Normal pressure hydrocephalus (NPH) is characterized by pathologically enlarged ventricles without elevated cerebrospinal fluid (CSF) pressure along with a triad of clinical symptoms including gait disturbances, urinary incontinence, and cognitive impairment. NPH is evaluated with lumbar drain trials (LDTs) where CSF is removed over several days to determine if patients would benefit from ventricular shunting. Candidate selection and success for these surgeries remains challenging because other diseases such as Alzheimer’s disease (AD) share common features with NPH in cognitive impairment and enlarged ventricles. Prior research has found that 20%-40% of presumed NPH cases have AD pathology as determined by brain biopsy or autopsy. CSF biomarkers of AD can be altered in NPH and are not always conclusive, complicating the interpretation of results when formulating diagnoses and prognoses. Studies to refine the analyses of AD CSF biomarkers in NPH are needed. We aimed to examine the frequency of CSF biomarker results among patients presenting for NPH evaluations with LDTs.

62 patients presented for LDTs upon physician recommendations. CSF specimens were sent to Mayo Clinic Laboratories for Alzheimer Disease Evaluation (ADEVL) that utilizes Elecsys (Lenexa, KS) CSF electrochemiluminescence immunoassays (Roche Diagnostics, Basel, Switzerland) to measure levels of amyloid-beta 42 (Aβ42), total tau (t-tau), and phosphorylatedtau (p-tau), and p-tau:Aβ42 ratio. Results were classified based on interpretation through the Amyloid/Tau/Neurodegeneration (ATN) framework1: 1) AD - biomarker profile consistent with AD pathologic change, 2) non-AD profile - biomarker levels normal or inconsistent with AD pathologic change, or 3) indeterminate - biomarkers were incongruous with only one or two abnormal levels of Aβ42, t-tau, p-tau, or ptau: Aβ42. Indeterminate cases may represent altered protein levels due to CSF dynamics or AD-related pathologic change. In reviewing recent research on CSF dynamics and AD biomarkers in NPH2 a p-tau threshold of 15 pg/mL was derived and implemented such that cases with Aß42 <=1026 pg/mL and p-tau <15 pg/mL were designated as suspected non-AD, and those with Aß42 <=1026 pg/mL and p-tau >15 pg/mL were designated suspected AD.

Of the 62 LDT cases, 12 (19.35%) were classified as AD, 31 (50%) were indeterminate and 22 (35.48%) were non-AD. Of the 31 indeterminate cases, 21 (33.87% of the overall sample) were suspected non-AD and 7 (11.29% of the full sample) were categorized as suspected AD.

Our findings show that 20%-30% of patients presenting for LDT showed evidence for AD-type pathologic change, consistent with prior reports of AD pathology in cases of possible NPH. Half of all LDT cases had indeterminate AD CSF biomarker results, the interpretations of which were confounded by the potential alterations of CSF biomarkers levels due to NPH independent of AD. Our findings emphasize the need to establish better approaches to interpreting CSF AD biomarkers in evaluating NPH. Future research should examine the discriminative utility of CSF AD biomarkers and the selected p-tau threshold in indeterminate cases for predicting response to LDT and shunting.

Patients and their families often ask clinicians to estimate when full-time care (FTC) will be needed after Alzheimer's Disease (AD) is diagnosed. Although a few algorithms predictive algorithms for duration to FTC have been created, these have not been widely adopted for clinical use due to questions regarding precision from limited sample sizes and lack of an easy, user friendly prediction model. Our objective was to develop a clinically relevant, data-driven predictive model using machine learning to estimate time to FTC in AD based on information gathered from a) clinical interview alone, and b) clinical interview plus neuropsychological data.

