Connectivity in fMRI: Blind Spots and Breakthroughs.

In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis.

On the Reliability of Individual Brain Activity Networks.

There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales.

Divergent Network Patterns of Amyloid-β Deposition in Logopenic and Amnestic Alzheimer's Disease Presentations.

Background: Despite divergent clinical features, language and amnestic presentations of Alzheimer's disease (AD) appear to show comparable regional amyloid-β (Aβ) burden. By using a statistical network approach, we aimed to identify complex network patterns of Aβ deposition and explore the effect of apolipoprotein E (APOE) ε4 allele on cortical Aβ burden across AD phenotypes.

Methods: Sixteen amnestic AD participants and 18 cases with logopenic-variant of primary progressive aphasia (lv-PPA) with a high cortical Aβ burden were selected.

Spatially Sparse, Temporally Smooth MEG Via Vector ℓ0 .

In this paper, we describe a new method for solving the magnetoencephalography inverse problem: temporal vector ℓ0-penalized least squares (TV-L0LS). The method calculates maximally sparse current dipole magnitudes and directions via spatial ℓ0 regularization on a cortically-distributed source grid, while constraining the solution to be smooth with respect to time. We demonstrate the utility of this method on real and simulated data by comparison to existing methods.

Brain activity: connectivity, sparsity, and mutual information.

We develop a new approach to functional brain connectivity analysis, which deals with four fundamental aspects of connectivity not previously jointly treated. These are: temporal correlation, spurious spatial correlation, sparsity, and network construction using trajectory (as opposed to marginal) Mutual Information. We call the new method Sparse Conditional Trajectory Mutual Information (SCoTMI).

Uncovering values-based practice: VBP's implicit commitments to subjectivism and relativism.

Despite assertions to the contrary, KWM Fulford's values-based practice is implicitly committed to subjectivism when it comes to reasoning about values. This renders the approach unworkable. The act of merely uncovering underlying values is not enough to effect change and, therefore, resolve problems if we have no way, even in principle, of determining which values are right and which are wrong.

Identifying FMRI model violations with Lagrange multiplier tests.

The standard modeling framework in functional magnetic resonance imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialized software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis.