Correcting motion induced fluorescence artifacts in two-channel neural imaging

Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal's movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One approach to mitigating these artifacts is to image two channels; one that captures an activity-dependent fluorophore, such as GCaMP, and another that captures an activity-independent fluorophore such as RFP. Because the activity-independent channel contains the same motion artifacts as the activity-dependent channel, but no neural signals, the two together can be used to remove the artifacts. Existing approaches for this correction, such as taking the ratio of the two channels, do not account for channel independent noise in the measured fluorescence. Moreover, no systematic comparison has been made of existing approaches that use two-channel signals. Here, we present Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove artifacts by specifying a generative model of the fluorescence of the two channels as a function of motion artifact, neural activity, and noise. We further present a novel method for evaluating ground-truth performance of motion correction algorithms by comparing the decodability of behavior from two types of neural recordings; a recording that had both an activity-dependent fluorophore (GCaMP and RFP) and a recording where both fluorophores were activity-independent (GFP and RFP). A successful motion-correction method should decode behavior from the first type of recording, but not the second. We use this metric to systematically compare five methods for removing motion artifacts from fluorescent time traces. We decode locomotion from a GCaMP expressing animal 15x more accurately on average than from control when using TMAC inferred activity and outperform all other methods of motion correction tested.Comment: 11 pages, 3 figure

Prevalence and clinical characteristics of non-malignant CT detected incidental findings in the SUMMIT lung cancer screening cohort

BACKGROUND: Pulmonary and extrapulmonary incidental findings are frequently identified on CT scans performed for lung cancer screening. Uncertainty regarding their clinical significance and how and when such findings should be reported back to clinicians and participants persists. We examined the prevalence of non-malignant incidental findings within a lung cancer screening cohort and investigated the morbidity and relevant risk factors associated with incidental findings. We quantified the primary and secondary care referrals generated by our protocol. METHODS: The SUMMIT study (NCT03934866) is a prospective observational cohort study to examine the performance of delivering a low-dose CT (LDCT) screening service to a high-risk population. Spirometry, blood pressure, height/weight and respiratory history were assessed as part of a Lung Health Check. Individuals at high risk of lung cancer were offered an LDCT and returned for two further annual visits. This analysis is a prospective evaluation of the standardised reporting and management protocol for incidental findings developed for the study on the baseline LDCT. RESULTS: In 11 115 participants included in this analysis, the most common incidental findings were coronary artery calcification (64.2%) and emphysema (33.4%). From our protocolised management approach, the number of participants requiring review for clinically relevant findings in primary care was 1 in 20, and the number potentially requiring review in secondary care was 1 in 25. CONCLUSIONS: Incidental findings are common in lung cancer screening and can be associated with reported symptoms and comorbidities. A standardised reporting protocol allows systematic assessment and standardises onward management

Growing small solid nodules in lung cancer screening: safety and efficacy of a 200 mm3 minimum size threshold for multidisciplinary team referral

The optimal management of small but growing nodules remains unclear. The SUMMIT study nodule management algorithm uses a specific threshold volume of 200 mm3 before referral of growing solid nodules to the multidisciplinary team for further investigation is advised, with growing nodules below this threshold kept under observation within the screening programme. Malignancy risk of growing solid nodules of size >200 mm3 at initial 3-month interval scan was 58.3% at a per-nodule level, compared with 13.3% in growing nodules of size ≤200 mm3 (relative risk 4.4, 95% CI 2.17 to 8.83). The positive predictive value of a combination of nodule growth (defined as percentage volume change of ≥25%), and size >200 mm3 was 65.9% (29/44) at a cancer-per-nodule basis, or 60.5% (23/38) on a cancer-per-participant basis. False negative rate of the protocol was 1.9% (95% CI 0.33% to 9.94%). These findings support the use of a 200 mm3 minimum volume threshold for referral as effective at reducing unnecessary multidisciplinary team referrals for small growing nodules, while maintaining early-stage lung cancer diagnosis

ERS International Congress 2023: highlights from the Thoracic Oncology Assembly

Lung cancer is the leading cause of cancer mortality in the world. It greatly affects the patients' quality of life, and is thus a challenge for the daily practice in respiratory medicine. Advances in the genetic knowledge of thoracic tumours' mutational landscape, and the development of targeted therapies and immune checkpoint inhibitors, have led to a paradigm shift in the treatment of lung cancer and pleural mesothelioma. During the 2023 European Respiratory Society Congress in Milan, Italy, experts from all over the world presented their high-quality research and reviewed best clinical practices. Lung cancer screening, management of early stages of lung cancer, application of artificial intelligence and biomarkers were discussed and they will be summarised here

Prognostic factors in chronic hypersensitivity pneumonitis

Hypersensitivity pneumonitis (HP) is an immunologically mediated lung disease resulting from exposure to inhaled environmental antigens. Prognosis is variable, with a subset of patients developing progressive fibrosis leading to respiratory failure and death. Therefore, there is an urgent need to identify factors which predict prognosis and survival in patients with HP. We undertook a narrative review of existing evidence to identify prognostic factors in patients with chronic HP. Patient demographics, smoking history, extent of antigen exposure and comorbidities all have reported associations with disease outcome, and physiological, radiological and laboratory markers have been shown to predict overall survival. While no single marker has been demonstrated to accurately and reliably predict prognosis, older age, more severe impairment of pulmonary function at baseline and established fibrosis on either biopsy or high-resolution computed tomography are consistently associated with worse survival. The vast majority of existing studies are retrospective, and this review identifies a need for prospective longitudinal studies with serial assessment of respiratory health to ascertain factors associated with nonfatal deterioration. Future developments, including the development of HP-specific composite scores may help further improve our ability to predict outcomes for individual patients

Data source for each figure.

(DOCX)</p

TMAC reduces decodable motion artifacts in experimental data.

A) Top, animal body curvature over time. Middle, GCaMP and RFP fluorescence from a neuron that TMAC estimates to have high signal to noise, recorded from a behaving worm. Bottom, GCaMP and RFP fluorescence from a different neuron in that same recording that TMAC estimates to have large motion artifacts. B) Time trace of animal curvature and predicted behavior, decoded from activity inferred by TMAC in a GCaMP worm. Gray shaded regions were used to train the decoder, white region was held out and used to evaluate decoding performance. C) Ratio of decoding accuracy (ρ2) when decoding GCaMP divided by the median accuracy for a GFP worm across different models (S2 Table). D) Histogram over all neurons of correlation squared between RFP and activity inferred by TMAC from a GFP worm. RFP vs GFP data the same as in Fig 1D and 1E. E) Same as F but in a GCaMP worm.</p

Accuracy of motion correction methods on synthetic data.

Each of the 5 methods for motion correction were tested on the synthetic dataset from Fig 2. The reported value is the distribution of correlation squared between inferred activity and true activity over instantiations of neurons. This synthetic data was generated from TMAC itself so it is unsurprising that it outperforms other methods on this dataset. The linear regression method also performs well because, like TMAC, it assumes an additive interaction between motion and activity. (PDF)</p