Spine Geometry Research Articles

Apical stress redistribution during anterior vertebral body tethering for thoracic adolescent idiopathic scoliosis: a finite element analysis of a novel surgical technique.

Apical stress redistribution (ASR) is proposed to mitigate failure risks after anterior vertebral body tethering for adolescent idiopathic scoliosis. It consists in releasing set-screws at peri-apical levels following curve tensioning to redistribute stresses within the construct. This study determines the biomechanical impact and curve correction obtained with ASR. Finite element models of anterior vertebral body tethering were constructed for three typical scoliotic patients with Lenke 1 curves. ASR was simulated by releasing tension on the cable at the level of the three apical set screws (i.e. untightening three consecutive periapical set screws), followed by retightening of the set screws without further tensioning. Cable tension, implant forces and spine geometry were compared before and after performing ASR. Periapical cable tension decreased post-ASR, and ASR alsoreduced the maximum tensions proximally and distally. Postoperative disc height was similar between conventional and ASR approaches. Apical intervertebral disc stresses were shifted from concave to convex compression intra and postoperatively, with a similar pattern between the conventional and ASR techniques. The ASR technique achieved scoliotic curve corrections of 54%, 68%, and 79%, while the conventional technique resulted in corresponding corrections (54%, 68%, and 80%) for subjects 1, 2, and 3. The periapical coronal curves exhibited similar patterns. ASR demonstrated promising apical cable and implant forces re-equilibrium compared to the conventional approach. This novel technique did not impair immediate and postoperative curve correction, while maintaining similar apical intervertebral stress distribution. ASR shows potential to modulate growth while reducing maximum cable tension infra- and supra-apical.

Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.

Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option to plan the outcome of surgery on a patient-based model. However, it requires considerable computing power and time, which may discourage its use. Machine learning (ML) models can be a helpful surrogate to the FEM, providing accurate real-time responses. This work implements ML algorithms to estimate post-operative spinal shapes. The algorithms are trained using features from 6400 simulations generated using the FEM from spine geometries of 64 patients. The features are selected using an autoencoder and principal component analysis. The accuracy of the results is evaluated by calculating the root-mean-squared error and the angle between the reference and predicted position of each vertebra. The processing times are also reported. A combination of principal component analysis for dimensionality reduction, followed by the linear regression model, generated accurate results in real-time, with an average position error of 3.75 mm and orientation angle error below 2.74 degrees in all main 3D axes, within 3 ms. The prediction time is considerably faster than simulations based on the FEM alone, which require seconds to minutes. It is possible to predict post-operative spinal shapes of patients with AIS in real-time by using ML algorithms as a surrogate to the FEM. Clinicians can compare the response of the initial spine shape of a patient with AIS to various target shapes, which can be modified interactively. These benefits can encourage clinicians to use software tools for surgical planning of scoliosis.

Biophysical Modeling of Synaptic Plasticity.

Dendritic spines are small, bulbous compartments that function as postsynaptic sites and undergo intense biochemical and biophysical activity. The role of the myriad signaling pathways that are implicated in synaptic plasticity is well studied. A recent abundance of quantitative experimental data has made the events associated with synaptic plasticity amenable to quantitative biophysical modeling. Spines are also fascinating biophysical computational units because spine geometry, signal transduction, and mechanics work in a complex feedback loop to tune synaptic plasticity. In this sense, ideas from modeling cell motility can inspire us to develop multiscale approaches for predictive modeling of synaptic plasticity. In this article, we review the key steps in postsynaptic plasticity with a specific focus on the impact of spine geometry on signaling, cytoskeleton rearrangement, and membrane mechanics. We summarize the main experimental observations and highlight how theory and computation can aid our understanding of these complex processes.

SCO-spondin knockout mice exhibit small brain ventricles and mild spine deformation

Reissner’s fiber (RF) is an extracellular polymer comprising the large monomeric protein SCO-spondin (SSPO) secreted by the subcommissural organ (SCO) that extends through cerebrospinal fluid (CSF)-filled ventricles into the central canal of the spinal cord. In zebrafish, RF and CSF-contacting neurons (CSF-cNs) form an axial sensory system that detects spinal curvature, instructs morphogenesis of the body axis, and enables proper alignment of the spine. In mammalian models, RF has been implicated in CSF circulation. However, challenges in manipulating Sspo, an exceptionally large gene of 15,719 nucleotides, with traditional approaches has limited progress. Here, we generated a Sspo knockout mouse model using CRISPR/Cas9-mediated genome-editing. Sspo knockout mice lacked RF-positive material in the SCO and fibrillar condensates in the brain ventricles. Remarkably, Sspo knockout brain ventricle sizes were reduced compared to littermate controls. Minor defects in thoracic spine curvature were detected in Sspo knockouts, which did not alter basic motor behaviors tested. Altogether, our work in mouse demonstrates that SSPO and RF regulate ventricle size during development but only moderately impact spine geometry.

