4.0 Results

Outline 4.0 Results                 

  - [ ] 4.1 Global Connectivity Patterns
      - [ ] validation of seed regions (FEF, IFJa)                                          
  - [ ] 4.2 Testing the “Where” Stream (FEF Connectivity)
      - [ ] Motion Areas reinnhemen MT/MST                                            
  - [ ] 4.3 Testing the “What” Stream (IFJa Connectivity)
      - [ ] Strong connection to semantic system                                            
      - [ ] vergleichen mit IFJp                                                            
      - [ ] vergleichen mit 44/45/47l                                                      
  - [ ] 4.4 Resolving Ambiguities                              
      - [ ] whether A5 aligns more with IFJ or FEF          
      - [ ] PSL alignment 
      - [ ] STV alignment                                                                  
  - [ ] 4.5 Functional Roles of Adjacent Areas                                                
      - [ ] 55b as alternative dorsal seed → couples with dorsal language network, not spatial/motion
      - [ ] 44 vs. 45 as seed regions → dissociation within Broca complex
      - [ ] suggests FEF drives spatial/motion-dorsal, 55b drives dorsal language stream    
  - [ ] 4.6 Behavioral Prediction

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

Data: 0 DATA for Bachelorarbeit

Figure: Functional Connectivity: left vs right hemisphere, part correlation
Data: 0 DATA for Bachelorarbeit

• 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:

Link zum Original

Transclude of 4.2-Testing-the-"Where"-Stream-(FEF-Connectivity)#42-testing-the-where-stream-fef-connectivity

Transclude of 4.3-Testing-the-"What"-Stream-(IFJa-Connectivity)#43-testing-the-what-stream-ifja-connectivity

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.

---

Link zum Original

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.

Data: 0 DATA for Bachelorarbeit

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 $z=8.93$, $\text{mean}=0.04$).

---

Link zum Original

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.

---

Link zum Original