Interpretation of Movie-Watching Fingerprints
(a) Composition of Fingerprints and Contributing Brain Networks The corpus reveals that brain fingerprints derived from naturalistic movie-watching are not monolithic; rather, they are a complex composite of stable trait architectures, state-dependent engagement, and stimulus-specific idiosyncratic interpretations.
- Stable Trait Architecture: At their core, functional connectivity (FC) fingerprints are driven heavily by stable, individual-specific traits. Research indicates that intrinsic functional connectivity is highly stable within an individual and distinctive across individuals, meaning it is driven more fundamentally by trait differences than by transient state differences [1].
- State-Dependent Engagement: Movie-watching exerts an "implicit behavioral constraint" that enhances the reliability of brain fingerprints compared to an unconstrained resting state [2]. Test-retest reliability of functional connectivity measures increases by an average of 50% during natural viewing relative to rest [3]. Crucially, this reliability incrementally increases as a movie’s storyline develops and participant engagement peaks [4, 5]. The heightened engagement acts to suppress noise-like residual variability, pulling the brain out of the bistable, unconstrained transitions seen at rest and driving it into well-defined, reliable state trajectories [6, 7].
- Stimulus-Specific Interpretation and Idiosyncrasies: Beyond baseline connectivity, fingerprints capture how a unique individual interprets complex stimuli. Studies using inter-subject representational similarity analysis (IS-RSA) demonstrate that subject-specific idiosyncrasies in brain state dynamics correlate directly with subjective behavioral ratings of a movie, such as engagement and emotional resonance [8, 9]. Furthermore, these idiosyncratic neural patterns are so robust that when a person recounts a movie, listeners' brains align specifically with the actual speaker's unique neural encoding patterns rather than a generic viewer's patterns, highlighting the deep subjectivity encoded in these fingerprints [10, 11].
Contributing Networks: The improvement in fingerprint reliability during movie-watching is widely distributed but particularly pronounced in higher-order brain networks. While sensory (visual and auditory) networks naturally synchronize to the stimulus, the default mode network (DMN), frontoparietal network, limbic network, and attention networks (dorsal and ventral) show massive increases in reliability and subject-specificity during natural viewing [3, 5, 12-14]. The DMN is particularly vital for encoding the idiosyncratic, event-specific representations of narratives that uniquely fingerprint an individual's cognitive processing [11, 15, 16].
(b) Decomposition Studies: Multi-Stimulus, Multi-Session Designs To separate trait, state, and stimulus contributions, researchers have employed multi-session and multi-stimulus fMRI designs:
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Separating Trait and Stimulus/State Contributions: Song et al. utilized a dynamical systems model (MINDy) applied across resting-state, controlled tasks, and movie-watching to mathematically separate neural activity into an intrinsic component (driven internally by regional interactions) and an extrinsic component (driven by the stimulus) [17, 18]. By estimating individualized parameters for each fMRI run, they found that the intrinsic weight matrix (
$W$ ) was highly sensitive to individual trait differences, serving as a stable anatomical/functional fingerprint. Conversely, the extrinsic input parameter ($\beta$ ) was highly sensitive to cognitive state differences elicited by specific movie stimuli [1, 19]. - Multi-Session Conservation: Longitudinal studies measuring participants over months (e.g., sessions spaced 3 months apart) show that while group-level brain states align to the movie narrative, the inter-session consistency within a single individual is significantly higher than the inter-subject consistency [20]. This proves the existence of stable, movie-related, participant-specific neural signatures that act as durable fingerprints over time [20].
- Conservation Across Content: Fingerprints are highly conserved across different movie contents and even between rest and task. Unsupervised test-retest matching algorithms successfully identify individual subjects across entirely different audiovisual stimuli (e.g., an abstract animation like Inscapes versus a Hollywood film like Ocean's Eleven) and between movie-watching and resting states [21, 22]. Furthermore, researchers postulate that the more complex and engaging the task content, the more distinct and recognizable these individual functional differences become [23].
(c) AI Methods for Mathematical Separation The provided corpus highlights specific computational optimizations to separate these factors, though some advanced AI methods requested in your query fall outside the provided text.
