0.0 Bachelorarbeit Gesamt

0.0 Bachelorarbeit - Gesamtübersicht

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0.2 Abstract

Abstract Text

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1.0 Introduction

1.1. The Auditory Dual-Stream Framework

The prefrontal cortex not only is the most important brain region for decision making, but also serves as the main hub for top-down attention. When we decide to look at the squirrel sitting on the table, it is our prefrontal cortex that first decides and then initiates the directing of the eyes and also the visual system to attend to the squirrel. While focusing on the squirrel, we hear the squirrel making noises - we then attend to the auditory stimuli as well. The question still remains which parts of the prefrontal cortex direct attention to the auditory stream. My thesis will examine the scope in which the prefrontal cortex directs attention top-down in different modalities. But first, we need to dive into the existing literature about the visual stream hypothesis.

1.1.1 The Visual Two-Streams Hypothesis

Lesion studies in macaque monkeys by Ungerleider and Mishkin (1982) set the start in developing the concept of parallel processing streams of the visual system in the brain. They described two distinct pathways that both originate in the primary visual cortex (V1): a ventral stream projecting toward inferotemporal cortex, processing objects such as a squirrel or the table. This stream is also called the ‘what’-stream and decodes object identity. The second, the dorsal stream, projects toward posterior parietal cortex and processes motion and object locations - also called the ‘where’-stream. Goodale & Milner (1992) later refined this framework, proposing that the dorsal stream is primarily involved in guiding how to interact with objects - while the ventral stream serves object perception and recognition. For our study it is relevant to ask whether the ‘how’/‘where’-stream not only performs visuomotor, but also audiomotor control over downstream modalities.

Critical for our study is recent work by Bedini and Baldauf (2021), identifying prefrontal hubs that perform top-down control over each stream. Based on evidence from functional and structural connectivity, they demonstrated a clear dissociation in functional connectivity: The Frontal Eye Field (FEF) - a core node of the Dorsal Attention Network (DAN)- shows predominant coupling with regions of the dorsal visual ‘where’-stream, while the anterior Inferior Frontal Junction (IFJa) - part of the Frontoparietal Network (FPN) - couples preferentially with the ventral visual ‘what’-stream. This connectivity-based division of labor was later supported by resting-state MEG data showing the same dissociation in oscillatory coupling and top-down directionality (Soyuhos, O., & Baldauf, D. (2022)). Together, these findings draw a clear picture of functional specialization in prefrontal top-down control.

1.1.2 From Wernicke’s Area to Auditory Dual-Stream Processing

In the auditory modality, the processing of input was thought to operate in a single cortical region, Wernicke’s area. It is located in the posterior left superior temporal gyrus (STG) and was considered the primary hub for auditory comprehension following Wernicke’s (1874) findings in aphasia - the missing ability to understand language. This model dominated neuroscience well into the twentieth century.
In the 1970s and 1980s, when neuropsychological evidence revealed that lesions to the left STG did not consistently produce comprehension deficits, but were instead more reliably associated with speech production deficits (Hickok & Poeppel 2007 - Nature). These findings changed the view, so that auditory processing could not be reduced to a single region and led to a fundamental re-evaluation of the cortical auditory organization.

The current view, developed most influentially by Hickok & Poeppel (2004, 2007) and Rauschecker & Scott (2009), puts the organization of the auditory cortex into two parallel processing streams analogous to those in the visual system. A posterodorsal stream projects from the superior temporal plane through parietal and premotor cortex and is associated with spatial processing and sensorimotor integration. An anteroventral stream projects from the STG forward through the temporal lobe toward inferior frontal regions and supports auditory object identification and semantic (speech) processing. Figure 1 illustrates this dual-stream architecture as proposed by Hickok & Poeppel (2007), mapping those two pathways onto a lateral view mainly of the left hemisphere. Section 3.2 ‘Selection of Regions of Interest’ will display in detail which regions most likely belong to which of both streams based on existing literature.

Figure 1. Schema of the auditory dual-stream model (adapted from Hickok & Poeppel, 2007). The dorsal pathway (blue) extends from the superior temporal plane toward posterior parietal and premotor cortex. The ventral pathway (purple) projects anteriorly through the temporal lobe toward inferior frontal regions. Both streams originate in the primary auditory cortex on the supratemporal plane.

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1.2 The Gap, Top-Down Control of Auditory Streams

In environments with competing sounds, the brain cannot attend to all auditory input simultaneously. Top-down attention functions as a control mechanism that filters relevant information and suppresses distractors - therefore focussing on few inputs in a goal-directed manner (De Vries & Baldauf (2021) - Journal of Neuroscience). The key question is: Which prefrontal regions act as the top-down controllers of these auditory streams?

The division into dorsal and ventral pathways is well-documented(Ahveninen et al. (2006) - PNAS, Hickok & Poeppel 2007 - Nature), which already sets the path for following the question about the top-down attention mechanisms. Existing work has identified IFG subregions as frontal nodes in auditory processing, particularly BA44 and BA45 within the semantic pathway (Rolls et al. (2023) - Cerebral Cortex) and BA44 also along the dorsal pathway for affective prosody (Frühholz (2015) - NeuroImage). Hickok & Poeppel 2007 - Nature described a dorsal pathway including the Spt - a region at the parietotemporal boundary within the Sylvian fissure - as a sensorimotor interface, connecting anteriorly to Broca’s region and premotor cortex. Hickok & Poeppel 2007 - Nature. However, these findings remain specific to the language-related dorsal prosody processing or the semantic ‘what’-stream. Especially the auditory ‘where’-stream lacks a clear prefrontal controller. The question of whether the FEF and IFJa also direct the auditory domain in a similar fashion to the visual system remains unanswered.

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1.3 Hypothesis, A supramodal organization

The preceding sections have outlined the missing piece: while the dual-stream architecture of the auditory cortex is well established, the prefrontal regions that coordinate these streams from the top-down remain under-explored. For the visual system, this question has been resolved - FEF and IFJa act as dissociable control hubs for the dorsal and ventral streams (Bedini & Baldauf (2021)); whether a comparable organization exists for the auditory domain is still unknown.

We hypothesize that the resting-state functional connectivity will show a clear dissociation of the auditory ‘what’ and ‘where’-streams: the auditory ‘where’-stream preferentially connects to the Frontal Eye Field (FEF), and the auditory ‘what’-stream to the anterior Inferior Frontal Junction (IFJa).

If confirmed, this dissociation would provide evidence for a supramodal organizational principle of the prefrontal cortex, with FEF and IFJa acting as domain-general hubs for top-down attentional control across sensory modalities. This study tests a first step toward that larger claim by examining whether FEF and IFJa connectivity profiles in the auditory domain mirror those established for vision.

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2.0 Theoretical Background

The auditory dual-stream framework proposes that sound processing diverges into two functionally and anatomically distinct pathways after being processed by the primary auditory cortex.
The ventral ‘what’-stream decodes object identity and the dorsal ‘where’-stream processes spatial localization and sensorimotor integration. This organization mirrors the well-established visual dual-stream architecture. Romanski (2004) demonstrated in non-human primates that this division is not modality-specific, but ‘domain’-specific: the dorsolateral prefrontal cortex (dlPFC), including areas 46, 8a and 8b, receives converging spatial input from both posterior parietal (visual) and caudal superior temporal (auditory) cortices, while the ventrolateral PFC (vlPFC), including areas 45 and 12, receives object-related input from inferotemporal and anterior auditory association cortices (Figure 1, Romanski 2004, originally based on Arnsten (2003)). This domain-specific principle provides the theoretical foundation for the present thesis. The mapping of these macaque pathways onto human prefrontal architecture - specifically the FEF for the dorsal stream and the IFJa for the ventral stream - is elaborated in Sections 2.1. and 2.2, grounding on neuroimaging evidence (Bedini & Baldauf (2021)).

