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3.0 Methods

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Outline 3.0 Methods

3.0 Methods

Outline 3.1 Data Acquisition & Preprocessing

  • Core Argument dieses Kapitels definieren
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3.2 Cortical Parcellation with The HCP-MMP1 Atlas

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3.3 Selection of Regions of Interest (ROIs)

To test the hypothesis of supramodal prefrontal control, we defined a set of Regions of Interest (ROIs) based on the multimodal parcellation of the Human Connectome Project (HCP-MMP1) by Glasser et al. (2016) - Nature. This atlas maps cortical areas based on a combination of cortical architecture (myelin), function (task-fMRI) and connectivity, providing a significantly higher precision than traditional Brodmann maps. ROIs were divided into three categories: (1) Prefrontal Seed Regions (the controllers), (2) Auditory Where-Stream targets (“dorsal/spatial”), (3) Auditory What-Stream targets (“Ventral/objects”).

The selection of ROIs is based on previous research, mainly by Rolls et al. (2023) - Cerebral Cortex about connectivity of auditory areas using the Glasser Altas Glasser et al. (2016) - Nature

In general the amount of ROIs found in both streams is far from balanced. There are more areas assigned to the what/ventral auditory stream. This makes sense since language comprehension requires more cotrical compute than locating sounds.

3.3.1 Prefrontal Seed Regions

We selected distict prefrontal “control hubs” as seed regions based on the functional dissociation described by Bedini & Baldauf (2021) in the visual stream.

  • Frontal Eye Field (FEF): We defined the FEF using the specific Glasser label FEF. This region is located in the caudal middle frontal gyrus, ventral to the junction of the superior sulcus (sPCS) and superior frontal sulcus (SFS).
    • The FEF contains topographic maps of contralateral space and is a core node of the Dorsal Attention Network (DAN), responsible for spatial attentional and oculomotor control.
  • anterior Inferior Frontal Junction (IFJa): We defined the IFJa using the specific Glasser label IFJa. This region is located at the junction of the inferior precentral sulcus (iPCS) and the inferior frontal sulcus (IFS).
    • Unlike the FEF, the IFJa is part of the Frontoparietal Network (FPN) and shows multiple-demand characteristics.

IFJa vs IFJp

Most papers soeak of the IFJ as a whole unit, but Bedini (2023) - Brain Structure found that IFJa and IFJp exhibit totally different connectivity patterns.

While the majority of neuroimaging literature treats the IFJ as a monolithic unit, recent evidence suggests a functional and conenctional dossiociation between its anterior (IFJa) and posterior (IFJp) subdivisions. We utilized the high resolution of the HCP-MMP1 to make use of this distinction.

According to Bedini & Baldauf (2021), IFJp is a core node of tthe multiple demand system and the FPN. It is activated by tasks involving reasoning and math tasks, serving as a general-purpose executive mediator

IFJp: According to the IFJp belongs to the multiple demand system and might serve as a mediator for those areas.

IFJa: functions as a mediator for lower-order regions especially for language related regions Bedini & Baldauf (2021)

This is why we contrasted IFJa and IFJp and focus on IFJa as a seed region for the auditory what-pathway. In figure … we contrasted functional connectivity of both the IFJa and IFJp.

3.3.2 Target Definitions

3.3.2.1 ROIs for the What-Stream

Since the what-stream mainly focusses on semantics and comprehension, it is the main speech processing pathway - as in the visual stream for object recognition.
There is a lot of research on it

Starting from the core and belt regions and moving anteriorly to the IFJ, we found following regions belonging certainly to the what-stream:

STGa (aSTG old description) is according to Ahveninen et al. (2006) - PNAS and the connectivity to TA2 from Glasser et al. (2016) - Nature certainly part of the what-stream. STGa has also connectivity to IFG. Also a bit of dorsal stream to IFG and fOP but weaker.

3.3.2.1 ROIs for the Where-Stream

The research on the auditory where-stream is a bit thin. Mainly I focussed on Rolls et al. (2023) - Cerebral Cortex for the connectivity between the areas.

Previous research shows that all STG subregions connect to the fOP via dorsal pathways, which could mean that STG generally belongs to the dorsal stream.
BUT

3.3.2.1.1 FOP

fOP areas are vage. the only source I found was Frühholz (2015) - NeuroImage in which it says that BA44, 44 is part of fOP and therefore it could be part of the dorsal pathway.

FOP1 (Frontal Opercular Area 1): Located in the frontal operculum. According to Frühholz (2015) - NeuroImage, the fOP is a primary target of the auditory dorsal pathway (Frühholz (2015) - NeuroImage). Rolls et al. (2023) - Cerebral Cortex associated fOP regions with the “Group 3” network (including A4, A5, MT), linking it to somatosensory and motion processing. Inclusion of FOP1 captures the frontal endpoint of the “Sound to Motor” stream described by Hickok & Poeppel (2004) - Cognition, Hickok & Poeppel 2007 - Nature.

3.3.2.1.2 Spatial supporting regions 7AL, 7AM and 7PC

According to Rolls et al. (2023) - Cerebral Cortex, the areas 7AL, 7Am, 7PC could all be involved in spatial processing within the auditory stream. Since we hypothesize supramodality in which the FEF directs visual as well as auditory signals, it is plausible that letter named regions also play a significant role in spatial processing of auditory tasks.

Though, one could argue MT and MST also play a role in motion, but as studies show, there is no response of MT/MST for auditory stimuli only ⇒ Study raussuchen, da gab es eine!! Siehe Source

3.3.2.1.3 PSL

According to Rolls et al. (2023) - Cerebral Cortex PSL is involved in language-semantic functions, but Dureux (2024) shows that PSL is unresponsive to auditive stimuli (Dureux (2024)) suggesting PSL could be a highly specialized area.
It has high conenctivity to STS along with TPOJ1, STV, PSL, TGv, TGd, PGi which could suggest PSL being part of the Auditory What-Stream (Ventral).

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3.4 Statistical Analysis

3.4.1 Functional Connectivity

This study utilizes functional connectivity (FC) as the statistical dependency of BOLD-signal-time stamps (Bloof Oxygen Level Dependent) defined in spatially divided cortical regions (Friston (1994)).

  • for each subject the middle time slots of the seed regions (FEF, IFJa) as well as the auditory target areas are extracted.
  • the study uses pearson-correlation coefficient as a primary measurement for functional coupling
  • using it for resting-state fMRI (Biswal (1995))

3.4.2 Partial vs. Full Correlation

When comparing brain regions (or variables in general) it is essential to differentiate between the following for the interpretation of networks:

Full Correlation: Calculates the relationship between A and B without taking other variables into account. If A and B both correlate strongly with C, full correlation shows a connection between A and B, which might not even exist (indirect correlation)

Partial Correlation: Calculates the realtionship betweenA and B after subtracting the infuence of other variables. Hence, it shows functional connectivity without the noise of other brain areas being involved. (Marrelec 2006 - NeuroImage) (Smith (2011))

In this paper, we use both full correlation for the global picture and partial correlation for the remaining connections after filtering out the other regions, whichis robust to indirect influences.

3.4.4 Correction for Multiple Tests (FDR)

The risk of more positive results due to many correlation analyses while computing several correlations for many cortical regions at the same time rises.

  • Therefore we use False Discovery Rate (FDR) correction to counter this effect.
  • In contrast to the rigorous Bonferroni-correction, which often turns out to be very conservative and might cover real effects, FDR correction offers a higher statistical power through checking the expected amount of rejected null hypotheses.
  • in all connecitivty results there is FDR corrrection for multiple comparisons, while we used the signifance of q < 0,05 (FDR-corrected p).

Quelle: Benjamini, Y., & Hochberg, Y. (1995)

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3.5 Brain Behaviour Correlation

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Notes & Scrapbook

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