Comparing auditory and visual aspects of multisensory working memory using bimodally matched feature patterns.

Working memory (WM) reflects the transient maintenance of information in the absence of external input, which can be attained via multiple senses separately or simultaneously. Pertaining to WM, the prevailing literature suggests the dominance of vision over other sensory systems. However, this imbalance may be stemming from challenges in finding comparable stimuli across modalities. Here, we addressed this problem by using a balanced multisensory retro-cue WM design, which employed combinations of auditory (ripple sounds) and visuospatial (Gabor patches) patterns, adjusted relative to each participant's discrimination ability. In three separate experiments, the participant was asked to determine whether the (retro-cued) auditory and/or visual items maintained in WM matched or mismatched the subsequent probe stimulus. In Experiment 1, all stimuli were audiovisual, and the probes were either fully mismatching, only partially mismatching, or fully matching the memorized item. Experiment 2 was otherwise same as Experiment 1, but the probes were unimodal. In Experiment 3, the participant was cued to maintain only the auditory or visual aspect of an audiovisual item pair. In two of the three experiments, the participant matching performance was significantly more accurate for the auditory than visual attributes of probes. When the perceptual and task demands are bimodally equated, auditory attributes can be matched to multisensory items in WM at least as accurately as, if not more precisely than, their visual counterparts.

Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI.

In graph theory, "multilayer networks" represent systems involving several interconnected topological levels. A neuroscience example is the hierarchy of connections between different cortical depths or "lamina". This hierarchy is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We then compared networks where the inter-regional connections were limited to a single cortical depth only ("layer-by-layer matrices") to those considering all possible connections between regions and cortical depths ("multilayer matrix"). We utilized global and local graph theory features that quantitatively characterize network attributes such as network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial aspects of the cortex dominated information transfer and deeper aspects clustering. These differences were largest in frontotemporal and limbic brain regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information. Multilayer connectomics could provide a methodological framework for studies on how information flows across this hierarchy.

Connectomic analysis of Alzheimer's disease using percolation theory.

Alzheimer's disease (AD) is a severe neurodegenerative disorder that affects a growing worldwide elderly population. Identification of brain functional biomarkers is expected to help determine preclinical stages for targeted mechanistic studies and development of therapeutic interventions to deter disease progression. Connectomic analysis, a graph theory-based methodology used in the analysis of brain-derived connectivity matrices was used in conjunction with percolation theory targeted attack model to investigate the network effects of AD-related amyloid deposition. We used matrices derived from resting-state functional magnetic resonance imaging collected on mice with extracellular amyloidosis (TgCRND8 mice, n = 17) and control littermates (n = 17). Global, nodal, spatial, and percolation-based analysis was performed comparing AD and control mice. These data indicate a short-term compensatory response to neurodegeneration in the AD brain via a strongly connected core network with highly vulnerable or disconnected hubs. Targeted attacks demonstrated a greater vulnerability of AD brains to all types of attacks and identified progression models to mimic AD brain functional connectivity through betweenness centrality and collective influence metrics. Furthermore, both spatial analysis and percolation theory identified a key disconnect between the anterior brain of the AD mice to the rest of the brain network.