Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study

Both Neurofibrillary tangles (NFT) and neuritic plaques (NP) are the primary neuropathologic markers of Alzheimer's disease (AD), although they are highly prevalent in normal brain aging [1–4].

Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls [5, 6]. While there are dramatic differences in neuropathologic lesion counts between middle-aged AD cases and controls, the difference in lesion counts, while significant, is of lesser magnitude in older adult AD cases and controls[5].

Advanced age at death is associated with somewhat less severe dementia and fewer senile plaques and neurofibrillary tangles[6].

Presently there is not a consensus on whether NFT constitute a specific effect of the disease or result, in part, from a non-specific age related process.

In fact, some investigators [7] have suggested that, since the NFT are very prevalent in the brains of non-demented older adults, the presence of NFT in the brain is not, by itself, diagnostic of AD, and that NFT should be viewed as a later occurrence in the pathological progression of the disease.

Overall, the exact role of NFT to AD, aging, and dementia remains unclear. Even universally accepted neuropathological criteria for Alzheimer's disease differ on the diagnostic role of NFT.

The current approach of determining different cut-off points for NFT and NP density and regional distribution do not allow a 100% sensitivity and specificity in discriminating between AD brains and control subjects with normal cognitive function.

Recent studies further suggest that NFT have a stronger correlation to cognitive function than NP, not only in AD but also in normal aging and mild cognitive impairment [1, 3, 8]. The degree of cognitive impairment is a function of the distribution of NTF within the brain [7]. In particular, the presence of high NFT density in the entorhinal and hippocampus neurons is strongly correlated to reduced cognitive performance in normal aging, whereas NFT formation in neocortical areas is associated with clinically overt AD [2–4, 9].

Neuropathologic studies [2–4, 9] have shown that the distribution of NFT in the human brain follows, in general, a predictable and hierarchical pattern whereas the distribution of NP varies among individuals. Neurofibrillary pathology is initially limited to the hippocampus and the entorhinal cortex [3, 9]. As the number of NFT increases in these areas, neurofibrillary pathology extends into the temporal cortex. Finally, tangles emerge and spread to the neocortical areas of the brain.

In a previous study [10] we have shown that Artificial Neural Networks analysis applied to demographic, clinical and genotype descriptors allowed a better prediction of the number of NFT in the neocortex and hippocampus than the number of NP in the same areas. These results indicate that a non-linear analysis of complex data is a valid approach in highlighting on the role of NP and NFT in the development of a degenerative process leading to AD. This supports the concept that the presence of NFT in aging may represent one of its earliest pathological substrates and play a significant role in the initial stages of memory impairment, confirming the findings [3, 9] by other authors.

An important way to challenge this hypothesis is to evaluate the predictive role of NFT and NP in two critical brain regions, i.e. neocortex and hippocampus, in distinguishing between normal subjects and those with AD.

The aim of this study is to discover the hidden and non-linear associations among Alzheimer's disease pathognomonic brain lesions and the clinical diagnosis of Alzheimer's disease in participants in the Nun Study.

As one can see, even if the average difference between the neuropathological lesion load in the two groups was substantial, a marked overlap of values was present for NFT in hippocampus, NP in neocortex, and NP in hippocampus.

A good linear relationship between each of the 4 selected input variables and the target of the study (AD cases/normal controls) was present: for Neurofibrillary Tangles in Neocortex, r-squared = 0.50; Neurofibrillary Tangles in Hippocampus, r-squared = 0.50; Neuritic Plaques in Neocortex, r-squared = 0.50; Neuritic Plaques in Hippocampus respectively. r-squared = 0.32 ;

These results were consistently obtained in ten separated experiments performed on different training and testing subsets. The corresponding results obtained with LDA were good but not excellent; in fact the mean accuracy rate was 92.30%.

Since some AD patients had severe cognitive impairment, in further experiments, we excluded from the analysis AD patients with MMSE score below 4.

A subset of 13 AD patients was obtained with a mean MMSE equal to 15.

In order to assess the relative importance of the four neuropathological AD markers in developing the model build by ANNs, in the ten experiments we evaluated the so called "input relevance " of each markers during the training phase of the neural network.

Figure 1 shows the average input relevance of each variable in the ten independent training sessions. As one can see, NFT Neocortex accounted for the highest input relevance followed by NFT Hippocampus, NP Neocortex, and lastly by Max NP Hippocampus.

Artificial neural networks have shown optimal performance on various medical applications because of their capacity to learn how to identify complex relationships among data.

At variance with statistical linear methods, ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on an individual basis and not as average trends.

In a previous paper [10] we have shown that ANNs can be used to predict the results of post-mortem brain evaluations from cognitive performance data among 117 participants in the Nun Study.

That is, we determined how demographic data and cognitive and functional variables of each subject during the last year of her life could predict: a) the presence of brain pathology expressed as Braak stages of AD pathology, NFT and NP count in the neocortex and hippocampus; and b) brain atrophy, a highly prevalent neuropathologic feature of AD.

In this study our goal was to understand what constitutes the relevant neuropathological pattern differentiating AD from normal control subjects, an issue which, so far, has never been solved.

Thanks to the ANNs analysis we succeeded in reaching a perfect distinction between the two groups which remained unchanged even when we analyzed only the clinically mild and moderate AD patients. Input relevance analysis confirmed the relative dominance of NFT in the neocortex in discriminating between normal controls and AD cases and indicated the low importance played by NP in hippocampus.

Input relevance is a practical way to open the so called "black box" of ANNs, allowing one to discover the role played by each variable in the developing the data model during the training phase. The numerical value of this parameter is proportionally related to the "weight" of a given variable in the model.

Another major challenge in comparing the prevalence of AD lesions in old individuals with AD and non-demented control subjects is the selection of appropriate criteria for excluding mild dementia in the controls. In fact, as regards to non-demented people most of the studies rely on the interview of a knowledgeable informant after the subject death, rather than direct observation of the control subject, according to the same protocol used to assess AD patients One example is the study published by Berg and co-workers in 1998 [5], in which experienced nurses or physicians interviewed informants and reviewed the records of previous clinical assessments to define the Clinical Dementia Rating score of controls. In addition, some controls were excluded because of neocortical senile plaques densities that met neuropathological criteria for AD, introducing in this way a circular reasoning.

A possible limitation of our analysis is linked to the relative small sample size. This issue can be considered at two different levels: the statistical and epidemiological one.

From a pure statistical point of view we can say that the small number of variables considered guarantees a balanced ratio between variables and records. In addition the use of a rigorous validation protocol with many training and testing procedures should protect against statistical imbalances.

From an epidemiological point of view we can't regard the 26 patients in this study as a representative population of AD patients. Therefore it is clear that the results presented in the paper are only valid for this particular environment and cannot be generalized. One should anyway consider the extreme scarcity in the general literature of autopsy data in groups of aged people with a substantial proportion of individuals without dementia symptoms.

Another potential limitation of our paper is that the markers that might best correlate with cognitive status (i.e. synaptic markers) are not included in the dataset ; nonetheless, we think that the information carried out by NFT and NP is sufficiently specific to make a considerable contribution to the understanding of pathology-clinical relation.

In conclusion, the results of this study confirm that the neuropathologic profile of AD subjects is complex but specific and thanks to ANNs it can be conveniently differentiated from that of normal subjects. Cortical NFT represent the key variable more likely related to the patho-physiology of the disease than the NP.