Neuroimaging and biomarker evidence of neurodegeneration in asthma

Background: Epidemiological studies have shown that Alzheimer’s disease and related dementias (ADRD) are seen more frequently with asthma, especially with greater asthma severity or exacerbation frequency. // Objective: To examine the changes in brain structure that may underlie this phenomenon, we examined diffusion-weighted magnetic resonance imaging (dMRI) and blood-based biomarkers of AD (p-Tau181), neurodegeneration (NfL) and glial activation (GFAP). // Methods: dMRI data were obtained in 111 individuals with asthma, ranging in disease severity from mild to severe, and 135 healthy controls. Regression analyses were used to test the relationships between asthma severity and neuroimaging measures, as well as AD pathology, neurodegeneration and glial activation, indexed by plasma p-Tau181, NfL and GFAP respectively. Additional relationships were tested with cognitive function. // Results: Asthma participants had widespread and large magnitude differences in several dMRI metrics, which were indicative of neuroinflammation and neurodegeneration, and robustly associated with GFAP and to a lesser extent, with NfL. The AD biomarker p-Tau181 was only minimally associated with neuroimaging outcomes. Further, asthma severity was associated with deleterious changes in neuroimaging outcomes, which in turn, were associated with slower processing speed, a test of cognitive performance. // Conclusion: These data suggest that asthma, particularly when severe, is associated with characteristics of neuroinflammation and neurodegeneration and may be a potential risk factor for neural injury and cognitive dysfunction. The results suggest a need to determine how asthma may affect brain health and whether treatment directed toward characteristics of asthma associated with these risks can mitigate these effects

CSF neurofilament light chain profiling and quantitation in neurological diseases.

Neurofilament light chain is an established marker of neuroaxonal injury that is elevated in CSF and blood across various neurological diseases. It is increasingly used in clinical practice to aid diagnosis and monitor progression and as an outcome measure to assess safety and efficacy of disease-modifying therapies across the clinical translational neuroscience field. Quantitative methods for neurofilament light chain in human biofluids have relied on immunoassays, which have limited capacity to describe the structure of the protein in CSF and how this might vary in different neurodegenerative diseases. In this study, we characterized and quantified neurofilament light chain species in CSF across neurodegenerative and neuroinflammatory diseases and healthy controls using targeted mass spectrometry. We show that the quantitative immunoprecipitation-tandem mass spectrometry method developed in this study strongly correlates to single-molecule array measurements in CSF across the broad spectrum of neurodegenerative diseases and was replicable across mass spectrometry methods and centres. In summary, we have created an accurate and cost-effective assay for measuring a key biomarker in translational neuroscience research and clinical practice, which can be easily multiplexed and translated into clinical laboratories for the screening and monitoring of neurodegenerative disease or acute brain injury

Relationships among neurocognition, symptoms and functioning in patients with schizophrenia: a path-analytic approach for associations at baseline and following 24 weeks of antipsychotic drug therapy

<p>Abstract</p> <p>Background</p> <p>Neurocognitive impairment and psychiatric symptoms have been associated with deficits in psychosocial and occupational functioning in patients with schizophrenia. This post-hoc analysis evaluates the relationships among cognition, psychopathology, and psychosocial functioning in patients with schizophrenia at baseline and following sustained treatment with antipsychotic drugs.</p> <p>Methods</p> <p>Data were obtained from a clinical trial assessing the cognitive effects of selected antipsychotic drugs in patients with schizophrenia. Patients were randomly assigned to 24 weeks of treatment with olanzapine (n = 159), risperidone (n = 158), or haloperidol (n = 97). Psychosocial functioning was assessed with the Heinrichs-Carpenter Quality of Life Scale [QLS], cognition with a standard battery of neurocognitive tests; and psychiatric symptoms with the Positive and Negative Syndrome Scale [PANSS]. A path-analytic approach was used to evaluate the effects of changes in cognitive functioning on subdomains of quality of life, and to determine whether such effects were direct or mediated via changes in psychiatric symptoms.</p> <p>Results</p> <p>At baseline, processing speed affected functioning mainly indirectly via negative symptoms. Positive symptoms also affected functioning at baseline although independent of cognition. At 24 weeks, changes in processing speed affected changes in functioning both directly and indirectly via PANSS negative subscale scores. Positive symptoms no longer contributed to the path-analytic models. Although a consistent relationship was observed between processing speed and the 3 functional domains, variation existed as to whether the paths were direct and/or indirect. Working memory and verbal memory did not significantly contribute to any of the path-analytic models studied.</p> <p>Conclusion</p> <p>Processing speed demonstrated direct and indirect effects via negative symptoms on three domains of functioning as measured by the QLS at baseline and following 24 weeks of antipsychotic treatment.</p

