Global cortical atrophy may be associated with fatigue in patients with parkinson's disease

Fatigue after spontaneous intracerebral haemorrhage: prevalence and associated factors

BACKGROUND

Fatigue is a major complaint in stroke survivors, but data focusing on intracerebral haemorrhage (ICH) survivors are scarce. In a cohort of spontaneous ICH survivors, we assessed the long-term prevalence of fatigue and its associated factors.

METHODS

We included consecutive 1-year ICH survivors from the prospective, observational, single-centre Prognosis of Intracerebral Haemorrhage (PITCH) study. We evaluated fatigue (defined as a score ≥ 4 in Chalder Fatigue Scale); the severity of neurological, depressive, and anxiety symptoms; and functional disability 1, 3, and 6 years after ICH. We performed univariable and multivariable models to evaluate clinical factors and brain magnetic resonance imaging (MRI) small vessel disease (SVD) markers associated with fatigue.

RESULTS

Of 255 1-year ICH survivors, 153 (60%) underwent fatigue screening and were included in this study. Seventy-eight patients (51%) reported fatigue at 1-year, 56/110 (51%) at 3-year, and 27/67 (40%) at 6-year follow-up. Patients with fatigue exhibited more severe concomitant depressive/anxiety symptoms, but the severity of depressive symptoms was the only clinical factor significantly associated with 1-year fatigue in multivariable analysis (adjusted odds ratio 1.4 for one-point increase; 95% confidence interval 1.2-1.6). Patients with severe cortical atrophy at baseline had increased risk of fatigue at 1-year follow-up compared to patients with mild/no cortical atrophy (adjusted odds ratio 2.5; 95% confidence interval 1.1-5.8).

CONCLUSIONS

Fatigue after ICH is frequent and long-lasting, and it is associated with cortical atrophy (but not with other MRI markers of cerebral SVD). The link between fatigue and depressive symptoms may represent a potential therapeutic target.

Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer's disease

AIMS

We mainly evaluate retinal alterations in Alzheimer's disease (AD) patients, investigate the associations between retinal changes with AD biomarkers, and explore an optimal machine learning (ML) model for AD diagnosis based on retinal thickness.

METHODS

A total of 159 AD patients and 299 healthy controls were enrolled. The retinal parameters of each participant were measured using optical coherence tomography (OCT). Additionally, cognitive impairment severity, brain atrophy, and cerebrospinal fluid (CSF) biomarkers were measured in AD patients.

RESULTS

AD patients demonstrated a significant decrease in the average, superior, and inferior quadrant peripapillary retinal nerve fiber layer, macular retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL) thicknesses, as well as total macular volume (TMV) (all p < 0.05). Moreover, TMV was positively associated with Mini-Mental State Examination and Montreal Cognitive Assessment scores, IPL thickness was correlated negatively with the medial temporal lobe atrophy score, and the GCL thickness was positively correlated with CSF Aβ₄₂ /Aβ₄₀ and negatively associated with p-tau level. Based on the significantly decreased OCT variables between both groups, the XGBoost algorithm exhibited the best diagnostic performance for AD, whose four references, including accuracy, area under the curve, f1 score, and recall, ranged from 0.69 to 0.74. Moreover, the macular retinal thickness exhibited an absolute superiority for AD diagnosis compared with other enrolled variables in all ML models.

CONCLUSION

We identified the retinal alterations in AD patients and found that macular thickness and volume were associated with AD severity and biomarkers. Furthermore, we confirmed that OCT combined with ML could serve as a potential diagnostic tool for AD.

Neuroimaging correlates of persistent fatigue in older adults: A secondary analysis from the Multidomain Alzheimer Preventive Trial (MAPT) trial

OBJECTIVES

Fatigue has been suggested as a marker of biological aging. It seems plausible that this symptom might be associated with changes in brain health. The objective of this study was to examine the associations between persistent fatigue and neuroimaging correlates in a non-disease-specific population of community-dwelling older adults.

METHODS

We performed a cross-sectional analysis using data from The Multidomain Alzheimer Preventive Trial (MAPT). We included 458 subjects. Persistent fatigue was defined as meeting exhaustion criterion of Fried frailty phenotype in two consecutive clinical visits six months apart between study baseline and one year. Brain imaging correlates, assessed by magnetic resonance imaging (MRI), were the outcomes. The associations between persistent fatigue and brain correlates were explored using mixed model linear regressions with random effect at the center level.

RESULTS

The mean age of the participants was 74.8 ± 4 years old, and 63% of the subjects were women. Forty-seven participants (10%) exhibited a persistent fatigue profile. People with persistent fatigue were older compared to subjects without persistent fatigue (76.2 years ± 4.3 vs.74.7 ± 3.9 p = 0.009). Persistent fatigue was associated with higher white matter hyperintensity volume in the fully adjusted analysis. We did not find any cross-sectional association between persistent fatigue and sub-cortical volumes and global and regional cortical thickness.

CONCLUSION

Persistent fatigue was cross-sectionnally associated with higher white matter hyperintensity volume in older adults. Further longitudinal studies, using an assessment tool specifically designed and validated for measuring fatigue, are needed to confirm our findings.

Anti-NMDA receptor encephalitis and brain atrophy in children and adults: A quantitative study

PURPOSE

To determine whether brain atrophy was present in patients with anti-N-methyl-d-aspartate receptor encephalitis (anti-NMDARE) using qualitative and quantitative analyses of brain magnetic resonance imaging (MRI) and to explore clinical differences in patients with anti-NMDARE with or without brain atrophy.

METHODS

A retrospective observational study encompassing the serologic, cerebrospinal fluid, and brain MRI data of 23 patients with anti-NMDARE was conducted. Median patient age was 14 years (interquartile range [IQR], 12 years). The cohort included 15 children (<18 years old) and 8 adults (≥18 years old). There were 6 male and 17 female patients. Imaging analysis involved 2 expert readers' observations of MRIs and automated volumetric quantification using NeuroQuant (CorTechs Labs, Inc.) software.

RESULTS

Of 23 pediatric and adult patients, 11 patients had 14 brain MRIs that were quantitatively analyzed. Quantitative NeuroQuant volumetric analysis showed atrophy in 9 of 14 MRIs for 7 of 11 patients compared to age-controlled normative data. In these 9 MRIs, atrophy was present in the temporal lobes (n = 9), cerebral cortex (n = 3), and cerebellum (n = 3). Qualitative analysis of 59 MRIs (23 patients) revealed volume loss in 6 patients: 5 with global cerebral and temporal lobe volume loss and 1 with temporal lobe volume loss. No patient showed cerebellar volume loss on qualitative analysis. Mean length of stay in the intensive care unit was not significantly different for patients with or without quantitative volume loss (3.5 [5.2] vs 27.4 [23.4] days; p = 0.08).

CONCLUSIONS

In this cohort of patients with anti-NMDARE, quantitative volumetric analysis showed brain atrophy, particularly affecting the temporal lobes, in 64% (7/11) of the patients. Qualitative analysis showed brain atrophy in 26% (6/23). These findings highlight the increased sensitivity of quantitative methods for volume loss detection. Larger studies are needed.