A Winding Road: Alzheimer’s Disease Increases Circuitous Functional Connectivity Pathways

Abstract

Neuroimaging has been successful in characterizing the pattern of cerebral atrophy that accompanies the progression of Alzheimer’s disease (AD). Examination of functional connectivity, the strength of signal synchronicity between brain regions, has gathered pace as another way of understanding changes to the brain that are associated with AD. It appears to have good sensitivity and detect effects that precede cognitive decline, and thus offers the possibility to understand the neurobiology of the disease in its earliest phases. However, functional connectivity analyzes to date generally consider only the strongest connections, with weaker links ignored. This proof-of-concept study compared patients with mild-to-moderate AD (N = 11) and matched control individuals (N = 12) based on functional connectivities derived from blood-oxygenation level dependent (BOLD) sensitive functional MRI acquired during resting wakefulness. All positive connectivities irrespective of their strength were included. Transitive closures of the resulting connectome were calculated that classified connections as either direct or indirect. Between-group differences in the proportion of indirect paths were observed. In AD, there was broadly increased indirect connectivity across greater spatial distances. Furthermore, the indirect pathways in AD had greater between-subject topological variance than controls. The prevailing characterization of AD as being a disconnection syndrome is refined by the observation that direct links between regions that are impaired are perhaps replaced by an increase in indirect functional pathways that is only detectable through inclusion of connections across the entire range of connection strengths.

Enhancement of indirect functional connections with shortest path length in the adult autistic brain

“…Resting-state functional MRI data were preprocessed using Analysis of Functional NeuroImages (AFNI, http://afni.nimh.nih.gov/; Cox, 1996) and the Oxford Centre for Functional MRI of the Brain Software Library (FSL, http://fsl.fmrib.ox.ac.uk/fsl/; Smith et al, 2004), according to pipelines that minimize motion artifacts (Patel et al, 2014). Based on previous semi-metric studies (Peeters et al, 2015;Simas et al, 2015;Suckling et al, 2015), the following preprocessing steps were applied to functional images: slice-time correction; rigid-body head motion correction; obliquity transformation to the structural image; affine co-registration to the skull-stripped structural image; spatial transformation to the MNI 152 template in Talairach space; spatial smoothing (6 mm full width at half maximum); and a within-run intensity normalization to a whole-brain median of 1,000. Processing steps for denoising head motion were then performed including: wavelet despiking using the BrainWavelet Toolbox (http://www.brainwavelet.org/); nuisance signal regression of the six motion parameters and their first order temporal derivatives and ventricular cerebrospinal fluid signal; and high-pass frequency filtering with a cutoff frequency of 0.02 Hz.…”

“…SMP at scales 1-3 had the same direction of effect and similar effect sizes for the main effect of diagnosis at whole-brain level, and similar patterns of F-value maps of between-group difference within and between atlas networks (Figure 1). Since the blood oxygenation level dependent signals in the frequency interval 0.06-0.125 Hz have been demonstrated to detect changes in the brain's functional organization (Bassett, Nelson, Mueller, Camchong, & Lim, 2012;Hermundstad et al, 2013;Suckling et al, 2015), the primary analyses focused on connectivities calculated at scale 3. A factorial analysis of variance results at other scales are provided at Table S1.…”

“…Information is more likely to be conveyed through the entire, fully connected connectome involving most, if not all edges (Simas & Suckling, 2016;Suckling et al, 2015), especially given temporal variations of brain organization (Allen et al, 2014;Hutchison et al, 2013). The discarding of weaker edges has been undertaken on the assumption that neural communication processes emerge preferentially through strong connections and along shortest paths.…”

“…However, the shortest path can be calculated only if an overall map of the structure of brain network is known, and it seems improbable that the brain might carry such a map for continuous and instantaneous reference. Information is more likely to be conveyed through the entire, fully connected connectome involving most, if not all edges (Simas & Suckling, 2016;Suckling et al, 2015), especially given temporal variations of brain organization (Allen et al, 2014;Hutchison et al, 2013). The significance of weak links has been widely acknowledged in the information transfer across friendship networks (Granovetter, 1973(Granovetter, , 1983; Weaker inter-personal relationships being the bridges between groups of strongly tied individuals, thus facilitating the sharing of information over long distances.…”

“…Many real-world weighted networks have been confirmed to have various degrees of semi-metric behavior (Rocha, 2002;Tiago & Rocha, 2015). Semi-metric analyses of brain networks indicate that the functional connectome exhibits high levels of semi-metricity, and psychiatric disorders are characterized by idiosyncratic semi-metric patterns (Peeters et al, 2015;Simas et al, 2015;Suckling et al, 2015).…”