PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation

With the advent of convolutional neural networks~(CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as $T_1$-weighted and $T_2$-weighted contrasts with only $T_1$-weighted training data. The segmentations generated are highly accurate with state-of-the-art results~(overall Dice overlap$=0.94$), with a fast run time~($\approx$ 45 seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev

P2T2: a Physically-primed deep-neural-network approach for robust T2T_{2} distribution estimation from quantitative T2T_{2}-weighted MRI

Estimation of the T2 distribution from multi-echo T2-Weighted MRI (T2W) data can provide insight into the microscopic content of tissue using macroscopic imaging. This information can be used as a biomarker for several pathologies, such as tumor characterization, osteoarthritis, and neurodegenerative diseases. Recently, deep neural network (DNN) based methods were proposed for T2 distribution estimation from MRI data. However, these methods are highly sensitive to distribution shifts such as variations in the echo-times (TE) used during acquisition. Therefore, DNN-based methods cannot be utilized in large-scale multi-institutional trials with heterogeneous acquisition protocols. We present P2T2, a new physically-primed DNN approach for T2 distribution estimation that is robust to different acquisition parameters while maintaining state-of-the-art estimation accuracy. Our P2T2 model encodes the forward model of the signal decay by taking as input the TE acquisition array, in addition to the MRI signal, and provides an estimate of the corresponding T2 distribution as its output. Our P2T2 model has improved the robustness against distribution shifts in the acquisition process by more than 50% compared to the previously proposed DNN model. When tested without any distribution shifts, our model achieved about the same accuracy. Finally, when applied to real human MRI data, our P2T2 model produced the most detailed Myelin-Water fraction maps compared to both the MIML model and classical approaches. Our proposed physically-primed approach improved the generalization capacity of DNN models for T2 distribution estimation and their robustness against distribution shifts compared to previous approaches without compromising the accuracy

Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint (TMJ) magnetic resonance imaging (MRI) protocol. Materials and methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement (ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement (ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.ope

Characterization of the BOLD signal in functional MRI

In the last two decades, functional magnetic resonance imaging (fMRI) has become an important and widely used imaging technique for functional brain mapping. However, blood oxygen level dependent (BOLD) technique is quite insensitive and task invoked BOLD signal change at 3T is typically in the order of a few percent. Furthermore, the coupling between BOLD signal changes and neuronal activities is quite complicated, involving a cascade of events remaining poorly understood even today. In this thesis, some of the basic characteristics of the BOLD signal are investigated. Better understanding of the BOLD signal characteristics can be beneficial for the design of BOLD fMRI experiment aimed to improve the time efficiency. It can also provide guidelines for developing fMRI data processing strategies. In study I and II, a single-shot dual-echo spiral acquisition technique was used for characterizing the T2* changes associated with motor activation task. In study I, the optimal strategy for head motion correction was investigated. Based on the improvement in the detection of brain activation, the best strategy is to perform the head motion correction using the imaging data from the second echo and then apply the derived motion correction parameters to the first echo, instead of conducting motion correction of the individual echoes independently. In study II, several aspects of brain mapping methods based on T2*-weighted imaging and T2* (R2*=1/T2*) mapping were quantitatively compared, including the detected activation volume, functional contrast, signal-to-noise ratio, and contrast-to-noise ratio. fMRI studies based T2* mapping have the following potential advantages: maximum functional contrast, independence of echo time; and reduced inflow effects. The sensitivity for brain activation detection is significantly correlated with the contrast-to-noise ratio, which is determined by both the signal-to-noise ratio and functional contrast. In study III, the hemodynamic responses to functional activation were characterized using T2*-weighted BOLD imaging, arterial spin labeling, and bolus tracking of MRI contrast agent. In addition to the BOLD signal change, the relative cerebral blood flow and cerebral blood volume associated with brain activation were independently determined. In study IV, the characteristics of the global signal in resting-state fMRI were investigated. It was found that the global signal time courses and regional contributions differ individually. However, after removing the contribution from the cerebral spinal fluid, a consistent brain network responsible for the remaining global signal changes was identified. The involved brain regions include: posterior cingulate cortex, precuneus, superior temporal gyrus, medial frontal gyrus and the cerebellar vermis, which is likely to be related to the perception and cognitive processes of the brain occurred in the specific environments during resting-state fMRI

Cortical Iron Disrupts Functional Connectivity Networks Supporting Working Memory Performance in Older Adults

Excessive brain iron negatively affects working memory and related processes but the impact of cortical iron on task-relevant, cortical brain networks is unknown. We hypothesized that high cortical iron concentration may disrupt functional circuitry within cortical networks supporting working memory performance. Fifty-five healthy older adults completed an N-Back working memory paradigm while functional magnetic resonance imaging (fMRI) was performed. Participants also underwent quantitative susceptibility mapping (QSM) imaging for assessment of non-heme brain iron concentration. Additionally, pseudo continuous arterial spin labeling scans were obtained to control for potential contributions of cerebral blood volume and structural brain images were used to control for contributions of brain volume. Task performance was positively correlated with strength of task-based functional connectivity (tFC) between brain regions of the frontoparietal working memory network. However, higher cortical iron concentration was associated with lower tFC within this frontoparietal network and with poorer working memory performance after controlling for both cerebral blood flow and brain volume. Our results suggest that high cortical iron concentration disrupts communication within frontoparietal networks supporting working memory and is associated with reduced working memory performance in older adults

Change In Processing Speed And Its Associations With Cerebral White Matter Microstructure

The decline of cognition with age is one of the most feared aspects of aging, while the slowing of responses, or reduced processing speed, is one of the most reliable aspects of aging. Slowing of processing has been hypothesized to affect other domains of cognition as well. Despite the well-known slowing-age relationship and central position processing speed plays in theories of cognitive aging the neurobiological mechanisms which underpin slowing is unclear. If we could identify the biology associated with processing speed we could then attempt to develop interventions to mitigate the effects of age on those variables. In turn we could test whether “preventing” or reducing the decline of processing speed helps alleviate the decline in other cognitive domains. Not only would this provide basic knowledge about aging, cognition, and their relationship but it may also have a broader societal impact. In this project, we tested whether the amount of myelin content, indexed by MWF, in white matter tracts hypothesized to be important for performing a choice reaction time task, could explain the variance in processing speed or in change of processing speed after controlling for age effects. While MWF did not explain any additional variance in our variables of interest we did found that the geomT2-IEW, a measure thought to be related to axonal density, in the genu was negatively associated with change in processing speed. These results suggest that axons maybe an important structure supporting processing speed and that future investigations should look at both myelin and axonal changes as potential substrates sub serving processing speed

Investigating the group-level impact of advanced dual-echo fMRI combinations

Multi-echo fMRI data acquisition has been widely investigated and suggested to optimize sensitivity for detecting the BOLD signal. Several methods have also been proposed for the combination of data with different echo times. The aim of the present study was to investigate whether these advanced echo combination methods provide advantages over the simple averaging of echoes when state-of-the-art group-level random-effect analyses are performed. Both resting-state and task-based dual-echo fMRI data were collected from 27 healthy adult individuals (14 male, mean age = 25.75 years) using standard echo-planar acquisition methods at 3T. Both resting-state and task-based data were subjected to a standard image pre-processing pipeline. Subsequently the two echoes were combined as a weighted average, using four different strategies for calculating the weights: (1) simple arithmetic averaging, (2) BOLD sensitivity weighting, (3) temporal-signal-to-noise ratio weighting and (4) temporal BOLD sensitivity weighting. Our results clearly show that the simple averaging of data with the different echoes is sufficient. Advanced echo combination methods may provide advantages on a single-subject level but when considering random-effects group level statistics they provide no benefit regarding sensitivity (i.e., group-level t-values) compared to the simple echo-averaging approach. One possible reason for the lack of clear advantages may be that apart from increasing the average BOLD sensitivity at the single-subject level, the advanced weighted averaging methods also inflate the inter-subject variance. As the echo combination methods provide very similar results, the recommendation is to choose between them depending on the availability of time for collecting additional resting-state data or whether subject-level or group-level analyses are planned

The semantic network at work and rest:Differential connectivity of anterior temporal lobe subregions

The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. SIGNIFICANCE STATEMENT Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these subregions has not been demarcated. Here, we show that these ventrolateral anterior temporal subregions form part of a network responsible for semantic processing during both rest and an explicit semantic task. This demonstrates the existence of a core functional network responsible for multimodal semantic cognition regardless of state. Distinct connectivity is identified in the superior ATL, which is connected to auditory and language areas. Understanding the functional connectivity of semantic cognition allows greater understanding of how this complex process may be performed and the role of distinct subregions of the anterior temporal cortex

The neurostructural effects of prenatal exposure to methamphetamine in an infant population in the Western Cape

Prenatal methamphetamine exposure is associated with functional and neurostructural alterations, but neuroimaging investigations of these effects in infants are almost non-existent. Studies in neonates permit a degree of separation of drug exposure effects from potential confounders in the postnatal environment. Magnetic resonance imaging (MRI) was used to investigate the neurostructural effects of prenatal methamphetamine exposure on neonates recruited from a Cape Town community. Mothers were recruited during pregnancy and interviewed regarding methamphetamine use. Women in the exposure group used methamphetamine at least twice per month during pregnancy, while control mothers did not use methamphetamine. MRI scans were acquired within the first postnatal month. Anatomical images were processed using FreeSurfer and subcortical and cerebellar structures manually segmented with Freeview. Volumes were regressed with methamphetamine exposure (days/month of pregnancy) and related confounding variables, including total brain volume, gestational age at scan, exposure to cigarette smoking and infant sex. Diffusion data were processed with FSL, and diffusion tensors and tensor parameters determined using AFNI. Probabilistic tractography defined white matter connections between target regions. For the first analysis, five major white matter networks (commissural, and bilateral projection and association networks) were defined between spherical targets. For the second analysis, regions traced in the anatomical study were used as targets. Averaged DTI parameters were then calculated for each connection, and multiple regression analysis determined associations between DTI parameters and methamphetamine exposure at network level and in the individual connections. Methamphetamine exposure was associated with reduced caudate nucleus volume bilaterally, and in the right caudate following adjustment for confounders. Exposure was associated with reduced fractional anisotropy in all major white matter networks, and in individual connections within the limbic meso-cortico-striatal circuit. Exposure was associated with increased radial diffusivity in a subset of these. These results support findings in older children of methamphetamine-induced neurostructural damage, and demonstrate that such effects are already measurable in neonates. Corticostriatal circuit changes may underlie the impaired executive function observed in prenatally exposed children, and suggest a specific mechanism of damage in dopaminergic-related circuits that is consistent with the neurotoxic actions of methamphetamine