Selective Motor Control is a Clinical Correlate of Brain Motor Tract Impairment in Children with Spastic Bilateral Cerebral Palsy

BACKGROUND AND PURPOSE

Selective voluntary motor control is an important factor influencing gross motor function, interjoint coordination, and the outcome of hamstring-lengthening surgery in spastic cerebral palsy. Using DTI, we investigated whether selective voluntary motor control would show strong correlations with WM motor tract microstructure and whether selective voluntary motor control is more sensitive to global WM impairment than gross motor function.

MATERIALS AND METHODS

Children with spastic bilateral cerebral palsy born preterm and typically developing children were recruited. The Selective Control Assessment of the Lower Extremity (SCALE) and Gross Motor Function Measure (GMFM) were assessed in participants with cerebral palsy. Participants underwent brain MR imaging to collect DWI data. Tract-Based Spatial Statistics was used to analyze the WM for between-group differences and correlations with SCALE and GMFM. ROI analyses compared motor regions.

RESULTS

Twelve children with cerebral palsy (mean age, 11.5 years) and 12 typically developing children (mean age, 10.3 years) participated. Altered DTI outcomes were found throughout the whole brain for the cerebral palsy group. SCALE, developed to evaluate selective voluntary motor control in cerebral palsy, showed significant positive correlations with fractional anisotropy in more WM voxels throughout the whole brain and for motor regions, including the corticospinal tract and corpus callosum, compared with GMFM. A significant negative correlation between radial diffusivity and SCALE, but not GMFM, was found within the corpus callosum.

CONCLUSIONS

SCALE was a more sensitive clinical correlate of motor and whole-brain WM tract impairment in children with spastic bilateral cerebral palsy, suggesting greater anisotropy and myelination in these regions for those with higher selective voluntary motor control.

A CNN and LSTM Network for Eye-Blink Classification from MRI Scanner Monitoring Videos

Eye closure changes brain activity, so eye-blink tracking of subjects undergoing resting-state functional magnetic resonance imaging (fMRI) is relevant for identifying when a subject blinks, falls asleep, or keeps their eyes closed. Existing MRI eye-tracking solutions use commercially available MR-compatible video cameras with tracking software that can fail on low-quality videos. In this paper, we propose a two-stage convolutional recurrent neural network to classify open and closed eyes from frames of MRI eye-tracking videos under variable camera conditions. The model extracts visual features from each video frame using a convolutional neural network based on the Inception-v3 model, then uses a long short-term memory network to incorporate temporal information encoded in the sequence of visual features over time. Our model is implemented in Keras and demonstrated on a dataset of MRI eye-tracking videos from the Human Connectome Project. We manually labelled frames from the dataset for training and evaluation. The network was able to classify eye-blink states with a precision of 0.739 and recall of 0.835 on a previously unseen holdout dataset under varying camera conditions, eye position, and video quality.Clinical relevance- Functional mapping studies in psychiatry and neuro-development which rely on a resting state fMRI protocol may yield divergent results depending on whether the subject keeps their eyes closed or open or whether the subject falls asleep. The clinical relevance of this work is to introduce the eye state (closed or open) in brain imaging studies as a prospective covariate, and as a feature that can potentially control for sleep state as a confounding factor.

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy

Depression symptom heterogeneity limits the identifiability of treatment‐response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing three latent symptom dimensions within the 17‐item Hamilton Depression Rating Scale (HDRS) and use data‐driven methods to relate multivariate patterns of patient clinical, demographic, and brain structural changes over electroconvulsive therapy (ECT) to dimensional changes in depressive symptoms. We included 110 ECT patients from Global ECT‐MRI Research Collaboration (GEMRIC) sites who underwent structural MRI and HDRS assessments before and after treatment. Cross validated random forest regression models predicted change along symptom dimensions. HDRS symptoms clustered into dimensions of somatic disturbances (SoD), core mood and anhedonia (CMA), and insomnia. The coefficient of determination between predicted and actual changes were 22%, 39%, and 39% (all p < .01) for SoD, CMA, and insomnia, respectively. CMA and insomnia change were predicted more accurately than HDRS‐6 and HDRS‐17 changes (p < .05). Pretreatment symptoms, body‐mass index, and age were important predictors. Important imaging predictors included the right transverse temporal gyrus and left frontal pole for the SoD dimension; right transverse temporal gyrus and right rostral middle frontal gyrus for the CMA dimension; and right superior parietal lobule and left accumbens for the insomnia dimension. Our findings support that recovery along depressive symptom dimensions is predicted more accurately than HDRS total scores and are related to unique and overlapping patterns of clinical and demographic data and volumetric changes in brain regions related to depression and near ECT electrodes.