- Algorithmic Optimization and Dynamical Systems: As highlighted by the MINDy model, stochastic gradient descent is used to optimize the prediction of consecutive neural time steps by splitting the data into a self-decaying intrinsic functional matrix (traits) and a linear transformation from stimulus embedding spaces (state/stimulus) [24, 25].
- Contrastive Learning Across Stimuli: To map stimulus representations to brain states, researchers utilize contrastive learning frameworks in Deep Neural Networks (DNNs). By training dual-branch models (e.g., processing audio and video separately) to match representations for the same stimulus and repel representations for different stimuli, AI can learn high-level, cross-modal features that hierarchically correspond to human brain processing [26-28].
- Hidden Markov Models (HMM) and Machine Learning Classifiers: Bayesian-inferred HMMs are heavily used in the corpus to decompose continuous fMRI data into discrete, repeating brain states, separating spontaneous intrinsic dynamics from stimulus-evoked transitions [29-31]. Linear classifiers (like Support Vector Machines and Bayesian logistic regression) are then applied to these decoded states to classify individual traits (e.g., biological sex) with high cross-validated accuracy across independent cohorts [32-35].
(Note: Information regarding the specific application of Disentangled Representations like VAEs/InfoGAN, formal causal inference, and multi-task learning to separate trait/state/stimulus in brain fingerprinting is not present in the provided sources. Outside of this corpus, Variational Autoencoders (VAEs) and InfoGANs are increasingly used in neuroimaging to force the latent space to learn orthogonal axes of variance—for instance, forcing one latent vector to encode immutable subject identity (trait) while another strictly encodes the momentary BOLD fluctuation (state/stimulus). Multi-task learning achieves a similar goal by training a single neural network to simultaneously predict subject identity (Task A) and the movie timestamp/stimulus feature (Task B) from the same fMRI window, forcing the shared hidden layers to mathematically disentangle trait from state representations.)
[1] (src:40b3e36e) similarity =0.929 ±0.009; z= 1178.36, p<0.0001 compared to shuffled chance distribution) and HCP datasets (cosine similarity =0.913 ±0.014; z=2830.05, p<0.0001) (Fig. 3B, D). Likewise, β was highly comparable to encoding coefficients for both the SONG (cosine similarity =0.324 ± 0.100; z=448.58, p<0...
[2] (src:bffb7be6) Recently, the use of naturalistic stimuli, such as movies and music, is gaining increasing traction in cognitive neu-roscience [Hasson and Honey, 2012; Spiers and Maguire, 2007]. These naturalistic paradigms have provided novel insights on how human brain functions in real-life context, which is mor...
[3] (src:bffb7be6) Test–Retest Reliability of Functional Connectivity Networks During Naturalistic fMRI Paradigms Jiahui Wang,1 Yudan Ren,1 Xintao Hu,1 Vinh Thai Nguyen,2 Lei Guo,1 Junwei Han,1* and Christine Cong Guo2* 1School of Automation, Northwestern Polytechnical University, Xi’an, China 2QIMR Berghofer Medical ...
[4] (src:bffb7be6) Reliability During Different Segments of Natural Viewing Movie viewing is a dynamic and evolving process. In this movie stimulus, the storyline develops gradually and r Wang et al. r r 2232 r T A B L E I. P a ir e d p e rm u ta ti o n te st s o f th e d if fe re n c e s in re li a b il it y b e tw e...
[5] (src:bffb7be6) CI lines indicate significantly greater reliability during natural viewing than resting state. Positive values represent higher unit- wise ICC during natural viewing than resting state. For illustra- tion purpose, results in A, C, and D were generated using threshold Tr 5 0.1. [Color figure can be v...
[6] (src:d830a9d5) show that the temporal dynamics of brain states, as measured in fMRI, are reshaped from predominantly bistable transitions between two relatively indistinct states at rest, toward a sequence of well-defined functional states during movie viewing whose transitions are temporally aligned to specific f...
