3.4 Brain Behavior Correlation

Outline 3.4 Brain Behavior Correlation

• Task fMRI
  • language story nehmen

To-Do’s

• [ ]

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.

Notes & Scrapbook

Hier Dinge abladen, die noch keinen Platz im Text haben, damit der Schreibfluss nicht stoppt.

Full correlation

language task story acc → nicht signifikant

alle sind linear correlated und threshold 0.05

Entwurf von Gemini

• Referenz: HCP S1200 Release Reference Manual (S. 48)

• Referenz: Barch et al., 2013: Function in the human connectome

• Referenz: NIH Toolbox Technical Manual - Words-in-Noise

---

Zusammenfassung der Methodik-Sektion

• Language Task (Story): Misst den “What”-Stream. RT operationalisiert die semantische Zugriffsgeschwindigkeit; Acc ist durch Exekutivfunktionen überlagert.

• WM Task (2-Back Place): Dient als supramodaler Proxy für den räumlichen “Where”-Stream, bringt jedoch die Limitation visueller Objekterkennungsstrategien mit sich.

• Noise Comp Task: Dient als sensorisches Kontrollmaß, um präfrontale Bedeutungsverarbeitung (“What”) von rein akustischer Signalfilterung (“Cocktail-Party-Effekt”) abzugrenzen.

• Akademischer Ton: Die Begründungen sind funktional formuliert und benennen die Schwächen (wie die Modalitäts-Diskrepanz beim Place-Task) proaktiv, was deine wissenschaftliche Eigenständigkeit unterstreicht.

see also

idee von gemini:

Zusammenfassung des endgültigen Setups

• IFJa + Language (STORY > MATH): Evaluiert ausschließlich die von dir in 3.2 definierten Ventral-Targets (STGa, STS-Cluster, TA2, TGv, 45, 47l, PSL, STV, TPOJ1).

• FEF + Working Memory (PLACE > AVG): Evaluiert ausschließlich die von dir in 3.2 definierten Dorsal-Targets (7AL, 7Am, 7PC, MT, MST, PF, PFop, 44, 55b, FOP1-3).