Central white matter integrity alterations in 2-3-year-old children following prenatal alcohol exposure

BACKGROUND

Prenatal alcohol exposure (PAE) remains a potentially preventable, but pervasive risk factor to neurodevelopment. Yet, evidence is lacking on the impact of alcohol on brain development in toddlers. This study aimed to investigate the impact of PAE on brain white matter integrity in 2-3-year-old children.

METHODS

Children (n = 83, 30-37 months old) of the Drakenstein Child Health Study birth cohort, underwent diffusion MRI on a 3 T Siemens scanner during natural sleep. Parameters were extracted in children with PAE (n = 25, 56 % boys) and unexposed controls (n = 58, 62 % boys) using Tract-based Spatial Statistics, and compared by group. The contribution of maternal tobacco smoking to white matter differences was also explored.

RESULTS

Children with PAE had altered fractional anisotropy, radial diffusivity and axial diffusivity in brain stem, limbic and association tracts compared to unexposed controls. Notably lower fractional anisotropy was found in the uncinate fasciculus, and lower mean and radial diffusivity were found in the fornix stria terminalis and corticospinal tract (FDR corrected p < 0.05). There was a significant interaction effect of PAE and prenatal tobacco exposure which lowered mean, radial and axial diffusivity in the corticospinal tract significantly in the PAE group but not controls.

CONCLUSION

Widespread altered white matter microstructural integrity at 2-3 years of age is consistent with findings in neonates in the same and other cohorts, indicating persistence of effects of PAE through early life. Findings also highlight that prenatal tobacco exposure impacts the association of PAE on white matter alterations, amplifying effects in tracts underlying motor function.

Hippocampal subregions and networks linked with antidepressant response to electroconvulsive therapy

Electroconvulsive therapy (ECT) has been repeatedly linked to hippocampal plasticity. However, it remains unclear what role hippocampal plasticity plays in the antidepressant response to ECT. This magnetic resonance imaging (MRI) study tracks changes in separate hippocampal subregions and hippocampal networks in patients with depression (n = 44, 23 female) to determine their relationship, if any, with improvement after ECT. Voxelwise analyses were restricted to the hippocampus, amygdala, and parahippocampal cortex, and applied separately for responders and nonresponders to ECT. In analyses of arterial spin-labeled (ASL) MRI, nonresponders exhibited increased cerebral blood flow (CBF) in bilateral anterior hippocampus, while responders showed CBF increases in right middle and left posterior hippocampus. In analyses of gray matter volume (GMV) using T1-weighted MRI, GMV increased throughout bilateral hippocampus and surrounding tissue in nonresponders, while responders showed increased GMV in right anterior hippocampus only. Using CBF loci as seed regions, BOLD-fMRI data from healthy controls (n = 36, 19 female) identified spatially separable neurofunctional networks comprised of different brain regions. In graph theory analyses of these networks, functional connectivity within a hippocampus-thalamus-striatum network decreased only in responders after two treatments and after index. In sum, our results suggest that the location of ECT-related plasticity within the hippocampus may differ according to antidepressant outcome, and that larger amounts of hippocampal plasticity may not be conducive to positive antidepressant response. More focused targeting of hippocampal subregions and/or circuits may be a way to improve ECT outcome.

SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Functional Alignment

We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data while simultaneously achieving alignment. Our network is constructed as a generative encoder-decoder architecture comprising fully-connected layers capable of producing a distribution space of the warping functions. We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data.