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3.0 Methods

3.1 Data Acquisition & Preprocessing

3.1.1 Dataset and Participants

This study utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from the S1200 release of the Human Connectome Project (HCP). To ensure high data quality and completeness, a strictly defined sub-group of $N=812$ healthy young adult subjects was selected. This specific cohort was chosen because these subjects completed all four rs-fMRI runs, and their data were reconstructed using the latest r227 reconstruction algorithm, which corrects for specific phase-encoding artifacts present in earlier releases.

3.1.2 Data Acquisition

Imaging data were acquired using a customized Siemens 3T Connectome Skyra scanner. The rs-fMRI data were collected utilizing a gradient-echo EPI sequence with the following standard HCP parameters: a repetition time (TR) of $720\text{ ms}$, an echo time (TE) of $33.1\text{ ms}$, a flip angle of $52^{\circ }$, and an isotropic spatial resolution of $2.0\text{ mm}$. Each subject completed two sessions on separate days. Each session consisted of two 15-minute runs with opposing phase-encoding directions (Right-to-Left and Left-to-Right), resulting in 1200 frames per run and a total of 4800 time points per subject. During the acquisition, subjects were instructed to keep their eyes open and maintain fixation on a red crosshair on a dark background.

3.1.3 Preprocessing

The raw fMRI data underwent preprocessing via the standardized HCP Minimal Preprocessing Pipelines. This first pipeline performs spatial distortion correction, motion correction, and registration to the structural final brain mask by reducing the bias field and normalization. Subsequently, the data were transformed from native mesh to fs_LR registered 32k mesh (2mm average vertex spacing).

To account for noise and motion artifacts, the data were denoised using ICA-FIX (Independent Component Analysis-based X-noiseifier). This automated method decomposes the data into independent components and regresses out those identified as structured noise (e.g., cardiac, respiratory, and head movement artifacts) without applying aggressive global signal regression.

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3.2 Selection of Regions of Interest (ROIs)

To test the hypothesis of supramodal prefrontal control over auditory processing, a comprehensive set of 36 target ROIs and 2 primary seed regions (plus one control seed) per hemisphere was defined. All regions were identified utilizing the multimodal cortical parcellation (HCP-MMP1.0) by Glasser et al. (2016) - Nature. This atlas provides superior neuroanatomical precision by integrating structural, functional, and connectivity data.

The selected ROIs are categorized into functional networks reflecting the auditory dual-stream architecture and their respective prefrontal controllers. The large sample size (N=812) provides sufficient statistical power to detect moderate connectivity effects across all ROI pairs after multiple comparison correction

3.2.1 The HCP-MMP1 Atlas

Before defining specific regions, the choice of the underlying atlas must be justified. Unlike traditional parcellations based solely on cytoarchitecture (e.g., Brodmann areas), the HCP-MMP1.0 atlas integrates four distinct modalities:

1. Cortical Myelin Content: Identified via T1w/T2w ratios.
2. Cortical Thickness: Measuring structural differences.
3. Task-fMRI Activation: Pinpointing functional hubs.
4. Resting-State Functional Connectivity: Mapping intrinsic networks.

This multimodal approach is essential for this study because it allows for the differentiation of functionally distinct areas that appear homogenous in classical maps. For instance, it enables the isolation of the IFJa from the surrounding prefrontal cortex, which is important for the auditory dual-stream architecture.

3.2.2 Prefrontal Seed Regions (The Conductors)

We selected distinct prefrontal control hubs based on the functional dissociation previously established in the visual system by Bedini & Baldauf (2021). This selection tests whether these visual control architectures map onto auditory processing.

• Frontal Eye Field (FEF): A core node of the Dorsal Attention Network (DAN), hypothesized to exert top-down control over the spatial auditory “Where”-stream.
• Anterior Inferior Frontal Junction (IFJa): Part of the Frontoparietal Network (FPN), hypothesized to mediate feature-based attention and semantic processing within the auditory “What”-stream Bedini & Baldauf (2021).
• Posterior Inferior Frontal Junction (IFJp) - Control Seed: Included strictly as a methodological baseline. IFJp is associated with the Multiple-Demand (MD) system for general-purpose executive tasks Bedini & Baldauf (2021). Factoring out its variance allows for a double dissociation, ensuring that the observed control over the auditory network is highly specific to the IFJa.

3.2.3 The Dorsal Pathways (Where & How)

The dorsal pathways process spatial localization (Where) and sensorimotor integration (How) (Hickok & Poeppel 2007 - Nature). ROI selection relies on the connectivity profiles outlined by Rolls et al. (2023) - Cerebral Cortex and recent functional data by Dureux (2024).

3.2.3.1 The Spatial-Orienting Network (Where-Stream)

This network tracks moving auditory objects and directs spatial focus.

• 7AL, 7Am, 7PC: Superior parietal areas receiving direct input from early auditory regions, crucial for auditory spatial attention and motion tracking Rolls et al. (2023) - Cerebral Cortex.
• PF, PFop: These areas correspond to the Inferior Perietal Lobule (Baker (2018)) and according to Rauschecker & Scott (2009) - Nature Neuroscience the IPL plays an important role in the dorsal auditory pathway (Glasser et al. (2016) - Nature)
• PFcm: Defined by Glasser et al. (2016) - Nature as a heavily myelinated inferior parietal region with strong somatosensory properties along with OP1-4 and FOP1. Although recent evidence indicates a lack of direct auditory responsiveness (Dureux et al., 2024), it was strategically included due to its anatomical position bridging the auditory-motor interface (OP4) and multimodal integration hubs (PSL).
• MT, MST: Classical supramodal motion hubs. They receive effective connectivity from auditory areas (A4/A5) to enable the tracking of auditory movement in space Rolls et al. (2023) - Cerebral Cortex.

3.2.3.2 The Motor Interface (How-Stream)

This interface translates auditory representations into articulatory motor plans (Sound-to-Motor mapping, Hickok & Poeppel (2004) - Cognition, Hickok & Poeppel 2007 - Nature)

• 55b: A premotor hub exhibiting responses to vocal stimuli being assigned to a language area. Interesting because 55b is a neighbor of the prefrontal seed region FEF and Glasser et al. (2016) - Nature; Dureux (2024).
• 44 (Broca’s pars opercularis): According to Rolls et al. (2023) - Cerebral Cortex Area 44 might be forming a dorsal auditory pathway with PBelt, A4, A5 and motion areas MT and MST.
• OP4, FOP1, FOP2, FOP3, 43: According to Frühholz the frontal operculum is connected to posterior STG and anterior IFG via dorsal pathways Frühholz (2015) - NeuroImage. OP4 specifically demonstrates exclusive sensitivity to human vocalizations Dureux (2024). Area 43 was included due to its anatomical position between the FOP and premotor cortex, potentially serving as an interface along the auditory-motor-pathway.
• SCEF (Supplementary and Cingulate Eye Field): A medial frontal cingulate area anatomically adjacent to the FEF. Despite responding almost exclusively to vocalizations (Dureux (2024)), its medial frontal anatomy and oculomotor system affiliation place it within the dorsal network. Dureux (2024) further groups SCEF functionally with premotor area 55b, OP4, and Broca’s areas 44 and 45 in the same vocalization-selective cluster.

3.2.4 The Ventral Pathways (What)

The ventral stream decodes auditory object identity, semantics, and speech perception.