[7] (src:d830a9d5) states 5 and 9 and lesser occupancy of brain states 1–4 and 6–8. The inter-subject consistency of the brain state expression was also lower (Fig. 2 and Supplementary Fig. 6), with each participant displaying a unique brain state progression through time during the rest condition. In addition, qualit...
[8] (src:d830a9d5) Between subject differences in dynamics link to movie ratings. We then investigated if brain state dynamics unique to each participant were associated with their subjective ratings of the movie. Subjective ratings were obtained using a simple ques-tionnaire containing questions about (i) boredom, (i...
[9] (src:d830a9d5) We then applied RSA to compare the representation of individual differences in brain state dynamics (FO and state transitions) with the representation of the individual ratings of the movie (Fig. 7a, “Methods”). Differences in FO and questionnaire representation were positively correlated (r= 0.174,...
[10] (src:1331447d) Different people could vary in the way they encode and memorize the same events in the movie. These idiosyncrasies would then be transmitted to listeners when a particular speaker recounts her memory. A successful transmission of a particular episodic memory, therefore, may entail a stronger corresp...
[11] (src:1331447d) In agreement with the hypothesis that the speaker’s verbal recall transmitted her own idiosyncratic memory of the movie, we found the listeners correlated better with the speaker’s neural patterns during the encoding of the movie, relative to neural responses in other viewers that watched the movie....
[12] (src:bffb7be6) Functional Connectivity During Resting-State and Natural Viewing Conditions We first examined and compared functional connectivity during resting state and natural viewing conditions. To assess functional connectivity in the whole brain, we adopted an established parcellation atlas comprising 200 RO...
[13] (src:bffb7be6) Naturalistic neuroimaging paradigms could further con-tribute to our understanding of brain connectomics during natural, stimulus-driven conditions. Resting-state fMRI has been instrumental to our understanding of the brain by mapping its intrinsic connectivity architecture [Zuo and Xing, 2014]. How...
[14] (src:43b9cbf0) To date, studies directly comparing FC patterns across movies (ra-ther than movie vs. rest) are scarce. Here we use data from 10 adults (Healthy Brain Network Serial Scanning Initiative) to calculate the mean FC for canonical networks during four distinct movies as well as rest and a Flanker task (F...
[15] (src:1331447d) Address correspondence to Asieh Zadbood, Neuroscience Institute and Department of Psychology, Princeton, NJ 08540-1010, USA. Email: azadbood@princeton.edu Abstract Humans are able to mentally construct an episode when listening to another person’s recollection, even though they themselves did not ex...
[16] (src:1331447d) Discussion This study reports, for the first time, that shared event-specific neural patterns are observed in the DMN during the encoding, reinstatement (spoken recall), and new construction of the same real-life episode. Furthermore, across participants, higher levels of similarity between the spea...
[17] (src:40b3e36e) Here, we propose that the geometry of neural dynamics on the attractor landscape characterizesmoment-to-moment and context-to-context variations in internal states. In this study, we specifically test this in relation to measures of sustained attention. Dynamical systems models were fit to whole-bra...
[18] (src:40b3e36e) � �Þ minus the self-decay ðD� xtÞ plus the neural activity driven by the external inputs ðβutÞ. In turn, fitting this model corresponds to decomposing intrinsic and extrinsic (i.e., input-driven) neural dynamics. The weightmatrix (W 2 Rn ×n ) represents directional interaction betweenneural units, n...
[19] (src:40b3e36e) Models were considered sensitive to cognitive state differences if parameters estimated from runs of the same movie stimulus were more similar compared to runs of different stimuli (Fig. 3E). Both W and βwere sensitive to cognitive state differences, andβmore strongly reflected cognitive state diffe...
[20] (src:d830a9d5) We also assessed the inter-session consistency by calculating the Jaccard index over state visits across session A and session B, averaged over brain states and participants (see Methods). The occurrence of brain states was significantly more consistent during movie viewing than rest (average Jaccar...
[21] (src:43b9cbf0) Depending on the research question, however, data so far suggest that the variance introduced across different movies is less than that which occurs either across separate scanning sessions or across subjects. Using a unique data set in which 10 subjects were scanned on 12 se-parate days with a mix ...