Modulation of inhibitory control networks relate to clinical response following ketamine therapy in major depression

Subanesthetic ketamine is found to induce fast-acting and pronounced antidepressant effects, even in treatment resistant depression (TRD). However, it remains unclear how ketamine modulates neural function at the brain systems-level to regulate emotion and behavior. Here, we examined treatment-related changes in the inhibitory control network after single and repeated ketamine therapy in TRD. Forty-seven TRD patients (mean age = 38, 19 women) and 32 healthy controls (mean age = 35, 18 women) performed a functional magnetic resonance imaging (fMRI) response inhibition task at baseline, and 37 patients completed the fMRI task and symptom scales again 24 h after receiving both one and four 0.5 mg/kg intravenous ketamine infusions. Analyses of fMRI data addressed effects of diagnosis, time, and differences between treatment remitters and non-remitters. Significant decreases in brain activation were observed in the inhibitory control network, including in prefrontal and parietal regions, and visual cortex following serial ketamine treatment, p < 0.05 corrected. Remitters were distinguished from non-remitters by having lower functional activation in the supplementary motor area (SMA) prior to treatment, which normalized towards controls following serial ketamine treatment. Results suggest that ketamine treatment leads to neurofunctional plasticity in executive control networks including the SMA during a response-inhibitory task. SMA changes relate to reductions in depressive symptoms, suggesting modulation of this network play an important role in therapeutic response. In addition, early changes in the SMA network during response inhibition appear predictive of overall treatment outcome, and may serve as a biomarker of treatment response.

Modularity and heterochrony in the evolution of the ceratopsian dinosaur frill

The fossil record provides compelling examples of heterochrony at macroevolutionary scales such as the peramorphic giant antlers of the Irish elk. Heterochrony has also been invoked in the evolution of the distinctive cranial frill of ceratopsian dinosaurs such as Triceratops. Although ceratopsian frills vary in size, shape, and ornamentation, quantitative analyses that would allow for testing hypotheses of heterochrony are lacking. Here, we use geometric morphometrics to examine frill shape variation across ceratopsian diversity and within four species preserving growth series. We then test whether the frill constitutes an evolvable module both across and within species, and compare growth trajectories of taxa with ontogenetic growth series to identify heterochronic processes. Evolution of the ceratopsian frill consisted primarily of progressive expansion of its caudal and caudolateral margins, with morphospace occupation following taxonomic groups. Although taphonomic distortion represents a complicating factor, our data support modularity both across and within species. Peramorphosis played an important role in frill evolution, with acceleration operating early in neoceratopsian evolution followed by progenesis in later diverging cornosaurian ceratopsians. Peramorphic evolution of the ceratopsian frill may have been facilitated by the decoupling of this structure from the jaw musculature, an inference that predicts an expansion of morphospace occupation and higher evolutionary rates among ceratopsids as indeed borne out by our data. However, denser sampling of the meager record of early-diverging taxa is required to test this further.

Brain Metabolism During A Lower Extremity Voluntary Movement Task in Children With Spastic Cerebral Palsy

Reduced selective voluntary motor control (SVMC) is a primary impairment due to corticospinal tract (CST) injury in spastic cerebral palsy (CP). There are few studies of brain metabolism in CP and none have examined brain metabolism during a motor task. Nine children with bilateral spastic CP [Age: 6-11 years, Gross Motor Function Classification System (GMFCS) Levels II-V] completed this study. SVMC was evaluated using Selective Control Assessment of the Lower Extremity (SCALE) ranging from 0 (absent) to 10 (normal). Brain metabolism was measured using positron emission tomography (PET) scanning in association with a selective ankle motor task. Whole brain activation maps as well as ROI averaged metabolic activity were correlated with SCALE scores. The contralateral sensorimotor and superior parietal cortex were positively correlated with SCALE scores (p < 0.0005). In contrast, a negative correlation of metabolic activity with SCALE was found in the cerebellum (p < 0.0005). Subsequent ROI analysis showed that both ipsilateral and contralateral cerebellar metabolism correlated with SCALE but the relationship for the ipsilateral cerebellum was stronger (R ² = 0.80, p < 0.001 vs. R ² = 0.46, p = 0.045). Decreased cortical and increased cerebellar activation in children with less SVMC may be related to task difficulty, activation of new motor learning paradigms in the cerebellum and potential engagement of alternative motor systems when CSTs are focally damaged. These results support SCALE as a clinical correlate of neurological impairment.

Deep Learning of Warping Functions for Shape Analysis

Rate-invariant or reparameterization-invariant matching between functions and shapes of curves, respectively, is an important problem in computer vision and medical imaging. Often, the computational cost of matching using approaches such as dynamic time warping or dynamic programming is prohibitive for large datasets. Here, we propose a deep neural-network-based approach for learning the warping functions from training data consisting of a large number of optimal matches, and use it to predict optimal diffeomorphic warping functions. Results show prediction performance on a synthetic dataset of bump functions and two-dimensional curves from the ETH-80 dataset as well as a significant reduction in computational cost.

Neuroimaging young children and associations with neurocognitive development in a South African birth cohort study

Magnetic resonance imaging (MRI) is an indispensable tool for investigating brain development in young children and the neurobiological mechanisms underlying developmental risk and resilience. Sub-Saharan Africa has the highest proportion of children at risk of developmental delay worldwide, yet in this region there is very limited neuroimaging research focusing on the neurobiology of such impairment. Furthermore, paediatric MRI imaging is challenging in any setting due to motion sensitivity. Although sedation and anesthesia are routinely used in clinical practice to minimise movement in young children, this may not be ethical in the context of research. Our study aimed to investigate the feasibility of paediatric multimodal MRI at age 2–3 years without sedation, and to explore the relationship between cortical structure and neurocognitive development at this understudied age in a sub-Saharan African setting. A total of 239 children from the Drakenstein Child Health Study, a large observational South African birth cohort, were recruited for neuroimaging at 2–3 years of age. Scans were conducted during natural sleep utilising locally developed techniques. T1-MEMPRAGE and T2-weighted structural imaging, resting state functional MRI, diffusion tensor imaging and magnetic resonance spectroscopy sequences were included. Child neurodevelopment was assessed using the Bayley-III Scales of Infant and Toddler Development. Following 23 pilot scans, 216 children underwent scanning and T1-weighted images were obtained from 167/216 (77%) of children (median age 34.8 months). Furthermore, we found cortical surface area and thickness within frontal regions were associated with cognitive development, and in temporal and frontal regions with language development (beta coefficient ≥0.20). Overall, we demonstrate the feasibility of carrying out a neuroimaging study of young children during natural sleep in sub-Saharan Africa. Our findings indicate that dynamic morphological changes in heteromodal association regions are associated with cognitive and language development at this young age. These proof-of-concept analyses suggest similar links between the brain and cognition as prior literature from high income countries, enhancing understanding of the interplay between cortical structure and function during brain maturation.

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MRI data are challenging to acquire in the early years of life.

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Paediatric MRI without sedation is feasible in sub-Saharan Africa, with 77% success.

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The Drakenstein Child Health study has novel MRI data of South African children.

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Morphological features of the cortex associate with neurocognitive development.

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Structure-cognition relationships in heteromodal association regions at 2–3 years.

BRAIN NETWORK CONNECTIVITY FROM MATCHING CORTICAL FEATURE DENSITIES

We present a new method for constructing structural inference brain networks from functional measures of cortical features. Instead of averaging vertex-wise cortical features, we propose the use of full functions of spatial densities of measures such as thickness and use two dimensional pairwise correlations between regions to construct population networks. We show increased within group correlations for both healthy controls and toddlers with prenatal alcohol exposure compared to the existing mean-based correlation approach. Further, we also show significant differences in brain connectivity between the healthy controls and the exposed group.

Single and repeated ketamine treatment induces perfusion changes in sensory and limbic networks in major depressive disorder

Ketamine infusion therapy can produce fast-acting antidepressant effects in patients with major depressive disorder (MDD). Yet, how single and repeated ketamine treatment induces brain systems-level neuroplasticity underlying symptom improvement is unknown. Advanced multiband imaging (MB) pseudo-continuous arterial spin labeling (pCASL) perfusion MRI data was acquired from patients with treatment resistant depression (TRD) (N = 22, mean age=35.2 ± 9.95 SD, 27% female) at baseline, and 24 h after receiving single, and four subanesthetic (0.5 mg/kg) intravenous ketamine infusions. Changes in global and regional CBF were compared across time points, and relationships with overall mood, anhedonia and apathy were examined. Comparisons between patients at baseline and controls (N = 18, mean age=36.11 ± 14.5 SD, 57% female) established normalization of treatment effects. Results showed increased regional CBF in the cingulate and primary and higher-order visual association regions after first ketamine treatment. Baseline CBF in the fusiform, and acute changes in CBF in visual areas were related to symptom improvement after single and repeated ketamine treatment, respectively. In contrast, after serial infusion therapy, decreases in regional CBF were observed in the bilateral hippocampus and right insula with ketamine treatment. Findings demonstrate that neurophysiological changes occurring with single and repeated ketamine treatment follow both a regional and temporal pattern including sensory and limbic regions. Initial changes are observed in the posterior cingulate and precuneus and primary and higher-order visual areas, which relate to clinical responses. However, repeated exposure to ketamine, though not relating to clinical outcome, appears to engage deeper limbic structures and insula. ClinicalTrials.gov: Biomarkers of Fast Acting Therapies in Major Depression, https://clinicaltrials.gov/ct2/show/NCT02165449, NCT02165449.

Multimodal Data Registration for Brain Structural Association Networks

We present a method for multimodal brain data registration that aligns shapes of nodal network configurations in an invertible manner. We use ideas from shape analysis to represent an individual subject data configuration as an element on a hypersphere, where geodesics have closed form solutions. The method not only performs inter-subject data registration, but also allows for the construction of a population data template to which all subject data configurations can be registered. Results show compression of data measures and significant reduction in variance after registration. We also observe increased predictive power of regions of interest (ROI) node identification, significant increases in pairwise network connectivity measures, as well as significant increases in canonical correlations with age after registration.

Cognitive Correlates of Hippocampal Atrophy and Ventricular Enlargement in Adults with or without Mild Cognitive Impairment

We analyzed structural magnetic resonance imaging data from 58 cognitively normal and 101 mild cognitive impairment subjects. We used a general linear regression model to study the association between cognitive performance with hippocampal atrophy and ventricular enlargement using the radial distance method. Bilateral hippocampal atrophy was associated with baseline and longitudinal memory performance. Left hippocampal atrophy predicted longitudinal decline in visuospatial function. The multidomain ventricular analysis did not reveal any significant predictors.

Mechanisms of Antidepressant Response to Electroconvulsive Therapy Studied With Perfusion Magnetic Resonance Imaging

BACKGROUND

Converging evidence suggests that electroconvulsive therapy (ECT) induces neuroplasticity in patients with severe depression, though how this relates to antidepressant response is less clear. Arterial spin-labeled functional magnetic resonance imaging tracks absolute changes in cerebral blood flow (CBF) linked with brain function and offers a potentially powerful tool when observing neurofunctional plasticity with functional magnetic resonance imaging.

METHODS

Using arterial spin-labeled functional magnetic resonance imaging, we measured global and regional CBF associated with clinically prescribed ECT and therapeutic response in patients (n = 57, 30 female) before ECT, after two treatments, after completing an ECT treatment "index" (∼4 weeks), and after long-term follow-up (6 months). Age- and sex-matched control subjects were also scanned twice (n = 36, 19 female), ∼4 weeks apart.

RESULTS

Patients with lower baseline global CBF were more likely to respond to ECT. Regional CBF increased in the right anterior hippocampus in all patients irrespective of clinical outcome, both after 2 treatments and after ECT index. However, hippocampal CBF increases postindex were more pronounced in nonresponders. ECT responders exhibited CBF increases in the dorsomedial thalamus and motor cortex near the vertex ECT electrode, as well as decreased CBF within lateral frontoparietal regions.

CONCLUSIONS

ECT induces functional neuroplasticity in the hippocampus, which could represent functional precursors of ECT-induced increases in hippocampal volume reported previously. However, excessive functional neuroplasticity within the hippocampus may not be conducive to positive clinical outcome. Instead, our results suggest that although hippocampal plasticity may contribute to antidepressant response in ECT, balanced plasticity in regions relevant to seizure physiology including thalamocortical networks may also play a critical role.

Elastic Registration of Single Subject Task Based fMRI Signals

Single subject task-based fMRI analyses generally suffer from low detection sensitivity with parameter estimates from the general linear model (GLM) lying below the significance threshold especially for similar contrasts or conditions. In this paper, we present a shape-based approach for alignment of condition-specific time course activity for single subject task-based fMRI. Our approach extracts signals for each condition from the entire time course, constructs an unbiased average of those signals, and warps each signal to the mean. As the warping is diffeomorphic, non-linear and allows large deformations of time series if required, we term this approach as elastic functional registration. On a single subject level, our method significantly detects more clusters and more activated voxels in relevant subcortical regions in healthy controls.

Superficial white matter damage in anti-NMDA receptor encephalitis

BACKGROUND

Clinical brain MRI is normal in the majority of patients with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. However, extensive deep white matter damage wasrecently identifiedin these patients using diffusion weighted imaging. Here, our aim was to study a particularly vulnerable brain compartment, the late myelinating superficial white matter.

METHODS

Forty-six patients with anti-NMDAR encephalitis were included. Ten out of these were considered neurologically recovered (modified Rankin scale of zero), while 36 patients were non-recovered. In addition, 30 healthy controls were studied. MRI data were collected from all subjects and superficial white matter mean diffusivity derived from diffusion tensor imaging was compared between groups in whole brain, lobar and vertex-based analyses. Patients underwent comprehensive cognitive testing, and correlation analyses were performed between cognitive performance and superficial white matter integrity.

RESULTS

Non-recovered patients showed widespread superficial white matter damage in comparison to recovered patients and healthy controls. Vertex-based analyses revealed that damage predominated in frontal and temporal lobes. In contrast, the superficial white matter was intact in recovered patients. Importantly, persistent cognitive impairments in working memory, verbal memory, visuospatial memory and attention significantly correlated with damage of the superficial white matter in patients.

CONCLUSIONS

Anti-NMDAR encephalitis is associated with extensive superficial white matter damage in patients with incomplete recovery. The strong association with impairment in several cognitive domains highlights the clinical relevance of white matter damage in this disorder and warrants investigations of the underlying pathophysiological mechanisms.

Fronto-Temporal Connectivity Predicts ECT Outcome in Major Depression

BACKGROUND

Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning.

METHODS

A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n = 46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features.

RESULTS

The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC).

CONCLUSION

Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra

We present a shape matching approach for functional magnetic resonance imaging (fMRI) time course and spectral alignment. We use ideas from differential geometry and functional data analysis to define a functional representation for fMRI signals. The space of fMRI functions is then equipped with a reparameterization invariant Riemannian metric that enables elastic alignment of both amplitude and phase of the fMRI time courses as well as their power spectral densities. Experimental results show significant increases in pairwise node to node correlations and coherences following alignment. We apply this method for finding group differences in connectivity between patients with major depression and healthy controls.

A Riemannian Framework for Linear and Quadratic Discriminant Analysis on the Tangent Space of Shapes

We present a Riemannian framework for linear and quadratic discriminant classification on the tangent plane of the shape space of curves. The shape space is infinite dimensional and is constructed out of square root velocity functions of curves. We introduce the notion of mean and covariance of shape-valued random variables and samples from a tangent space to the pre-shapes (invariant to translation and scaling) and then extend it to the full shape space (rotational invariance). The shape observations from the population are approximated by coefficients of a Fourier basis of the tangent space. The algorithms for linear and quadratic discriminant analysis are then defined using reduced dimensional features obtained by projecting the original shape observations on to the truncated Fourier basis. We show classification results on synthetic data and shapes of cortical sulci, corpus callosum curves, as well as facial midline curve profiles from patients with fetal alcohol syndrome (FAS).

DATA-DRIVEN CLUSTER SELECTION FOR SUBCORTICAL SHAPE AND CORTICAL THICKNESS PREDICTS RECOVERY FROM DEPRESSIVE SYMPTOMS

Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using structural MRI data from 42 ECT patients scanned prior to ECT treatment, we developed a random forest classifier based on data-driven shape cluster selection and cortical thickness features to predict remission. Right hemisphere hippocampal shape and right inferior temporal cortical thickness was most predictive of remission, with the predicted probability of recovery decreasing when these regions were thicker prior to treatment. Remission was predicted with an average 73% balanced accuracy. Classification of MRI data may help identify treatment-responsive patients and aid in clinical decision-making. Our results show promise for the development of personalized treatment strategies.

Neural correlates of proactive and reactive aggression in adolescent twins

Verbal and physical aggression begin early in life and steadily decline thereafter in normal development. As a result, elevated aggressive behavior in adolescence may signal atypical development and greater vulnerability for negative mental and health outcomes. Converging evidence suggests that brain disturbances in regions involved in impulse control, emotional regulation, and sensation seeking may contribute to heightened aggression. However, little is known regarding the neural mechanisms underlying subtypes of aggression (i.e., proactive and reactive aggression) and whether they differ between males and females. Using a sample of 106 14-year-old adolescent twins, this study found that striatal enlargement was associated with both proactive and reactive aggression. We also found that volumetric alterations in several frontal regions including smaller middle frontal and larger orbitofrontal cortex were correlated with higher levels of aggression in adolescent twins. In addition, cortical thickness analysis showed that thickness alterations in many overlapping regions including middle frontal, superior frontal, and anterior cingulate cortex and temporal regions were associated with aggression in adolescent twins. Results support the involvement of fronto-limbic-striatal circuit in the etiology of aggression during adolescence. Aggr. Behav. 43:230-240, 2017. © 2016 Wiley Periodicals, Inc.

Neurochemical correlates of rapid treatment response to electroconvulsive therapy in patients with major depression

BACKGROUND

Electroconvulsive therapy (ECT) is a highly effective brain stimulation treatment for severe depression. Identifying neurochemical changes linked with ECT may point to biomarkers and predictors of successful treatment response.

METHODS

We used proton magnetic resonance spectroscopy (1H-MRS) to measure longitudinal changes in glutamate/glutamine (Glx), creatine (Cre), choline (Cho) and N-acetylaspartate (NAA) in the dorsal (dACC) and subgenual anterior cingulate cortex (sgACC) and bilateral hippocampus in patients receiving ECT scanned at baseline, after the second ECT session and after the ECT treatment series. Patients were compared with demographically similar controls at baseline. Controls were assessed twice to establish normative values and variance.

RESULTS

We included 50 patients (mean age 43.78 ± 14 yr) and 33 controls (mean age 39.33 ± 12 yr) in our study. Patients underwent a mean of 9 ± 4.1 sessions of ECT. At baseline, patients showed reduced Glx in the sgACC, reduced NAA in the left hippocampus and increased Glx in the left hippocampus relative to controls. ECT was associated with significant increases in Cre in the dACC and sgACC and decreases in NAA in the dACC and right hippocampus. Lower NAA levels in the dACC at baseline predicted reductions in depressive symptoms. Both ECT and symptom improvement were associated with decreased Glx in the left hippocampus and increased Glx in the sgACC.

LIMITATIONS

Attrition and clinical heterogeneity may have masked more subtle findings.

CONCLUSION

ECT elicits robust effects on brain chemistry, impacting Cre, NAA and Glx, which suggests restorative and neurotrophic processes. Differential effects of Glx in the sgACC and hippocampus, which approach control values with treatment, may reflect previously implicated underactive cortical and overactive subcortical limbic circuitry in patients with major depression. NAA levels at baseline are predictive of therapeutic outcome and could inform future treatment strategies.