The Compression Flow as a Measure to Estimate the Brain Connectivity Changes in Resting State fMRI and 18FDG-PET Alzheimer's Disease Connectomes

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.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

“…A topological metric, compression flow, derived from network centrality criteria, outperformed individual small-world metrics alone. It monotonically followed impairment progression in each patient group, and discriminated between all patient groups (CN, EMCI, LMCI, and AD) [433]. Other studies developed a novel multifractal feature [434] and created a compact representation of the brain network [395] for classification purposes.…”

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome

“…This theoretical approach is, seemingly, time-independent and active at most different time-scales. Furthermore, in our previous work we conjectured that compression flow is inversely related with the free-energy (Zippo et al, 2015), namely, when brain networks increase the extent of compression flow, the system free-energy decreases. Therefore, we could suggest that the new information needed by the classification learning task induces a bump of free-energy that, theoretically, is likely to be cut by means of topological modifications of the functional connectivity.…”

“…On the functional connectomes we computed a common set of complex network statistics (Table 3) to assess the network information processing capability between the first (run 1) and the second group (run 2) of trials revealing a significant increment of classification accuracy (Figure 1I). Eventually, we used a recently presented (Zippo et al, 2015) refined functional integration measure, of functional integration, the compression flow (CF), stochastically estimating the network capability to learn and predict external inputs.…”

“…Analyses on the extracted functional brain networks were performed in Matlab by the Brain Connectivity Toolbox (BCT) (Rubinov and Sporns, 2010), by the Python graph toolbox (Peixoto, 2014c), and by other ad hoc-routines developed in our lab. Specifically, we used a complementary measure of information integration, called Compression Flow (CF), that we previously showed to effectively discriminate patients diagnosed with mild cognitive impairment from those with probable Alzheimer's disease (Zippo et al, 2015). For a better numerical treatment of the results, the original fourth stage, consisting in a summation, was replaced by an average, as follows.…”

A Bayesian Double Fusion Model for Resting-State Brain Connectivity Using Joint Functional and Structural Data

“…Applications of functional magnetic resonance imaging (fMRI) have shed light on brain research, including studies of the functional and effective connectivity between two regions of the brain providing insights into understanding the functional networks in the brain. Functional connectivity (FC) has been used as a biomarker for neurological and psychiatric disorders, for example, Alzheimer's disease ( Joo et al, 2016;Wang et al, 2007;Zippo et al, 2015) and bipolar disorder (Altinay et al, 2015;Dickstein et al, 2010).…”