Association of Typical versus Atypical Antipsychotics with Symptoms and Quality of Life in Schizophrenia

BACKGROUND: Several reports on patients with chronic schizophrenia suggest that atypical versus typical antipsychotics are expected to lead to better quality of life (QOL) and cognitive function. Our aim was to examine the association of chronic treatment with typical or atypical antipsychotics with cognitive function, psychiatric symptoms, QOL, and drug-induced extrapyramidal symptoms in long-hospitalized patients with schizophrenia. METHODOLOGY AND PRINCIPAL FINDINGS: The Hasegawa Dementia Scale-Revised (HDS-R), Brief Psychiatric Rating Scale (BPRS), the Schizophrenia Quality of Life Scale, translated into Japanese (JSQLS), and the Drug-Induced Extrapyramidal Symptoms Scale (DIEPSS) were used to evaluate cognitive function, psychiatric symptoms, QOL, and drug-induced extrapyramidal symptoms. We examined the correlation between the dose of antipsychotics and each measure derived from these psychometric tests. The student t-test was used to compare scores obtained from psychometric tests between patients receiving typical and atypical antipsychotics. Results showed significant correlations between chlorpromazine (CPZ)-equivalent doses of typical antipsychotics and atypical antipsychotics, and the total BPRS score and BPRS subscale scores for positive symptoms. CPZ-equivalent doses of typical antipsychotics were correlated with the JSQLS subscale score for dysfunction of psycho-social activity and DIEPSS score. Furthermore, the total BPRS scores, BPRS subscale score for positive symptoms, the JSQLS subscale score for dysfunction of psycho-social activity, and the DIEPSS score were significantly higher in patients receiving typical antipsychotics than atypical antipsychotics. CONCLUSION AND SIGNIFICANCE: These findings suggest that long-term administration of typical antipsychotics has an unfavorable association with feelings of difficulties mixing in social situations in patients with chronic schizophrenia

Relationships between cognitive deficits, symptoms and quality of life in schizophrenia

Objective: Schizophrenia is a complex disorder characterized by impairment in a number of domains, all of which contribute to disability. The aim of the present study was to investigate the relationships between cognitive function, symptoms and quality of life (QOL) in schizophrenia. Method: This cross-sectional study measured cognition, positive and negative symptom severity, and quality of life (measured with the Quality of Life Scale) in 57 outpatients with schizophrenia. Correlations between the different measures were sought. Multiple regression analyses were used to develop models of the contributions of cognitive deficits and symptomatology to QOL. Results: More severe positive and negative symptoms and cognitive impairment each correlated with poorer QOL. There was a moderate association between negative symptoms and cognition and a small association between positive symptoms and cognition. Age, gender, and drug and alcohol abuse did not significantly predict QOL. In the multiple regression analysis, entering the total cognition and total symptom scores produced a model that accounted for an additional 57% of the variance in QOL. Conclusions: Improving quality of life for people with schizophrenia requires that positive and negative symptoms and cognition are each addressed as separate domains of impairment. But, given that these account for only 57% of the variance in QOL, other factors such as unemployment, poverty, social isolation and stigma may also be important.Kara Savilla, Lisa Kettler, Cherrie Galletl

Calibrating the classifier: siamese neural network architecture for end-to-end arousal recognition from ECG

Affective analysis of physiological signals enables emotion recognition in mobile wearable devices. In this paper, we present a deep learning framework for arousal recognition from ECG (electrocardio- gram) signals. Specifically, we design an end-to-end convolutional and recurrent neural network architecture to (i) extract features from ECG; (ii) analyse time-domain variation patterns; and (iii) non-linearly relate those to the user's arousal level. The key novelty is our use of a shared- parameter siamese architecture to implement user-specific feature cali- bration. At each forward and backward pass, we concatenate to the input a user-dependent template that is processed by an identical copy of the network. The siamese architecture makes feature calibration an integral part of the training process, allowing modelling of general dependencies between the user's ECG at rest and those during emotion elicitation. On leave-one-user-out cross validation, the proposed architecture obtains +21:5% score increase compared to state-of-the-art techniques. Compari- son with alternative network architectures demonstrates the effectiveness of the siamese network in achieving user-specific feature calibration