Deep learning assisted quantitative analysis of Aß and microglia in patients with idiopathic normal pressure hydrocephalus in relation to cognitive outcome.

Neuropathologic changes of Alzheimer disease (AD) including Aß accumulation and neuroinflammation are frequently observed in the cerebral cortex of patients with idiopathic normal pressure hydrocephalus (iNPH). We created an automated analysis platform to quantify Aß load and reactive microglia in the vicinity of Aß plaques and to evaluate their association with cognitive outcome in cortical biopsies of patients with iNPH obtained at the time of shunting. Aiforia Create deep learning software was used on whole slide images of Iba1/4G8 double immunostained frontal cortical biopsies of 120 shunted iNPH patients to identify Iba1-positive microglia somas and Aß areas, respectively. Dementia, AD clinical syndrome (ACS), and Clinical Dementia Rating Global score (CDR-GS) were evaluated retrospectively after a median follow-up of 4.4 years. Deep learning artificial intelligence yielded excellent (>95%) precision for tissue, Aß, and microglia somas. Using an age-adjusted model, higher Aß coverage predicted the development of dementia, the diagnosis of ACS, and more severe memory impairment by CDR-GS whereas measured microglial densities and Aß-related microglia did not correlate with cognitive outcome in these patients. Therefore, cognitive outcome seems to be hampered by higher Aß coverage in cortical biopsies in shunted iNPH patients but is not correlated with densities of surrounding microglia.

Exploring the Association Between Visual Field Testing and CERAD Neuropsychological Battery in Idiopathic Normal Pressure Hydrocephalus Patients.

Background: Association between visual field test indices and The Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Battery (CERAD-NB) is unknown. Idiopathic normal pressure hydrocephalus (iNPH) patients provide a unique set of patient data for analysis. Objective: To assess the reliability of visual field testing using the CERAD-NB in patients with iNPH and to investigate the association between visual field test results and cognitive function. Methods: 62 probable iNPH patients were subjected to comprehensive ophthalmological examination, ophthalmological optical coherence tomography imaging studies, visual field testing, and CERAD-NB. Based on visual field indices, the patients were divided into two groups: unreliable (n = 19) and reliable (n = 43). Independent T-test analysis was performed to examine the relationship between visual field test results and cognitive function. Pearson Chi-square test was used for non-continuous variables. Results: The unreliable group performed worse in CERAD-NB subtests compared to the reliable group. Statistically significant differences were observed in nine out of ten subtests, with only Clock Drawing showing no statistical significance. Pairwise comparison of the groups showed no statistical significance between amyloid-ß (Aß) biopsy, hyperphosphorylated tau biopsy, apolipoprotein E allele or the ophthalmological status of the patient. But there was a statistically significant difference in cerebrospinal fluid Aß42 and age between the groups. Conclusions: Patients with unreliable visual field tests performed worse on CERAD-NB subtests. CERAD-NB subtests do not provide a specific cut-off value to refrain patients from visual field testing. Should patients with unreliable visual field tests be screened for cognitive impairment?

Predicting Development of Alzheimer's Disease in Patients with Shunted Idiopathic Normal Pressure Hydrocephalus.

BACKGROUND: Idiopathic normal pressure hydrocephalus (iNPH) patients often develop Alzheimer's disease (AD) related brain pathology. Disease State Index (DSI) is a method to combine data from various sources for differential diagnosis and progression of neurodegenerative disorders. OBJECTIVE: To apply DSI to predict clinical AD in shunted iNPH-patients in a defined population. METHODS: 335 shunted iNPH-patients (median 74 years) were followed until death (n = 185) or 6/2015 (n = 150). DSI model (including symptom profile, onset age of NPH symptoms, atrophy of medial temporal lobe in CT/MRI, cortical brain biopsy finding, and APOE genotype) was applied. Performance of DSI model was evaluated with receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 70 (21%) patients developed clinical AD during median follow-up of 5.3 years. DSI-model predicted clinical AD with moderate effectiveness (AUC = 0.75). Significant factors were cortical biopsy (0.69), clinical symptoms (0.66), and medial temporal lobe atrophy (0.66). CONCLUSION: We found increased occurrence of clinical AD in previously shunted iNPH patients as compared with general population. DSI supported the prediction of AD. Cortical biopsy during shunt insertion seems indicated for earlier diagnosis of comorbid AD.

Multimodal analysis to predict shunt surgery outcome of 284 patients with suspected idiopathic normal pressure hydrocephalus.

OBJECTIVES: Optimal selection of idiopathic normal pressure hydrocephalus (iNPH) patients for shunt surgery is challenging. Disease State Index (DSI) is a statistical method that merges multimodal data to assist clinical decision-making. It has previously been shown to be useful in predicting progression in mild cognitive impairment and differentiating Alzheimer's disease (AD) and frontotemporal dementia. In this study, we use the DSI method to predict shunt surgery response for patients with iNPH. METHODS: In this retrospective cohort study, a total of 284 patients (230 shunt responders and 54 non-responders) from the Kuopio NPH registry were analyzed with the DSI. Analysis included data from patients' memory disorder assessments, age, clinical symptoms, comorbidities, medications, frontal cortical biopsy, CT/MRI imaging (visual scoring of disproportion between Sylvian and suprasylvian subarachnoid spaces, atrophy of medial temporal lobe, superior medial subarachnoid spaces), APOE genotyping, CSF AD biomarkers, and intracranial pressure. RESULTS: Our analysis showed that shunt responders cannot be differentiated from non-responders reliably even with the large dataset available (AUC = 0.58). CONCLUSIONS: Prediction of the treatment response in iNPH is challenging even with our extensive dataset and refined analysis. Further research of biomarkers and indicators predicting shunt responsiveness is still needed.