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. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.

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. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH2 network construction method outperforms other approaches at most data spatiotemporal resolutions.

Divergent Network Patterns of Amyloid-ß 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-ß (Aß) burden. By using a statistical network approach, we aimed to identify complex network patterns of Aß deposition and explore the effect of apolipoprotein E (APOE) ε4 allele on cortical Aß 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 Aß burden were selected. A comprehensive clinical assessment, Aß imaging, and APOE genotyping were performed in all cases. Statistical network analysis was undertaken based on the estimation of sparse partial correlations of Aß burden between cortical regions. Global and regional network statistical parameters as well as the effect of APOEε4 genotype on cortical Aß were explored. RESULTS: The two groups showed equivalent distribution of cortical amyloid burden and frequency of APOEε4 genotype. Statistical network analysis, however, demonstrated divergent connectivity properties. The lv-PPA group demonstrated higher mean network degree and shorter characteristic path length than the amnestic AD group. Amnestic AD cases showed connectivity hubs confined to the mesial temporal and prefrontal lobes bilaterally, whereas lv-PPA cases showed hubs scattered across the whole cortical mantle. An interaction effect on total Aß burden between APOE genotype and AD presentations was also detected. CONCLUSIONS: The network analysis reveals interregional network differences not evident using a simple comparison of Aß burden. This suggests that regional neurotoxic effects may explain the phenotypical differences in AD presentation and that these can be modulated by APOE genotype.

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). We demonstrate SCoTMI on simulated and real fMRI data, showing that SCoTMI gives more accurate and more repeatable detection of network links than competing network estimation methods.

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. Fulford's only departure from subjectivism about value is his commitment to 'framework values', which seems grounded in a version of ethical relativism. I argue that we need to reject both subjectivism and relativism if progress within ethical discussions about practice is to be meaningful and a real possibility.

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. Using Lagrange multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as nonlinearity, nonstationarity and validity of the common double gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples.