Latent states are effective for modeling complex brain data
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Multiple peer-reviewed studies establish that hidden Markov models, variational autoencoders, and state-space approaches effectively model and decode complex, high-dimensional brain imaging data by mapping them to latent states.
There exists growing interest in understanding the dynamics of resting state functional magnetic resonance imaging (rs-fMRI) to establish mechanistic links between individual patterns of spontaneous neural activation and corresponding behavioral measures in both normative and clinical populations. Here we propose and validate a novel approach in which whole-brain rs-fMRI data are mapped to a specific low-dimensional representation—affective valence and arousal processing—prior to dynamic analysis. This mapping process constrains the state space such that both independent validation and visualization of the system's dynamics become tractable. To test this approach, we constructed neural decoding models of affective valence and arousal processing from brain states induced by International Affective Picture Set image stimuli during task-related fMRI in (n = 97) healthy control subjects. We applied these models to decode moment-to-moment affect processing in out-of-sample subjects' rs-fMRI data and computed first and second temporal derivatives of the resultant valence and arousal time-series. Finally, we fit a second set of neural decoding models to these derivatives, which function as neurally constrained ordinary differential equations (ODE) underlying affect processing dynamics. To validate these decodings, we simulated affect processing by numerical integration of the true temporal sequence of neurally decoded derivatives for each subject and demonstrated that these decodings generate significantly less (p < 0.05) group-level simulation error than integration based upon decoded derivatives sampled uniformly randomly from the true temporal sequence. Indeed, simulations of valence and arousal processing were significant for up to four steps of closed-loop simulation (Δt = 2.0 s) for both valence and arousal, respectively. Moreover, neural encoding representations of the ODE decodings include significant clusters of activation within brain regions associated with affective reactivity and regulation. Our work has methodological implications for efforts to identify unique and actionable biomarkers of possible future or current psychopathology, particularly those related to mood and emotional instability.
The application of hidden Markov models (HMMs) to neural data has uncovered hidden states and signatures of neural dynamics that are relevant for sensory and cognitive processes. However, training an HMM on cortical data requires a careful handling of model selection, since models with more numerous hidden states generally have a higher likelihood on new (unseen) data. A potentially related problem is the occurrence of very rapid state switching after decoding the data with an HMM. The first problem can lead to overfitting and over-segmentation of the data. The second problem is due to intermediate-to-low self-transition probabilities and is at odds with many reports that hidden states in cortex tend to last from hundred of milliseconds to seconds. Here, we show that we can alleviate both problems by regularizing a Poisson-HMM during training so as to enforce large self-transition probabilities. We call this algorithm the 'sticky Poisson-HMM' (sPHMM). The sPHMM successfully eliminates rapid state switching, outperforming an alternative strategy based on an HMM with a large prior on the self-transition probabilities. When used together with the Bayesian Information Criterion for model selection, the sPHMM also captures the ground truth in surrogate datasets built to resemble the statistical properties of the experimental data.
For decades, psychiatric neuroimaging has searched for biomarkers of depression and other disorders, but they remain elusive in clinical practice. While the last 5 years have seen rapid progress, other large-scale correlative studies have found only small, unreliable links between brain measures and clinical symptoms. Growing evidence suggests that such limitations are not just about sample size but depend critically on how models represent data. This review traces a recent shift away from univariate methods to multivariate/multiview approaches that learn more effective representations of biological and symptom measures by flexibly learning multimodal latent representations. First, we review how linear multiview embedding methods have revealed reproducible biological depression subtypes but do not perform well in small samples or samples enriched for mild symptoms. Then, we consider newer work exploring more sophisticated representations for neuroimaging data, including deep-learning and graph-based representations, and multimodal extensions that uncover complex latent patterns that single-modality studies miss. Then, we review recent developments in foundation models, which, once trained on large corpora, can "transfer learn" readily to small clinical cohorts, potentially bringing the advantages of large-scale learning to small, privacy-limited data. Finally, we highlight emerging representation tools that treat the brain as a dynamic, stateful multivariate process. Taken together, these advances point to a future in which the value of neuroimaging will be determined not only by ever-larger sample sizes but also by data quality and by how well our algorithms capture the distributed, multimodal, and evolving nature of psychiatric disorders.
