Results for predicting conversion to AD in MCI subjects with baseline scores (whole sample).

<p>Results for predicting conversion to AD in MCI subjects with baseline scores (whole sample).</p

Characteristics of the samples used to model normal brain aging.

<p>Characteristics of the samples used to model normal brain aging.</p

Results in the CSF subsample for predicting conversion to AD in MCI subjects with baseline scores.

<p>n.s. = not significant.</p

Cumulative probability of remaining AD-free in the quartiles of baseline <b><i>BrainAGE</i></b><b> score.</b>

<p>Kaplan-Meier survival curves based on Cox regression comparing cumulative AD incidence in subjects with MCI at baseline by <i>BrainAGE</i> score quartiles (p for trend <0.001). Duration of follow-up is truncated at 1250 days.</p

ROC curves of individual subject classification to sMCI or pMCI in the CSF subsample.

<p>ROC curves of individual subject classification to sMCI^CSF or pMCI^CSF based on baseline <i>BrainAGE</i> scores and CSF biomarkers for (A) early converters and (B) the whole CSF subsample. The areas under the ROC curves (AUCs) of the CSF biomarkers were tested against the AUC of <i>BrainAGE</i>: **p<0.01; *p<0.05.</p

ROC curves of individual subject classification to sMCI or pMCI.

<p>ROC curves of individual subject classification to sMCI or pMCI based on baseline <i>BrainAGE</i> scores, cognitive scores, and hippocampus volumes for (A) early converters and (B) the whole sample. The areas under the ROC curves (AUCs) of cognitive scores and hippocampus volumes were tested against the AUC of <i>BrainAGE</i>: ***p<0.001; **p<0.01; *p<0.05.</p

Table_1_The moderating effects of sex, age, and education on the outcome of combined cognitive training and transcranial electrical stimulation in older adults.DOCX

Computerized cognitive training (CCT) has been shown to improve cognition in older adults via targeted exercises for single or multiple cognitive domains. Combining CCT with non-invasive brain stimulation is thought to be even more effective due to synergistic effects in the targeted brain areas and networks. However, little is known about the moderating effects of sex, age, and education on cognitive outcomes. Here, we investigated these factors in a randomized, double-blind study in which we administered CCT either combined with transcranial direct (tDCS), alternating (tACS) current stimulation or sham stimulation. 59 healthy older participants (mean age 71.7 ± 6.1) received either tDCS (2 mA), tACS (5 Hz), or sham stimulation over the left dorsolateral prefrontal cortex during the first 20 min of a CCT (10 sessions, 50 min, twice weekly). Before and after the complete cognitive intervention, a neuropsychological assessment was performed, and the test scores were summarized in a composite score. Our results showed a significant three-way interaction between age, years of education, and stimulation technique (F(6,52) = 5.53, p = 0.007), indicating that the oldest participants with more years of education particularly benefitted from tDCS compared to the sham group, while in the tACS group the youngest participants with less years of education benefit more from the stimulation. These results emphasize the importance of further investigating and taking into account sex, age, and education as moderating factors in the development of individualized stimulation protocols.Clinical Trial RegistrationClinicalTrials.gov, identifier NCT03475446.</p

Model statistics of Cox regression for CSF-biomarker baseline levels in the CSF subsample (adjusted for age, gender, and education).

<p>(+) = higher values mean higher risk for AD; (<b><i>–</i></b>) = lower values mean higher risk for AD;</p>***<p>p<0.001;</p>*<p>p<0.05; n.s. = not significant.</p><p><b>Bold</b> type = best performance of all markers.</p

Cognitive scores during follow-up.

<p>Mean (A) MMSE, (B) CDR-SB, (C) ADAS scores in pMCI_early, pMCI_late, and sMCI subjects at baseline examination as well as all follow-up assessments. Error bars depict the standard error of the mean (SEM).</p

<i>BrainAGE</i> in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease

<div><p>Alzheimer’s disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07–1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.</p></div