3.2.3.1 Ventral Semantic and Identity Network

• STGa, STSda, STSdp, STSva, STSvp, TA2: The semantic core of the ventral pathway processing complex auditory objects. The STS complex integrates vocal inputs with facial motor representations to decode identity and message (Glasser et al. (2016) - Nature, Rolls (2022) - NeuroImage, Rolls et al. (2023) - Cerebral Cortex)
• AVI: The Anterior Ventral Insula shows activations to auditory stimuli alongside inferior frontal regions 55b, IFJp, IFJa, IFSp, 44, 45, and OP4. AVI exhibits highly specific capabilities in distinguishing vocalizations from noise, extending the semantic network into insular evaluation regions Dureux (2024).
• 45, 47l, IFSp: The frontal termini of the ventral meaning pathway. Area 45 (Broca’s pars triangularis) and 47l handle semantic selection, while IFSp shows responses to vocalization along with 44, 45, IFJp, OP4 and 55b ; Rolls et al. (2023) - Cerebral Cortex, Dureux (2024)

3.2.4.2 Anterior Temporal Semantic Regions

• TE1a, TGd, TGv (Temporal Pole): Following the effective connectivity analysis of Rolls (2022) - NeuroImage, these areas represent the semantic integration hubs of the temporal lobe. We intentionally differentiated between these regions to test the specificity of prefrontal control:
  • TE1a & TGd (Group 1): Associated with an inferior, visual-semantic system. Their lack of effective connectivity with the IFJa makes them ideal candidates to evaluate the boundaries of the IFJa-controlled auditory network.
  • TGv (Group 2): Integrated into a frontal system involving speech production and syntax. Its robust connectivity with the IFJa, FEF, and Area 55b makes it an interesting target for top-down modulation during linguistic and executive tasks.

3.2.5 Hierarchical Gateways and Connectors

These regions serve as routing hubs and convergence zones between early acoustic analysis and higher-order integration.

• PBelt: A4, A5: The primary “routers” exiting the auditory core. PBelt and A4 show effective connectivity to parietal regions 7AL, 8AM, 7PC. Rolls et al. (2023) - Cerebral Cortex suggests that PBelt, A4 and A5 might form a language-related dorsal pathway, adding them to the auditory where-stream. Glasser et al. (2016) - Nature parcellation on the other hand places A4 and A5 together with STSdp, STSda, STSvp, STSva, STGa, and TA2 in a region naming them auditory association cortex. This area rather belongs to the ventral what-stream, making a clear classification difficult. Ambiguities will be resolved and discussed in chapters 4.4 and 5. (wo auch immer ich das mache)   
• PGi: Effective connectivity to semantic areas as STS, TGv, TGd and TE1a place PGi in the auditory what-pathway. Rolls (2022) - NeuroImage places PGi in Group 1 along with STSva, STSvp and TE1a and TGd.
• PSL, STV, TPOJ1: Multimodal convergence zones (Group 3 networks) bridging auditory semantics with visual and somatosensory inputs (Rolls (2022) - NeuroImage, Rolls et al. (2023) - Cerebral Cortex) showing effective connectivity with PBelt, A4 and A5. While PSL is anatomically integrated into the semantic network, functional models suggest it acts as an abstract linguistic interface rather than a primary auditory responder Dureux (2024). That’s why we placed these regions in the semantic what-pathway for the following analyses. TPOJ1 shows weak responses to non-vocal sounds according to Dureux (2024).

3.2.6 Tables

Table 1: Prefrontal Seed Regions

Kürzel	Voller Name	Location (Stream)	Quelle
FEF	Frontal Eye Field	Prefrontal (Dorsal Attention)	Bedini & Baldauf (2021); Salmi et al. (2009)
IFJa	Anterior Inferior Frontal Junction	Prefrontal (Frontoparietal)	Bedini & Baldauf (2021)
IFJp	Posterior Inferior Frontal Junction	Prefrontal (Multiple-Demand)	Bedini & Baldauf (2021)

Table 2: Where & How Stream (Dorsal)

Kürzel	Voller Name	Location (Stream)	Quelle
7AL	Area 7 Anterior Lateral	Dorsal (Where - Spatial)	Rolls et al. (2023)
7Am	Area 7 Anterior Medial	Dorsal (Where - Spatial)	Rolls et al. (2023)
7PC	Area 7 Posterior Capsular	Dorsal (Where - Spatial)	Rolls et al. (2023)
PF	Area PF (Inferior Parietal)	Dorsal (Where - Spatial)	Baker (2018)
PFop	Area PF Opercular	Dorsal (Where - Spatial)	Rauschecker & Scott (2009)
PFcm	Area PF Complex Medial	Dorsal (Where/Somatosensory)	Glasser (2016)
MT	Middle Temporal Area	Dorsal (Where - Motion)	Rolls et al. (2023)
MST	Medial Superior Temporal Area	Dorsal (Where - Motion)	Rolls et al. (2023)
55b	Area 55b	Dorsal (How - Motor Relay)	Dureux (2024)
44	Area 44 (Pars Opercularis)	Dorsal (How - Motor)	Rolls et al. (2023)
OP4	Frontal Opercular Area 4	Dorsal (How - Motor interface)	Dureux (2024)
FOP1-3	Frontal Operculum 1, 2, 3	Dorsal (How - Motor planning)	Frühholz (2015)
43	Area 43	Dorsal (How - Motor planning)	Frühholz (2015)
SCEF	Supp. & Cingulate Eye Field	Dorsal (How - Cingulate Attn.)	Dureux (2024)
A4	Auditory Area 4	Gateway (Sensory Router)	Rolls et al. (2023)
PBelt	Parabelt Complex	Gateway (Sensory Router)	Rolls et al. (2023)

Table 3: What pathway (ventral)

Kürzel	Voller Name	Location (Stream)	Quelle
A5	Auditory Area 5	Ventral (What - Semantic Gateway)	Rolls et al. (2022)
STGa	Superior Temporal Gyrus Ant.	Ventral (What - Identity)	Glasser (2016)
STSda/dp	STS Dorsal Ant. / Post.	Ventral (What - Semantic)	Rolls et al. (2023)
STSva/vp	STS Ventral Ant. / Post.	Ventral (What - Semantic)	Glasser (2016)
TA2	Area TA2	Ventral (What - Semantic)	Glasser (2016)
STV	Superior Temporal Visual Area	Ventral (What - Multimodal)	Rolls (2023)
TPOJ1	Temp.-Par.-Occ. Junction 1	Ventral (What - Convergence)	Rolls (2023)
PGi	Area PGi (Inferior Parietal)	Ventral (What - Visual Semantic)	Rolls (2022)
AVI	Anterior Ventral Insula	Ventral (What - Evaluation)	Dureux (2024)
45	Area 45 (Pars Triangularis)	Ventral (What - Semantic)	Rolls et al. (2023)
47l	Area 47 Lateral	Ventral (What - Semantic)	Rolls et al. (2023)
IFSp	Inferior Frontal Sulcus Post.	Ventral (What - Semantic)	Dureux (2024)
TE1a	Area TE1 Anterior	Ventral (What - Periphery)	Rolls (2022)
TGd / TGv	Temporal Gyrus Dor. / Ven.	Ventral (What - Periphery)	Rolls (2022)
PSL	Perisylvian Language Area	Connector (Linguistic Interface)	Rolls (2023)

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3.3 Matrix Construction

3.3.1 Functional Connectivity

Resting-state functional connectivity (RSFC) is defined as the temporal correlation between neurophysiological events in spatially distinct brain regions Friston (1994). In this study, RSFC is operationalized as the statistical dependency between BOLD (Blood Oxygen Level Dependent) signal time series, reflecting the intrinsic functional architecture of the brain during resting-state Biswal (1995). Unlike EC, which models directed causal influences, RSFC is a symmetric, undirected measure that captures the degree to which two regions show correlated activity over time, independent of any explicit task or stimulus.

3.3.2 Time Series Extraction

We utilized the preprocessed dense CIFTI timeseries from the HCP S1200 release. For each subject, the BOLD signal was spatially averaged across all vertices within each of the 360 parcels (180 per hemisphere) of the HCP MMP1 atlas (Glasser et al. (2016) - Nature). Mean BOLD time series were extracted for all seed regions (FEF, IFJa, IFJp) [WELCHE noch? 55b, 44, 45?], as well as all auditory target regions defined in section 3.2.

3.3.3 Connectivity Matrix Construction

Pairwise RSFC values were computed as Pearson correlation coefficients between the extracted BOLD time series of each seed region and all parcels of the HCP-MMP1 atlas - consistent with the HCP’s own connectivity analyses (Glasser et al. (2016) - Nature). This led to a connectivity vector for each subject representing the full RSFC profile for each seed across the cortex. All matrix construction steps were implemented using a custom MATLAB toolbox developed within the Baldauf Research Group. [TOOLBOX Ref - Daniel fragen]

3.3.4 Single Seed vs. Contrast Analysis

We used two complementary analysis modes. In the single-seed analysis, the functional connectivity of a given seed region (FEF or IFJa) was assessed against the mean whole-brain connectivity of that subject, detecting parcels that are significantly more correlated with the seed region than the average cortical region. In the contrast analysis, the connectivity profiles of two seed regions were directly compared, revealing parcels with preferential connectivity to one seed over the other.

3.3.5 Partial vs Full Correlation

When characterizing functional brain networks, a critical distinction needs to be made between full and partial correlation. Full Correlation calculates the pairwise statistical relationship between two regions A and B without controlling for other variables. Because any two regions that both correlate with a third region C will appear correlated with each other - therefore displaying indirect correlation without true connectivity. Partial Correlation on the other hand, estimates the relationship between A and B after regressing out the shared variance to all other simultaneously measured regions (e.g. region C, Marrelec 2006 - NeuroImage, Smith (2011)). As a result, the partial correlation connectome is considerably sparser than its full correlation counterpart, while retaining the most direct functional connections (Glasser et al. (2016) - Nature).

In this study, both measures are applied: full correlation provides a global view of each seed’s connectivity landscape, while partial correlation isolates direct connections after filtering out contributions from the other regions included in the analysis.

3.3.6 Statistical Testing: Wilcoxon Signed-Rank Test & FDR Correction

Statistical significance of RSFC estimates was assessed using the paired Wilcoxon signed-rank test, an alternative to the Gaussian-based paired t-test. This test was chosen because RSFC values derived from Pearson correlations do not necessarily follow a Gaussian distribution across subjects (Soyuhos, O., & Baldauf, D. (2022)) For the single-seed analyses, a one-tailed test was applied, evaluating whether connectivity with the seed region exceeded the mean whole-brain connectivity. For contrast analyses, a two-tailed test was used to determine whether connectivity was significantly higher for one seed than for the other.

Each seed region (FEF or IFJa) was tested independently; their connectivity profiles were not averaged before statistical testing, preserving each region’s functional fingerprint.

To control for the elevated risk of false positives arising from testing all 360 cortical parcels simultaneously, all p-values were corrected for multiple comparisons using the False Discovery Rate (FDR) procedure (Benjamini, Y., & Hochberg, Y. (1995)). In contrast to the more stringent Bonferroni correction, FDR correction maintains higher statistical power by controlling the expected proportion of false positives among all rejected null hypotheses, rather than the probability of any single false positives. A significance threshold of q < 0.05 (FDR-corrected) was applied in all analyses.

For visualization, significant p-values were converted to z-scores. The z-scores were summed across all seeds for which a given parcel reached significance, and then divided by the total number of seeds showing significance for that parcel. The normalization generates a score that weights connectivity strength by consistency of the effect across seeds.

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3.4 Brain-Behavior Correlation

To validate our RSFC patterns performed in Section 4, we assessed whether individual differences in RSFC could predict performance on behavioral tasks that specifically test the auditory ‘what’ and ‘where’ pathways. This approach links the network architecture observed during rest to cognitive function during task engagement, contributing validity for the supramodal organization hypothesis.

3.4.1 Behavioral Subject Subsample

For the connectome-based predictive modeling, we used behavioral data of N = 371 participants from the HCP S1200 release. While the full resting-state cohort counts $N=812$ participants, restricting the behavioral analyses to the smaller subset guarantees complete data across all evaluated tasks. For each task, subjects were additionally excluded if they exceeded a head motion threshold of 0.15 mm framewise displacement or had missing behavioral scores, resulting in slightly varying effective sample sizes per analysis (see sections 3.4.2.1 and 3.4.2.2).

3.4.2 Behavioral fMRI Tasks

Three fMRI tasks were selected to evaluate the auditory dual-stream framework, defined in our study. The tasks were selected to enable a functional dissociation between semantic processing ‘what’, auditory-spatial decoding ‘where’, and low-level acoustic filtering, while acknowledging the limitations of the HCP task set.

3.4.2.1 Language Task (Story Comprehension)

In the story condition of the HCP Language Task, participants listened to short auditory stories adapted from Aesop’s fables, followed by a two-alternative forced-choice (2-AFC) question testing the semantic content of the stories (Binder (2011), Barch et al. (2013)). Two performance measures were extracted: Story Accuracy measures the proportion of correct responses, and since the vocabulary of the stories is simple, we observe a ceiling effect in which the sustained attention is measured rather than semantic access. However, Story Median Reaction Time captures the speed of semantic retrieval and therefore is used as the primary measurement for ventral ‘what’-stream efficiency. For this analysis, 20 subjects were excluded from the N = 371 subsample (18 due to head motion > 0.15 mm, 2 due to missing scores), yielding an effective N = 351. The Spearman correlation between behavioral scores and head motion was $r=0.00$ ($p=0.939$), confirming that motion artifacts did not confound the results.

3.4.2.2 Working Memory Task (2-Back Place)

The HCP Working Memory Task consists of a visual N-Back design in which participants track sequential images and respond whether the current image matches the one presented two trials before (Barch et al. (2013)). The place subcondition, in which stimuli consisted of landscape and scene photographs, was extracted as a measure for visuo-spatial working memory (Barch et al. (2013)).
This task was chosen as the closest available test of spatial processing capacity, linked to the FEF’s dorsal stream. Because the HCP dataset does not include an auditory spatial task, this visual task tests the supramodal hypothesis. Participants might solve these tasks via object recognition rather than spatial navigation, which might reduce observed effects in the dorsal network. Additionally, since IFJa has been shown to be involved in working memory (Bedini & Baldauf (2021)), its connectivity may contribute to Place task predictions independently of any semantic strategy, making it difficult to cleanly attribute ventral model effects to a single mechanism. For this analysis, 19 subjects were excluded from the N = 371 subsample (18 due to head motion > 0.15 mm, 1 due to missing scores), yielding an effective N = 352. However, the Spearman correlation between behavioral scores and head motion was $r=-0.16$ ($p=0.002$), indicating a significant association between motion and Place Acc performance. This constitutes a potential confound and is discussed as a limitation in section 5.7.

3.4.2.3 Words-in-Noise (NIH Toolbox Noise Comparison)

The NIH Toolbox Words-in-Noise test assesses the ability to recognize spoken words in background noise by adaptively decreasing the signal-to-noise ratio across trials (Zecker (2013)). The task is a valid measure of low-level acoustic signal separation rather than high-level semantic processing. This test was included as a control paradigm to determine whether IFJa network connectivity generalizes across auditory tasks or whether acoustic signal separation relies on a functionally different network.

3.4.3 Connectivity-Based Behavioral Prediction

The connectivity values were derived from partial correlation matrices computed across the N = 371 subsample, reflecting direct functional coupling between ROIs with shared variance removed. The prediction model itself was fitted using linear correlation between the connectivity predictor matrix and the behavioural outcome variable. Separate models were computed for the ventral ROIs subset and the dorsal ROIs subset, enabling predictions specific to the streams.

We evaluated model performance using K-fold cross-validation (K = 371, leave-one-out, as the primary analysis; K = 10 for comparison), providing an R value (correlation) as the indicator of predictive accuracy. A significance threshold of p < 0.05 was applied for all tasks.

