A Hierarchical Machine Learning Solution for the Non-Invasive Diagnostic of Autonomic Dysreflexia

More than half of patients with high spinal cord injury (SCI) suffer from episodes of autonomic dysreflexia (AD), a condition that can lead to lethal situations, such as cerebral haemorrhage, if not treated correctly. Clinicians assess AD using clinical variables obtained from the patient’s history and physiological variables obtained invasively and non-invasively. This work aims to design a machine learning-based system to assist in the initial diagnosis of AD. For this purpose, 29 patients with SCI participated in a test at Cruces University Hospital in which data were collected using both invasive and non-invasive methods. The system proposed in this article is based on a two-level hierarchical classification to diagnose AD and only uses 35 features extracted from the non-invasive stages of the experiment (clinical and physiological features). The system achieved a 93.10% accuracy with a zero false negative rate for the class of having the disease, an essential condition for treating patients according to medical criteria.This work was partially funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT-980-16), by the Spanish Ministry of Science, Innovation and Universities-National Research Agency and the European Regional Development Fund-ERDF (PhysComp, TIN2017-85409-P), and from the State Research Agency (AEI, Spain) under grant agreement No. RED2018-102312-T (IA-Biomed)

Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies

Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT-980-16); by the Spanish Ministry of Science, Innovation and Universities - National Research Agency and the European Regional Development Fund - ERDF (PhysComp, TIN2017-85409-P), and from the State Research Agency (AEI, Spain) under grant agreement No RED2018-102312-T (IA-Biomed); by Michael J. Fox Foundation [RRIA 2014 (Rapid Response Innovation Awards) Program (Grant ID: 10189)]; by the Instituto de Salud Carlos III through the project “PI14/00679” and “PI16/00005”, the Juan Rodes grant “JR15/00008” (IG) (Co-funded by European Regional Development Fund/European Social Fund - “Investing in your future”); and by the Department of Health of the Basque Government through the projects “2016111009” and “2019111100”

Brain fog of post-COVID-19 condition and Chronic Fatigue Syndrome, same medical disorder?

Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is characterized by persistent physical and mental fatigue. The post-COVID-19 condition patients refer physical fatigue and cognitive impairment sequelae. Given the similarity between both conditions, could it be the same pathology with a different precipitating factor? Objective: To describe the cognitive impairment, neuropsychiatric symptoms, and general symptomatology in both groups, to find out if it is the same pathology. As well as verify if the affectation of smell is related to cognitive deterioration in patients with post-COVID-19 condition. Methods: The sample included 42 ME/CFS and 73 post-COVID-19 condition patients. Fatigue, sleep quality, anxiety and depressive symptoms, the frequency and severity of different symptoms, olfactory function and a wide range of cognitive domains were evaluated. Results: Both syndromes are characterized by excessive physical fatigue, sleep problems and myalgia. Sustained attention and processing speed were impaired in 83.3% and 52.4% of ME/CFS patients while in post-COVID-19 condition were impaired in 56.2% and 41.4% of patients, respectively. Statistically significant differences were found in sustained attention and visuospatial ability, being the ME/CFS group who presented the worst performance. Physical problems and mood issues were the main variables correlating with cognitive performance in post-COVID-19 patients, while in ME/CFS it was anxiety symptoms and physical fatigue. Conclusions: The symptomatology and cognitive patterns were similar in both groups, with greater impairment in ME/CFS. This disease is characterized by greater physical and neuropsychiatric problems compared to post-COVID-19 condition. Likewise, we also propose the relevance of prolonged hyposmia as a possible marker of cognitive deterioration in patients with post-COVID-19.This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project PI20/01076 and co-funded by the European Union, EITB maratoia (BIOS21/COV/006) and grants for health research projects from the Basque Government (2021111006). Azcue, N. received a pre-doctoral research grant from the basque government (PRE_2021_1_0186)

Myocardial MIBG scintigraphy in genetic Parkinson’s disease as a model for Lewy body disorders

