Fine temporal brain network structure modularizes and localizes differently in men and women: insights from a novel explainability framework.

Deep learning has become an effective tool for classifying biological sex based on functional magnetic resonance imaging (fMRI). However, research on what features within the brain are most relevant to this classification is still lacking. Model interpretability has become a powerful way to understand "black box" deep-learning models, and select features within the input data that are most relevant to the correct classification. However, very little work has been done employing these methods to understand the relationship between the temporal dimension of functional imaging signals and the classification of biological sex. Consequently, less attention has been paid to rectifying problems and limitations associated with feature explanation models, e.g. underspecification and instability. In this work, we first provide a methodology to limit the impact of underspecification on the stability of the measured feature importance. Then, using intrinsic connectivity networks from fMRI data, we provide a deep exploration of sex differences among functional brain networks. We report numerous conclusions, including activity differences in the visual and cognitive domains and major connectivity differences.

Deep multimodal predictome for studying mental disorders.

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.

Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia.

This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs. Results show (1) schizophrenia is highly multimodal and includes changes in specific networks, (2) non-linear relationships with schizophrenia are observed when interpolating among shared latent dimensions, and (3) we observe a decrease in the modularity of functional connectivity and decreased visual-sensorimotor connectivity for schizophrenia patients for the FA-sFNC and sMRI-sFNC modality pairs, respectively. Additionally, our results generally indicate decreased fractional corpus callosum anisotropy, and decreased spatial ICA map and voxel-based morphometry strength in the superior frontal lobe as found in the FA-sFNC, sMRI-FA, and sMRI-ICA modality pair clusters. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data which we hope challenges the reader to think differently about how modalities interact.

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function.

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.

Posterior column reconstruction improves fusion rates at the level of osteotomy in three-column posterior-based osteotomies.

PURPOSE: To determine the incidence of pseudarthrosis at the osteotomy site after three-column spinal osteotomies (3-COs) with posterior column reconstruction. METHODS: 82 consecutive adult 3-COs (66 patients) with a minimum of 2-year follow-up were retrospectively reviewed. All cases underwent posterior 3-COs with two-rod constructs. The inferior facets of the proximal level were reduced to the superior facets of the distal level. If that was not possible, a structural piece of bone graft either from the local resection or a local rib was slotted in the posterior column defect to re-establish continual structural posterior bone across the lateral margins of the resection. No interbody cages were used at the level of the osteotomy. RESULTS: There were 34 thoracic osteotomies, 47 lumbar osteotomies and one sacral osteotomy with a mean follow-up of 52 (24-126) months. All cases underwent posterior column reconstructions described above and the addition of interbody support or additional posterior rods was not performed for fusion at the osteotomy level. Among them, 29 patients underwent one or more revision surgeries. There were three definite cases of pseudarthrosis at the osteotomy site (4%). Six revisions were also performed for pseudarthrosis at other levels. CONCLUSION: Restoration of the structural integrity of the posterior column in three-column posterior-based osteotomies was associated with > 95% fusion rate at the level of the osteotomy. Pseudarthrosis at other levels was the second most common reason for revision following adjacent segment disease in the long-term follow-up.

A molecular-level mechanistic framework for interfacial proton-coupled electron transfer kinetics.

Electrochemical proton-coupled electron transfer (PCET) reactions can proceed via an outer-sphere electron transfer to solution (OS-PCET) or through an inner-sphere mechanism by interfacial polarization of surface-bound active sites (I-PCET). Although OS-PCET has been extensively studied with molecular insight, the inherent heterogeneity of surfaces impedes molecular-level understanding of I-PCET. Herein we employ graphite-conjugated carboxylic acids (GC-COOH) as molecularly well-defined hosts of I-PCET to isolate the intrinsic kinetics of I-PCET. We measure I-PCET rates across the entire pH range, uncovering a V-shaped pH-dependence that lacks the pH-independent regions characteristic of OS-PCET. Accordingly, we develop a mechanistic model for I-PCET that invokes concerted PCET involving hydronium/water or water/hydroxide donor/acceptor pairs, capturing the entire dataset with only four adjustable parameters. We find that I-PCET is fourfold faster with hydronium/water than water/hydroxide, while both reactions display similarly high charge transfer coefficients, indicating late proton transfer transition states. These studies highlight the key mechanistic distinctions between I-PCET and OS-PCET, providing a framework for understanding and modelling more complex multistep I-PCET reactions critical to energy conversion and catalysis.

Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment.

Alzheimer's Disease (AZD) is a neurodegenerative disease for which there is now no known effective treatment. Mild cognitive impairment (MCI) is considered a precursor to AZD and affects cognitive abilities. Patients with MCI have the potential to recover cognitive health, can remain mildly cognitively impaired indefinitely or eventually progress to AZD. Identifying imaging-based predictive biomarkers for disease progression in patients presenting with evidence of very mild/questionable MCI (qMCI) can play an important role in triggering early dementia intervention. Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly studied in brain disorder diseases. In this work, employing a recent developed a time-attention long short-term memory (TA-LSTM) network to classify multivariate time series data. A gradient-based interpretation framework, transiently-realized event classifier activation map (TEAM) is introduced to localize the group-defining "activated" time intervals over the full time series and generate the class difference map. To test the trustworthiness of TEAM, we did a simulation study to validate the model interpretative power of TEAM. We then applied this simulation-validated framework to a well-trained TA-LSTM model which predicts the progression or recovery from questionable/mild cognitive impairment (qMCI) subjects after three years from windowless wavelet-based dFNC (WWdFNC). The FNC class difference map points to potentially important predictive dynamic biomarkers. Moreover, the more highly time-solved dFNC (WWdFNC) achieves better performance in both TA-LSTM and a multivariate CNN model than dFNC based on windowed correlations between timeseries, suggesting that better temporally resolved measures can enhance the model's performance.

Metal nanoparticles supported on a nonconductive oxide undergo pH-dependent spontaneous polarization.

Electrochemical polarization, which often plays a critical role in driving chemical reactions at solid-liquid interfaces, can arise spontaneously through the exchange of ions and/or electrons across the interface. However, the extent to which such spontaneous polarization prevails at nonconductive interfaces remains unclear because such materials preclude measuring and controlling the degree of interfacial polarization via standard (i.e., wired) potentiometric methods. Herein, we circumvent the limitations of wired potentiometry by applying infrared and ambient pressure X-ray photoelectron spectroscopies (AP-XPS) to probe the electrochemical potential of nonconductive interfaces as a function of solution composition. As a model class of macroscopically nonconductive interfaces, we specifically probe the degree of spontaneous polarization of ZrO2-supported Pt and Au nanoparticles immersed in aqueous solutions of varying pH. Shifts in the Pt-adsorbed CO vibrational band position evince electrochemical polarization of the Pt/ZrO2-water interface with changing pH, and AP-XPS reveals quasi-Nernstian shifts of the electrochemical potential of Pt and Au with pH in the presence of H2. These results indicate that spontaneous proton transfer via equilibrated H+/H2 interconversion spontaneously polarizes metal nanoparticles even when supported on a nonconductive host. Consequently, these findings indicate that solution composition (i.e., pH) can be an effective handle for tuning interfacial electrical polarization and potential at nonconductive interfaces.

Chromatic fusion: generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia.

This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. By linking colors to private and shared information from modalities, we introduce chromatic fusion, a framework that allows for intuitively interpreting multimodal data. We test our framework on structural, functional, and diffusion modality pairs. In this framework, we use a multimodal variational autoencoder to learn separate latent subspaces; a private space for each modality, and a shared space between both modalities. These subspaces are then used to cluster subjects, and colored based on their distance from the variational prior, to obtain meta-chromatic patterns (MCPs). Each subspace corresponds to a different color, red is the private space of the first modality, green is the shared space, and blue is the private space of the second modality. We further analyze the most schizophrenia-enriched MCPs for each modality pair and find that distinct schizophrenia subgroups are captured by schizophrenia-enriched MCPs for different modality pairs, emphasizing schizophrenia's heterogeneity. For the FA-sFNC, sMRI-ICA, and sMRI-ICA MCPs, we generally find decreased fractional corpus callosum anisotropy and decreased spatial ICA map and voxel-based morphometry strength in the superior frontal lobe for schizophrenia patients. To additionally highlight the importance of the shared space between modalities, we perform a robustness analysis of the latent dimensions in the shared space across folds. These robust latent dimensions are subsequently correlated with schizophrenia to reveal that for each modality pair, multiple shared latent dimensions strongly correlate with schizophrenia. In particular, for FA-sFNC and sMRI-sFNC shared latent dimensions, we respectively observe a reduction in the modularity of the functional connectivity and a decrease in visual-sensorimotor connectivity for schizophrenia patients. The reduction in modularity couples with increased fractional anisotropy in the left part of the cerebellum dorsally. The reduction in the visual-sensorimotor connectivity couples with a reduction in the voxel-based morphometry generally but increased dorsal cerebellum voxel-based morphometry. Since the modalities are trained jointly, we can also use the shared space to try and reconstruct one modality from the other. We show that cross-reconstruction is possible with our network and is generally much better than depending on the variational prior. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data that we hope challenges the reader to think differently about how modalities interact.

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk.

BACKGROUND: Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification. METHODS: We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data. RESULTS: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks. CONCLUSIONS: Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.

Interpreting models interpreting brain dynamics.

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. Results successfully demonstrate that the proposed framework enables learning the dynamics of resting-state fMRI directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction.

Tools of the trade: estimating time-varying connectivity patterns from fMRI data.

Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diagnosed using self-reported symptoms. This makes it difficult to pose a confident prediction on the cases and does not provide insight into the underlying neural and biological mechanisms of these disorders. Combining neuroimaging and genomic data with a multi-modal 'predictome' paves the way for biologically informed markers and may improve prediction reliability. With that, we develop a multi-modal deep learning framework by fusing data from different modalities to capture the interaction between the latent features and evaluate their complementary information in characterizing schizophrenia. Our deep model uses structural MRI, functional MRI, and genome-wide polymorphism data to perform the classification task. It includes a multi-layer feed-forward network, an encoder, and a long short-term memory (LSTM) unit with attention to learn the latent features and adopt a joint training scheme capturing synergies between the modalities. The hybrid network also uses different regularizers for addressing the inherent overfitting and modality-specific bias in the multi-modal setup. Next, we run the network through a saliency model to analyze the learned features. Integrating modalities enhances the performance of the classifier, and our framework acquired 88% (P < 0.0001) accuracy on a dataset of 437 subjects. The trimodal accuracy is comparable to the state-of-the-art performance on a data collection of this size and outperforms the unimodal and bimodal baselines we compared. Model introspection was used to expose the salient neural features and genes/biological pathways associated with schizophrenia. To our best knowledge, this is the first approach that fuses genomic information with structural and functional MRI biomarkers for predicting schizophrenia. We believe this type of modality blending can better explain the disorder's dynamics by adding cross-modal prospects.Clinical Relevance- This study combinedly learns imaging and genomic features for the classification of schizophrenia. The data fusion scheme extracts modality interactions, and the saliency experiments report multiple functional and structural networks closely connected to the disorder.

Fusing multimodal neuroimaging data with a variational autoencoder.

Neuroimaging studies often collect multimodal data. These modalities contain both shared and mutually exclusive information about the brain. This work aims to find a scalable and interpretable method to fuse the information of multiple neuroimaging modalities into a lower-dimensional latent space using a variational autoencoder (VAE). To assess whether the encoder-decoder pair retains meaningful information, this work evaluates the representations using a schizophrenia classification task. The linear classifier, trained on the representations obtained through dimensionality reduction, achieves an area under the curve of the receiver operating characteristic (ROC-AUC) of 0.8609. Thus, training on a multimodal dataset with functional brain networks and a structural magnetic resonance imaging (sMRI) scan, leads to dimensionality reduction that retains meaningful information. The proposed dimensionality reduction outperforms both early and late fusion principal component analysis on the classification task.Clinical relevance - This work examines the interplay between neuroimaging modalities and their relation to mental disorders. This allows for more complex and rigorous analysis of multimodal neuroimaging data throughout clinical settings.

Decentralized distribution-sampled classification models with application to brain imaging.

BACKGROUND: In this age of big data, certain models require very large data stores in order to be informative and accurate. In many cases however, the data are stored in separate locations requiring data transfer between local sites which can cause various practical hurdles, such as privacy concerns or heavy network load. This is especially true for medical imaging data, which can be constrained due to the health insurance portability and accountability act (HIPAA) which provides security protocols for medical data. Medical imaging datasets can also contain many thousands or millions of features, requiring heavy network load. NEW METHOD: Our research expands upon current decentralized classification research by implementing a new singleshot method for both neural networks and support vector machines. Our approach is to estimate the statistical distribution of the data at each local site and pass this information to the other local sites where each site resamples from the individual distributions and trains a model on both locally available data and the resampled data. The model for each local site produces its own accuracy value which are then averaged together to produce the global average accuracy. RESULTS: We show applications of our approach to handwritten digit classification as well as to multi-subject classification of brain imaging data collected from patients with schizophrenia and healthy controls. Overall, the results showed comparable classification accuracy to the centralized model with lower network load than multishot methods. COMPARISON WITH EXISTING METHODS: Many decentralized classifiers are multishot, requiring heavy network traffic. Our model attempts to alleviate this load while preserving prediction accuracy. CONCLUSIONS: We show that our proposed approach performs comparably to a centralized approach while minimizing network traffic compared to multishot methods.

Static and dynamic functional connectivity analysis of cerebrovascular reactivity: An fMRI study.