Effects of a microgravity SkinSuit on lumbar geometry and kinematics

PurposeAstronauts returning from long ISS missions have demonstrated an increased incidence of lumbar disc herniation accompanied by biomechanical and morphological changes associated with spine elongation. This research describes a ground-based study of the effects of an axial compression countermeasure Mk VI SkinSuit designed to reload the spine and reduce these changes before return to terrestrial gravity.MethodsTwenty healthy male volunteers aged 21–36 without back pain participated. Each lay overnight on a Hyper Buoyancy Flotation (HBF) bed for 12 h on two occasions 6 weeks apart. On the second occasion participants donned a Mk VI SkinSuit designed to axially load the spine at 0.2 Gz during the last 4 h of flotation. Immediately after each exposure, participants received recumbent MRI and flexion–extension quantitative fluoroscopy scans of their lumbar spines, measuring differences between spine geometry and intervertebral kinematics with and without the SkinSuit. This was followed by the same procedure whilst weight bearing. Paired comparisons were performed for all measurements.ResultsFollowing Mk VI SkinSuit use, participants evidenced more flexion RoM at L3–4 (p = 0.01) and L4–5 (p = 0.003), more translation at L3–4 (p = 0.02), lower dynamic disc height at L5–S1 (p = 0.002), lower lumbar spine length (p = 0.01) and greater lordosis (p = 0.0001) than without the Mk VI SkinSuit. Disc cross-sectional area and volume were not significantly affected.ConclusionThe MkVI SkinSuit restores lumbar mobility and lordosis following 4 h of wearing during hyper buoyancy flotation in a healthy control population and may be an effective countermeasure for post space flight lumbar disc herniation.

New method to apply the lumbar lordosis of standing radiographs to supine CT-based virtual 3D lumbar spine models

Standing radiographs play an important role in the characterization of spinal sagittal alignment, as they depict the spine under physiologic loading conditions. However, there is no commonly available method to apply the lumbar lordosis of standing radiographs to supine CT-based virtual 3D models of the lumbar spine. We aimed to develop a method for the sagittal rigid-body registration of vertebrae to standing radiographs, using the exact geometry reconstructed from CT-data. In a cohort of 50 patients with monosegmental spinal degeneration, segmentation and registration of the lumbar vertebrae and sacrum were performed by two independent investigators. Intersegmental angles and lumbar lordosis were measured both in CT scans and radiographs. Vertebrae were registered using the X-ray module of Materialise Mimics software. Postregistrational midsagittal sections were constructed of the sagittal midplane sections of the registered 3D lumbar spine geometries. Mean Hausdorff distance was measured between corresponding registered vertebral geometries. The registration process minimized the difference between the X-rays’ and postregistrational midsagittal sections’ lordoses. Intra- and inter-rater reliability was excellent based on angle and mean Hausdorff distance measurements. We propose an accessible, accurate, and reproducible method for creating patient-specific 3D geometries of the lumbar spine that accurately represent spinal sagittal alignment in the standing position.

Spatiotemporal modelling reveals geometric dependence of AMPAR dynamics on dendritic spine morphology.

The modification of neural circuits depends on the strengthening and weakening of synaptic connections. Synaptic strength is often correlated to the density of the ionotropic, glutamatergic receptors, AMPARs, (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors) at the postsynaptic density (PSD). While AMPAR density is known to change based on complex biological signalling cascades, the effect of geometric factors such as dendritic spine shape, size and curvature remain poorly understood. In this work, we developed a deterministic, spatiotemporal model to study the dynamics of AMPARs during long-term potentiation (LTP). This model includes a minimal set of biochemical events that represent the upstream signalling events, trafficking of AMPARs to and from the PSD, lateral diffusion in the plane of the spine membrane, and the presence of an extrasynaptic AMPAR pool. Using idealized and realistic spine geometries, we show that the dynamics and increase of bound AMPARs at the PSD depends on a combination of endo- and exocytosis, membrane diffusion, the availability of free AMPARs and intracellular signalling interactions. We also found non-monotonic relationships between spine volume and the change in AMPARs at the PSD, suggesting that spines restrict changes in AMPARs to optimize resources and prevent runaway potentiation. KEY POINTS: Synaptic plasticity involves dynamic biochemical and physical remodelling of small protrusions called dendritic spines along the dendrites of neurons. Proper synaptic functionality within these spines requires changes in receptor number at the synapse, which has implications for downstream neural functions, such as learning and memory formation. In addition to being signalling subcompartments, spines also have unique morphological features that can play a role in regulating receptor dynamics on the synaptic surface. We have developed a spatiotemporal model that couples biochemical signalling and receptor trafficking modalities in idealized and realistic spine geometries to investigate the role of biochemical and biophysical factors in synaptic plasticity. Using this model, we highlight the importance of spine size and shape in regulating bound AMPA receptor dynamics that govern synaptic plasticity, and predict how spine shape might act to reset synaptic plasticity as a built-in resource optimization and regulation tool.

Protein drift-diffusion dynamics and phase separation in curved cell membranes and dendritic spines: Hybrid discrete-continuum methods.

We develop methods for investigating protein drift-diffusion dynamics in heterogeneous cell membranes and the roles played by geometry, diffusion, chemical kinetics, and phase separation. Our hybrid stochastic numerical methods combine discrete particle descriptions with continuum-level models for tracking the individual protein drift-diffusion dynamics when coupled to continuum fields. We show how our approaches can be used to investigate phenomena motivated by protein kinetics within dendritic spines. The spine geometry is hypothesized to play an important biological role regulating synaptic strength, protein kinetics, and self-assembly of clusters. We perform simulation studies for model spine geometries varying the neck size to investigate how phase-separation and protein organization is influenced by different shapes. We also show how our methods can be used to study the roles of geometry in reaction-diffusion systems including Turing instabilities. Our methods provide general approaches for investigating protein kinetics and drift-diffusion dynamics within curved membrane structures.

A Population-Based 3D Atlas of the Pathological Lumbar Spine Segment.

The spine is the load-bearing structure of human beings and may present several disorders, with low back pain the most frequent problem during human life. Signs of a spine disorder or disease vary depending on the location and type of the spine condition. Therefore, we aim to develop a probabilistic atlas of the lumbar spine segment using statistical shape modeling (SSM) and then explore the variability of spine geometry using principal component analysis (PCA). Using computed tomography (CT), the human spine was reconstructed for 24 patients with spine disorders and then the mean shape was deformed upon specific boundaries (e.g., by or standard deviation). Results demonstrated that principal shape modes are associated with specific morphological features of the spine segment such as Cobb’s angle, lordosis degree, spine width and height. The lumbar spine atlas here developed has evinced the potential of SSM to investigate the association between shape and morphological parameters, with the goal of developing new treatments for the management of patients with spine disorders.

Biomechanical analysis of the impact of increasing levels of body mass index on the ability of a bracing orthosis to alter the asymmetric compressive growth plate loading in a scoliotic spine

BackgroundThe impact of increasing body mass index (BMI) on the magnitude of alterations in the asymmetric compressive vertebral growth plate loading of a scoliotic spine following 50% in-brace correction is studied using the finite element method, and subsequent alteration of the longitudinal bone growth rate is quantified. MethodsUpper body weight loading corresponding to a single patient spine geometry representing 4 BMI classifications – overweight (BMI = 25 kg/m2), and class-1, -2, and -3 obesity (BMI – 30 kg/m2, 35 kg/m2, and 40 kg/m2, respectively) – is applied to a three-dimensional finite element model (FEM) of the human thoracolumbosacral spine. The FEM represents a Lenke 1AN scoliotic curve type with 30-degree Cobb angle and 10-degree apical vertebral rotation. Bracing simulation is performed until 50% in-brace correction is achieved. Changes in bending moment at each motion segment and on each growth plate after bracing is simulated and compared between each BMI level. FindingsAs BMI increases, the average magnitude of change in bending moment on each growth plate following bracing decreases. For BMIs 30 kg/m2, 35 kg/m2, and 40 kg/m2, the average change in bending moment on each vertebral growth plate following 50% in-brace correction is 31% smaller (P = .00006), 60% smaller (P = .00007), and 96% smaller (P = .00006), respectively, than for BMI 25 kg/m2. InterpretationsResults indicate that BMI significantly impacts the ability of a bracing orthosis to alter the asymmetric compressive growth plate loading in a scoliotic spine, which is the design function of bracing. This suggests that increasing BMI may significantly impact the ability of a bracing orthosis to halt curve progression in a scoliotic patient.

