Biology subjects

Jovicich, J.

Publications and source records attributed to Jovicich, J..

15 recordsLinked to original sources

bioRxiv2026

Cortical long-range dynamics track excitation-inhibition balance across cognitive states and organizational principles

A fundamental question in systems neuroscience is how the brain's excitation-inhibition (E/I) balance shapes the temporal structure of neural activity and whether we can non-invasively recover this relationship from neural scaling properties. We show that MRS-derived E/I balance is expressed in long-range temporal dynamics and constrained by cytoarchitecture. The laminar organisation of inhibitory circuits, spanning agranular to granular cortex, determines how faithfully regional dynamics reflect E/I balance. This neurochemical signature is state-dependent: its relationship with cortical dynamics reverses during task engagement, consistent with traversal of dynamical regimes around a critical point. It extends beyond fMRI to arousal-linked dynamics, indexed by pupil diameter and microsaccades, indicating a global mode of cortical state rather than a regionally isolated property. The imprint of E/I balance on long-range dynamics follows the sensory-association axis at rest, and is reconfigured during task engagement. These findings establish E/I balance as a neurochemical quantity with structured, scalable signatures across the temporal and spatial organization of the human cortex.

neuroscience↗

bioRxiv2026

Exploring the sensitivity limits of neuronal current imaging with MRI and MEG in the human brain

IntroductionConventional BOLD-fMRI relies on hemodynamic responses that are temporally and spatially indirect markers of neural activity. Developing alternative contrasts, sensitive to neuroelectrical phenomena, is a critical challenge in brain imaging. Spin-lock (SL) fMRI has shown promise in phantom studies for detecting magnetic field changes associated with neuronal activity, but its in-vivo sensitivity and practicality remain unclear. This study evaluated whether SL contrast can effectively detect and localize human neuronal activation, benchmarked against complementary functional modalities, magnetoencephalography (MEG) and 3T BOLD-fMRI, to assess the sensitivity of MR-based neuronal current imaging. MethodsThirteen healthy young volunteers underwent SL-based imaging during 8 Hz visual stimulation, along with BOLD and MEG acquisitions. Subjects viewed quadrant-checkerboard stimuli to elicit localized cortical responses. Two balanced SL contrast mechanisms, rotary excitation (REX) and stimulus-induced rotary saturation (SIRS), were employed. Postprocessing targeted stimulus-locked signal fluctuations using a regression-filtering-rectification strategy. Phantom experiments tested sensitivity and analysis pipeline performance. ResultsMEG revealed robust stimulus-locked responses in occipital cortex, with estimated local magnetic field amplitudes of [~]0.07 nT. Conventional BOLD-fMRI confirmed reliable hemodynamic activation. In contrast, neither balanced REX nor balanced SIRS produced consistent stimulus-related activation in vivo. Phantom experiments subsequently yielded detection thresholds of 0.2 nT for REX and 0.6 nT for SIRS, exceeding the MEG-estimated physiological field amplitudes. ConclusionsUnder the present experimental conditions, the tested spin-lock fMRI implementations did not achieve sufficient sensitivity for reliable in-vivo detection of neuronal magnetic fields at 3T. Phantom and MEG-based estimates indicate that physiological field amplitudes in the visual cortex lie below current detection limits. These findings establish quantitative constraints on direct neuronal current imaging with MRI and provide a benchmark for future methodological developments aimed at bridging electrophysiology and functional MRI. Key pointsO_LIWe assessed spin-lock fMRI sensitivity using combined SL-fMRI, BOLD-fMRI, MEG, and phantom measurements during visual stimulation. C_LIO_LIMEG and BOLD-fMRI confirmed robust neuronal and hemodynamic activation in the visual cortex. C_LIO_LISL-fMRI did not achieve reliable in-vivo detection of neuronal magnetic fields; phantom sensitivity limits exceeded MEG-estimated physiological field amplitudes. C_LI

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bioRxiv2025

Supervised white matter bundle segmentation in glioma patients with transfer learning