BACKGROUND: Cerebrovascular reactivity (CVR) is an important aspect of brain function, and as such it is important to understand relationship between CVR and functional connectivity. METHODS: This research studied the role of CVR, or the brain's ability to react to vasoactive stimuli on brain functional connectivity by scanning subjects with blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) while they periodically inhale room air and a CO 2-enriched gas mixture. We developed a new metric to measure the effect of CVR on each intrinsic connectivity network (ICN), which contrasts to voxel-wise CVR. We also studied the changes in whole-brain connectivity patterns using both static functional network connectivity (sFNC) and dynamic FNC (dFNC). RESULTS: We found that network connectivity is generally weaker during vascular dilation, which is supported by previous research. The dFNC analysis revealed that participants did not return to the pre-CO 2 inhalation state, suggesting that one-minute periods of room-air inhalation is not enough for the CO 2 effect to fully dissipate. CONCLUSIONS: Cerebrovascular reactivity is one tool that the cerebrovascular system uses to ensure the constant, finely-tuned flow of oxygen to function properly. Understanding the relationship between CVR and brain dynamism can provide unique information about cerebrovascular diseases and general brain function. We observed that CVR has a wide, but consistent relationship to connectivity patterns between functional networks.

The Deformity Angular Ratio: Does It Correlate With High-Risk Cases for Potential Spinal Cord Monitoring Alerts in Pediatric 3-Column Thoracic Spinal Deformity Corrective Surgery?

STUDY DESIGN: A retrospective analysis. OBJECTIVE: The purpose of this study was to determine whether the deformity angular ratio (DAR) can reliably assess the neurological risks of patients undergoing deformity correction. SUMMARY OF BACKGROUND DATA: Identifying high-risk patients and procedures can help ensure that appropriate measures are taken to minimize neurological complications during spinal deformity corrections. Subjectively, surgeons look at radiographs and evaluate the riskiness of the procedure. However, 2 curves of similar magnitude and location can have significantly different risks of neurological deficit during surgery. Whether the curve spans many levels or just a few can significantly influence surgical strategies. Lenke et al have proposed the DAR, which is a measure of curve magnitude per level of deformity. METHODS: The data from 35 pediatric spinal deformity correction procedures with thoracic 3-column osteotomies were reviewed. Measurements from preoperative radiographs were used to calculate the DAR. Binary logistic regression was used to model the relationship between DARs (independent variables) and presence or absence of an intraoperative alert (dependent variable). RESULTS: In patients undergoing 3-column osteotomies, sagittal curve magnitude and total curve magnitude were associated with increased incidence of transcranial motor evoked potential changes. Total DAR greater than 45° per level and sagittal DAR greater than 22° per level were associated with a 75% incidence of a motor evoked potential alert, with the incidence increasing to 90% with sagittal DAR of 28° per level. CONCLUSION: In patients undergoing 3-column osteotomies for severe spinal deformities, the DAR was predictive of patients developing intraoperative motor evoked potential alerts. Identifying accurate radiographical, patient, and procedural risk factors in the correction of severe deformities can help prepare the surgical team to improve safety and outcomes when carrying out complex spinal corrections. LEVEL OF EVIDENCE: 3.

Modified T4 hemivertebrectomy for persistent high left shoulder after surgery for double thoracic scoliosis.

STUDY DESIGN: Case report. OBJECTIVE: To surgically regain shoulder balance in patients with adolescent idiopathic scoliosis after loss of alignment after posterior fusion for Lenke II deformity correction. SUMMARY OF BACKGROUND DATA: Shoulder balance is known to have a large effect on patient satisfaction after deformity correction. Previous studies have outlined guidelines for determining levels of instrumentation to prevent postoperative high left shoulder. However, to our knowledge, no study has provided instructions on how to correct coronal imbalance in patients with previously fused scoliosis. We describe a case using a T4 unilateral pedicle subtraction osteotomy and contralateral Smith-Petersen osteotomy to treat high left shoulder in a patient who had previously undergone posterior instrumented fusion for adolescent idiopathic scoliosis. METHODS: The radiographs and clinical charts were reviewed for a 17-year-old female patient treated with a revision fusion and modified T4 hemivertebrectomy for a persistently high left shoulder after previous correction of a Lenke II idiopathic scoliosis. RESULTS: A reduction in the T1 tilt angle from 19.2° to 10.1° and an improvement in the coronal Cobb angle of the proximal thoracic curve from 37° to 17° were obtained. Shoulder balance was greatly improved. CONCLUSION: A proximal thoracic partial vertebrectomy with unilateral pedicle subtraction osteotomy and contralateral Smith-Petersen osteotomy is a technique that can be used to successfully correct fixed shoulder imbalance after posterior instrumented fusion of a double thoracic adolescent idiopathic scoliosis. LEVEL OF EVIDENCE: N/A.