Optimal transport- and kernel-based early detection of mild cognitive impairment patients based on magnetic resonance and positron emission tomography images

Background To help clinicians provide timely treatment and delay disease progression, it is crucial to identify dementia patients during the mild cognitive impairment (MCI) stage and stratify these MCI patients into early and late MCI stages before they progress to Alzheimer’s disease (AD). In the process of diagnosing MCI and AD in living patients, brain scans are collected using neuroimaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). These brain scans measure the volume and molecular activity within the brain resulting in a very promising avenue to diagnose patients early in a minimally invasive manner. Methods We have developed an optimal transport based transfer learning model to discriminate between early and late MCI. Combing this transfer learning model with bootstrap aggregation strategy, we overcome the overfitting problem and improve model stability and prediction accuracy. Results With the transfer learning methods that we have developed, we outperform the current state of the art MCI stage classification frameworks and show that it is crucial to leverage Alzheimer’s disease and normal control subjects to accurately predict early and late stage cognitive impairment. Conclusions Our method is the current state of the art based on benchmark comparisons. This method is a necessary technological stepping stone to widespread clinical usage of MRI-based early detection of AD.

Multimodality Characterization of the Clot in Acute Stroke

Aim: Current treatment of occluded cerebral vessels can be done by a variety of endovascular techniques. Sometimes, the clot responds in varying degrees to the treatment chosen. The Ex vivo characterization of the clot occluding the arteries in acute ischemic stroke can help in understanding the underlying imaging features obtained from pre-treatment brain scans. For this reason, we explored the potential of microCT when combined with electron microscopy for clot characterization. Results were compared to the clinical CT findings.Methods: 16 patients (9 males, 8 females, age range 54–93 years) who were referred to our institution for acute stroke underwent dual-source CT.Results: Clinical CT clots were seen as either iso or hyperdense. This was corroborated with micro-CT, and electron microscopy can show the detailed composition.Conclusion: MicroCT values can be used as an indicator for red blood cells-rich composition of clots. Meaningful information regarding the clot composition and modalities of embedding along the stent retrievers can be obtained through a combination of microCT and electron microscopy.

Loneliness is linked to specific subregional alterations in hippocampus-default network co-variation

Social interaction complexity makes humans unique. But in times of social deprivation this strength risks to expose important vulnerabilities. Human social neuroscience studies have placed a premium on the default network (DN). In contrast, hippocampus (HC) subfields have been intensely studied in rodents and monkeys. To bridge these two literatures, we here quantified how DN subregions systematically co-vary with specific HC subfields in the context of subjective social isolation (i.e., loneliness). By co-decomposition using structural brain scans of ~40,000 UK Biobank participants, loneliness was specially linked to midline subregions in the uncovered DN patterns. These association cortex signatures coincided with concomitant HC patterns implicating especially CA1 and molecular layer. These patterns also showed a strong affiliation with the fornix white-matter tract and the nucleus accumbens. In addition, separable signatures of structural HC-DN co-variation had distinct associations with the genetic predisposition for loneliness at the population level.

Doppler Slicing for Ultrasound Super-Resolution Without Contrast Agents

Much of the information needed for diagnosis and treatment monitoring of diseases like cancer and cardiovascular disease is found at scales below the resolution limit of classic ultrasound imaging. Recently introduced vascular super-localization methods provide more than a ten-fold improvement in spatial resolution by precisely estimating the positions of microbubble contrast agents. However, most vascular ultrasound scans are currently performed without contrast agents due to the associated cost, training, and post-scan monitoring. Here we show that super-resolution ultrasound imaging of dense vascular structures can be achieved using the natural contrast of flowing blood cells. Instead of relying on separable targets, we used Fourier-based decomposition to separate signals arising from the different scales of vascular structures while removing speckle noise using multi-ensemble processing. This approach enabled the use of compressed sensing for super-resolution imaging of the underlying vascular structures, improving resolution by a factor of four. Reconstruction of ultrafast mouse brain scans revealed details that could not be resolved in regular Doppler images, agreeing closely with bubble-based super-localization microscopy of the same fields of view. By combining multi-ensemble Doppler acquisitions with narrowband Fourier decomposition and computational super-resolution imaging, this approach opens new opportunities for affordable and scalable super-resolution ultrasound imaging.

