demonstrate different levels of female song, in one of the most studied song bird species, the zebra finch, female song is absent (Odom et al., 2014). Observations like these led to the discovery of one of the largest sexual dimorphisms in the brain of vertebrate species (Bernard et al., 1993; Nottebohm and Arnold, 1976). Such sexual dimorphisms in singing behavior and brain structure drove a bias in research of the song control system toward male songbirds (Figure 1B). Besides the large sexual dimorphism in song and song control nuclei, seasonal songbirds expose an additional peculiarity as they display naturally re-occurring seasonal cycles of singing and neuroplasticity of the song control system. However, the male focus proceeded in the research of seasonal neuroplasticity, since this process was shown to be largely driven by photoperiod-induced increases in testosterone, which results in a pre-optic area (POA)-mediated increase in motivation to sing and subsequent singing activity-induced neuroplasticity (Alward et al., 2013). The largest differences in song control nuclei volumes are found between breeding and non-breeding season (Riters et al., 2002). This led seasonal neuroplasticity studies to focus mainly on the breeding season or photostimulated phase, which they often compare to the non-breeding season or photorefractory and photosensitive phases. The main emphasis on the role of photoperiodic-induced increase in testosterone has led to many manipulation studies involving castration and/or testosterone implantation and its effect on song production and the song control system (e.g. Hall and Macdougall-Shackleton, 2012; Orije et al., 2020; Stevenson and Ball, 2010). Figure 1 with 1 supplement see all Download asset Open asset Simplified overview of the experimental setup. (A). Schematic overview of the song control and auditory system of the songbird brain and the cerebellar and hippocampal connections to the rest of the brain (B) and unilateral DWI-based 3D representation of the different nuclei and the interconnecting tracts as deduced from the tractogram (C). Male and female starlings were measured repeatedly as they went through different photoperiods. At each time point, their songs were recorded, blood samples were collected and T2-weighted 3D anatomical and diffusion weighted images (DWI) were acquired. The 3D anatomical images were used to extract whole brain volume (A). The song control system is subdivided in the anterior forebrain pathway (blue arrows) and the song motor pathway (red arrows). The auditory pathway is indicated by green arrows. The orange arrows indicate the connection of the lateral cerebellar nucleus (CbL) to the dorsal thalamic region further connecting to the song control system as suggested by Person et al., 2008; Pidoux et al., 2018 (B,C). Nuclei in (C) are indicated in gray, the tractogram is color-coded according to the standard red-green-blue code (red = left right orientation (L-R), blue = dorso ventral (D-V) and green = rostro caudal (R-C)). Prior studies have also pointed out that testosterone implants only have an effect when the birds have experienced a photosensitive phase with short day length exposure (Bernard and Ball, 1997; Rouse et al., 2015). Although little research is devoted to brain neuroplasticity during this state (Larson et al., 2019; Riters et al., 2002), multiple studies have shown that during the photosensitive period, testes size (Cornez et al., 2017) and GnRH expression in the POA slowly start to increase (Bentley et al., 2013; Hurley et al., 2008; Stevenson et al., 2012), while some song control nuclei slowly start to grow (Riters et al., 2002). The photosensitive period might be seen as a sensitive window, defined as a period in which experiences lead to long-lasting changes (that remain in the subsequent photostimulated phase or breading season) in the brain and song behavior (Knudsen, 2004). The photosensitive period plays a crucial role in creating permissive circumstances for enhanced neuroplasticity resulting in enlarged song control nuclei as observed during the photostimulated stage. The studies on sexual dimorphisms and seasonal neuroplasticity in songbirds have been preoccupied with vocal control areas as the neural substrate for the behavioral sexual dimorphism in song performance. However, in vivo Magnetic Resonance Imaging (MRI) allows us to visualize the entire brain and study the involvement of other structures like the hippocampus and cerebellum, which recently have been shown to be involved in song preference and song processing, respectively (Bailey et al., 2009; Person et al., 2008; Pidoux et al., 2018; Striedter, 2016). Prior MRI studies from our team focusing on the entire songbird brain have demonstrated seasonal neuroplasticity and functional changes in both auditory and olfactory systems of starlings (De Groof et al., 2017; De Groof et al., 2010; De Groof et al., 2013). This outcome hints toward a seasonal re-occurring cycle of multisensory neuroplasticity and raises the question whether the photosensitive stage perhaps represents a renewed ‘sensitive window’ for heightened neuroplasticity and rewiring of various sensory and sensorimotor systems. The current study aims to fill the gaps highlighted above and answers the following questions with an extensive data-driven exploratory brain-wide structural MRI study of both male and female starlings throughout different seasons: (1) Is the sexual dimorphism limited to the song control system in starlings, as in zebra finches where only the male sings (Hamaide et al., 2017) or does this dimorphism extend to other sensory systems? Can it be attributed to a difference in total brain size between the sexes, similar to what has been observed in mammals? (2) Do female and male starlings display parallel or different seasonal structural neuroplasticity? Is this neuroplasticity limited to the song control system or does it extend to other neural circuits? When do these (neuroplasticity) changes take place, already during the photosensitive period when gonadal hormone levels are still very low? (3) How does seasonal neuroplasticity correlate – at the group and individual level – with gonadal hormone levels and with song-production? Since MRI is an in vivo method, we were able to measure the same subjects repeatedly and monitor the structural neuroplasticity longitudinally in 13 male and 12 female starlings as they undergo different subsequent photoperiodic phases: at baseline photorefractory state (PR), after 4, 8, and 12 weeks of short days during the photosensitive state (SD4, SD8, SD12), after 4 weeks of photostimulation (LD4) and after 16 weeks on long days when the birds are photorefractory again (LD16) (Figure 1A). At each time point we: collected blood samples; measured body weight; recorded song; and acquired structural MRI data including 3D anatomical scans and diffusion weighted images (DWI). Using voxel-based diffusion tensor imaging (DTI) and fixel-based analysis (De Groof et al., 2006; Raffelt et al., 2017), we could visualize different sensory and sensorimotor circuits and their mutual connections (Figure 1C). We established spatio-temporal statistical maps revealing microstructural changes in gray and white matter, specifically in fiber connections throughout the different seasons in the entire brain in both sexes. To learn specifically about the contribution of singing activity-induced neuroplasticity in the seasonal neuroplasticity, a voxel-based multiple regression between seasonal song rate and corresponding DTI parameter maps was performed to identify the neuronal correlates of song behavior in both sexes (Hamaide et al., 2020; Sagi et al., 2012). Results Population-based tractogram of the starling brain using fixel-based analysis In the current study, we analyzed the DWI scans in two distinct ways: (1) using the common approach of diffusion tensor derived metrics such as fractional anisotropy (FA) and; (2) using a novel method of fiber orientation distribution (FOD)-derived fixel-based analysis. Both techniques infer the microstructural information based on the diffusion of water molecules, but they are conceptually different (Table 1). Common DTI analysis extracts for each voxel several diffusion parameters, which are sensitive to various microstructural changes in both gray and white matter specified in Table 1. Fixel-based analysis on the other hand explores both microscopic changes in apparent fiber density (FD) or macroscopic changes in fiber-bundle cross-section (log FC) (Table 1). Positive fiber-bundle cross-section values indicate expansion, whereas negative values reflect shrinkage of a fiber bundle relative to the template (Raffelt et al., 2017). Table 1 Overview of the parameters studied by common DTI analysis and novel fixel-based analysis, including a comprehensive, but not exhaustive, list of some of the microstructural changes to which they are sensitive. MetricLevelPara-meterMeasuresSensitive to changes in:DTI analysisDiffusion tensor VoxelFADirectionality of water diffusionAxon number and density, axon diameter, myelination, fiber organization MDAverage diffusion across all directionsCell size, cell spacing, cell density, dendrite branching, extracellular space ADDiffusion along the fiber directionAxon number and density RDDiffusion perpendicular to the fiber directionAxon diameter and myelinationFixel-based