Purpose To identify myocardial sympathetic denervation patterns suggestive of Lewy body (LB) pathology in patients with genetic and idiopathic parkinsonisms by 123I-metaiodobenzylguanidine (MIBG) scintigraphy. Methods We retrospectively analysed myocardial MIBG images acquired with a dual-head gamma camera and low-energy high- resolution collimator (LEHR) in 194 patients with suspected synucleinopathy or atypical parkinsonism, including 34 with genetic Parkinson’s disease (PD; 4 PARK1, 8 PARK2 and 22 PARK8), 85 with idiopathic PD (iPD), 6 with idiopathic REM sleep behaviour disorder (iRBD), 17 with dementia with LB (DLB), 40 with multiple system atrophy (MSA) and 12 with progressive supranuclear palsy (PSP), and in 45 healthy controls. We calculated heart-to-mediastinum MIBG uptake ratios (HMR) at 15 min and 4 h (HMR4H) for the LEHR and standardized medium-energy collimators, to obtain classification accuracies and optimal cut-off values for HMR using supervised classification and ROC analyses. Results While patients with LB disorders had markedly lower HMR4HLEHR than controls (controls 1.86 ± 0.26, iPD 1.38 ± 0.29, iRBD 1.23 ± 0.09, PARK1 1.20 ± 0.09, DLB 1.17 ± 0.11; p 0.05). The diag- nostic accuracy of HMR4HLEHR was highest in patients with LB disorders (PARK1, iPD, DLB, iRBD; 89% to 97%) and lowest in those with PARK2, PARK8, PSP and MSA (65% to 76%), with an optimal HMR4HLEHR cut-off value of 1.72 for discrim- inating most patients with LB disorders including iPD and 1.40 for discriminating those with aggressive LB spectrum phenotypes (DLB, PARK1 and iRBD). Conclusion Our study including patients with a wide spectrum of genetic and idiopathic parkinsonisms with different degrees of LB pathology further supports myocardial MIBG scintigraphy as an accurate tool for discriminating patients with LB spectrum disorders

Plasma Neurofilament Light Chain: A Potential Biomarker for Neurological Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex disorder characterized by heterogeneous symptoms, which lack specific biomarkers for its diagnosis. This study aimed to investigate plasma neurofilament light chain (NfL) levels as a potential biomarker for ME/CFS and explore associations with cognitive, autonomic, and neuropathic symptoms. Here, 67 ME/CFS patients and 43 healthy controls (HCs) underwent comprehensive assessments, including neuropsychological evaluation, autonomic nervous system (ANS) testing, and plasma NfL level analysis. ME/CFS patients exhibited significantly higher plasma NfL levels compared to HC (F = 4.30, p < 0.05). Correlations were observed between NfL levels and cognitive impairment, particularly in visuospatial perception (r = −0.42; p ≤ 0.001), verbal memory (r = −0.35, p ≤ 0.005), and visual memory (r = −0.26; p < 0.05) in ME/CFS. Additionally, higher NfL levels were associated with worsened autonomic dysfunction in these patients, specifically in parasympathetic function (F = 9.48, p ≤ 0.003). In ME/CFS patients, NfL levels explained up to 17.2% of the results in cognitive tests. Unlike ME/CFS, in HC, NfL levels did not predict cognitive performance. Elevated plasma NfL levels in ME/CFS patients reflect neuroaxonal damage, contributing to cognitive dysfunction and autonomic impairment. These findings support the potential role of NfL as a biomarker for neurological dysfunction in ME/CFS. Further research is warranted to elucidate underlying mechanisms and clinical implications.This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project PI20/01076 and co-funded by the European Union, BIOEF through EITB maratoia (BIOS21/COV/006), and grants for health research projects from the Basque Government (2021111006). The first author received a pre-doctoral research grant from the Basque Government (PRE_2023_2_0138)

Retinal thickness as a biomarker of cognitive impairment in manifest Huntington’s disease

Background Cognitive decline has been reported in premanifest and manifest Huntington’s disease but reliable biomarkers are lacking. Inner retinal layer thickness seems to be a good biomarker of cognition in other neurodegenerative diseases. Objective To explore the relationship between optical coherence tomography-derived metrics and global cognition in Huntington’s Disease. Methods Thirty-six patients with Huntington’s disease (16 premanifest and 20 manifest) and 36 controls matched by age, sex, smoking status, and hypertension status underwent macular volumetric and peripapillary optical coherence tomography scans. Disease duration, motor status, global cognition and CAG repeats were recorded in patients. Group differences in imaging parameters and their association with clinical outcomes were analyzed using linear mixed-effect models. Results Premanifest and manifest Huntington’s disease patients presented thinner retinal external limiting membrane-Bruch’s membrane complex, and manifest patients had thinner temporal peripapillary retinal nerve fiber layer compared to controls. In manifest Huntington’s disease, macular thickness was significantly associated with MoCA scores, inner nuclear layer showing the largest regression coefficients. This relationship was consistent after adjusting for age, sex, and education and p-value correction with False Discovery Rate. None of the retinal variables were related to Unified Huntington’s Disease Rating Scale score, disease duration, or disease burden. Premanifest patients did not show a significant association between OCT-derived parameters and clinical outcomes in corrected models. Conclusions In line with other neurodegenerative diseases, OCT is a potential biomarker of cognitive status in manifest HD. Future prospective studies are needed to evaluate OCT as a potential surrogate marker of cognitive decline in HD.Open Access funding provided by the University of Basque Country (UPV/EHU). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been partially funded by the EITB Maratoia call for Rare Diseases (BIO17/ND/009) and by the Health Department of the Basque Government (2019111004)