Environmental enrichment enhances patterning and remodeling of synaptic nanoarchitecture as revealed by STED nanoscopy.

Synaptic plasticity underlies long-lasting structural and functional changes to brain circuitry and its experience-dependent remodeling can be fundamentally enhanced by environmental enrichment. It is however unknown, whether and how the environmental enrichment alters the morphology and dynamics of individual synapses. Here, we present a virtually crosstalk-free two-color in vivo stimulated emission depletion (STED) microscope to simultaneously superresolve the dynamics of endogenous PSD95 of the post-synaptic density and spine geometry in the mouse cortex. In general, the spine head geometry and PSD95 assemblies were highly dynamic, their changes depended linearly on their original size but correlated only mildly. With environmental enrichment, the size distributions of PSD95 and spine head sizes were sharper than in controls, indicating that synaptic strength is set more uniformly. The topography of the PSD95 nanoorganization was more dynamic after environmental enrichment; changes in size were smaller but more correlated than in mice housed in standard cages. Thus, two-color in vivo time-lapse imaging of synaptic nanoorganization uncovers a unique synaptic nanoplasticity associated with the enhanced learning capabilities under environmental enrichment.

A statistical lumbar spine geometry model accounting for variations by Age, Sex, Stature, and body mass index

The objective of this study was to develop a statistical lumbar spine geometry model accounting for morphological variations among the adult population. Five lumber vertebrae and lumber spine curvature were collected from CT scans of 82 adult subjects through CT segmentation, landmark identification, and template mesh mapping. Generalized Procrustes Analysis (GPA), Principal Component Analysis (PCA), and multivariate regression analysis were conducted to develop the statistical lumbar spine model. Two statistical models were established to predict the vertebrae geometry and lumbar curvature respectively. Using the statistical models, a lumbar spine finite element (FE) model could be rapidly generated with a given set of age, sex, stature, and body mass index (BMI). The results showed that the lumbar spine vertebral size was significantly affected by stature, sex and age, and the lumbar spine curvature was significantly affected by stature and age. This statistical lumbar spine model could serve as the geometric basis for quantifying effects of human characteristics on lumbar spine injury risks in impact conditions.

Geometry of the parallelism in polar spine spaces and their line reducts

The concept of the spine geometry over a polar Grassmann space belongs to a wide family of partial affine line spaces. It is known that the geometry of a spine space over a projective Grassmann space can be developed in terms of points, so called affine lines, and their parallelism (in this case the parallelism is not intrinsically definable as it is not Veblenian). This paper aims to prove an analogous result for the polar spine spaces. As a by-product we obtain several other results on primitive notions for the geometry of polar spine spaces.

Constitutive tumor necrosis factor (TNF)‐deficiency causes a reduction in spine density in mouse dentate granule cells accompanied by homeostatic adaptations of spine head size

The majority of excitatory synapses terminating on cortical neurons are found on dendritic spines. The geometry of spines, in particular the size of the spine head, tightly correlates with the strength of the excitatory synapse formed with the spine. Under conditions of synaptic plasticity, spine geometry may change, reflecting functional adaptations. Since the cytokine tumor necrosis factor (TNF) has been shown to influence synaptic transmission as well as Hebbian and homeostatic forms of synaptic plasticity, we speculated that TNF-deficiency may cause concomitant structural changes at the level of dendritic spines. To address this question, we analyzed spine density and spine head area of Alexa568-filled granule cells in the dentate gyrus of adult C57BL/6J and TNF-deficient (TNF-KO) mice. Tissue sections were double-stained for the actin-modulating and plasticity-related protein synaptopodin (SP), a molecular marker for strong and stable spines. Dendritic segments of TNF-deficient granule cells exhibited ∼20% fewer spines in the outer molecular layer of the dentate gyrus compared to controls, indicating a reduced afferent innervation. Of note, these segments also had larger spines containing larger SP-clusters. This pattern of changes is strikingly similar to the one seen after denervation-associated spine loss following experimental entorhinal denervation of granule cells: Denervated granule cells increase the SP-content and strength of their remaining spines to homeostatically compensate for those that were lost. Our data suggest a similar compensatory mechanism in TNF-deficient granule cells in response to a reduction in their afferent innervation.