analysisFiber orientation distribution FixelFDMicroscopic density within fiber populationAxon number and density, axon diameter FCMacroscopic change in cross-sectional area perpendicular to fiber bundleFiber bundle size due to changes in extra-axonal space and/or myelination, number of axons FA, fractional anisotropy; MD, mean diffusivity; AD, axial diffusivity; RD, radial diffusivity; FD, fiber density; FC, fiber-bundle cross-section. Fixel-based analysis can distinguish between different fiber bundles within a voxel and estimate fiber density for each fiber bundle separately (FD1 and FD2). For a complete description and interpretation of these measures we refer to the following reviews of DTI analysis (Beaulieu, 2002; Beaulieu, 2013; Song et al., 2002; Zatorre et al., 2012) and fixel based analysis (Genc et al., 2020; Raffelt et al., 2017). A population-based template created for the fixel-based analysis can be used as a study-based atlas in which many of the avian anatomical structures can be identified (Figure 2). We recognize many of the white matter structures such as the different lamina, occipito-mesencephalic tract (OM) and optic tract (TrO) among others. Interestingly, many of the nuclei within the song control system (i.e. HVC, robust nucleus of the arcopallium (RA), lateral magnocellular nucleus of the anterior nidopallium (LMAN), and Area X), auditory system (i.e. intercollicular nucleus complex, nucleus ovoidalis) and visual system (i.e. entopallium, nucleus rotundus) are identified by the empty spaces between tracts. The applied fixel-based approach is inherently sensitive to changes in white matter and cannot report on the microstructure within gray matter like brain nuclei; but rather sheds light on the fiber tracts surrounding and interconnecting them. As such, it provides an excellent tool to investigate neuroplasticity of different brain networks, and in the case of a nodular song control system focusing on changes in the fibers surrounding the song control nuclei, referred to as HVC surr, RA surr, and Area X surr. Figure 2 with 1 supplement see all Download asset Open asset Overview of the population-based tractogram of male and female starlings over the seasons created for the fixel-based analysis with indications of different lamina, interconnecting tracts, nuclei, and brain regions displayed on axial (A) and coronal slices (B) throughout the brain. The different intervals of the coronal and axial sectioning are indicated on the saggittal inset in the left corner of each panel. The coronal slices do not follow a strict interval to visualise small nuclei such as DLM and Ov. The crosshair indicates the color-coding of the tractogram following the standard red-green-blue code (red = left right orientation (L-R), blue = dorso ventral (D-V) and green = rostro caudal (R-C)). Data-driven analysis of sexual dimorphisms in brain structure We established the general structural differences between male and female starling brains by applying a full-factorial design voxel-based analysis on our DTI data (Figures 3 and 4, Table 2). This includes data from all time points with time as a within-subject factor that needs to be averaged over (McFarquhar, 2019). This analysis confirmed the well-established sexual dimorphism of vocal control areas in songbirds (Bernard et al., 1993; Hamaide et al., 2017; Nottebohm and Arnold, 1976), where male starlings have higher fractional anisotropy in several parts of the song control system (the surroundings of HVC, Area X and RA) and auditory system (Field L and caudomedial nidopallium [NCM]) compared to female starlings. Interestingly, even though most differences occur bilaterally, these structures are visible as sub-peaks within a single significant cluster. This cluster was asymmetric toward the left hemisphere and comprised large parts of the mesopallium and nidopallium, but not the lamina mesopallialis (LaM) dividing these regions. Figure 3 with 1 supplement see all Download asset Open asset Overview of the general structural sexual dimorphism within the starling brain attributed to differences in fractional anisotropy (FA). For each sex comparison, the statistical parametric maps are displayed on axial and coronal sections throughout the brain (upper and lower row, respectively). The results are displayed with puncorr <0.001 and kE ≥ 10 voxels, and overlaid on the population fractional anisotropy map. The T-values are color-coded according to the scale on the right. In males, fractional anisotropy was higher in a large cluster lateralized to the left hemisphere and covering parts of the nidopallium and mesopallium, including several regions of the auditory system and surrounding the song