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4.0 Results

Our analyses reveal a clear functional dissociation between the auditory where- and what-streams at the level of their prefrontal top-down regulators. Partial correlation of the two seed regions FEF and IFJa shows a distinct connectivity that aligns with the proposed supramodal organization. The FEF is selectively coupled to spatial-parietal and motor circuits, while IFJa is preferentially connected to the ventral semantic network and the Broca complex. This double dissociation is further supported by seed-specificity validations (FEF vs. 55b, IFJa vs. IFJp) and by the analysis of Broca’s areas 44 and 45.

All z-scores reported below are from partial correlation analyses unless otherwise stated. Full correlation results are discussed only where they show meaningful contrasts compared to the partial correlation pattern.

4.1 Global Connectivity Patterns

Before examining each stream individually, we assess the overall functional dissociation between the auditory ‘what’ and ‘where’ streams. Using full and partial correlation, we compare the connectivity profiles of the two prefrontal seed regions FEF and IFJa, and validate their anatomical specificity against neighboring control seeds.

4.1.1 FEF vs. IFJa: Validation of Prefrontal Seed Regions

To validate the anatomical specificity of our seed regions, we compared their partial correlation connectivity fingerprints across both hemispheres (Figure FEF vs IFJa full connectivity.png).

The functional connectivity analysis with full correlation reveals a clear double dissociation (Figure: FEF vs IFJa full corr.png) with FEF significantly coupling with spatial-parietal areas ((full corr.: OP4 $z=21.13$, 7Am $z=24.44$, 7PC $z=23.53$, RH)) with exceptions of FEF coupling with TA2 (full corr.: $z=16.34$, RH) stronger than IFJa and TA2 and 55b connecting stronger to IFJa than to FEF.

Then we applied partial correlation to partial out third party connectors to see a clear picture of the connectivity patterns within the auditory streams. The partial correlation analysis reveals that the most direct auditory connections to FEF are consistently rooted in the spatial orienting network. Both hemispheres show strong coupling with the inferior parietal cluster (PF, PFop, PFcm). However, the network shows a distinct right-hemispheric dominance in specific regions, consistent with widely accepted right-lateralization framework for spatial auditory processing (Hickok & Poeppel, 2007). While the left hemisphere dominates in FOP3 ($z=3.43$), 7Am ($z=8.37$) and 7PC ($z=5.82$), the right hemisphere demonstrates a clear dominance in PF ($z=18.15$ vs. left $z=8.61$) and shows partial correlation with the perisylvian language area (PSL, $z=12.64$), suggesting a stronger specialized right-lateralized fronto-parietal integration for spatial auditory processing (Hickok & Poeppel 2007 - Nature).

Figure: Functional Connectivity: Left vs right hemisphere, full correlation

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Figure: Functional Connectivity: left vs right hemisphere, part correlation
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• FEF: The FEF seed exhibits robust full correlation with the superior parietal lobe (7AL, 7Am, 7PC). This replicates the where-stream, similar to the visual stream Bedini & Baldauf (2021), together with auditory and motor regions such as A5, PBelt, FOP1, STV and PSL
• IFJa: In contrast, the IFJa showed strong coupling with the anterior language network (HCP language task)
  

4.1.2 Topographical Validation: IFJp Control Seed

To validate the top-down control over the auditory what-stream is driven by the anterior subdivision of IFJ, a control partial correlation model substituted IFJa for IFJp, its immediate neighbor.
The control analysis confirms the functional dissociation of where and what stream. The FEF maintains its robust connectivity to the dorsal auditory and motor network with 23 significant regions in the left and 20 in the right hemisphere.

In the profound contrast substituting the what-hub with IFJp leads to a significant drop in the connectivity pattern of the semantic what-stream. The IFJp fails to communicate with the temporal lobe in comparison to the FEF.
This contrast provides the evidence that the prefrontal control hub over auditory identity processing is functionally exclusively anchored in the IFJa.

FEF vs. IFJp right hemisphere: part corr

FEF vs IFJp part left hemisphere:

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4.4 Resolving Ambiguities

During the analysis, some areas as A4 or PSL seem to have an ambiguous connectivity pattern. This chapter will dissolve ambiguities found in the literature and results of the RSFC analysis.

4.4.1 A4 and A5

Areas A4 and A5 exhibit connectivity profiles that position them as early gateways for the auditory pathways targeting robustly IFJa (A4: left z = 8.25, right z = 8.76; A5: left z = 11.63, right z = 6.96). Partial Correlation shows only in left hemisphere low coupling from A4 with FEF ($z=4.06$, $mean=0.01$). This connectivity pattern aligns with the Glasser parcellation

As noted in the ROI selection (Section 3.2.5), A4 and A5 were

  provisionally placed in the dorsal pathway pending empirical resolution.

   The present data provides that resolution…

According to Rolls et al. (2023) - Cerebral Cortex A4 and A5 could form a dorsal language-relates stream with PBelt and Broca’s area 44. A4 and A5 both exhibit effective connectivity to MT and MST (Rolls et al. (2023) - Cerebral Cortex), which connect further to parietal regions, therefore, according to Rolls, A4 and A5 might form a language related dorsal stream.
Glasser

A4

In Glasser et al. (2016) - Nature A4 is defined as functionally different from its neighbors:

• in contrast to STV, A4 shows a weaker connectivity to visual area PCV
• • RSFC between A4, PBelt, 7AL, 8Am, 7PC (Rolls et al. (2023) - Cerebral Cortex)
• • to BA44, 44 (Rolls et al. (2023) - Cerebral Cortex)
• We identified auditory association cortex as a region mainly on the superior temporal gyrus and within the superior temporal sulcus that is activated in the LANGUAGE STORY, MATH, and STORY-MATH contrasts. It is strongly functionally connected with the inferior frontal gyrus, including areas 44, 45, and 47l. This auditory region likely becomes progressively less purely auditory and more multi-modal as one progresses inferiorly, anteriorly, and posteriorly (away from early auditory cortex, e.g. Main Text Figure 3). Indeed, functional connectivity with early auditory cortex progressively decreases along those directions. This region includes eight areas that we identify as A4, A5, STSdp, STSda, STSvp, STSva, STGa, and TA2.

A5
Rolls (2023) treats A5 as an ambiguous
Rolls 2022 puts A5 in group 3 with STGa, STS, da, STSdp, PSL, STV and TPOJ1. Those regions show connectivity to Brocas areas 44 and 45, to TGv and TGd
also EC to MT/MST

• laut rolls ambiguous

• bei mir klar IFJa

• aber vielleicht language und deshalb kein FEF

• A5 vs STGa

  • more myelin (Glasser et al. (2016) - Nature)
  • differs in RSFC (Glasser et al. (2016) - Nature)
  • differs in RSFC (Glasser et al. (2016) - Nature)

Connectivity

• effective connectivity to MT and MST (Rolls et al. (2023) - Cerebral Cortex)
  • which connect to superior parietal regions forming a Auditory Where-Stream (Dorsal) involved in actions in space. (Rolls et al. (2023) - Cerebral Cortex)
• unscertain whether where or what pathway. (Rolls et al. (2023) - Cerebral Cortex)
  • but connectivity to MT/MST might indicate rather a being part of Auditory Where-Stream (Dorsal) (Rolls et al. (2023) - Cerebral Cortex)

Projections

• to BA44, 44 (Rolls et al. (2023) - Cerebral Cortex)

PFC targets

• connectivity with IFJa IFJa and IFSp
  • implicates short-term working memory for the Auditory What-Stream (Ventral) (Rolls et al. (2023) - Cerebral Cortex)

4.4.2 PSL the division

PSL presents a fundamental paradox within the auditory dual-stream framework. On one hand, Rolls et al. (2023) - Cerebral Cortex classify PSL as part of the auditory ventral what-stream, based on its effective connectivity along with TPOJ1, STV, TGv, TGd and PGi, as language-related semantic regions connecting to Broca’s area 45. On the other hand, Dureux (2024) demonstrates that PSL remained entirely unresponsive to all tested auditory stimuli - including vocallizations, non-vocal sounds and white noise.
In our analysis, PSL presents a complex, asymmetric connectivity profile. It is integrated into the left ventral “what” stream (L_IFJa: $z=9.65$, $mean=0.01$, single seed), but simultaneously forms a strong right-lateralized connection with the right spatial “where” stream (R_FEF: $z=21.37$, $mean=0.03$, single seed).
Our partial correlation data resolves this apparent contradiction. The massive prefrontal connectivity, combined with Rolls et al. (2023) - Cerebral Cortex’s language-related integration and acoustic silence (Dureux (2024)), suggests that PSL does not process low-level auditory input. Instead, it might function as a high-level convergence zone similar to TPOJ, which calls Rolls (2022) - NeuroImage a multimodal convergence region. PSL might link semantic identity (via left IFJa) with spatial attention (via right FEF) at an abstract, modality-independent level.