Anterior temporal lobectomy: A cross-sectional observational study of potential surgical candidates at a single institute

Background: Epilepsy is a common neurological disorder, associated with serious cognitive, physical, and psychosocial burdens. Mesial temporal lobe epilepsy (mTLE) is the commonest form of focal epilepsy. The aim of this study was to establish the incidence of patients with electroencephalographic epileptiform discharges consistent with mTLE attending a tertiary hospital in South Africa, and determine whether these patients may be candidates for anterior temporal lobectomy. Methods: This was a cross-sectional observational study of all patients receiving scalp electroencephalograms (EEG) performed at the Groote Schuur Hospital Neurophysiology laboratory during the period January 1, 2017–December 31, 2019. Where magnetic resonance imaging (MRI) brain scans had been performed, these were assessed for corroborative evidence of mTLE. Results: Over the 3-year period, 4 342 EEGs were assessed. A total of 411 (11%) showed epileptiform discharges consistent with all epilepsy types. Of these, 327 (69%) were of focal onset and 108 (33% of all focal onset epilepsies) were consistent with mTLE. Of the patients with electroencephalographic features of mTLE, only 27 (25%) had had MRI brain scans performed according to an epilepsy surgery protocol. None of these patients had been considered for surgery. Conclusion: Surgery, especially anterior temporal lobectomy, is widely acknowledged to be an efficacious and cost-effective intervention in patients with drug-resistant mTLE. The findings of our study suggest that patients with mTLE in our setting are under-investigated for potential surgery; and that it is under-utilized. These findings are in line with similar studies in both well-resourced and resource-constrained countries. Our study also highlights the utility of EEG as a practical screening tool to identify potential surgical candidates, as well as the establishment of an EEG and MRI database to assist in recognizing these patients.

RS-FISH: Precise, interactive, fast, and scalable FISH spot detection

Fluorescent in-situ hybridization (FISH)-based methods are powerful tools to study molecular processes with subcellular resolution, relying on accurate identification and localization of diffraction-limited spots in microscopy images. We developed the Radial Symmetry-FISH (RS-FISH) software that accurately, robustly, and quickly detects single-molecule spots in two and three dimensions, making it applicable to several key assays, including single-molecule FISH (smFISH), spatial transcriptomics, and spatial genomics. RS-FISH allows interactive parameter tuning and scales to large sets of images as well as tera-byte sized image volumes such as entire brain scans using straight-forward distributed processing on workstations, clusters, and in the cloud.

Reasons for Requesting a CT Scan and Amount of Radiation Exposure in Hospitalized Children, the Issue Needs Attention

Background: Computerized Tomographic (CT) scan is a rapid, non-invasive, and common diagnostic modality in radiology. More and faster growth of cells, as well as longer lifespan of a child, may lead to a cumulative effect of radiation and a greater chance of mutation, especially in children who are more vulnerable. Objectives: This study was designed to evaluate the current trend in the use of this modality. Methods: In this descriptive study, all cases of children admitted to Taleghani Hospital who were studied by CT scan during the years 2018 - 2019, entered in survey. Demographic information, anatomical location of the scan, findings from the CT scan report, initial and final diagnosis, number of scans, patient’s complaint that led to his hospitalization and total amount of radiation received by the patient were analyzed. The data were analyzed by SPSS software version 23 with descriptive statistics (frequency, frequency percentage, and mean and standard deviation). Results: In this study, 344 scans of 280 patients were reviewed. Out of 280 patients, 222 patients underwent scan from one, 55 patients from two and four patients from three different anatomical locations. There were 146 male patients (52.1%) with a mean age of 57.7 months and 134 female patients (47.9%) with a mean age of 54.7 months. Out of all scans, the brain was reported with 45%, chest 30%, and other areas 25%. The CT scan was performed at the request of the doctor in charge. Pneumonia (19%) followed by convulsion (17%) and febrile seizures (12%) were the most common complaints. The number of completely normal CT was 151 scans (44.02%), with brain scans having the highest rate of normal scans with 74.19%. Brain scans with an average of 358.66 milliGray.centimeter (mGy.cm) and a maximum of 995.30 mG/cm had the highest average absorbed dose. Conclusions: In this study, the rate of normal CT scans was relatively high and the range of rays available for each modality was significantly wide. Effective interventions must be made in this regard.

Simultaneous Multislice Brain MRI T1 Mapping with Improved Low-Rank Modeling

To accelerate data acquisition speed in magnetic resonance imaging (MRI), multiple slices are simultaneously acquired using multiband pulses. Simultaneous multislice (SMS) imaging typically unfolds slice aliasing from the acquired collapsed slices. In this study, we extended the SMS framework to accelerated MR parameter quantification such as T1 mapping. Assuming that the slice-specific null space and signal subspace are invariant along the parameter dimension, we formulated the SMS framework as a constrained optimization problem under a joint reconstruction framework such that the noise and signal subspaces are used for slice separation and recovery, respectively. The proposed method was validated on 3T MR human brain scans. We successfully demonstrated that the proposed method outperforms competing methods in suppressing aliasing artifacts and noise at high SMS accelerations, thus leading to accurate T1 maps.

Population modeling with machine learning can enhance measures of mental health

Background Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? Results Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data. Conclusion Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations.