Parcels and particles: Markov blankets in the brain

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions-and analyses-of distributed brain responses: namely, functional segregation and integration. There are currently two main approaches to characterizing functional integration. The first is a mechanistic modeling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterizes undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organized criticality, dynamical instability, and so on. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organization of far from equilibrium systems. Using the apparatus of the renormalization group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.

Degradation in intrinsic connectivity networks across the Alzheimer's disease spectrum

Introduction

Changes in intrinsic functional connectivity (iFC) have been reported at various stages of the Alzheimer's disease (AD) spectrum. We aimed to investigate such alterations over a variety of large-scale intrinsic brain networks (iBNs) across the spectrum of amyloid β positivity and uncover their relation to cognitive impairment.

Methods

Eight iBNs were defined from resting-state functional magnetic resonance imaging data. In amyloid β–positive healthy subjects, prodromal, and AD patients (N = 70), within-network iFC (intra-iFC) and between-network iFC (inter-iFC) were correlated with scores of cognitive impairment.

Results

Across all iBNs, a general degradation in intra-iFC along the scale of cognitive impairment severity was found. Only subtle changes in inter-iFC were identified.

Discussion

Across the AD spectrum, changes in iFC that are strongly related to cognitive impairment occur within an extensive variety of networks.

Imaging Neurodegeneration: Steps Toward Brain Network-Based Pathophysiology and Its Potential for Multi-modal Imaging Diagnostics

PURPOSE

Multi-modal brain imaging provides different in vivo windows into the human brain and thereby different ways to characterize brain disorders. Particularly, resting-state functional magnetic resonance imaging facilitates the study of macroscopic intrinsic brain networks, which are critical for development and spread of neurodegenerative processes in different neurodegenerative diseases. The aim of the current study is to present and highlight some paradigmatic findings in intrinsic network-based pathophysiology of neurodegenerative diseases and its potential for new network-based multimodal tools in imaging diagnostics.

METHODS

Qualitative review of selected multi-modal imaging studies in neurodegenerative diseases particularly in Alzheimer's disease (AD).

RESULTS

Functional connectivity of intrinsic brain networks is selectively and progressively impaired in AD, with changes likely starting before the onset of symptoms in fronto-parietal key networks such as default mode or attention networks. Patterns of distribution and development of both amyloid-β plaques and atrophy are linked with network connectivity changes, suggesting that start and spread of pathology interacts with network connectivity. Qualitatively similar findings have been observed in other neurodegenerative disorders, suggesting shared mechanisms of network-based pathophysiology across diseases.

CONCLUSION

Spread of neurodegeneration is intimately linked with the functional connectivity of intrinsic brain networks. These pathophysiological insights pave the way for new multi-modal network-based tools to detect and characterize neurodegeneration in individual patients.