Human brain structural connectivity matrices–ready for modelling

Škoch, Antonín; Bučková, Barbora Rehák; Mareś, Jan; Tintěra, Jaroslav; Šanda, Pavel; Jajcay, Lucia; Horáček, Jiří; Španiel, Filip; Hlinka, Jaroslav

doi:10.1038/s41597-022-01596-9

Cited by 13 publications

(17 citation statements)

References 41 publications

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“…We started our workflow by computing a template structural connectivity matrix from openly available data, since we did not record individual DTI data. We used data from 88 healthy control individuals participating in the Early-Stage Schizophrenia Outcome study 117 . Individual structural connectivity matrices were already provided and arranged according to the Automated Anatomical Labeling (AAL) atlas 118 with 90 ROIs.…”

Section: Methodsmentioning

confidence: 99%

Neural dynamics of semantic control underlying generative storytelling

Rastelli,

Greco,

Finocchiaro

et al. 2024

Preprint

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Storytelling has been pivotal for the transmission of knowledge and cultural norms across human history. A crucial process underlying the generation of narratives is the exertion of cognitive control on the semantic representations stored in memory, a phenomenon referred as semantic control. Despite the extensive literature investigating the neural mechanisms of semantic control in generative language tasks, little effort has been done towards storytelling under naturalistic conditions. Here, we probed human participants to generate stories in response to a set of instructions which triggered a narrative that was either appropriate (ordinary), novel (random), or balanced (creative), while recording functional magnetic resonance imaging (fMRI) signal. By leveraging deep language models, we demonstrated how participants ideally balanced the level of semantic control during story generation. At the neural level, creative stories were differentiated by a multivariate pattern of neural activity in frontal cortices compared to ordinary ones and in fronto- temporo-parietal cortices with respect to randomly generated stories. Crucially, similar brain regions were also encoding the features that distinguished the stories behaviourally. Moreover, we decomposed the neural dynamics into connectome harmonic modes and found specific spatial frequency patterns underlying the modulation of semantic control during story generation. Finally, we found different functional coupling within and between the default mode, salience and control networks when contrasting creative stories with their controls. Together, our findings highlight the neural mechanisms underlying the regulation of semantic exploration during narrative ideation and contribute to a deeper understanding of the neural dynamics underpinning the role of semantic control in generative storytelling.

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Section: Methodsmentioning

confidence: 99%

Neural dynamics of semantic control underlying generative storytelling

Rastelli,

Greco,

Finocchiaro

et al. 2024

Preprint

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“…In general, non-random localizations of lesions are identified through specific patterns and marks in capture points or ROI [ 76 ]. Then, brain’s information is processed from characteristics at both structural and functional levels [ 77 ]. A common goal is to infer causal influences from experimental measures and exploit the interconnectivity between multiple data to provide mechanistic insights on the nature of their relationships.…”

Section: Radiomicsmentioning

confidence: 99%

Assessment of brain cancer atlas maps with multimodal imaging features

Capobianco,

Dominietto

2023

J Transl Med

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Background Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. Main text Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. Conclusions The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy. Graphical Abstract

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“…There have been significant theoretical and experimental developments in comprehending the wiring diagrams (connection matrices) of the cerebral cortex of invertebrates and mammals over the last thirty years; see, for example, [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] and the references therein. The topology of cortical neural networks is described by connection matrices.…”

Section: -Adic Kernels and Connection Matricesmentioning

confidence: 99%

“…Nowadays, there is a large number of experimental data about the connection matrices of the cerebral cortex of invertebrates and mammalians. Based on these data, several researchers hypothesized that cortical neural networks are arranged in fractal or self-similar patterns and have the small-world property; see, e.g., [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] and the references therein. Connection matrices provide a static view of neural connections.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Wilson–Cowan Models and Connection Matrices

Zúñiga–Galindo

Zambrano-Luna

2023

Entropy

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This work aims to study the interplay between the Wilson–Cowan model and connection matrices. These matrices describe cortical neural wiring, while Wilson–Cowan equations provide a dynamical description of neural interaction. We formulate Wilson–Cowan equations on locally compact Abelian groups. We show that the Cauchy problem is well posed. We then select a type of group that allows us to incorporate the experimental information provided by the connection matrices. We argue that the classical Wilson–Cowan model is incompatible with the small-world property. A necessary condition to have this property is that the Wilson–Cowan equations be formulated on a compact group. We propose a p-adic version of the Wilson–Cowan model, a hierarchical version in which the neurons are organized into an infinite rooted tree. We present several numerical simulations showing that the p-adic version matches the predictions of the classical version in relevant experiments. The p-adic version allows the incorporation of the connection matrices into the Wilson–Cowan model. We present several numerical simulations using a neural network model that incorporates a p-adic approximation of the connection matrix of the cat cortex.

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Human brain structural connectivity matrices–ready for modelling

Cited by 13 publications

References 41 publications

Neural dynamics of semantic control underlying generative storytelling

Neural dynamics of semantic control underlying generative storytelling

Assessment of brain cancer atlas maps with multimodal imaging features

Hierarchical Wilson–Cowan Models and Connection Matrices

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