control system. Females, on the other hand, have higher fractional anisotropy at the level of the cerebellum and several tracts such as OM, TSM, and TrO. Figure 4 with 3 supplements see all Download asset Open asset Overview of the general structural sexual dimorphism within the starling brain attributed to microscopic differences in fiber density (FD) (A, B) and macroscopic differences in fiber-bundle cross-section (log FC) (C, D). For each sex comparison, the statistical parametric maps are displayed on axial and coronal sections throughout the brain (upper and lower row respectively). The results are displayed with puncorr <0.001, and overlaid on the population based tractogram. Only significant tracks are displayed in a color representing the T-value. Figure 4—source data 1 This file contains the source data used to make the graphs presented in Figure 4—figure supplement 2 and 3. https://cdn.elifesciences.org/articles/66777/elife-66777-fig4-data1-v2.xlsx Download elife-66777-fig4-data1-v2.xlsx Table 2 Clusters displaying a sex difference in fractional anisotropy (FA). Table summarizing the significance at cluster level and peak level. Some clusters are large and cover multiple regions, indicated by the single statistic at cluster level. For each sub-region within this large cluster the peak significance (pFWE) and T-value (T) are reported. p-Values are FWE corrected. KE indicates the number of continuous voxels within a cluster. Main sex difference for FAClusterHemisphereClusterPeakpFWEkEpFWETMale > femaleNidopallium/mesopaliumLeft<0.000136936<0.000114.25HVC surrLeft<0.00018.35 Right<0.00017.53Field L dorsalLeft<0.000110.00 Right<0.00016.93NCMLeft<0.00017.72 Right<0.00016.72Area X caudal surrLeft0.0015.69 Right0.0016.07RA surrLeft<0.00017.86HVC-RA tractLeft<0.00016.68RA surrRight<0.00014410.0016.08HVC-RA tractRight<0.00016.42TeO (superior)Left<0.0001189<0.00019.44 Right<0.0001125<0.00017.47LaMRight<0.0001165<0.00016.97CerebellumLobule VII0.011101<0.00016.54Female > maleTrORight<0.000124846<0.000112.08 Left<0.00018.84TSMLeft<0.00019.62 Right<0.00019.74Commisura anteriorCenter0.2224.85Commisura posteriorCenter0.0665.19OMLeft<0.00018.77 Right<0.00017.78CStLeft<0.00018.50 Right<0.00017.64CerebellumLobule VIII<0.00019.12 Lobule VII<0.00019.68 Lobule VI<0.00017.77 Lobule V<0.00017.12 Lobule IV<0.00016.58tFARight<0.000110.50 Left0.006113<0.00018.64Area XLeft0.13957<0.00016.34 Right<0.0001254<0.00016.91 Importantly, females generally have higher fractional anisotropy in tracts interconnecting both hemispheres (commisura anterior and posterior) and in bilateral tracts interconnecting nuclei within a hemisphere including the TrO, OM, septopallio-mesencephalic tract (TSM), and fronto-arcopallial tract (tFA). In addition, females have higher fractional anisotropy within the cerebellum and caudal part of the lateral striatum (CSt) compared to males. These findings are further confirmed by fixel-based analysis; as many of the sexual dimorphisms in fractional anisotropy match with the fiber density statistical analysis (Figure 4A,B). Certain subregions of the cerebellum, the molecular layer of cerebellar lobules II, III, IV have higher fiber density in females, whereas the cerebellar lobule VIII has higher fiber density in males. This diversifies the finding of increased fractional anisotropy in the cerebellum of female starlings. The molecular layer of the cerebellum consists of parallel fibers emitted by the granule cells of the cerebellar cortex (D'Angelo, 2018). Fixel-based analysis is able to detect significant changes in this highly organized fiber structure, which is depicted as the red parallel fibers running left to right in Figure 4—figure supplement 1B. In addition to these microscopic changes, fiber-bundle cross-section statistical analysis revealed some macroscopic differences between male and female starlings (Figure 4C,D). Male starlings generally have a larger volume in the rostral part of the left hemisphere at the level of the hyperpallium apicale, which further supports the asymmetry toward the left hemisphere. Furthermore, the left HVC surroundings and cerebellar lobule VIII are larger in males compared to females. However, certain regions within the superior frontal lamina (LFS), nidopallium and mesopallium are larger in females, compared to males. Extracted values from the identified clusters are plotted to demonstrate the extent of the sex difference in the respective diffusion parameters (Figure 3—figure supplement 1, Figure 4—figure supplements 2 and 3). Alongside the well-established difference in the song control system, the applied data-driven approach sheds new light on sexual dimorphisms in the rest of the starling brain, including a microstructural hemispheric asymmetry toward the left hemisphere in males and a more structurally organized cerebellum as well as several white matter tracts in females. Role of brain size in the general sexual dimorphism The hypothesis of neuronal interconnectivity is used to explain some of the sexual dimorphisms in brain structure of mammals and postulates that larger brains have relatively stronger intrahemispheric connections, resulting in stronger lateralization in larger brains, whereas smaller brains benefit more from higher interhemispheric connectivity (Hänggi et al., 2014). To ensure that the general sexual dimorphisms are attributed to a genuine sex difference and not to a difference in brain size, we examined: (1) whether there is a difference in brain size between males and females and; (2) if this difference in brain size can explain sexual dimorphisms like the higher interhemispheric interconnectivity found in females. Even though there was a small difference in brain size between male and female starlings (respectively, Mean male = 1815, SE = 8 mm³ and Mean female = 1770, SE = 13 mm³), it was not significant (F(1, 23)=1.78, p=0.195). Next, we artificially divided each sex in two groups based on their brain size using a median split similar to the analysis in Kurth et al., 2018. These artificially generated groups were used for a voxel-based analysis with brain size as a fixed factor to assess the effect of brain size on the fractional anisotropy, fiber density and fiber-bundle cross-section analysis (Figures 5 and 6, Figure 6—figure supplement 1, Table 3). This analysis could not explain the hemispheric asymmetry in males nor the increased interhemispheric connections in females. Only a few sex differences matched with the voxel-based analysis for brain size. Large brain male and female starlings had higher fractional anisotropy values in specific sections of the song control system including the caudal surroundings of the right Area X, surroundings of RA and HVC (Figure 5A). The brain size difference in fractional anisotropy in right Area X surroundings and cerebellum was matched with a difference in fiber density (Figure 6A), whereas the higher fractional anisotropy in the surroundings of HVC of large brain starlings, is complemented by a higher fiber-bundle cross-section in the surroundings of HVC (Figure 6C). Figure 5 with 1 supplement see all Download asset Open asset Overview of general structural difference between large and small brain starling in fractional anisotropy (FA). The statistical parametric maps are displayed on axial and coronal sections throughout the brain (upper and lower row, respectively). The results are displayed with puncorr <0.001 and kE ≥ 10 voxels, and overlaid on the population fractional anisotropy map. The T-values are color-coded according to the scale on the right. Figure 6 with 3 supplements see all Download asset Open asset Overview of general structural difference between large and small brain starling in microscopic differences in fiber density (FD) (A, B) and macroscopic differences in fiber-bundle cross-section (log FC) (C, D). The statistical parametric maps are displayed on axial and coronal sections throughout the brain (upper and lower row respectively). The results are displayed with puncorr <0.001, and overlaid on the population-based tractogram. Only significant tracks are displayed and colored according to their significance by T-value. Figure 6—source data 1 This file contains the source data used to make the graphs presented in Figure 6—figure supplement 2 and 3. https://cdn.elifesciences.org/articles/66777/elife-66777-fig6-data1-v2.xlsx Download elife-66777-fig6-data1-v2.xlsx Table 3 Clusters displaying a difference in brain size versus a difference in sex in fractional anisotropy (FA). Table summarizing the significance at cluster level and peak level. Some clusters are large and cover multiple regions, indicated by the single statistic at cluster level. For each sub-region within this large cluster the peak significance (pFWE) and T-value (T) are reported. p-Values are FWE corrected. KE indicates the number of continuous voxels within a cluster. The final column reports if a region with a difference in brain size is analogue to other relevant differences such as the general sexual dimorphism in fractional anisotropy (M > F or F > M) or the correlation between song rate and fractional anisotropy in females (↑Singing F). Large > small brainClusterHemisphereClusterPeakRelevant differencepFWEkEpFWETHVC surrLeft<0.0001227<0.00016.43M > FHippocampusLeft<0.0001155<0.00015.63↑singing FLaM/LFSLeft<0.00017.51HVC surrRight<0.0001417<0.00016.61M > FHippocampusRight<0.00016.94Area