4.4.3 STV

[DRAFT — bitte prüfen und in eigene Stimme übersetzen]

The Superior Temporal Visual Area (STV) presents a nominative paradox: despite its name implying visual processing, it is consistently recruited in both auditory streams and classified by Rolls et al. (2023) - Cerebral Cortex as part of the language-related ventral network alongside PSL and TPOJ1 — with strong effective connectivity directed towards Broca’s areas 44 and 45.

Our partial correlation data reveals significant bilateral coupling of STV with both prefrontal seeds — assessed in separate single-seed analyses. In the IFJa single-seed analysis (section 4.3), STV couples directly with IFJa (left $z=18.90$, $\text{mean}=0.02$; right $z=18.55$, $\text{mean}=0.02$), after partialling out FEF and all other ROIs. Conversely, in the FEF single-seed analysis (section 4.2.4), STV couples directly with FEF (left $z=19.50$, $\text{mean}=0.02$; right $z=17.82$, $\text{mean}=0.02$), after partialling out IFJa and all other ROIs. In both cases, the coupling is direct — not mediated through the other seed — indicating that STV maintains independent functional connections with each controller.

This dual connectivity resolves the apparent paradox. STV does not process unimodal visual input in isolation; rather, it functions as a supramodal integration zone at the temporal–parietal boundary, binding auditory identity signals from the ventral stream with spatial and cross-modal information from the dorsal stream. This interpretation aligns with its anatomical position at the posterior superior temporal sulcus, a region consistently implicated in audiovisual and multimodal convergence (Rolls et al. (2023) - Cerebral Cortex, Rolls (2022) - NeuroImage). Its strong IFJa coupling suggests a primary affiliation with the “what” stream, while its weaker FEF connection positions it as a candidate gateway through which semantic identity representations gain access to spatial attentional modulation.

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4.5 Functional Roles of Adjacent Areas

4.5.3 Specificity Validation: FEF vs. 55b

A parallel validation compared FEF with its immediate premotor neighbor, area 55b, using partial correlation to rule out nonspecific regional signals. Despite their direct anatomical neighbourhood, the two seed regions exhibit clearly dissociable connectivity patterns. In both hemispheres, the FEF maintains dominant coupling with parietal, motor areas and the ventral connectivity to TA2 and TE1a already shown in Fig FEF vs IFJa part corr.png. In contrast, area 55b dominates language-related connectivity to areas A5, PSL (left $z=23.24$, right $z=17.53$), superior temporal lobe and Broca’s areas 44 and 45 (44: left $z=23.33$, right $z=18.45$; 45: left $z=23.60$, right $z=23.79$). suggesting it functions as a language-relay for the spatial-dominated prefrontal dorsal domain led by the FEF. This dissociation confirms that the spatial connectivity profile is specific to the FEF, and not general for the entire dorso-prefrontal region.

Notably, 55b maintains strong direct coupling with auditory association areas A5 (left: $z=13.27$, right $z=19.42$) and A4 (right: $z=7.51$). These connections are mostly absent in the FEF profile (only in left hemisphere to A4, $z=4.06$, $mean=0.01$). This suggests that 55b serves as the primary auditory-language relay within the dorsal prefrontal domain, connecting acoustic input with the spatial attention network rather than the FEF itself.

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4.5.4 Results of the Broca-Seed Validation

To evaluate recent anatomical categorizations proposing a functional subdivision of Broca’s area within the auditory dual-stream architecture (Rolls et al. (2023) - Cerebral Cortex), the connectivity patterns of Areas 44 and 45 were independently assessed.

Applying partial correlation to isolate direct functional pathways reveals highly divergent connectivity profiles, highlighting the distinct specializations within the Broca’s area.

Area 45: The Ventral Anchor
Area 45 exhibits a strong integration into the ventral what-stream. The connectivity profile extends massively into the temporal lobe (e.g., STSdp: left $z=11.68$, $\text{mean}=0.06$; right $z=20.12$, $\text{mean}=0.05$) and demonstrates strong coupling with the PSL (left $z=5.36$, $\text{mean}=0.06$; right $z=16.91$, $\text{mean}=0.06$). This robust temporal-prefrontal coupling firmly anchors Area 45 as a core semantic node.

Area 44: The Motor-Articulatory Interface
Area 44 reveals a fundamentally different architecture. The partial correlation model shows no meaningful integration with the parietal or visual motion networks. Regions such as MT, MST, and PFop demonstrate near-zero or negative shared variance ($\text{mean}\le 0.00$). Instead, Area 44 strictly isolates its functional coupling to the anterior ventral insula (AVI: left $z=19.18$, $\text{mean}=0.03$; right $z=23.91$, $\text{mean}=0.05$), the premotor language node 55b (left $z=5.89$, $\text{mean}=0.07$), and the IFJa (left $z=11.15$, $\text{mean}=0.04$; right $z=8.93$, $\text{mean}=0.04$).

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4.6 Behavioral Prediction

To check whether the resting-state networks identified in sections 4.2–4.5 are functionally meaningful, we tested if individual differences in RSFC could predict how well participants performed on behavioral tasks designed to target the auditory ‘what’ and ‘where’ pathways.

4.6.1 “What” Stream: Predicting Semantic Processing Speed

The main test of the ventral ‘what’ stream hypothesis used the Language Task Story median reaction time (Median RT). The ventral ROI model significantly predicted story Median RT ($R=0.18$, $p=0.001$), with area A5 and STGa as the strongest positive predictors — meaning that stronger resting-state coupling to area A5 and STGa was associated with slower response times. The dorsal ROI model did not reach significance ($R=0.04$, n.s.). This provides a clean dissociation: the semantic ‘what’ network predicts language comprehension speed, while the spatial ‘where’ network does not.

[PLOT A — Number of edges per area, Language Task Median RT, ventral model, part371, K=371]
What to see: Area 45 should dominate the positive edge count, with few or no other areas contributing substantially. This visualizes why Area 45 drives the prediction and anchors the IFJa–Area 45 coupling established in section 4.3.2 as the behavioral correlate of semantic processing speed.

The story accuracy (Acc) results told a different story. The full network predicted story Acc ($R=0.38$, $p<0.001$), and both the ventral ($R=0.32$, $p<0.001$) and dorsal ($R=0.24$, $p<0.001$) submodels reached significance. Interestingly, area 45 appeared as a negative predictor of accuracy — stronger Broca’s area coupling was associated with lower accuracy. Given the simple vocabulary of the Aesop fable stories, this might point towards an over-engagement of deep semantic and syntactic machinery that actually disrupts fluent comprehension rather than helping it. An alternative explanation cannot be ruled out, though: because accuracy was near ceiling, the remaining variance may simply reflect attentional lapses or button errors, which would make the negative Area 45 contribution more likely a product of noise-fitting than a real effect. The dorsal model’s contribution, driven by areas 43, 7PC, and PF, likely reflects general attentional demand rather than stream-specific semantic processing. Reaction time is therefore the more meaningful and stream-specific measure for the ‘what’ stream hypothesis.

