Faculty Bibliography

BACKGROUND:There is growing interest in detecting cerebro-cerebellar circuits, which requires adequate blood oxygenation level dependent contrast and signal-to-noise ratio (SNR) throughout the brain. Although 7T scanners offer increased SNR, coverage of commercial head coils is currently limited to the cerebrum. PURPOSE/OBJECTIVE:To improve cerebellar functional MRI (fMRI) at 7T with high permittivity material (HPM) pads extending the sensitivity of a commercial coil. STUDY TYPE/METHODS:Simulations were used to determine HPM pad configuration and assess radiofrequency (RF) safety. In vivo experiments were performed to evaluate RF field distributions and SNR and assess improvements of cerebellar fMRI. SUBJECTS/METHODS:Eight healthy volunteers enrolled in a prospective motor fMRI study with and without HPM. FIELD STRENGTH/SEQUENCE/UNASSIGNED:Gradient echo (GRE) echo planar imaging for fMRI, turbo FLASH for flip angle mapping, GRE sequence for SNR maps, and T1 -weighted MPRAGE were acquired with and without HPM pads at 7T. ASSESSMENT/RESULTS:Field maps, SNR maps, and anatomical images were evaluated for coverage. Simulation results were used to assess SAR levels of the experiment. Activation data from fMRI experiments were compared with and without HPM pads. STATISTICAL TESTS: fMRI data were analyzed using FEAT FSL for each subject followed by group level analysis using paired t-test of acquisitions with and without HPM. RESULTS:Simulations showed 52% improvement in transmit efficiency in cerebellum with HPM and SAR levels well below recommended limits. Experiments showed 27% improvement in SNR in cerebellum and improvement in coverage on T1 -weighted images. fMRI showed greater cerebellar activation in individual subjects with the HPM pad present (Z > = 4), especially in inferior slices of cerebellum, with 59% average increase in number of activated voxels in the cerebellum. Group-level analysis showed improved functional activation (Z > = 2.3) in cerebellar regions with HPM pads without loss of measured activation elsewhere. DATA CONCLUSION/UNASSIGNED:HPM pads can improve cerebellar fMRI at 7T with a commonly-used head coil without compromising RF safety. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017.

BACKGROUND: Resting state fMRI (rsfMRI) is task independent mapping of individual brain function. Here we demonstrated the clinical feasibility of rsfMRI in patients with brain tumor. MATERIALS AND METHODS: Resting-state fMRI data from a total of 39 cases (32 primary brain tumor patients [8 tumors in sensorimotor cortices] and 7 normal healthy control cases) were analyzed based on independent component analysis (ICA). For each patient, ipsilesional(IL) and contralesional(CL) regions of interest (ROI) from supra-threshold Z-score voxels (Z-score > 2.3) on sensorimotor network (SMN) were used to evaluate BOLD signal changes on Z score maps. Asymmetry score were also calculated based on numbers of supra-thershold voxels between IL and CL in tumor patients and between I and C ROIs in control cases. Statistically significant differences between the each subgroup were tested using a two-tailed t-test. RESULTS: In patients with tumor involving SMN, overall decreased BOLD signal by supra-threshold voxel count and mean Z-score was noted in both IL and CL ROIs (mean +/- standard deviation, voxel number: IL: 2376 +/- 1366, CL: 2906 +/- 1210, mean Z-score: IL: 3.7 +/- 0.3, CL: 3.65 +/- 0.3) compared to brain tumor located outside SMN (voxel number: IL: 3258 +/- 1370, CL: 3413 +/- 1467, mean Zscore: IL: 4.0 +/- 0.4, CL: 3.9 +/- 0.4). The asymmetry score was also higher in tumor involving SMN group than those not (0.1 vs 0.02). Overall increased amplitude of BOLD signal and decreased spatial extent of network of whole SMN were depicted in patients with brain tumor as compared with normal subjects. However, these differences were not statistically significant. CONCLUSION: We have demonstrated that brain tumor might cause changes of the spatial extent and amplitude of SMN on rsfRMI without significant differences observed. Resting fMRI and connectivity analysis are task-independent, and have potential clinical utility in the presurgical evaluation of patients with brain tumors, and may help in uncooperative or pediatric patients

Background/UNASSIGNED:The corpus callosum is implicated in the pathophysiology of autism spectrum disorder (ASD). However, specific structural deficits and underlying mechanisms are yet to be well defined. Methods/UNASSIGNED:) diffusivities, which reflect myelination and microstructural organization of the extracellular space. The relationships between DKI metrics and processing speed, a cognitive feature known to be impaired in ASD, were also examined. Results/UNASSIGNED: > .05). Conclusion/UNASSIGNED:Decreased DKI metrics suggested that ASD may be associated with axonal deficits such as reduced axonal caliber and density in the corpus callosum, especially in the mid and posterior callosal areas. These data suggest that impaired interhemispheric connectivity may contribute to decreased processing speed in ASD participants.

