Function subspace clustering based on sparse Representation of Intrinsic Dynamics (FRID), an unsupervised approach for identifying neurons with shared microcircuit connectivity and information encoding properties (referred to as ‘Functionomics’) from mesoscale neural recordings significantly outperforms correlated-firing-based methods in both simulated complex networks and empirical calcium recordings.
Abstract
ABSTRACT Building a fine-grained functional atlas of the brain is crucial for deciphering bio-intelligence. However, the intricate and non-linear interactions at single-neuron level lead to asynchronous and heterogeneous firing patterns among closely interacting neural populations, thereby challenging correlated-firing-based clustering methods. Here, we present Functional subspace clustering based on sparse Representation of Intrinsic Dynamics (FRID), an unsupervised approach for identifying neurons with shared microcircuit connectivity and information encoding properties (referred to as ‘Functionomics’) from mesoscale neural recordings. FRID significantly outperforms correlated-firing-based methods in both simulated complex networks and empirical calcium recordings. The accuracy and utility of FRID are validated across sensory integration and decision-making tasks to produce functionomic clusters with improved resolution and coding specificity than those defined by anatomical functional areas. As representative findings, we demonstrate single-cell level functional remapping during recovery from ischemic stroke and the learning-induced reorganization of functional modularization. Together with large-scale neural recordings, FRID could serve as a general data-driven paradigm to accelerate the understanding of mesoscale brain functions.
Neural activity is organized in low-dimensional structure, yet functional connectivity in fMRI typically represents each brain parcel by a single voxel-averaged time series. This scalar representation makes whole-brain connectivity tractable but discards potentially informative dimensions of within-parcel BOLD activity. Here, we represent each parcel by a low-dimensional temporal subspace derived from its principal-component time series and use the RV coefficient to quantify connectivity between regional subspaces. Across Human Connectome Project resting-state and working-memory data, progressively expanding these subspaces reveals reproducible connectivity regimes with distinct network and identifiability profiles. At rest, connectivity constructed from the first principal component identifies individuals more strongly than either voxel-averaged functional connectivity or higher-dimensional subspace representations. During working memory, identifiability instead peaks after secondary components are included, indicating that the distribution of individual-specific information across the regional PCA spectrum depends on cognitive state. These patterns replicate across independent samples and remain robust across multiple parcellation resolutions. Together, our findings show that within-parcel BOLD structure contains identity- and statedependent information that is obscured by scalar regional summaries. Functional connectivity may therefore be better understood as a family of related connectomes indexed by the regional subspace retained, providing a general framework for mapping interactions between low-dimensional neural representations.
P. Velidi, Enrico Amico, F. Nathoo et al.· bioRxiv· 0 citations
It is found that the method alone can change the apparent structure of the network as much as real biological differences do, and practical recommendations for parameter selection, reporting, and cross-method validation in MEA-based network neuroscience are provided.
J. Ort, V. Witzig, Aniella Bak et al.· bioRxiv· 0 citations
Topological richness grows with network size, and second homology emerges significantly above the null only in the larger networks, and results show that persistent homology resolves structured topology in neural recordings at the scale experiments actually deliver.
Eve Bodnia, M. Basart, S. Hai et al.· bioRxiv· 0 citations
Whole-brain functional connectivity, estimated from magnetoencephalography (MEG) data, provides a compact representation of long-range neuronal communication, making it suitable for predictive biomarker discovery. In this work, we propose a deep learning framework (FC-CNN) for predicting brain states from frequency-resolved functional connectivity estimates derived from resting-state MEG recordings. We systematically compare the performance of FC-CNN to that of conventional regression methods using amplitude and phase-based functional connectivity in the well-studied age-prediction task on the Cam-CAN cohort (n = 576). We show that FC-CNN outperforms conventional approaches, and that, compared to phase synchronization, amplitude envelope correlation consistently leads to higher prediction performance. Moreover, we present quantitative evidence that the weights of a trained deep learning model can enable neurophysiological interpretation of the activity patterns that inform successful predictions. Our work demonstrates that the proposed approach successfully decodes brain states from MEG functional connectivity and is promising for discovery of predictive biomarkers for brain disorders.
Eero Saarro, Santeri Ruuskanen, Carola Maria Caivano et al.· bioRxiv· 0 citations
The brain can rapidly adjust sensory processing according to behavioral context from moment to moment without altering its underlying anatomical wiring. Such flexibility is thought to arise from dynamic reconfiguration of the effective network through which signals propagate, yet the principles governing such reconfiguration and its consequences for coding remain unclear. Here, we exploited the distinct stationary and locomotion states within the same recording sessions and addressed this question by inferring directed, millisecond-scale signal-transmission networks at single-neuron resolution in behaving mice. Locomotion was accompanied by a counterintuitive structured sparsification of the inferred network: interactions became fewer but more temporally precise, while the remaining interactions were more local, modular, feature-specific, and feedforward. We then used theoretical analysis and controlled perturbations of multi-area rate models to systematically determine how each empirically observed component of this reorganization affects population coding. We found that sparsification reduced shared variability; local organization reduced signal–noise alignment; feature-specific interactions sharpened selectivity; and a more feedforward architecture accelerated decoding. These results provide an experimentally grounded mechanism by which behavioral state reorganizes neuronal interactions given the same sensory inputs, and suggest structured sparsification as an underlying principle of network reconfiguration that supports accurate and faster sensory coding.
Whole-brain transcriptomic atlases are now widely available, yet computational neural models are almost exclusively parameterized from rodent data and used to infer human brain function, an extrapolation whose cost remains unquantified. To address this, we constructed a biophysically detailed, conductance-based Hodgkin–Huxley spiking microcircuit of a five-population prefrontal network, where every ion-channel, receptor, and gap-junction conductance was scaled by cell-type-specific gene expression. We parameterized the identical circuit using single-nucleus RNA-seq from mouse mPFC and human DLPFC, alongside a literature-derived baseline, and compared their high-frequency-oscillation (HFO) outputs across seven physiological and pathological states. While population firing rates differed only modestly between the two refinements (∼20% for pyramidal and PV cells), the oscillatory dynamics diverged dramatically. The human-refined circuit generated strongly synchronized PV activity and robust ripple- and fast-ripple-band power (e.g., healthy-wake ripple power, in arbitrary units: 322 vs. 24 and 22), whereas the mouse-refined and literature arms remained asynchronous (interneuron synchrony: 0.21 vs. 0.02). This human ≫mouse ≈ original hierarchy was statistically consistent across all seven states (significant arm differences in 75/77 comparisons). Mechanistically, the human transcriptome drove markedly stronger PV–PV electrical coupling (gap-junction scale: 1.78 vs. 0.96) paired with stronger recurrent pyramidal excitation, which collectively synchronized the fast-spiking PV population into a coherent rhythm that perisomatic inhibition then imposed on the local field potential. Critically, these results are model-dependent; the gene-to-conductance mapping is phenomenological, and mRNA expression does not linearly translate to functional conductance. Nonetheless, under this mapping the divergence localizes PV-mediated coupling and excitation–inhibition balance as the parameters most in need of human-specific recalibration. More broadly, this work establishes transcriptome-informed spiking simulation as a powerful strategy for uncovering species-specific computational principles and for building mechanistically grounded, human-relevant models of prefrontal circuit dysfunction, an approach that moves beyond generic rodent defaults to enable targeted, species-appropriate modeling of neurological and psychiatric disorders.
