Biology subjects

Cionca, A.

Publications and source records attributed to Cionca, A..

4 recordsLinked to original sources

bioRxiv2026

Directed Brain Connectomics Revealed by Bicommunity Structure

The brain is a complex, interconnected system in which structural wiring underpins the flow of information between functional units. After two decades of advances in connectomics, the asymmetry of edge structures in large-scale brain networks remains largely unexplored. Here, we build a directed connectome combining structure and function, and dissect its organization into bicommunities where sending and associated receiving sets of nodes, through clustering of directed-edge information, are fully acknowledged. Our findings reveal a primary directional axis from sensory to association cortices, indicative of bottom-up information flow. Validation with invasive electrophysiological measures demonstrates that these bicommunities capture an intermediate organizational scale at which connection asymmetry aligns across modalities. Finally, sets of distinct known anatomical fiber bundles and their functional mapping are recovered as specific bicommunities. This work identifies asymmetric pathways onto which neural computation can be expressed, and establishes directed connectomics as a novel framework for understanding brain organization.

neuroscience↗

bioRxiv2025

Attentional focus and emotion modulate voice recognition deficits in cerebellar stroke patients

The cerebellum, long regarded as a motor structure, is increasingly recognized for its role in higher-order cognitive and socio-emotional functions. Its contribution to vocal emotion decoding, however, remains insufficiently understood. While prior work has linked the cerebellum to attentional control and predictive coding, direct evidence for its role in modulating prosody recognition under explicit versus implicit attentional demands is lacking. This study investigated how cerebellar stroke--controlled for time since stroke in months, and with an equal number of left- and right-lateralized lesions--alters vocal emotion processing depending on attentional focus. We aimed to disentangle sensory encoding and integration from decisional contributions of the cerebellum by combining behavioral analyses, drift diffusion modeling (DDM), and functional MRI in cerebellar stroke. Fifteen patients with chronic, isolated cerebellar stroke and fifteen matched controls performed two tasks on vocal stimuli expressing anger, happiness, or neutrality. In the explicit task, participants categorized the expressed emotion; in the implicit task, they categorized speaker gender while ignoring emotional tone. Behavioral performance was analyzed using mixed-effects logistic regression and DDM (angle model). Functional MRI analyses included conventional contrasts as well as model-based regressors derived from behavioral and computational parameters. Patients showed lower accuracy than controls, with deficits particularly pronounced in the explicit task. Performance also varied by emotion: gender recognition for angry voices (implicit processing) was relatively preserved, whereas happy and neutral voices were more vulnerable. Contrary to predictions, DDM parameters did not differ between groups. Instead, task effects dominated: implicit processing yielded higher drift rates, larger boundary separation, faster non-decision times, and stronger attentional focus compared to explicit recognition, suggesting that explicit evaluation imposes additional cognitive costs. At the neural level, patients recruited extended networks during explicit processing, including orbitofrontal cortex, amygdala, anterior insula, and cerebellar lobule IX, alongside cerebello-cortical tracts. Implicit processing was associated with more restricted activations, particularly in frontal opercular and cerebellar regions (lobule IX). Model-based analyses further revealed that successful categorization in patients relied on the left inferior frontal gyrus, inferior parietal lobule, and pre-supplementary motor area, although these effects were not significant when controlling for time since stroke. Our findings demonstrate that the cerebellum does not primarily shape decision dynamics but optimizes sensory representations of prosodic cues for downstream evaluation. When predictive tuning is compromised, patients maintain intact decision processes yet rely on compensatory cortical recruitment, particularly during explicit tasks. This pattern supports predictive coding accounts of cerebellar function in socio-emotional communication and highlights the need to consider subtle socio-affective deficits in cerebellar patients.

neuroscience↗

bioRxiv2025

Non-invasive prediction of conduction velocities in the human brain from MRI-derived microstructure features at 7 Tesla

The conduction velocity of neuronal signals along axons is a key neurophysiological property that can be altered in various disease processes. While cortico-cortical evoked potentials (CCEPs) can be measured in presurgical assessment to provide information about conduction delay between a subset of brain regions, it is currently not possible to efficiently and systematically estimate conduction velocity in vivo across the whole brain. Given the established link between conduction velocity and axon morphology (most notably axon diameter but also myelination), mapping a reliable and quantitative metric linked to axon properties could fill the gap of inferring conduction velocity across the entire human brain. By integrating multiple MRI-derived microstructural measures - including axon radius, axonal water fraction, extra-axonal perpendicular diffusivity, and longitudinal relaxation time - and conduction velocity estimates obtained from a large database of CCEPs, we developed a whole-brain prediction model of conduction velocity. Our multivariate MRI-based model explained 29% of variance in neurophysiological conduction velocity, making it possible to partially predict whole-brain conduction velocity and delay matrices along connections for which no direct measurement is commonly available from epilepsy surgery investigations. This integrative MRI-based approach could provide a non-invasive framework for comprehensively characterising conduction delays in vivo across the human brain white matter.

neuroscience↗

bioRxiv2024

Differential Engagement of Associative-Limbic and Sensorimotor Regions of the Cerebellum and Basal Ganglia in Explicit vs. Implicit Emotional Processing

Emotional prosody processing involves a widespread network of brain regions, but the specific roles of the cerebellum and basal ganglia in explicit and implicit tasks are not well known or understood. This study investigated how the cerebellum and basal ganglia contribute to explicit (emotion categorization) and implicit (gender categorization) processing of emotional prosody, namely when attention is directly versus implicitly oriented towards the emotion of the voice stimuli, respectively. Twenty-eight healthy French-speaking participants (average age: 65 years old) underwent high-resolution functional MRI while performing explicit and implicit vocal emotion processing tasks. Neuroimaging results revealed--and replicated--that both tasks recruited a widespread network, including the superior temporal cortex, inferior frontal cortex, primary motor and somatosensory cortices, basal ganglia, and cerebellum. The explicit task elicited stronger activations in the basal ganglia (caudate nucleus, putamen) and cerebellar regions (Crus I/II, lobules VI, VIIb, and X), consistent with higher cognitive control demands. In contrast, the implicit task was associated with activations in cerebellar lobules IV-V, VI, VIII, and IX, along with the thalamus. Regression-based functional connectivity analyses further demonstrated stronger connectivity between the right cerebellar lobule IX and the putamen, as well as the cerebellar vermis (XII), particularly during implicit processing. These findings highlight the distinct contributions of the cerebellum and basal ganglia to emotional prosody processing, with explicit tasks engaging associative and cognitive control networks, while implicit tasks rely more on sensorimotor and automatic neural processing mechanisms.

neuroscience↗