The National Alzheimer's Coordinating Center dataset was used to examine 3,809 participants (M age at AD diagnosis = 76.05, SD = 9.76; 47.10% male; 87.20% Caucasian) with AD dementia who were aged >50 years, had no history of stroke, and not dependent on others for basic activities of daily living at time of diagnosis based on qualitative self or informant report. To develop a predictive model for time until FTC, supervised machine learning algorithms (e.g., gradient descent, gradient boosting) were implemented. In Model 1, 29 variables captured at the time of AD diagnosis and often gathered in a clinical interview, including sociodemographic factors, psychiatric conditions, medical history, and MMSE, were included. In Model 2, additional neuropsychological variables assessing episodic memory, language, attention, executive function, and processing speed were added. To train and test the algorithm(s), data were split into a 70:30 ratio. Prediction optimization was examined via cross validation using 1000 bootstrapped samples. Model evaluation included assessment of confusion matrices and calculation of accuracy and precision.

The average time to requiring FTC after AD diagnosis was 3.32 years (Range = 0.53-14.57 years). For the clinical interview only model (Model 1), younger age of onset, use of cholinesterase inhibitor medication, incontinence, and apathy were among the clinical variables that significantly predicted duration to FTC, with the largest effects shown for living alone, a positive family history of dementia, and lower MMSE score. In Model 2, the clinical predictors remained significant, and lower Boston Naming Test and Digit-Symbol Coding scores showed the largest effects in predicting duration to FTC among the neuropsychological measures. Final prediction models were further tested using five randomly selected cases. The average estimated time to FTC using the clinical interview model was within an average of 5.2 months of the recorded event and within an average of 5.8 months for the model with neuropsychological data.

Predicting when individuals diagnosed with AD will need FTC is important as the transition often carries significant financial costs related to caregiving. Duration to FTC was predicted by clinical and neuropsychological variables that are easily obtained during standard dementia evaluations. Implementation of the model for prediction of FTC in cases showed encouraging prognostic accuracy. The two models show promise as a first step towards creation of a user friendly prediction calculator that could help clinicians better counsel patients on when FTC after AD diagnosis may occur, though the development of separate models for use in more diverse populations will be essential.

Episodic memory functioning is distributed across two brain circuits, one of which courses through the dorsal anterior cingulate cortex (dACC). Thus, delivering non-invasive neuromodulation technology to the dACC may improve episodic memory functioning in patients with memory problems such as in amnestic mild cognitive impairment (aMCI). This preliminary study is a randomized, double-blinded, sham-controlled clinical trial to examine if high definition transcranial direct current stimulation (HD-tDCS) can be a viable treatment in aMCI.

Eleven aMCI participants, of whom 9 had multidomain deficits, were randomized to receive 1 mA HD-tDCS (N=7) or sham (N=4) stimulation. HD-tDCS was applied over ten 20-minute sessions targeting the dACC. Neuropsychological measures of episodic memory, verbal fluency, and executive function were completed at baseline and after the last HD-tDCS session. Changes in composite scores for memory and language/executive function tests were compared between groups (one-tailed t-tests with a = 0.10 for significance). Clinically significant change, defined as > 1 SD improvement on at least one test in the memory and non-memory domains, was compared between active and sham stimulation based on the frequency of participants in each.

No statistical or clinically significant change (N-1 X2; p = 0.62) was seen in episodic memory for the active HD-tDCS (MDiff = 4.4; SD = 17.1) or sham groups (MDiff = -0.5; SD = 9.7). However, the language and executive function composite showed statistically significant improvement (p = 0.04; MDiff = -15.3; SD = 18.4) for the active HD-tDCS group only (Sham MDiff = -5.8; SD = 10.7). Multiple participants (N=4) in the active group had clinically significant enhancement in language and executive functioning tests, while nobody in the sham group did (p = 0.04).

HD-tDCS targeting the dACC had no direct benefit for episodic memory deficits in aMCI based on preliminary findings for this ongoing clinical trial. However, significant improvement in language and executive function skills occurred in response to HD-tDCS, suggesting HD-tDCS in this configuration has promising potential as an intervention for language and executive function deficits in MCI.