In clinical settings, the virtual dissection of white matter tracts represents an informative source of information for monitoring neurological conditions or to support the planning of a treatment. The automation of this task using data-driven approaches, and in particular deep learning models, provides promising evidence of good accuracy when carried out on healthy individuals. However, the lack of large clinical datasets and the profound differences between healthy and clinical populations hinder the translation of these results to patients. Here, we investigated for the first time the effectiveness of transfer learning in adapting a deep learning architecture trained on a healthy population to glioma patients. Importantly, we provided the first thorough characterization of domain shift and its complexity, distinguishing systematic (i.e. measurement and pre-processing related) from tumor specific components. Our results suggest that (i) models trained on a large normative healthy population have a significant performance drop when the inference is carried out on patients; (ii) transfer learning can be an effective strategy to overcome the shortage of clinical data and to manage the systematic shift; (iii) fine tuning of the learning model cannot accommodate large white matter deformations induced by the tumor. The results were coherent across the five white matter bundles and three different input modalities tested, highlighting their robustness and generalizability. Our work provides valuable insights for advancing automated white matter segmentation in clinical populations and enhancing clinical transfer learning applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/659452v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1c2bbbcorg.highwire.dtl.DTLVardef@1b3555eorg.highwire.dtl.DTLVardef@a9bb4dorg.highwire.dtl.DTLVardef@147dc5e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWhite matter bundle segmentation is essential for planning glioma tumor resection. C_LIO_LIWe trained a deep learning model on a healthy dataset to segment white matter bundles. C_LIO_LIWe observed a sub-optimal performance on glioma patients due to domain shift. C_LIO_LITransfer learning mitigated domain shift between healthy and clinical populations. C_LIO_LIWe highlighted domain shift domains solved/not solved by transfer learning. C_LI

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bioRxiv2024

Multiscale excitation-inhibition balance dynamics: integrating metabolite kinetics with time-varying executive networks

The balance between neural excitation and inhibition (EIB) is an essential mechanism supporting cognitive processes. Yet, little is understood about how EIB shifts with cognitive load and its impact on functional connectivity dynamics. In this study, we investigate temporal profiles of the reciprocal modulation between EIB and functional network dynamics during working memory tasks, revealing that EIB prefrontal kinetics scale with increasing cognitive load. Notably, prefrontal EIB kinetics correlated with cognitive load, impacting stability of networks crucial for cognitive function. On one hand, brain dynamics adapt to meet increasing cognitive challenges with a shift towards more focused and sustained neural activity patterns in terms of connectivity. On the other, imbalances favouring excitation may hinder cognitive adaptability. Importantly, this experimental approach demonstrates a link between EIB kinetics, brain network dynamics and cognitive performance, defining the groundwork for exploring healthy and aberrant cognitive states. TeaserHighly focused or less responsive? Chemical signalling and network dynamics are coupled to produce persistent cognitive states.

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bioRxiv2024

Advancing Thalamic Nuclei Segmentation: The Impact of Compressed Sensing and FastSurfer on MRI Processing

The thalamus is a collection of gray matter nuclei that play a crucial role in sensorimotor processing and modulation of cortical activity. Characterizing thalamic nuclei non-invasively with structural MRI is particularly relevant for patient populations with Parkinsons disease, epilepsy, dementia, and schizophrenia. However, severe head motion in these populations poses a significant challenge for in vivo mapping of thalamic nuclei. Recent advancements have leveraged the compressed sensing (CS) framework to accelerate structural MRI acquisition times in MPRAGE sequence variants, while fast segmentation tools like FastSurfer have reduced processing times in neuroimaging research. In this study, we evaluated thalamic nuclei segmentations derived from six different MPRAGE variants with varying degrees of CS acceleration (from about 9 to about 1 minute acquisitions), using both FreeSurfer and FastSurfer for segmentation. Our findings show minimal sequence effects with no systematic bias, and low volume variability across sequences for the whole thalamus and major thalamic nuclei. Notably, CS-accelerated sequences produced less variable volumes compared to non-CS sequences. Additionally, segmentations of thalamic nuclei by FreeSurfer and FastSurfer were highly comparable. We provide first evidence supporting that a good segmentation quality of thalamic nuclei with compressed sensing T1-weighted image acceleration in a clinical 3T MRI system is possible. Our findings encourage future applications of fast T1-weighted MRI to study deep gray matter. CS-accelerated sequences and rapid segmentation methods are promising tools for future studies aiming to characterize thalamic nuclei in vivo at 3T in both healthy individuals and clinical populations.