Story accuracy did not replicate in the larger full812 sample (n.s.) — consistent with the ceiling effect interpretation, since without matched behavioral data for each subject, there is simply not enough variance to predict. The Median RT effect, however, held up in the full812 all-ROI model with $K=10$ cross-validation ($R=0.14$, $p<0.001$), showing that the semantic speed signal is robust enough to survive even when subject-level behavioral data is incomplete.

4.6.2 “Where” Stream: Predicting Visuo-Spatial Working Memory

We tested the dorsal ‘where’ stream hypothesis against Working Memory Task Place accuracy (Place Acc) and reaction time (Place RT). In the global model (all ROIs, part371, $K=371$), Place Acc was significantly predicted ($R=0.14$, $p=0.007$), with SCEF (supplementary and cingulate eye field) and the anterior ventral insula (AVI) as the strongest positive predictors.

[PLOT B — Connectivity mask, positive predictive edges, WM Place Acc, full network model, part371, K=371]
What to see: SCEF should appear as the dominant node with the highest number of positive predictive edges. AVI may appear as a secondary node. FEF should be absent or show minimal edges — its absence from the positive predictors is notable given that SCEF sits anatomically adjacent to it in medial dorsal frontal cortex.

When we looked at stream-specific models, however, the picture reversed in a striking way. The dorsal ROI subset predicted Place Acc with a negative cross-validated R ($R=-0.15$, $p=0.004$), with superior parietal areas 7AL and 7Am contributing positively in an otherwise inverse model — a result that is hard to reconcile with a straightforward dorsal ‘where’ stream contribution. By contrast, the ventral ROI model predicted Place Acc significantly and positively ($R=0.20$, $p<0.001$), driven by area 47l (lateral orbitofrontal/inferior frontal cortex). This suggests that performance on the visuo-spatial working memory task is better explained by ventral semantic network connectivity than by dorsal spatial network connectivity.

For Place RT, the dorsal model produced an artefactual result ($R=-1.00$) caused by matrix rank collapse under partial correlation with too few ROIs — this result is excluded from interpretation (see section 5.7). The ventral model yielded a marginally significant prediction ($R=-0.11$, $p=0.035$), with area STSda contributing positively and IFJa negatively. The negative contribution of IFJa — stronger IFJa connectivity associated with faster Place RT — is consistent with the semantic hijacking interpretation above. If participants solve the Place task via object recognition rather than spatial navigation, then IFJa engagement reflects its role as a semantic controller (Soyuhos, O., & Baldauf, D. (2023), Bedini, M., & Baldauf, D. (2021)), not a domain-general executive response. Notably, Bedini & Baldauf (2021) also report IFJa involvement in working memory, adding another layer of consistency to this finding — IFJa’s engagement in the Place task may reflect both its semantic control function and its role in maintaining object representations in working memory. This reading is reinforced by area 47l also driving Place Accuracy in the same ventral model: both predictors point toward a semantic processing strategy as the dominant mechanism. In other words, the fact that IFJa appears in a visuo-spatial task does not contradict the ventral stream specificity from section 4.6.1 — it instead provides further evidence that the task itself was solved semantically rather than spatially. The full-network model ($R=-0.20$, $p<0.001$) identified MT and TA2 as the strongest negative predictors of Place RT — stronger connectivity to visual motion and auditory association cortex was linked to faster responses — while PSL emerged as the leading positive predictor, suggesting that stronger perisylvian language area coupling was associated with slower Place RT.

Taken together, these results suggest that the 2-Back Place task was predominantly solved via ventral object-recognition strategies rather than dorsal spatial navigation, consistent with the methodological constraint noted in section 3.4.2. The FEF did not emerge as a significant predictor in either the full-network or the stream-specific models — a null result consistent with both the task’s visual nature and the semantic hijacking interpretation above.

The dominant predictor in the full-network model was instead SCEF (supplementary and cingulate eye field). SCEF is located in medial frontal cortex as part of the SMA/cingulate motor complex and is classified within the dorsal network in section 3.2. Despite its vocalization selectivity per Dureux (2024) — it responds almost exclusively to vocalizations, suggesting cingulate attention monitoring of biologically relevant sounds — its anatomical position firmly places it in the dorsal attentional architecture adjacent to the FEF. Its emergence as the top predictor for a visuo-spatial working memory task is therefore consistent with a dorsal attentional contribution, though the modest effect size ($R=0.14$) and the failure of the isolated dorsal submodel to reach a positive result suggest that SCEF’s role operates through distributed network interactions rather than a dedicated spatial processing pathway.

4.6.3 Control Paradigm: Acoustic Signal Filtering (Noise Comparison)

The NIH Toolbox Noise Comparison score served as a control for low-level acoustic signal filtering. In contrast to the language comprehension results, the ventral ROI model failed to predict Noise Comparison performance ($R=-0.07$, n.s.). This means that the RSFC of the IFJa-anchored semantic network does not generalise to acoustic noise exclusion.

Significant predictions came instead from the full-network model ($R=0.22$, $p<0.001$) and the dorsal ROI model ($R=0.17$, $p=0.001$). In the full-network model, area PFcm (inferior parietal cortex, medial PF complex) was the dominant positive predictor, with FOP3 as the main negative predictor. The dorsal submodel showed a different pattern: OP4 (parietal operculum, area 4) was the dominant positive predictor, while FOP3, 7AL, and PBelt contributed negatively. Across both models, the leading predictors are opercular and parietal rather than prefrontal — which dissociates acoustic filtering from the IFJa-anchored semantic control network.

[PLOT C — R-value overview, all tasks × ventral/dorsal/full models, part371, K=371]
What to see: Three task groups (Language RT, WM Place Acc, Noise Comp), each with three bars (ventral, dorsal, full). The pattern should show: (1) ventral dominates for Language RT, dorsal flat — the ‘what’-stream dissociation; (2) ventral positive, dorsal negative for Place Acc — the semantic hijacking pattern; (3) ventral near zero, dorsal and full positive for Noise Comp — the double dissociation with acoustic filtering. Significant bars marked. This figure summarizes the entire argument of section 4.6 in a single view.

This double dissociation — ventral specificity for semantic RT, parieto-opercular dominance for acoustic filtering — suggests a hierarchical organization: the ‘cocktail party’ problem of separating physical signals is solved at an earlier, lower-level auditory processing stage before the IFJa-anchored prefrontal network steps in for meaning extraction.

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5.0 Discussion

5.1 Resting-State Evidence for a Supramodal Prefrontal Architecture

Our central objective of this study was to examine whether the prefrontal hubs known to govern top-down attentional control in the visual domain extend their functional architecture into the auditory system. Our resting-state connectivity patterns are consistent with this hypothesis. Applying single-seed partial correlation analyses, we observe a functional dissociation between the FEF and the IFJa: the FEF shows preferential resting-state coupling with auditory-spatial and motion-sensitive cortical regions, whereas the IFJa couples selectively with temporal-semantic and language-related areas. This pattern supports our primary hypothesis (Section 1.3) that the auditory where-stream connects preferentially to FEF and the what-stream to IFJa. The observed dissociation is analogous to the spatial–non-spatial segregation previously established for these regions in the visual domain by Bedini & Baldauf (2021), who characterized the FEF as a spatial attention hub embedded in the dorsal attention system and the IFJa as anchoring for non-spatial, object-level processing. That our auditory connectivity profiles mirror this division suggests a prefrontal organization that may not be modality-specific but supramodal in its logic.

5.1.1 The FEF as an Auditory-Spatial Controller

The partial correlation profile of the FEF is consistent with its established role as a spatial attention controller, now extended to the auditory domain. Rather than projecting to the superior parietal lobule regions typically associated with visuospatial attention (7PC, 7Am, 7AL) — connections that vanish when shared ROI variance is partialled out — the FEF couples selectively with the inferior parietal lobule, specifically PF (left $z=13.89$; right $z=18.22$) and PFcm, as well as with the motion-sensitive area MST (bilateral, $z\approx 8.95$) and multimodal convergence zones including STV and TPOJ1. Crucially, the FEF shows no significant effect-size connectivity with any temporal regions of the ventral “what” stream (e.g., TE1a, TA2, TGd), suggesting a topographically clean dissociation.