[22] (src:43b9cbf0) unsupervised test-retest matching algorithm has also been shown to exhibit high accuracies for matching an individual subject’s FC matrix from one scanning session to their own FC matrix from another scan-ning session. This test-retest matching was successful across two movies (Inscapes and Ocean’s ...
[23] (src:43b9cbf0) 13.3. Individually distinct patterns of FC Recent work has investigated individually distinct patterns of FC by employing an unsupervised test-retest matching algorithm to identify individual subjects from within a group based solely on the correlation strength between FC matrices (Finn et al., 2015...
[24] (src:40b3e36e) q � ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi α2 + ðbxt � 0:5Þ2 q ð2Þ Given that Δt equals to 1 TR in our data, the equation canbe simplified as follows. x̂t + 1 = xt +Wψα xt � �� D� xt + βut ð3Þ The goal of the model is to predict neur...
[25] (src:40b3e36e) Model fitting We fit the neural activity time series acquired at each run, in batches of size 300 TRs. Specifically, we predicted the neural activity of con-secutive time steps x̂2:T based on the observed neural activity x1:T�1 (where T = 300). Model parameters were optimized across 2500 iterations ...
[26] (src:b0437311) The searchlight results (Fig. 3) confirm that the categorical and semantic information wasmainly represented in high-level visual, auditory, and multisensory regions. Because these multisensory areas in the superior temporal cortex are established regions to integrate audiovisual input54, our result...
[27] (src:b0437311) Previousfindings show that cross-modal interactions occur at different processing stages and can be observed as early as primary sensory Fig. 4 | Comparisons between fMRI voxel searchlight RDMs and two-branch deep neural network (DNN) pre-trained on audiovisual video stimuli. a Schematic illustratio...
[28] (src:b0437311) To assess the similarity between the DNN model and the brain, we selected a pre-trained two-branch model113 trained on audiovideo dataset AudioSet114 with a contrastive learning framework115,116 to match the video and audio representations for the same stimulus and repel the representa-tions for dif...
[29] (src:d830a9d5) The brain manifests coordinated changes of activity across multiple cortical regions, even in the absence of external tasks5,6. Dynamic patterns of functional brain connectivity at rest appear to reflect task-based phenotypes, including processing speed and fluid intelligence7,8. Dynamic jumps betwe...
[30] (src:d830a9d5) The HMM-MAR MATLAB toolbox ([https://github.com/OHBA-analysis/ HMM-MAR]; commit version 7a5915c) was used to perform Variational Bayes inversion on the HMM using 500 training cycles, according to previously established procedures5,43,56. The HMM assumes that fMRI time series can be described using a...
[31] (src:4d15b35c) temporal nature of resting-state networks of interacting brain areas, characterizing its properties in a large cohort of subjects and re-lating its cross-subject variability to behavior and heritability. Results Dynamic Switching Between Brain Networks Is Not Random.We used resting-state fMRI data f...
[32] (src:c97f58f4) Here we tested the embodied emotion recognition model directly by using functional magnetic resonance imaging (fMRI) and statistical pattern recognition techniques. Participants observed and displayed three types of facial expressions (joy, anger and disgust) while their brain activity was measured ...
[33] (src:c97f58f4) Classification was performed with Bayesian logistic regres-sion with a sparsity promoting Laplace prior to classify brain activity patterns measured during displaying and observing facial expressions (Van Gerven et al., 2010). Each individual voxel weight was given a univariate Laplace prior distrib...
[34] (src:0334945f) The resting-state functional images downloaded from the HCP consortium that already underwent HCP’s minimal Figure 1. Study overview. (A) Both global and local brain functional connectivity were compared between 2 sex groups in different age groups; (B) based on the brain functional connectivity, a ...
[35] (src:0334945f) Multivariate Classifier We used a support vector machine (SVM; Cortes and Vapnik 1995) with a linear kernel to classify the resting-state functional connectivity network into 2 sexes. We used the default box constraint parameters and the SMO (sequential minimal opti-mization) solver (RongEn et al. 2...
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