Intermittent Theta-Burst Stimulation Transcranial Magnetic Stimulation Increases GABA in the Medial Prefrontal Cortex: A Preliminary Sham-Controlled Magnetic Resonance Spectroscopy Study in Acute Bipolar Depression.

Background: Magnetic resonance spectroscopy (MRS) has been used to identify gamma-aminobutyric acid (GABA) alterations in mood disorders, particularly in the medial prefrontal cortex (mPFC) where decreased concentrations have been associated with anhedonia. In major depressive disorder (MDD), prior work suggests that repetitive transcranial magnetic stimulation (rTMS) increases mPFC GABA concentrations proportional to antidepressant response. To our knowledge, this has not been examined in acute bipolar depression. Methods: As part of a multicentre 4-week randomized, double-blind, sham-controlled trial using intermittent theta-burst stimulation (iTBS) of the left dorsolateral prefrontal cortex (DLPFC) in individuals with acute bipolar depression, we quantified mPFC GABA and Glx (glutamate+glutamine) concentrations using a 3T MRS scan at baseline and after the intervention. Depressive symptoms were measured using the Montgomery-Asberg Depression Rating Scale (MADRS) and the Hamilton Depression Rating Scale-17 (HRDS-17), and anhedonia was measured using the Snaith-Hamilton Pleasure Scale (SHAPS). Results: The trial was terminated for futility and magnetic resonance spectroscopy data was acquired for 18 participants. At baseline, there were no associations between GABA or Glx concentrations and anhedonia, however GABA was negative correlated with depressive symptom severity on the HRDS-17. Compared to the sham-iTBS group, participants receiving active-iTBS had a significant increase in mPFC GABA concentrations. This was unrelated to antidepressant outcomes or improvements in anhedonia. Conclusion: Our data suggests that iTBS targeting the DLPFC is associated with physiological changes in the mPFC. In acute bipolar depression, our preliminary data suggests that mPFC GABA is dissociated from antidepressant iTBS treatment outcomes and anhedonia.

Semiautomatic classification of acetabular shape from three-dimensional ultrasound for diagnosis of infant hip dysplasia using geometric features.

PURPOSE: Developmental dysplasia of the hip (DDH) is a congenital deformity which in severe cases leads to hip dislocation and in milder cases to premature osteoarthritis. Image-aided diagnosis of DDH is partly based on Graf classification which quantifies the acetabular shape seen at two-dimensional ultrasound (2DUS), which leads to high inter-scan variance. 3D ultrasound (3DUS) is a promising alternative for more reliable DDH diagnosis. However, manual quantification of acetabular shape from 3DUS is cumbersome. METHODS: Here, we (1) propose a semiautomated segmentation algorithm to delineate 3D acetabular surface models from 3DUS using graph search; (2) propose a fully automated method to classify acetabular shape based on a random forest (RF) classifier using features derived from 3D acetabular surface models; and (3) test diagnostic accuracy on a dataset of 79 3DUS infant hip recordings (36 normal, 16 borderline, 27 dysplastic based on orthopedic surgeon assessment) in 42 patients. For each 3DUS, we performed semiautomated segmentation to produce 3D acetabular surface models and then calculated geometric features including the automatic [Formula: see text]lpha (AA), acetabular contact angle (ACA), kurtosis (K), skewness (S) and convexity (C). Mean values of features obtained from surface models were used as inputs to train a RF classifier. RESULTS: Surface models were generated rapidly (user time 46.2 s) via semiautomated segmentation and visually closely correlated with actual acetabular contours (RMS error 1.39 ± 0.7 mm). A paired nonparametric u test on of feature values in each category showed statistically significant variation (p < 0.001) for AA, ACA and convexity. The RF classifier was 100 % specific and 97.2 % sensitive in classifying normal versus dysplastic hips and yielded true positive rates of 94.4, 62.5 and 89.9 % for normal, borderline and dysplastic hips. CONCLUSIONS: The proposed technique reduces the subjectivity of image-aided DDH diagnosis and could be useful in clinical practice.

Hip segmentation from MRI volumes in infants for DDH diagnosis and treatment planning.

Diagnosis and surgical management of Developmental Dysplasia of the Hip (DDH) relies on physical examination and 2D ultrasound scanning. Magnetic Resonance Imaging (MRI) can be used to complement existing techniques and could be advantageous in treatment planning due to its larger field of view. In this paper we propose a semi-automatic method to segment surface models of the acetabulum from MRI images. The method incorporates clinical knowledge in the form of intensity priors which are integrated into a Random Walker (RW) formulation. We use a modified RW framework which compensates for incomplete or blurred boundaries in the image by using information from neighboring slices in the sequence incorporated as node weights. We conducted a pilot study to evaluate the segmentation on a set of 10 infant hip MRI sequences using a 1.5 Tesla MR scanner. Contours obtained from the semi-automated segmentation were compared against manually segmented hip contours using Dice Ratio (DR), Hausdorff Distance (HD) and Root Mean Square (RMS) distance. The proposed method gave values of (DR = 0.84 ± 0.5, HD =3.0 ± 0.7, RMS =1.9 ± 0.3) and (DR=0.86 ± 0.2, HD=3.0 ± 0.1, RMS= 2.0 ± 0.6) for right and left acetabular contours respectively which was higher than the corresponding values obtained from conventional RW segmentation. The execution time of the segmentation algorithm was less than ~4 seconds on a 3.5 GHz CPU.

Cross-Modality Validation of Acetabular Surface Models Using 3-D Ultrasound Versus Magnetic Resonance Imaging in Normal and Dysplastic Infant Hips.

Current imaging diagnosis of developmental dysplasia of the hip (DDH) in infancy relies on 2-D ultrasound (US), which is highly operator-dependent. 3-D US offers more complete, and potentially more reliable, imaging of infant hip geometry. We sought to validate the fidelity of acetabular surface models obtained by 3-D US against those obtained concurrently by magnetic resonance imaging (MRI). 3-D US and MRI scans were performed on the same d in 20 infants with normal to severely dysplastic hips (mean age, 57 d; range 13-181 d). 3-D US was performed by two observers using a Philips VL13-5 probe. Coronal 3-D multi-echo data image combination (MEDIC) magnetic resonance (MR) images (1-mm slice thickness) were obtained, usually without sedation, in a 1.5 T Siemens unit. Acetabular surface models were generated for 40 hips from 3-D US and MRI using semi-automated tracing software, separately by three observers. For each hip, the 3-D US and MRI models were co-registered to overlap as closely as possible using Amira software, and the root mean square (RMS) distances between points on the models were computed. 3-D US scans took 3.2 s each. Inter-modality variability was visually minimal. Mean RMS distance between corresponding points on the acetabular surface at 3-D US and MRI was 0.4 ± 0.3 mm, with 95% confidence interval <1 mm. Mean RMS errors for inter-observer and intra-observer comparisons were significantly less for 3-D US than for MRI, while inter-scan and inter-modality comparisons showed no significant difference. Acetabular geometry was reproduced by 3-D US surface models within 1 mm of the corresponding 3-D MRI surface model, and the 3-D US models were more reliable. This validates the fidelity of 3-D US modeling and encourages future use of 3-D US in assessing infant acetabulum anatomy, which may be useful to detect and monitor treatment of hip dysplasia.