This pattern aligns with the functional logic described by Rauschecker & Scott (2009), who demonstrated that the posterior-dorsal auditory stream links posterior superior temporal cortex and parietal areas for spatial processing, with activation in regions adjacent to MT/MST specifically for auditory motion. Our resting-state data extend this framework to the prefrontal level by showing that the FEF — a node absent from classical auditory stream models — shows functional coupling with these motion-tracking areas, suggesting it may exert top-down control over auditory spatial orienting via the inferior parietal lobule. This stands in contrast to the superior parietal lobule route that characterizes visuospatial FEF connectivity Bedini & Baldauf (2021), a shift that may reflect the inherently multimodal nature of auditory spatial processing: auditory spatial localization operates within a distributed, multisensory reference frame rather than a retinotopic, egocentric one Rauschecker & Scott (2009).

5.1.2 The IFJa as a Semantic-Auditory Controller

The partial correlation profile of the IFJa reveals a complementary picture. After controlling for shared ROI variance, IFJa couples robustly with superior temporal sulcus regions central to auditory object identity processing (STSdp: left $z=13.94$, right $z=14.64$; STSda: left $z=10.30$, right $z=16.53$), with the Broca’s area subregions BA44 (bilateral $z\approx 24$) and BA45 (bilateral $z\approx 18$), and with early auditory association areas A4 and A5, while showing no substantial coupling with parietal spatial areas. In direct contrast to the FEF, the IFJa’s partial connectivity profile is predominantly language- and identity-oriented, and spatially disjoint from the parietal networks that define the dorsal where-stream.

This selectivity is consistent with the IFJa’s network membership and functional characterization. Per the resting-state fMRI parcellation of Ji et al. (2019, as reviewed in Bedini & Baldauf (2021)), the IFJa belongs to the language network, while the FEF is assigned to the cingulo-opercular network in that same partition — a classification our data directly support for the IFJa: its strongest auditory couplings are with the STS and Broca’s area. The concurrent coupling with early auditory association areas A4 and A5 suggests that IFJa may exert top-down modulation at an early stage of auditory feature extraction, biasing the processing of spectrotemporal features relevant to auditory object identity. The coupling with Broca’s area suggests coordination with the language working memory system, consistent with the IFJa’s proposed role in feature- and object-based attention encoding Bedini & Baldauf (2021). This pattern further resonates with the broader finding that top-down prefrontal control over auditory cortex is implemented via anticipatory alpha oscillations in object-based attention tasks De Vries & Baldauf (2021).

5.1.3 A Supramodal Prefrontal Architecture

Taken together, the complementary dissociation between FEF and IFJa connectivity profiles is consistent with a supramodal organization of prefrontal attentional control. The connectivity patterns suggest that the brain may not deploy dedicated, modality-specific attention controllers for each sensory stream; instead, the same two prefrontal hubs — FEF for spatial and IFJa for non-spatial control — appear to couple preferentially with corresponding auditory cortical regions in ways that parallel their known functional roles in the visual domain.

This is compatible with the view that the lateral prefrontal cortex organizes top-down attention according to a spatial/non-spatial axis that cuts across sensory modalities. Such a supramodal architecture would be computationally efficient — allowing a single prefrontal control system to flexibly coordinate attention across vision, audition, and potentially other modalities — and would be consistent with the evolutionary argument that visuospatial attention systems, which are phylogenetically older, are preserved in primates and subsequently recruited for the control of other sensory domains Bedini & Baldauf (2021). Whether these resting-state coupling patterns reflect genuine top-down control signals remains to be established with directed connectivity methods.

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5.2 Re-evaluating the auditory where stream

As Hickock und Poeppel proposed a How pathway which might be the language oatheway rolls talked about

1. Neighboring Areas

The Resulkts frm 4.5 show another dissoctation within the where stream where FEF and 55b share the labour of this stream.

• this could

2. Subheading

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6.0 References

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Bedini, M., Olivetti, E., Avesani, P., & Baldauf, D. (n.d.). Surface-based probabilistic tractography uncovers segregated white matter pathways underlying spatial and non-spatial control [Manuscript in preparation].

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7.0 Appendix

7.1 Glasser Atlas (2016)

Here is the parcellation created by Glasser

2. Subheading

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8.0 Declaration

1. Subheading

Hier schreiben…

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9.0 Abbreviations

1. Brain Region Abbreviations

Abbreviation	Full Name
43	Area 43
44 / BA44	Area 44, Pars Opercularis (Broca’s area)
45 / BA45	Area 45, Pars Triangularis (Broca’s area)
47l	Area 47 Lateral
55b	Area 55b
7AL	Area 7 Anterior Lateral
7Am	Area 7 Anterior Medial
7PC	Area 7 Posterior Capsular
A1	Primary Auditory Cortex (Area A1)
A4	Auditory Area 4
A5	Auditory Area 5
AVI	Anterior Ventral Insula
BA	Brodmann Area
FEF	Frontal Eye Field
FOP1–3	Frontal Operculum 1, 2, 3
IFJ	Inferior Frontal Junction
IFJa	anterior Inferior Frontal Junction
IFJp	posterior Inferior Frontal Junction
IFS	Inferior Frontal Sulcus
IFSp	Inferior Frontal Sulcus posterior
iPCS	inferior precentral sulcus
IPL	Inferior Parietal Lobule
MST	Medial Superior Temporal Area
MT	Middle Temporal Area
OP1–4	Opercular Areas 1–4
PBelt	Parabelt Complex
PF	Area PF (Inferior Parietal)
PFC	Prefrontal Cortex
PFcm	Area PF Complex Medial
PFop	Area PF Opercular
PGi	Area PGi (Inferior Parietal)
PMC	Premotor Cortex
PSL	Perisylvian Language Area
SCEF	Supplementary and Cingulate Eye Field
SPL	Superior Parietal Lobule
Spt	Sylvian parieto-temporal area
SFS	Superior Frontal Sulcus
sPCS	superior precentral sulcus
STS	Superior Temporal Sulcus
STSda	Superior Temporal Sulcus, dorsal anterior
STSdp	Superior Temporal Sulcus, dorsal posterior
STSva	Superior Temporal Sulcus, ventral anterior
STSvp	Superior Temporal Sulcus, ventral posterior
STG	Superior Temporal Gyrus
STGa	Superior Temporal Gyrus, anterior
STV	Superior Temporal Visual Area
TA2	Area TA2
TE1a	Area TE1 Anterior
TGd	Temporal Gyrus dorsal (Temporal Pole)
TGv	Temporal Gyrus ventral (Temporal Pole)
TPOJ1	Temporoparietal Occipital Junction Area 1
V1	Primary Visual Cortex

2. Other Abbreviations

Abbreviation	Full Name
BOLD	Blood Oxygen Level Dependent
CIFTI	Connectivity Informatics Technology Initiative (HCP file format)
DAN	Dorsal Attention Network
EC	Effective Connectivity
EPI	Echo-Planar Imaging
RSFC	Resting-State Functional Connectivity
FDR	False Discovery Rate
fMRI	functional Magnetic Resonance Imaging
FPN	Frontoparietal Network
HCP	Human Connectome Project
HCP-MMP1	HCP Multimodal Parcellation (version 1.0)
ICA	Independent Component Analysis
ICA-FIX	ICA-based X-noiseifier
LH	left hemisphere
MD	Multiple Demand (system)
MEG	Magnetoencephalography
RH	right hemisphere
ROI	Region of Interest
rs-fMRI	resting-state functional Magnetic Resonance Imaging
SLF	Superior Longitudinal Fasciculus
T1w	T1-weighted (MRI)
T2w	T2-weighted (MRI)
TE	Echo Time
TR	Repetition Time
VAN	Ventral Attention Network

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