Publications by Year: 2016

Disrupted thalamo-cortical connectivity is regarded as a core psychopathology in patients diagnosed with schizophrenia. However, whether the thalamo-cortical white matter connectivity is disrupted before the onset of psychosis is still unknown. To determine this gap in knowledge, the strength of thalamo-cortical white matter anatomical connectivity in subjects at clinical-high risk for psychosis (CHR) was compared to that of first-episode psychosis (FEP) and healthy controls. A total of 37 CHR, 21 FEP, and 37 matched healthy controls underwent diffusion-weighted magnetic resonance imaging to examine the number of probabilistic tractography "counts" representing thalamo-cortical white matter connectivity. We also investigated the relationship with psychopathology. For FEP, the connectivity between the thalamus and parietal cortex was significantly increased (F= 5.65,P .05) compared to that of healthy controls. However, the connectivity between thalamus and orbitofrontal cortex was significantly reduced compared to both healthy controls (F= 11.86,P .005) and CHR (F= 6.63,P .05). Interestingly, CHR exhibited a similar pattern as FEP, albeit with slightly reduced magnitude. Compared to healthy controls, there was a significant decrease (F= 4.16,P .05) in CHR thalamo-orbitofrontal connectivity. Also, the strength of the thalamo-orbitofrontal connectivity was correlated with the Global Assessment of Functioning score in CHR (r= .35,P .05). This observed pattern of white matter connectivity disruptions in FEP and in CHR suggests that this pattern of disconnectivity not only highlights the involvement of thalamus but also might be useful as an early biomarker for psychosis.

The great density and structural complexity of pulmonary vessels and airways impose limitations on the generation of accurate reference standards, which are critical in training and in the validation of image processing methods for features such as pulmonary vessel segmentation or artery-vein (AV) separations. The design of synthetic computed tomography (CT) images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image is differentiated unequivocally. This work demonstrates a complete framework to generate computational anthropomorphic CT phantoms of the human lung automatically. Starting from biological and image-based knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. A dataset of 24 labeled anthropomorphic pulmonary CT phantoms were synthesized with the proposed system. Visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems show good correspondence between real and synthetic lungs (p > 0.05 with low Cohen’s d effect size and AUC values), supporting the potentiality of the tool and the usefulness of the generated phantoms in the biomedical image processing field.

The lateral and third ventricles, as well as the corpus callosum (CC), are known to be affected in schizophrenia. Here we investigate whether abnormalities in the lateral ventricles (LVs), third ventricle, and corpus callosum are related to one another in first episode schizophrenia (FESZ), and whether such abnormalities show progression over time. Nineteen FESZ and 19 age- and handedness-matched controls were included in the study. MR images were acquired on a 3-Tesla MRI at baseline and  1.2 years later. FreeSurfer v.5.3 was employed for segmentation. Two-way or univariate ANCOVAs were used for statistical analysis, where the covariate was intracranial volume. Group and gender were included as between-subjects factors. Percent volume changes between baseline and follow-up were used to determine volume changes at follow-up. Bilateral LV and third ventricle volumes were significantly increased, while central CC volume was significantly decreased in patients compared to controls at baseline and at follow-up. In FESZ, the bilateral LV volume was also inversely correlated with volume of the central CC. This inverse correlation was not present in controls. In FESZ, an inverse correlation was found between percent volume increase from baseline to follow-up for bilateral LVs and lesser improvement in the Global Assessment of Functioning score. Significant correlations were observed for abnormalities of central CC, LVs and third ventricle volumes in FESZ, suggesting a common neurodevelopmental origin in schizophrenia. Enlargement of ventricles was associated with less improvement in global functioning over time.

