Autoimmune comorbidity in type 1 diabetes and its association with metabolic control and mortality risk in young people: a population-based study.

AIMS/HYPOTHESIS: This register-based study aimed to describe autoimmune comorbidity in children and young adults from type 1 diabetes onset, and to investigate whether such comorbidity was associated with a difference in HbA1c or mortality risk compared with children/young adults with type 1 diabetes without autoimmune comorbidity. METHODS: A total of 15,188 individuals from the Swedish National Diabetes Register, registered with type 1 diabetes before 18 years of age between 2000 and 2019, were included. Five randomly selected control individuals from the Swedish population (Statistics Sweden) were matched to each individual with type 1 diabetes (n=74,210 [346 individuals with type 1 diabetes were not found in the Statistics Sweden register at the date of type 1 diabetes diagnosis, so could not be matched to control individuals]). The National Patient Register was used to attain ICD-10 codes on autoimmune diseases and the Cause of Death Register was used to identify deceased individuals. RESULTS: In the total type 1 diabetes cohort, mean±SD age at onset of type 1 diabetes was 9.5±4.4 years and mean disease duration at end of follow-up was 8.8±5.7 years. Of the individuals with type 1 diabetes, 19.2% were diagnosed with at least one autoimmune disease vs 4.0% of the control group. The HRs for comorbidities within 19 years from onset of type 1 diabetes were 11.6 (95% CI 10.6, 12.6) for coeliac disease, 10.6 (95% CI 9.6, 11.8) for thyroid disease, 1.3 (95% CI 1.1, 1.6) for psoriasis, 4.1 (95% CI 3.2, 5.3) for vitiligo, 1.7 (95% CI 1.4, 2.2) for rheumatic joint disease, 1.0 (95% CI 0.8, 1.3) for inflammatory bowel disease, 1.0 (95% CI 0.7, 1.2) for systemic connective tissue disorder, 1.4 (95% CI 1.1, 1.9) for uveitis, 18.3 (95% CI 8.4, 40.0) for Addison's disease, 1.8 (95% CI 0.9, 3.6) for multiple sclerosis, 3.7 (95% CI 1.6, 8.7) for inflammatory liver disease and 19.6 (95% CI 4.2, 92.3) for atrophic gastritis. Autoimmune disease in addition to type 1 diabetes had no statistically significant effect on HbA1c or mortality risk. CONCLUSIONS/INTERPRETATION: To our knowledge, this is the first comprehensive study where young individuals with type 1 diabetes were followed regarding development of a wide spectrum of autoimmune diseases, from onset of type 1 diabetes. In this nationwide and population-based study, there was already a high prevalence of autoimmune diseases in childhood, especially coeliac and thyroid disease. The presence of autoimmune comorbidity did not have a statistically significant effect on metabolic control or mortality risk.

Both stronger and weaker cerebro-cerebellar functional connectivity patterns during processing of spoken sentences in autism spectrum disorder.

Cerebellar differences have long been documented in autism spectrum disorder (ASD), yet the extent to which such differences might impact language processing in ASD remains unknown. To investigate this, we recorded brain activity with magnetoencephalography (MEG) while ASD and age-matched typically developing (TD) children passively processed spoken meaningful English and meaningless Jabberwocky sentences. Using a novel source localization approach that allows higher resolution MEG source localization of cerebellar activity, we found that, unlike TD children, ASD children showed no difference between evoked responses to meaningful versus meaningless sentences in right cerebellar lobule VI. ASD children also had atypically weak functional connectivity in the meaningful versus meaningless speech condition between right cerebellar lobule VI and several left-hemisphere sensorimotor and language regions in later time windows. In contrast, ASD children had atypically strong functional connectivity for in the meaningful versus meaningless speech condition between right cerebellar lobule VI and primary auditory cortical areas in an earlier time window. The atypical functional connectivity patterns in ASD correlated with ASD severity and the ability to inhibit involuntary attention. These findings align with a model where cerebro-cerebellar speech processing mechanisms in ASD are impacted by aberrant stimulus-driven attention, which could result from atypical temporal information and predictions of auditory sensory events by right cerebellar lobule VI.

Quantification of acceleration as activity counts in ActiGraph wearable.

Digital clinical measures based on data collected by wearable devices have seen rapid growth in both clinical trials and healthcare. The widely-used measures based on wearables are epoch-based physical activity counts using accelerometer data. Even though activity counts have been the backbone of thousands of clinical and epidemiological studies, there are large variations of the algorithms that compute counts and their associated parameters-many of which have often been kept proprietary by device providers. This lack of transparency has hindered comparability between studies using different devices and limited their broader clinical applicability. ActiGraph devices have been the most-used wearable accelerometer devices for over two decades. Recognizing the importance of data transparency, interpretability and interoperability to both research and clinical use, we here describe the detailed counts algorithms of five generations of ActiGraph devices going back to the first AM7164 model, and publish the current counts algorithm in ActiGraph's ActiLife and CentrePoint software as a standalone Python package for research use. We believe that this material will provide a useful resource for the research community, accelerate digital health science and facilitate clinical applications of wearable accelerometry.

Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks.

Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases.

Spatial fidelity of MEG/EEG source estimates: A general evaluation approach.

Low spatial resolution is often cited as the most critical limitation of magneto- and electroencephalography (MEG and EEG), but a unifying framework for quantifying the spatial fidelity of M/EEG source estimates has yet to be established; previous studies have focused on linear estimation methods under ideal scenarios without noise. Here we present an approach that quantifies the spatial fidelity of M/EEG estimates from simulated patch activations over the entire neocortex superposed on measured resting-state data. This approach grants more generalizability in the evaluation process that allows for, e.g., comparing linear and non-linear estimates in the whole brain for different signal-to-noise ratios (SNR), number of active sources and activation waveforms. Using this framework, we evaluated the MNE, dSPM, sLORETA, eLORETA, and MxNE methods and found that the spatial fidelity varies significantly with SNR, following a largely sigmoidal curve whose shape varies depending on which aspect of spatial fidelity that is being quantified and the source estimation method. We believe that these methods and results will be useful when interpreting M/EEG source estimates as well as in methods development.

Detectability of cerebellar activity with magnetoencephalography and electroencephalography.

Electrophysiological signals from the cerebellum have traditionally been viewed as inaccessible to magnetoencephalography (MEG) and electroencephalography (EEG). Here, we challenge this position by investigating the ability of MEG and EEG to detect cerebellar activity using a model that employs a high-resolution tessellation of the cerebellar cortex. The tessellation was constructed from repetitive high-field (9.4T) structural magnetic resonance imaging (MRI) of an ex vivo human cerebellum. A boundary-element forward model was then used to simulate the M/EEG signals resulting from neural activity in the cerebellar cortex. Despite significant signal cancelation due to the highly convoluted cerebellar cortex, we found that the cerebellar signal was on average only 30-60% weaker than the cortical signal. We also made detailed M/EEG sensitivity maps and found that MEG and EEG have highly complementary sensitivity distributions over the cerebellar cortex. Based on previous fMRI studies combined with our M/EEG sensitivity maps, we discuss experimental paradigms that are likely to offer high M/EEG sensitivity to cerebellar activity. Taken together, these results show that cerebellar activity should be clearly detectable by current M/EEG systems with an appropriate experimental setup.

Poor metabolic control in childhood strongly correlates to diabetes-related premature death in persons <30 years of age-A population-based cohort study.

BACKGROUND/OBJECTIVE: The importance of metabolic control in childhood regarding excess risk of death in young persons has not been well studied. This registry-based study aimed to investigate mortality rates and cause of death related to metabolic control in young persons (≤29 years) in Sweden with type 1 diabetes. METHODS: All 12 652 subjects registered in the Swedish pediatric diabetes quality register, from 2006 to 2014, were included. Data were merged with the Swedish Cause of Death Register. Standardized mortality rates were calculated using the official Swedish population register. RESULTS: Of 68 deaths identified, 38.2% of the deaths were registered as being due to diabetes whereof the major cause of death was acute complications. Overall standardized mortality ratio was 2.7 (2.1-3.4, 95% CI). Subjects who died from diabetes had a mean HbA1c of 74 ± 19 mmol/mol (8.9 ± 1.7%) during childhood vs 62 ± 12 mmol/mol (7.8 ± 1.1%) in those still alive (P < .001). CONCLUSIONS: In this nationwide cohort of young subjects with type 1 diabetes, there was a high mortality rate compared to the general population. Mean HbA1c in childhood was significantly higher in those who died from diabetes, compared to subjects who were still alive. To decrease mortality in young persons with type 1 diabetes it is essential not only to achieve but also to maintain a good metabolic control during childhood and adolescence.

Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG.

Magnetoencephalography (MEG) and electroencephalography (EEG) use non-invasive sensors to detect neural currents. Since the contribution of superficial neural sources to the measured M/EEG signals are orders-of-magnitude stronger than the contribution of subcortical sources, most MEG and EEG studies have focused on cortical activity. Subcortical structures, however, are centrally involved in both healthy brain function as well as in many neurological disorders such as Alzheimer's disease and Parkinson's disease. In this paper, we present a method that can separate and suppress the cortical signals while preserving the subcortical contributions to the M/EEG data. The resulting signal subspace of the data mainly originates from subcortical structures. Our method works by utilizing short-baseline planar gradiometers with short-sighted sensitivity distributions as reference sensors for cortical activity. Since the method is completely data-driven, forward and inverse modeling are not required. In this study, we use simulations and auditory steady state response experiments in a human subject to demonstrate that the method can remove the cortical signals while sparing the subcortical signals. We also test our method on MEG data recorded in an essential tremor patient with a deep brain stimulation implant and show how it can be used to reduce the DBS artifact in the MEG data by ~ 99.9% without affecting low frequency brain rhythms.