X caudal surrRight<0.00011900.0025.99M > F ↑Singing FLPSLeft0.0041260.0065.78Lps/LFSRight<0.00013370.0425.29tFARight0.925200.0295.39F > MTrORight0.001160<0.00017.29F > MRA surrLeft<0.00014510.1934.88M > FCerebellumLobule VI<0.00017.50CerebellumLobule VI0.259480.0105.64CerebellumLobule IV0.114620.0035.94CerebellumLobule VII0.101640.0415.30Small > large brainNCMLeft0.006116<0.00016.39M > FLaMRight0.0141000.0255.43M > FVentricle rostralLeft0.21751<0.00017.21 Right<0.0001246<0.00016.83Ventricle caudalCenter0.0021380.0035.90 Center0.0041220.2144.84TrOCenter0.0111050.2324.82F > M For the neuronal interconnectivity hypothesis to hold up in starlings, we expected that small brain starlings exhibit higher fractional anisotropy in several interconnecting tracts. However, only a small part of the TrO had higher fractional anisotropy values in small brain starlings (Figure 5A), which closely matches the brain size difference of fiber-bundle cross-section in TrO (Figure 6D). In left NCM and a right LaM, both male and small brain starlings exhibit higher fractional anisotropy and fiber density values (Figures 5A and 6B), which might be partially explained by the finding of higher fiber-bundle cross-section values in the NCM of large compared to small brain starlings (Figure 6C). Furthermore, some regions such as the hippocampus in large brain starlings and ventricle in small brain birds were not detected in the voxel-based sex difference analysis and are solely attributed to a difference in brain size. In the fiber-bundle cross-section analysis for brain size differences, more regions unrelated to a sex difference showed a significant larger fiber-bundle cross-section value in large brains compared to smaller brains, including parts of the pallial-subpallial lamina, lateral forebrain bundle, medial and lateral surroundings of the entopallium. Interestingly, many of the fiber tracts that had a pronounced sex difference, such as the anterior and posterior commissure, TSM and OM, were not found in the brain size statistical maps. Furthermore, the microstructural hemispheric asymmetry covering the nidopallium and mesopallium detected in the voxel-based sex difference analysis can also not be attributed to a mere difference in brain size. Importantly, further statistical analysis is required to ensure that the brain size effect is not confounded by other factors such as song behavior differences. Sexual dimorphism in seasonal neuroplasticity After establishing the general structural sexual dimorphisms, we investigated whether seasonal neuroplasticity occurs differently between sexes. Therefore, we performed a voxel-based flexible factorial analysis looking at the general fractional anisotropy changes over time and the interactions between time and sex. These results are summarized in Figure 7 and Table 4. We extracted the mean fractional anisotropy values from the significant clusters and plotted them to determine how fractional anisotropy changes over time (Figure 8). Fractional anisotropy changes can be caused by a multitude of underlying microstructural changes, however fractional anisotropy is most sensitive to changes in axon number and density, axon diameter and myelination (see Table 1). Additionally, we extracted the mean of the other diffusion parameters (mean, axial and radial diffusion) and fixel-based measures from these ROIs to provide more insight into the basis of the fractional anisotropy change (Figure 8—figure supplements 1–4). For the statistical analysis of the extracted parameters, we conducted a linear mixed model analysis with Tukey’s Honest Significant Difference (HSD) multiple comparison correction during post hoc statistics. Figure 7 Download asset Open asset Time effect and interaction between sex and time in fractional anisotropy (FA). The statistical maps were assessed at puncorr <0.001 and kE ≥ 10 voxels with a small volume correction including regions of the white matter tracts, auditory and song control system. Main time effect of the cerebellum cluster was assessed without the small volume correction. Color scale represents significance by F-value. C, caudal; RL, rostro-lateral; R, rostral; surr, surroundings. Figure 8 with 4 supplements see all Download asset Open asset Summary of the longitudinal changes over time of fractional anisotropy (FA) extracted from ROI-based clusters that showed a significant interaction (first row), a significant change over time in the surroundings of song control nuclei (row 2 and 3) or a significant change over time in the fiber tracts (row 4–6). The gray area indicates the entire photosensitive period of short days (8L:16D).
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