Abstract
The human brain appears organized in compartments characterized by seemingly specific functional purposes on many spatial scales. A complementary functional state binds information from specialized districts to return what is called integrated information. These fundamental network dynamics undergoes to severe disarrays in diverse degenerative conditions such as Alzheimer's Diseases (AD). The AD represents a multifarious syndrome characterized by structural, functional, and metabolic landmarks. In particular, in the early stages of AD, adaptive functional modifications of the brain networks mislead initial diagnoses because cognitive abilities may result indiscernible from normal subjects. As a matter of facts, current measures of functional integration fail to catch significant differences among normal, mild cognitive impairment (MCI) and even AD subjects. The aim of this work is to introduce a new topological feature called Compression Flow (CF) to finely estimate the extent of the functional integration in the brain networks. The method uses a Monte Carlo-like estimation of the information integration flows returning the compression ratio between the size of the injected information and the size of the condensed information within the network. We analyzed the resting state connectomes of 75 subjects of the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI) repository. Our analyses are focused on the 18FGD-PET and functional MRI (fMRI) acquisitions in several clinical screening conditions. Results indicated that CF effectively discriminate MCI, AD and normal subjects by showing a significant decrease of the functional integration in the AD and MCI brain connectomes. This result did not emerge by using a set of common complex network statistics. Furthermore, CF was best correlated with individual clinical scoring scales. In conclusion, we presented a novel measure to quantify the functional integration that resulted efficient to discriminate different stages of dementia and to track the individual progression of the impairments prospecting a proficient usage in a wide range of pathophysiological and physiological studies as well.
Highlights
The human brain exhibits an incredibly large repertoire of computations
Compression Flow (CF) could track the disease progression within each of three pathological classes (EMCI, late MCI (LMCI), Alzheimer’s Diseases (AD)), a property that we did not observe with other standard complex network statistics
We presented an in silico biomarker to quantify the functional integration that resulted efficient to discriminate different stages of dementia and to differentiate the follow-up groups in AD, early MCI (EMCI), and LMCI and would be proficiently applied into a wider range of pathophysiological and physiological studies as well
Summary
The human brain exhibits an incredibly large repertoire of computations. From the spinal cord and the retina, most of input flows along subcortical regions and to cortices. In a concurrent and fundamental functional state, the human brain combines information from the specialized districts exhibiting what is called functional information integration (Tononi et al, 1994). The most accepted formulation defines the human brain network in terms of the average shortest path length among all possible node couples, a concept called characteristic path length (L) (Watts and Strogatz, 1998). Despite the very intuitive and simple definition, the characteristic path length suffers from many limitations and pitfalls. Two among all, it considers all paths as equiprobable neglecting preferential routes fostered by the topology and, secondary, it averages over all non-infinite paths underestimating the contribution of unconnected nodes
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