<Paper Reading>Brain dynamics

• 2 kinds of analysis:
  
  • sliding window
  • time-frequency analysis

Beyond the Connectome: The Dynome

• However, further understanding of brain function and dysfunction will require an integrated framework that links brain connectivity with brain dynamics, as well as the biological details that relate this connectivity more directly to function.
• What is needed is not only what is connected, but how and in what directions regions of the brain are connected: what signals they convey and how those signals are acted upon as part of a neural computational process.
• This expanded description of brain activity is what we call the “dynome.”It includes what is usually known as the functional connectome but expands the notion to go beyond statistical associations to the mechanisms involved in producing and processing signals within the brain.
• This is similar to the idea of a node, but encompasses all the anatomical and physiological structure within the local network, including cortical layers, distinct neuronal populations, intrinsic currents, local synaptic connectivity, and responses to neuromodulation.
• Experiments and modeling have begun to illuminate the possible mechanisms of cross-frequency coupling, including an important role for inhibition.(. Hippocampal theta rhythm and its coupling with gamma oscillations
  require fast inhibition onto parvalbumin-positive interneurons, 2009)
• nonsinusoidal nature of brain activity
• This modeling approach has been used, for example, to suggest important contributions of general features (such as signal transmission delays and noise) to the organization of dynamic resting state functional networks

0037 Individual Variability in Functional Connectivity Architecture of the Human Brain

• intersubject variability in connectivity
• The connectivity variability was also related to variability in sulcal depth but not cortical thickness
• Individual differences were largest in heteromodal association cortex including the lateral prefrontal lobe
  and the temporal-parietal junction and minimal in unimodal sensory and motor cortices.
• Functional connectivity variability was found to be highest in frontal, temporal, and parietal association cortex areas. These brain regions are phylogenetically late-developing regions
• Sulcal depth and cortical thickness were estimated for each subject using FreeSurfer
• cortical thickness demonstrated a very distinct pattern with high variability in the motor area but low variability in the frontoparietal network

The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery

• The chronnectome is a model of the brain in which nodal activity and connectivity patterns are changing in fundamental ways through time. In the context of this paper, “dynamics” is thus referring to intrinsic nonstationarities rather than to the behavior of model oscillators with effectively static activation and coupling parameters.