Intra-operative forecasting of growth modulation spine surgery outcomes with spatio-temporal dynamic networks.

In adolescent idiopathic scoliosis (AIS), non-invasive surgical techniques such as anterior vertebral body tethering (AVBT) enable to treat patients with mild and severe degrees of deformity while maintaining lower lumbar motion by avoiding spinal fusion. However, multiple features and characteristics affect the overall patient outcome, notably the 3D spine geometry and bone maturity, but also from decisions taken intra-operatively such as the selected tethered vertebral levels, which makes it difficult to anticipate the patient response. We propose here a forecasting method which can be used during AVBT surgery, exploiting the spatio-temporal features extracted from a dynamic networks. The model learns the corrective effect from the spine's different segments while taking under account the time differences in the initial diagnosis and between the serial acquisitions taken before and during surgery. Clinical parameters are integrated through an attention-based decoder, allowing to associate geometrical features to patient status. Long-term relationships allow to ensure regularity in geometrical curve prediction, using a manifold-based smoothness term to regularize geometrical outputs, capturing the temporal variations of spine correction. A dataset of 695 3D spine reconstructions was used to train the network, which was evaluated on a hold-out dataset of 72 scoliosis patients using the baseline 3D reconstruction obtained prior to surgery, yielding an overall reconstruction error of [Formula: see text]mm based on pre-identified landmarks on vertebral bodies. The model was also tested prospectively on a separate cohort of 15 AIS patients, demonstrating the integration within the OR theatre. The proposed predictive network allows to intra-operatively anticipate the geometrical response of the spine to AVBT procedures using the dynamic features.

Estimation and assessment of sagittal spinal curvature and thoracic muscle morphometry in different postures.

Spine models are typically developed from supine clinical imaging data, and hence clearly do not fully reflect postures that replicate subjects' clinical symptoms. Our objectives were to develop a method to: (i) estimate the subject-specific sagittal curvature of the whole spine in different postures from limited imaging data, (ii) obtain muscle lines-of-action in different postures and analyze the effect of posture on muscle fascicle length, and (iii) correct for cosine between the magnetic resonance imaging (MRI) scan plane and dominant fiber line-of-action for muscle parameters (cross-sectional area (CSA) and position). The thoracic spines of six healthy volunteers were scanned in four postures (supine, standing, flexion, and sitting) in an upright MRI. Geometry of the sagittal spine was approximated with a circular spline. A pipeline was developed to estimate spine geometry in different postures and was validated. The lines-of-action for two muscles, erector spinae (ES) and transversospinalis (TS) were obtained for every posture and hence muscle fascicle lengths were computed. A correction factor based on published literature was then computed and applied to the muscle parameters. The maximum registration error between the estimated spine geometry and MRI data was small (average RMSE∼1.2%). The muscle fascicle length increased (up to 20%) in flexion when compared to erect postures. The correction factor reduced muscle parameters (∼5% for ES and ∼25% for TS) when compared to raw MRI data. The proposed pipeline is a preliminary step in subject-specific modeling. Direction cosines of muscles could be used while improving the inputs of spine models.

Geometry and the Organizational Principle of Spine Synapses along a Dendrite.

Precise information on synapse organization in a dendrite is crucial to understanding the mechanisms underlying voltage integration and the variability in the strength of synaptic inputs across dendrites of different complex morphologies. Here, we used focused ion beam/scanning electron microscope (FIB/SEM) to image the dendritic spines of mice in the hippocampal CA1 region, CA3 region, somatosensory cortex, striatum, and cerebellum (CB). Our results show that the spine geometry and dimensions differ across neuronal cell types. Despite this difference, dendritic spines were organized in an orchestrated manner such that the postsynaptic density (PSD) area per unit length of dendrite scaled positively with the dendritic diameter in CA1 proximal stratum radiatum (PSR), cortex, and CB. The ratio of the PSD area to neck length was kept relatively uniform across dendrites of different diameters in CA1 PSR. Computer simulation suggests that a similar level of synaptic strength across different dendrites in CA1 PSR enables the effective transfer of synaptic inputs from the dendrites toward soma. Excitatory postsynaptic potentials (EPSPs), evoked at single spines by glutamate uncaging and recorded at the soma, show that the neck length is more influential than head width in regulating the EPSP magnitude at the soma. Our study describes thorough morphologic features and the organizational principles of dendritic spines in different brain regions.