Understanding the olfactory role in post-COVID cognitive and neuropsychiatric manifestations

Introduction: Olfactory dysfunction (OD) is frequent after SARS-CoV-2 infection. The aim of this study was to examine if long-term OD is common in post-COVID condition, and the relationship between olfaction, cognition, neuropsychiatric symptoms, and disease duration in these patients. Methods: This study included 121 participants with post-COVID condition and 51 healthy controls (HC). A comprehensive neuropsychological and neuropsychiatric assessment was conducted, encompassing various domains, including general cognition, processing speed, verbal fluency, attention, verbal memory, visual memory, visuoconstructive ability, visuospatial ability, abstraction, executive functions, anxious-depressive symptoms, general health perception, fatigue level, sleep quality, and olfaction. Statistical analyses were carried out to understand the relationship of OD with cognition, and its role as moderator variable. Results: In total, 25% of the post-covid patients had a reduced smell capacity, while only 9.3% of HC presented OD. Post-COVID patients had statistically significantly worse cognitive performance and clinical status than HC. Verbal fluency (AUC = 0.85, p < 0.001), and attention (AUC = 0.82, p < 0.001) were the variables that best discriminate between groups. OD seemed to be a moderator between fatigue and cognition, and between disease duration and attention (β = −0.04; p = 0.014). Discussion: The study highlights marked cognitive and neuropsychiatric sequelae in individuals post-COVID relative to HC. Olfactory impairment exhibits correlations with both cognitive performance and general health. Olfaction emerges as a potential prognostic marker owing to its moderating influence on disease severity indicators.The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project PI20/01076 and co-funded by the European Union, EITB maratoia (BIOS21/COV/006), and grants for health research projects from the Basque Government (2021111006). The first author received a pre-doctoral research grant from the Basque government (PRE_2023_2_0138)

Dysautonomia and small fiber neuropathy in post-COVID condition and Chronic Fatigue Syndrome

Background Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and post-COVID condition can present similarities such as fatigue, brain fog, autonomic and neuropathic symptoms. Methods The study included 87 patients with post-COVID condition, 50 patients with ME/CFS, and 50 healthy controls (HC). The hemodynamic autonomic function was evaluated using the deep breathing technique, Valsalva maneuver, and Tilt test. The presence of autonomic and sensory small fiber neuropathy (SFN) was assessed with the Sudoscan and with heat and cold evoked potentials, respectively. Finally, a complete neuropsychological evaluation was performed. The objective of this study was to analyze and compare the autonomic and neuropathic symptoms in post-COVID condition with ME/CFS, and HC, as well as, analyze the relationship of these symptoms with cognition and fatigue. Results Statistically significant differences were found between groups in heart rate using the Kruskal–Wallis test (H), with ME/CFS group presenting the highest (H = 18.3; p ≤ .001). The Postural Orthostatic Tachycardia Syndrome (POTS), and pathological values in palms on the Sudoscan were found in 31% and 34% of ME/CFS, and 13.8% and 19.5% of post-COVID patients, respectively. Concerning evoked potentials, statistically significant differences were found in response latency to heat stimuli between groups (H = 23.6; p ≤ .01). Latency was highest in ME/CFS, and lowest in HC. Regarding cognition, lower parasympathetic activation was associated with worse cognitive performance. Conclusions Both syndromes were characterized by inappropriate tachycardia at rest, with a high percentage of patients with POTS. The prolonged latencies for heat stimuli suggested damage to unmyelinated fibers. The higher proportion of patients with pathological results for upper extremities on the Sudoscan suggested a non-length-dependent SFN.This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project PI20/01076 and co‑funded by the European Union, EITB maratoia (BIOS21/COV/006) and grants for health research projects from the Basque Government (2021111006). Azcue, N. received a pre‑doctoral research grant from the basque government (PRE_2022_2_0111)