Laminar specific fMRI reveals directed interactions in distributed networks during language processing

Abstract

Interactions between top-down and bottom-up information streams are integral to brain function but challenging to measure noninvasively. Laminar resolution, functional MRI (lfMRI) is sensitive to depth-dependent properties of the blood oxygen level-dependent (BOLD) response, which can be potentially related to top-down and bottom-up signal contributions. In this work, we used lfMRI to dissociate the top-down and bottom-up signal contributions to the left occipitotemporal sulcus (LOTS) during word reading. We further demonstrate that laminar resolution measurements could be used to identify condition-specific distributed networks on the basis of whole-brain connectivity patterns specific to the depth-dependent BOLD signal. The networks corresponded to top-down and bottom-up signal pathways targeting the LOTS during word reading. We show that reading increased the top-down BOLD signal observed in the deep layers of the LOTS and that this signal uniquely related to the BOLD response in other language-critical regions. These results demonstrate that lfMRI can reveal important patterns of activation that are obscured at standard resolution. In addition to differences in activation strength as a function of depth, we also show meaningful differences in the interaction between signals originating from different depths both within a region and with the rest of the brain. We thus show that lfMRI allows the noninvasive measurement of directed interaction between brain regions and is capable of resolving different connectivity patterns at submillimeter resolution, something previously considered to be exclusively in the domain of invasive recordings.

Fine-scale computations for adaptive processing in the human brain

“…We interpret our results within a framework of feedback vs. feedforward connectivity across cortical depths ( Figure 1A), as proposed by previous UHF imaging studies (e.g., Huber et al, 2017;Kok et al, 2016;Moerel, Yacoub, Gulban, Lage-Castellanos, & De Martino, 2020;Sharoh et al, 2019). In particular, sensory inputs are known to enter the cortex at the level of the middle layer (middle layer 4) and output information is fed forward through the superficial layer (superficial layer 2/3).…”

“…We did not test connectivity between superficial V1 layers and deeper layers of higher areas, as these connections are known to relate to both feedback and feedforward processing (Maunsell & van Essen, 1983;Rockland & Virga, 1989). Despite, the fact that the UHF imaging resolution does not support one-to-one mapping between MRIdefined cortical depths and cyto-architectonically defined layers, Figure 1A provides a framework of feedback vs. feedforward connections across superficial, middle and deeper cortical depths, as proposed and tested by previous UHF imaging studies (e.g., Huber et al, 2017;Kok et al, 2016;Moerel, Yacoub, Gulban, Lage-Castellanos, & De Martino, 2020;Sharoh et al, 2019).…”

Forging a path to mesoscopic imaging success with ultra-high field functional magnetic resonance imaging

“…Further, different stimulus conditions evoke variations in the distribution of the BOLD response across cortical depth for visual stimuli [33]. Depth profiles have also been demonstrated in studies investigating somatosensory stimuli [41], working memory [45], language processing [46] and feedback responses in early visual cortex [33,42,47].…”

“…The presence of objectively identifiable landmarks and the functional significance of the GM have led to layer-dependent fMRI becoming an enormously popular subfield in UFH fMRI. Several studies have taken advantage of UFH fMRI capabilities to resolve functional responses at different cortical depths [33,[39][40][41][42][43][44][45][46][47]. Visual stimuli evoke patterns of BOLD response across cortical depth in V1 [39,43] and MT [40].…”

An in-vivo study of BOLD laminar responses as a function of echo time and static magnetic field strength

“…Our approach, and that of others, has generally been to identify activated regions on the basis of their aggregate response to the task, either by means of an independent activation study, or by smoothing the high resolution data and using this for a non-laminar analysis. Given that we are interested in the characteristics of the ROI as a whole 5 , 19 , 47 , an integration approach, or similar, represents the most logical strategy. Small variations caused by vasculature, or indeed gradients in functionality, will then not be accessible, and would require a different approach.…”