Volume entropy for modeling information flow in a brain graph

Triple network activation causes tinnitus in patients with sudden sensorineural hearing loss: A model-based volume-entropy analysis

Tinnitus can be defined as the conscious perception of phantom sounds in the absence of corresponding external auditory signals. Tinnitus can develop in the setting of sudden sensorineural hearing loss (SSNHL), but the underlying mechanism is largely unknown. Using electroencephalography, we investigated differences in afferent node capacity between 15 SSNHL patients without tinnitus (NT) and 30 SSNHL patients with tinnitus (T). Where the T group showed increased afferent node capacity in regions constituting a "triple brain network" [default mode network (DMN), central executive network (CEN), and salience network (SN)], the NT group showed increased information flow in regions implicated in temporal auditory processing and noise-canceling pathways. Our results demonstrate that when all components of the triple network are activated due to sudden-onset auditory deprivation, tinnitus ensues. By contrast, auditory processing-associated and tinnitus-suppressing networks are highly activated in the NT group, to overcome the activation of the triple network and effectively suppress the generation of tinnitus.

The balance between Bayesian inference and default mode determines the generation of tinnitus from decreased auditory input: A volume entropy-based study

Along with phantom pain, tinnitus, a phantom auditory perception occurring in the absence of an external acoustic stimulus, is one of the most representative phantom perceptions that develops in subjects with decreased peripheral sensory input. Although tinnitus is closely associated with peripheral hearing loss (HL), it remains unclear why only some individuals with HL develop tinnitus. In this study, we investigated the differences between 65 HL with tinnitus (HL‐T) and 104 HL with no tinnitus (HL‐NT) using a resting‐state electroencephalography data‐based volume entropy model of the brain network, by comparing the afferent node capacities, that quantify the contribution of each node to the spread of information, of all Brodmann areas. While the HL‐T group showed increased information flow in areas involved in Bayesian inference (the left orbitofrontal cortex, the left subgenual anterior cingulate cortex, and the left ventrolateral prefrontal cortex) and auditory memory storage (the right hippocampus/parahippocampus), the HL‐NT group showed increased afferent node capacity in hub areas of the default mode network (DMN; the right posterior cingulate cortex and the right medial temporal gyrus). These results suggest that the balance of activity between the Bayesian inferential network (updating missing auditory information by retrieving auditory memories from the hippocampus/parahippocampus) and DMN (maintaining the “silent status quo”) determines whether phantom auditory perception occurs in a brain with decreased peripheral auditory input.

Maturational delay and asymmetric information flow of brain connectivity in SHR model of ADHD revealed by topological analysis of metabolic networks

Attention-deficit hyperactivity disorder (ADHD) is a complex brain development disorder characterized by hyperactivity/impulsivity and inattention. A major hypothesis of ADHD is a lag of maturation, which is supported mainly by anatomical studies evaluating cortical thickness. Here, we analyzed changes of topological characteristics of whole-brain metabolic connectivity in twelve SHR rats selected as ADHD-model rats by confirming behavior abnormalities using the marble burying test, open field test, and delay discounting task and 12 Wistar Kyoto rats as the control group, across development from 4 weeks old (childhood) and 6 weeks old (entry of puberty). A topological approach based on graph filtrations revealed a lag in the strengthening of limbic-cortical/subcortical connections in ADHD-model rats. This in turn related to impaired modularization of memory and reward-motivation associated regions. Using mathematical network analysis techniques such as single linkage hierarchical clustering and volume entropy, we observed left-lateralized connectivity in the ADHD-model rats at 6 weeks old. Our findings supported the maturational delay of metabolic connectivity in the SHR model of ADHD, and also suggested the possibility of impaired and compensative reconfiguration of information flow over the brain network.

Clinical Personal Connectomics Using Hybrid PET/MRI

Brain connectivity can now be studied with topological analysis using persistent homology. It overcame the arbitrariness of thresholding to make binary graphs for comparison between disease and normal control groups. Resting-state fMRI can yield personal interregional brain connectivity based on perfusion signal on MRI on individual subject bases and FDG PET produces the topography of glucose metabolism. Assuming metabolism perfusion coupling and disregarding the slight difference of representing time of metabolism (before image acquisition) and representing time of perfusion (during image acquisition), topography of brain metabolism on FDG PET and topologically analyzed brain connectivity on resting-state fMRI might be related to yield personal connectomics of individual subjects and even individual patients. The work of association of FDG PET/resting-state fMRI is yet to be warranted; however, the statistics behind the group comparison of connectivity on FDG PET or resting-state MRI was already developed. Before going further into the connectomics construction using directed weighted brain graphs of FDG PET or resting-state fMRI, I detailed in this review the plausibility of using hybrid PET/MRI to enable the interpretation of personal connectomics which can lead to the clinical use of brain connectivity in the near future.