Importance: Clinical overlap between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is increasingly appreciated, but the underlying brain mechanisms remain unknown to date. Objective: To examine associations between white matter organization and 2 commonly co-occurring neurodevelopmental conditions, ASD and ADHD, through both categorical and dimensional approaches. Design, Setting, and Participants: This investigation was a cross-sectional diffusion tensor imaging (DTI) study at an outpatient academic clinical and research center, the Department of Child and Adolescent Psychiatry at New York University Langone Medical Center. Participants were children with ASD, children with ADHD, or typically developing children. Data collection was ongoing from December 2008 to October 2015. Main Outcomes and Measures: The primary measure was voxelwise fractional anisotropy (FA) analyzed via tract-based spatial statistics. Additional voxelwise DTI metrics included radial diffusivity (RD), mean diffusivity (MD), axial diffusivity (AD), and mode of anisotropy (MA). Results: This cross-sectional DTI study analyzed data from 174 children (age range, 6.0-12.9 years), selected from a larger sample after quality assurance to be group matched on age and sex. After quality control, the study analyzed data from 69 children with ASD (mean [SD] age, 8.9 [1.7] years; 62 male), 55 children with ADHD (mean [SD] age, 9.5 [1.5] years; 41 male), and 50 typically developing children (mean [SD] age, 9.4 [1.5] years; 38 male). Categorical analyses revealed a significant influence of ASD diagnosis on several DTI metrics (FA, MD, RD, and AD), primarily in the corpus callosum. For example, FA analyses identified a cluster of 4179 voxels (TFCE FEW corrected P < .05) in posterior portions of the corpus callosum. Dimensional analyses revealed associations between ASD severity and FA, RD, and MD in more extended portions of the corpus callosum and beyond (eg, corona radiata and inferior longitudinal fasciculus) across all individuals, regardless of diagnosis. For example, FA analyses revealed clusters overall encompassing 12121 voxels (TFCE FWE corrected P < .05) with a significant association with parent ratings in the social responsiveness scale. Similar results were evident using an independent measure of ASD traits (ie, children communication checklist, second edition). Total severity of ADHD-traits was not significantly related to DTI metrics but inattention scores were related to AD in corpus callosum in a cluster sized 716 voxels. All these findings were robust to algorithmic correction of motion artifacts with the DTIPrep software. Conclusions and Relevance: Dimensional analyses provided a more complete picture of associations between ASD traits and inattention and indexes of white matter organization, particularly in the corpus callosum. This transdiagnostic approach can reveal dimensional relationships linking white matter structure to neurodevelopmental symptoms.

Total N-acetyl-aspartate + N-acetyl-aspartate-glutamate (NAA), total creatine (Cr) and total choline (Cho) proton MRS (1 H-MRS) signals are often used as surrogate markers in diffuse neurological pathologies, but spatial coverage of this methodology is limited to 1%-65% of the brain. Here we wish to demonstrate that non-localized, whole-head (WH) 1 H-MRS captures just the brain's contribution to the Cho and Cr signals, ignoring all other compartments. Towards this end, 27 young healthy adults (18 men, 9 women), 29.9 +/- 8.5 years old, were recruited and underwent T1 -weighted MRI for tissue segmentation, non-localizing, approximately 3 min WH 1 H-MRS (TE /TR /TI = 5/10/940 ms) and 30 min 1 H-MR spectroscopic imaging (MRSI) (TE /TR = 35/2100 ms) in a 360 cm3 volume of interest (VOI) at the brain's center. The VOI absolute NAA, Cr and Cho concentrations, 7.7 +/- 0.5, 5.5 +/- 0.4 and 1.3 +/- 0.2 mM, were all within 10% of the WH: 8.6 +/- 1.1, 6.0 +/- 1.0 and 1.3 +/- 0.2 mM. The mean NAA/Cr and NAA/Cho ratios in the WH were only slightly higher than the "brain-only" VOI: 1.5 versus 1.4 (7%) and 6.6 versus 5.9 (11%); Cho/Cr were not different. The brain/WH volume ratio was 0.31 +/- 0.03 (brain approximately 30% of WH volume). Air-tissue susceptibility-driven local magnetic field changes going from the brain outwards showed sharp gradients of more than 100 Hz/cm (1 ppm/cm), explaining the skull's Cr and Cho signal losses through resonance shifts, line broadening and destructive interference. The similarity of non-localized WH and localized VOI NAA, Cr and Cho concentrations and their ratios suggests that their signals originate predominantly from the brain. Therefore, the fast, comprehensive WH-1 H-MRS method may facilitate quantification of these metabolites, which are common surrogate markers in neurological disorders.

Working memory, the ability to transiently keep, process, and use information as part of ongoing mental processes is an essential feature of cognitive functioning. The largest number of items that people can hold in their working memory, referred to as the capacity of working memory, is limited and varies substantially among individuals. Uncovering the biological factors that underlie these two defining properties of working memory capacity remains a key undertaking of modern cognitive neuroscience since capacity strongly predicts how well we reason, learn, and even do math. In this work we review data that highlights the role white matter, which provides the wiring of the extensive neural networks that activate during working memory tasks, may play in interindividual variations in capacity. We also describe advanced diffusion imaging methods, which may be uniquely suited in capturing those white matter features that are most relevant to capacity. Finally, we discuss several possible mechanisms through which white matter may both contribute to and limit working memory.