Development of a multiscale model of the human lumbar spine for investigation of tissue loads in people with and without a transtibial amputation during sit-to-stand.

Quantification of lumbar spine load transfer is important for understanding low back pain, especially among persons with a lower limb amputation. Computational modeling provides a helpful solution for obtaining estimates of in vivo loads. A multiscale model was constructed by combining musculoskeletal and finite element (FE) models of the lumbar spine to determine tissue loading during daily activities. Three-dimensional kinematic and ground reaction force data were collected from participants with ([Formula: see text]) and without ([Formula: see text]) a unilateral transtibial amputation (TTA) during 5 sit-to-stand trials. We estimated tissue-level load transfer from the multiscale model by controlling the FE model with intervertebral kinematics and muscle forces predicted by the musculoskeletal model. Annulus fibrosis stress, intradiscal pressure (IDP), and facet contact forces were calculated using the FE model. Differences in whole-body kinematics, muscle forces, and tissue-level loads were found between participant groups. Notably, participants with TTA had greater axial rotation toward their intact limb ([Formula: see text]), greater abdominal muscle activity ([Formula: see text]), and greater overall tissue loading throughout sit-to-stand ([Formula: see text]) compared to able-bodied participants. Both normalized (to upright standing) and absolute estimates of L4-L5 IDP were close to in vivo values reported in the literature. The multiscale model can be used to estimate the distribution of loads within different lumbar spine tissue structures and can be adapted for use with different activities, populations, and spinal geometries.

An energy approach describes spine equilibrium in adolescent idiopathic scoliosis.

The adolescent idiopathic scoliosis (AIS) is a 3D deformity of the spine whose origin is unknown and clinical evolution unpredictable. In this work, a mixed theoretical and numerical approach based on energetic considerations is proposed to study the global spine deformations. The introduced mechanical model aims at overcoming the limitations of computational cost and high variability in physical parameters. The model is constituted of rigid vertebral bodies associated with 3D effective stiffness tensors. The spine equilibrium is found using minimization methods of the mechanical total energy which circumvents forces and loading calculation. The values of the model parameters exhibited in the stiffness tensor are retrieved using a combination of clinical images post-processing and inverse algorithms implementation. Energy distribution patterns can then be evaluated at the global spine scale to investigate given time patient-specific features. To verify the reliability of the numerical methods, a simplified model of spine was implemented. The methodology was then applied to a clinical case of AIS (13-year-old girl, Lenke 1A). Comparisons of the numerical spine geometry with clinical data equilibria showed numerical calculations were performed with great accuracy. The patient follow-up allowed us to highlight the energetic role of the apical and junctional zones of the deformed spine, the repercussion of sagittal bending in sacro-illiac junctions and the significant role of torsion with scoliosis aggravation. Tangible comparisons of output measures with clinical pathology knowledge provided a reliable basis for further use of those numerical developments in AIS classification, scoliosis evolution prediction and potentially surgical planning.

Circadian Changes of Dendritic Spine Geometry in Mouse Barrel Cortex.

The circadian rhythmicity changes the density and shape of dendritic spines in mouse somatosensory barrel cortex, influencing their stability and maturation. In this study, we analyzed the main geometric parameters of dendritic spines reflecting the strength of synapses located on these spines under light/dark (12:12) and constant darkness conditions, in order to distinguish between endogenously regulated and light-driven parameters. Using morphological analysis of serial electron micrographs, as well as three-dimensional reconstructions, we found that the light induces elongation of single-synapse spine necks and increases in the diameter of double-synapse spine necks, increasing and decreasing the isolation of synapses from the parent dendrite, respectively. During the subjective night of constant darkness, we observed an enlargement of postsynaptic density area in inhibitory synapses and an increase in the number of polyribosomes inside double-synapse spines. The results show that both endogenous effect (circadian clock/locomotor activity) and light affect the morphological parameters of single- and double-synapse spines in the somatosensory cortex: light reduces the efficiency of excitatory synapses on single-synapse spines, increases the effect of synaptic transmission in double-synapse spines, and additionally masks the endogenous clock-driven enlargement of inhibitory synapses located on double-synapse spines. This indicates a special role of double-synapse spines and their inhibitory synapses in the regulation of synaptic transmission during both circadian and diurnal cycles in the mouse somatosensory cortex.