Metabolic connectivity for differential diagnosis of dementing disorders

Abstract

Presently, visual and quantitative approaches for image-supported diagnosis of dementing disorders rely on regional intensity rather than on connectivity measurements. Here, we test metabolic connectivity for differentiation between Alzheimer's disease and frontotemporal lobar degeneration. Positron emission tomography with 18F-fluorodeoxyglucose was conducted in 47 patients with mild Alzheimer's disease, 52 patients with mild frontotemporal lobar degeneration, and 45 healthy elderly subjects. Sparse inverse covariance estimation and selection were used to identify patterns of metabolic, inter-subject covariance on the basis of 60 regional values. Relative to healthy subjects, significantly more pathological within-lobe connections were found in the parietal lobe of patients with Alzheimer's disease, and in the frontal and temporal lobes of subjects with frontotemporal lobar degeneration. Relative to the frontotemporal lobar degeneration group, more pathological connections between the parietal and temporal lobe were found in the Alzheimer's disease group. The obtained connectivity patterns differentiated between two patients groups with an overall accuracy of 83%. Linear discriminant analysis and univariate methods provided an accuracy of 74% and 69%, respectively. There are characteristic patterns of abnormal metabolic connectivity in mild Alzheimer's disease and frontotemporal lobar degeneration. Such patterns can be utilized for single-subject analyses and might be more accurate in the differential diagnosis of dementing disorders than traditional intensity-based analyses.

Axonal damage and loss of connectivity in nigrostriatal and mesolimbic dopamine pathways in early Parkinson's disease

“…Here we applied anatomically-driven partial correlation analysis to test dopamine network connectivity since it is a common strategy to study connectivity with brain PET molecular images. Correlation analysis has already been applied in studies of brain metabolism (Caminiti et al, 2016, Huang et al, 2010, Titov et al, 2015) and in DAT, D 2 receptor, serotonin transporter and μ-opioid receptor studies (Cervenka et al, 2010, Premi et al, 2016, Tuominen et al, 2014), in order to provide patterns of molecular connectivity in healthy subjects and in various neurodegenerative diseases.…”

“…The number of edges grows like the square of the number of nodes, therefore, when many nodes are present, regularization techniques are used to estimate which partial correlation coefficients are non-zero (Huang et al, 2010). This procedure lacks information regarding the strength of the connection, and additional hypotheses and computations are required to obtain surrogate measures of connection strength (Titov et al, 2015). To assess the dopamine network connectivity, however, we selected N  = 10 nodes, that constitute two separate pathways (mesolimbic and nigrostriatal).…”

“…Recent studies applied such approach to different PET metabolic and neurotransmission targets that provided patterns of connectivity in healthy subjects and in neurodegenerative diseases (Caminiti et al, 2016, Cervenka et al, 2010, Huang et al, 2010, Premi et al, 2016, Titov et al, 2015, Tuominen et al, 2014). In particular, a recent report exploring the pattern of covariance with DATSCAN imaging, showed a specific loss of both inter- and intra-hemispheric dopamine connectivity in PD patients affected by impulse control disorder symptoms (Premi et al, 2016).…”

A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia

“…Automated quantification of bFDRP expression on a single-case basis allowed a discriminatory power of 92.2% and a sensitivity of 91.5% with specificity of 82% in distinguishing bvFTD from AD. That said, a direct numerical comparison with previous reports may be inconclusive due to differences in the characteristics of the research samples and the various measures used to determine early-stage dementia [3,27,28]. For instance, previous studies have merged language and behavioral variants of FTD into one group leading to a heterogeneous sample [22,27].…”

Metabolic covariance networks combining graph theory measuring aberrant topological patterns in mesial temporal lobe epilepsy

“…Metabolic connectivity is a valuable concept in the fast‐developing field of brain connectivity, which less dependent on neurovascular coupling as in fMRI and are indicative of a presumed steady state of neuronal activity during the recording interval . Growing evidence indicates that metabolic connectivity may serve a marker of normal and pathological cognitive function . So far, many analysis models of metabolic connectivity are best established in neurodegenerative disorders, such as seed correlation or IRCA, PCA and independent components analysis, sparse inverse covariance estimation, and graph theory .…”