Speech comprehension involves the dynamic interplay of multiple cognitive processes, from basic sound perception, to linguistic encoding, and finally to complex semantic-conceptual interpretations. How the brain handles the diverse streams of information processing remains poorly understood. Applying Hidden Markov Modeling to fMRI data obtained during spoken narrative comprehension, we reveal that the whole brain networks predominantly oscillate within a tripartite latent state space. These states are, respectively, characterized by high activities in the sensory-motor (State #1), bilateral temporal (State #2), and default mode networks (DMN; State #3) regions, with State #2 acting as a transitional hub. The three states are selectively modulated by the acoustic, word-level semantic, and clause-level semantic properties of the narrative. Moreover, the alignment with both the best performer and the group-mean in brain state expression can predict participants’ narrative comprehension scores measured from the post-scan recall. These results are reproducible with different brain network atlas and generalizable to two datasets consisting of young and older adults. Our study suggests that the brain underlies narrative comprehension by switching through a tripartite state space, with each state probably dedicated to a specific component of language faculty, and effective narrative comprehension relies on engaging those states in a timely manner.
Abstract The brain is a complex dynamic system that constantly evolves. Characterization of the spatiotemporal dynamics of brain activity is fundamental to understanding how brain works. Current studies with functional connectivity and linear models are limited by low temporal resolution and insufficient model capacity. With a generative variational auto encoder (VAE), the present study mapped the high-dimensional transient co-activity patterns (CAPs) of functional magnetic resonance imaging data to a low-dimensional latent representation that followed a multivariate gaussian distribution. We demonstrated with multiple datasets that the VAE model could effectively represent the transient CAPs in the latent space. Transient CAPs from high-intensity and low-intensity values reflected the same functional structure of brain and could be reconstructed from the same distribution in the latent space. With the reconstructed latent time courses, preceding CAPs successful predicted the following transient CAP with a long short-term memory recurrent neural network. Our methods provide a new avenue to characterize the brain’s transient co-activity maps and model the complex dynamics between them in a framewise manner.
ger, our method can fit the multidimensional signals much more accurately than the group ICA, while learning the same number of latent states. 3.3 |. Sensitivity analysis
We conducted a sensitivity analysis to investigate the model performance under the misspecification of the number of latent states. We fit the same simulated data ( Section 3.1 ) generated from two latent states but assumed that there were three underlying latent states in the analysis. Although the algorithm will produce three estimated latent states, we expect that one column of the loading matrix Θ will have estimates close to zero, indicating that the corresponding latent state does not contribute to the data and overfitting is present with too many latent states.
Web Figure 3a shows the true loadings (i.e., Θ 1 and Θ 2 ) and the estimated loadings assuming three latent states (i.e., Θ ^ 1 , Θ ^ 2 , and Θ ^ 3 ). We see that the additional loadings in Θ ^ 3 are very close to zero, which suggests that two latent states are sufficient to fit the data. Although we have specified the wrong number of latent states, Θ 1 and Θ 2 can still be accurately estimated. In fact, Web Figure 3b shows that all the other parameters not related to the third latent state are estimated with small biases and standard errors. Therefore, under misspecification of a larger number of latent states, our method can still provide robust and valid estimates. 4 |. REAL DATA APPLICATION
We applied the state space model to an EEG study of alcoholism. The original work in Zhang et al. (1995) contained control subjects in a larger study to examine genetic predisposition to alcoholism where EEG experiences were conducted in individuals with a variety of conditions ( Ingber, 1998 ). Our data analysis contained 77 alcoholic subjects (cases) and 45 healthy controls ( Asuncion & Newman, 2007 ). All subjects were exposed to three types of visual stimuli conditions: a single image (C1, single condition), two identical repeated images (C
In the rapidly advancing field of neuroscience, sophisticated imaging techniques such as functional magnetic resonance imaging (fMRI) enable detailed analysis of brain activity. Researchers increasingly seek to disentangle distinct brain states, recognizing that fMRI data typically comprise a mixture of these states. To enable independent analysis of individual brain states, numerous methodologies have been proposed, each requiring careful consideration in practical application. This review provides a comprehensive survey of decomposition methods, covering classical, probabilistic, and tensor-based approaches and their applications. Furthermore, the review discusses additional methodological considerations essential for the effective use of these techniques. By comparing decomposition algorithms with other widely used techniques in fMRI data analysis, this review highlights their methodological strengths and limitations, and further demonstrates their broad applicability for extracting brain states from fMRI data.
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