Conventional single tensor diffusion analysis models have provided mixed findings in the substantia nigra of Parkinson’s disease, but recent work using a bi-tensor analysis model has shown more promising results. Using a bi-tensor model, free-water values were found to be increased in the posterior substantia nigra of Parkinson’s disease compared with controls at a single site and in a multi-site cohort. Further, free-water increased longitudinally over 1 year in the posterior substantia nigra of Parkinson’s disease. Here, we test the hypothesis that other parkinsonian disorders such as multiple system atrophy and progressive supranuclear palsy have elevated free-water in the substantia nigra. Equally important, however, is whether the bi-tensor diffusion model is able to detect alterations in other brain regions beyond the substantia nigra in Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy and to accurately distinguish between these diseases. Free-water and free-water-corrected fractional anisotropy maps were compared across 72 individuals in the basal ganglia, midbrain, thalamus, dentate nucleus, cerebellar peduncles, cerebellar vermis and lobules V and VI, and corpus callosum. Compared with controls, free-water was increased in the anterior and posterior substantia nigra of Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. Despite no other changes in Parkinson’s disease, we observed elevated free-water in all regions except the dentate nucleus, subthalamic nucleus, and corpus callosum of multiple system atrophy, and in all regions examined for progressive supranuclear palsy. Compared with controls, free-water-corrected fractional anisotropy values were increased for multiple system atrophy in the putamen and caudate, and increased for progressive supranuclear palsy in the putamen, caudate, thalamus, and vermis, and decreased in the superior cerebellar peduncle and corpus callosum. For all disease group comparisons, the support vector machine 10-fold cross-validation area under the curve was between 0.93-1.00 and there was high sensitivity and specificity. The regions and diffusion measures selected by the model varied across comparisons and are consistent with pathological studies. In conclusion, the current study used a novel bi-tensor diffusion analysis model to indicate that all forms of parkinsonism had elevated free-water in the substantia nigra. Beyond the substantia nigra, both multiple system atrophy and progressive supranuclear palsy, but not Parkinson’s disease, showed a broad network of elevated free-water and altered free-water corrected fractional anisotropy that included the basal ganglia, thalamus, and cerebellum. These findings may be helpful in the differential diagnosis of parkinsonian disorders, and thereby facilitate the development and assessment of targeted therapies.

The characterization of neurodevelopmental aspects of brain alterations require neuroimaging methods that reflect correlates of neurodevelopment, while being robust to other progressive pathological processes. Newly developed neuroimaging methods for measuring geometrical features of the white matter fall exactly into this category. Our recent work shows that such features, measured in the anterior corpus callosum in diffusion MRI data, correlate with psychosis symptoms in patients with adolescent onset schizophrenia and subside a reversal of normal sexual dimorphism. Here, we test the hypothesis that similar developmental deviations will also be present in nonpsychotic subjects at familial high risk (FHR) for schizophrenia, due to genetic predispositions. Demonstrating such changes would provide a strong indication of neurodevelopmental deviation extant before, and independent of pathological changes occurring after disease onset. We examined the macrostructural geometry of corpus callosum white matter in diffusion MRI data of 35 non-psychotic subjects with genetic (familial) risk for schizophrenia, and 26 control subjects, both male and female. We report a reversal of normal sexual dimorphism in callosal white matter geometry consistent with recent results in adolescent onset schizophrenia. This pattern may be indicative of an error in neurogenesis and a possible trait marker of schizophrenia.

Deep Brain Stimulation (DBS) is a neurosurgical procedure that can reduce symptoms in medically intractable obsessive-compulsive disorder (OCD). Conceptually, DBS of the ventral capsule/ventral striatum (VC/VS) region targets reciprocal excitatory connections between the orbitofrontal cortex (OFC) and thalamus, decreasing abnormal reverberant activity within the OFC-caudate-pallidal-thalamic circuit. In this study, we investigated these connections using diffusion magnetic resonance imaging (dMRI) on human connectome datasets of twenty-nine healthy young-adult volunteers with two-tensor unscented Kalman filter based tractography. We studied the morphology of the lateral and medial orbitofrontothalamic connections and estimated their topographic variability within the VC/VS region. Our results showed that the morphology of the individual orbitofrontothalamic fibers of passage in the VC/VS region is complex and inter-individual variability in their topography is high. We applied this method to an example OCD patient case who underwent DBS surgery, formulating an initial proof of concept for a tractography-guided patient-specific approach in DBS for medically intractable OCD. This may improve on current surgical practice, which involves implanting all patients at identical stereotactic coordinates within the VC/VS region.

Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time.

UNLABELLED: Brain masking of MRI images separates brain from surrounding tissue and its accuracy is important for further imaging analyses. We implemented a new brain masking technique based on multi-atlas brain segmentation (MABS) and compared MABS to masks generated using FreeSurfer (FS; version 5.3), Brain Extraction Tool (BET), and Brainwash, using manually defined masks (MM) as the gold standard. We further determined the effect of different masking techniques on cortical and subcortical volumes generated by FreeSurfer. METHODS: Images were acquired on a 3-Tesla MR Echospeed system General Electric scanner on five control and five schizophrenia subjects matched on age, sex, and IQ. Automated masks were generated from MABS, FS, BET, and Brainwash, and compared to MM using these metrics: a) volume difference from MM; b) Dice coefficients; and c) intraclass correlation coefficients. RESULTS: Mean volume difference between MM and MABS masks was significantly less than the difference between MM and FS or BET masks. Dice coefficient between MM and MABS was significantly higher than Dice coefficients between MM and FS, BET, or Brainwash. For subcortical and left cortical regions, MABS volumes were closer to MM volumes than were BET or FS volumes. For right cortical regions, MABS volumes were closer to MM volumes than were BET volumes. CONCLUSIONS: Brain masks generated using FreeSurfer, BET, and Brainwash are rapidly obtained, but are less accurate than manually defined masks. Masks generated using MABS, in contrast, resemble more closely the gold standard of manual masking, thereby offering a rapid and viable alternative.

Diffusion tensor imaging (DTI) is the most widely used method for characterizing noninvasively structural and architectural features of brain tissues. However, the assumption of a Gaussian spin displacement distribution intrinsic to DTI weakens its ability to describe intricate tissue microanatomy. Consequently, the biological interpretation of microstructural parameters, such as fractional anisotropy or mean diffusivity, is often equivocal. We evaluate the clinical feasibility of assessing brain tissue microstructure with mean apparent propagator (MAP) MRI, a powerful analytical framework that efficiently measures the probability density function (PDF) of spin displacements and quantifies useful metrics of this PDF indicative of diffusion in complex microstructure (e.g., restrictions, multiple compartments). Rotation invariant and scalar parameters computed from the MAP show consistent variation across neuroanatomical brain regions and increased ability to differentiate tissues with distinct structural and architectural features compared with DTI-derived parameters. The return-to-origin probability (RTOP) appears to reflect cellularity and restrictions better than MD, while the non-Gaussianity (NG) measures diffusion heterogeneity by comprehensively quantifying the deviation between the spin displacement PDF and its Gaussian approximation. Both RTOP and NG can be decomposed in the local anatomical frame for reference determined by the orientation of the diffusion tensor and reveal additional information complementary to DTI. The propagator anisotropy (PA) shows high tissue contrast even in deep brain nuclei and cortical gray matter and is more uniform in white matter than the FA, which drops significantly in regions containing crossing fibers. Orientational profiles of the propagator computed analytically from the MAP MRI series coefficients allow separation of different fiber populations in regions of crossing white matter pathways, which in turn improves our ability to perform whole-brain fiber tractography. Reconstructions from subsampled data sets suggest that MAP MRI parameters can be computed from a relatively small number of DWIs acquired with high b-value and good signal-to-noise ratio in clinically achievable scan durations of less than 10min. The neuroanatomical consistency across healthy subjects and reproducibility in test-retest experiments of MAP MRI microstructural parameters further substantiate the robustness and clinical feasibility of this technique. The MAP MRI metrics could potentially provide more sensitive clinical biomarkers with increased pathophysiological specificity compared to microstructural measures derived using conventional diffusion MRI techniques.

Computed tomographic measures of central airway morphology have been used in clinical, epidemiologic, and genetic investigation as an inference of the presence and severity of small-airway disease in smokers. Although several association studies have brought us to believe that these computed tomographic measures reflect airway remodeling, a careful review of such data and more recent evidence may reveal underappreciated complexity to these measures and limitations that prompt us to question that belief. This Perspective offers a review of seminal papers and alternative explanations of their data in the light of more recent evidence. The relationships between airway morphology and lung function are observed in subjects who never smoked, implying that native airway structure indeed contributes to lung function; computed tomographic measures of central airways such as wall area, lumen area, and total bronchial area are smaller in smokers with chronic obstructive pulmonary disease versus those without chronic obstructive pulmonary disease; and the airways are smaller as disease severity increases. The observations suggest that (1) native airway morphology likely contributes to the relationships between computed tomographic measures of airways and lung function; and (2) the presence of smaller airways in those with chronic obstructive pulmonary disease versus those without chronic obstructive pulmonary disease as well as their decrease with disease severity suggests that smokers with chronic obstructive pulmonary disease may simply have smaller airways to begin with, which put them at greater risk for the development of smoking-related disease.