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bioRxiv2023

TR(acking) individuals down: exploring the effect of temporal resolution in resting-state functional MRI fingerprinting

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisitions temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s. We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make substantial contributions to subject identifiability, whereas sensory-motor regions become influential only when integrating data from different TRs. We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signals sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

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bioRxiv2023

Supervised White Matter Bundle Segmentation in Glioma Patients with Transfer Learning

In clinical neuroscience, the segmentation of the main white matter bundles is propaedeutic for many tasks such as preoperative neurosurgical planning and monitoring of neuro-related diseases. Automating bundle segmentation with data-driven approaches and deep learning models has shown promising accuracy in the context of healthy individuals. The lack of large clinical datasets is preventing the translation of these results to patients. Inference on patients data with models trained on healthy population is not effective because of domain shift. This study aims to carry out an empirical analysis to investigate how transfer learning might be beneficial to overcome these limitations. For our analysis, we consider a public dataset with hundreds of individuals and a clinical dataset of glioma patients. We focus our preliminary investigation on the corticospinal tract. The results show that transfer learning might be effective in partially overcoming the domain shift.

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bioRxiv2023

Efficient PCA denoising of spatially correlated MRI data

Mar[c]enko-Pastur (MP) PCA denoising is emerging as an effective means for noise suppression in MRI acquisitions with redundant dimensions. However, MP-PCA performance is severely compromised by spatially correlated noise - an issue typically affecting most modern MRI acquisitions - almost to the point of returning the original images with little or no noise removal. In this study, we develop and apply two new strategies that enable efficient and robust denoising even in the presence of severe spatial correlations. This is achieved by measuring a-priori information about the noise variance and combing these estimates with PCA denoising thresholding concepts. The two denoising strategies developed here are: 1) General PCA (GPCA) denoising that uses a-priori noise variance estimates without assuming specific noise distributions; and 2) Threshold PCA (TPCA) denoising which removes noise components with a threshold computed from a-priori estimated noise variance to determine the upper bound of the MP distribution. These strategies were tested in simulations with known ground truth and applied for denoising diffusion MRI data acquired using pre-clinical (16.4T) and clinical (3T) MRI scanners. In synthetic phantoms, MP-PCA failed to denoise spatially correlated data, while GPCA and TPCA correctly classified all signal/noise components. In cases where the noise variance was not accurately estimated (as can be the case in many practical scenarios), TPCA still provides excellent denoising performance. Our experiments in pre-clinical diffusion data with highly corrupted by spatial correlated noise revealed that both GPCA and TPCA robustly denoised the data while MP-PCA denoising failed. In in vivo diffusion MRI data acquired on a clinical scanner in healthy subjects, MP-PCA weakly removed noised, while TPCA was found to have the best performance, likely due to misestimations of the noise variance. Thus, our work shows that these novel denoising approaches can strongly benefit future pre-clinical and clinical MRI applications.

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bioRxiv2022

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson's disease cohorts

An open discussion in studies of intrinsic brain functional connectivity is the mitigation of head motion-related artifacts, particularly in the presence of peculiar symptomatology such as in Parkinsons disease (PD). Previous studies show that Independent Component Analysis (ICA) denoising improves the reproducibility of functional connectivity findings by detecting sources of non-neural signals. However, there is still no consensus about which pre-processing pipeline should be applied in natural high motion populations such as PD, particularly in relation to novel functional network descriptions derived from dynamic connectivity analyses. In this study, we investigated how different pre-processing pipelines affect intrinsic brain connectivity metrics, both static and dynamic, derived from a group of young healthy controls (HC) and a group of PD participants. A total of 20 HC and 20 PD subjects participated in this 3 T MRI study. Resting-state functional MRI images were used to test the effects of the pre-processing pipeline of static (sFC) and temporal-varying functional connectivity (dFC) estimations. Both MRI datasets were pre-processed using three different workflows differing in the motion correction approach: (i) standard motion realignment (mc); (ii) motion outlier detection and deweighting based on image intensity change estimations (DVARS) and (iii) ICA-based noise removal using reference noise features (AROMA). Furthermore, the PD dataset was also processed with a fourth method by applying an ICA-based denoising (FIX), previously trained on the HC group. sFC analysis was performed using Group ICA, by temporally concatenating different pre-processing types in pairs of different runs. Two types of dFC analyses were considered: innovation-driven co-activation patterns (iCAPs) and co-activation patterns (CAPs). CAPs allow dFC estimations that do not require the deconvolution of the hemodynamic response function and its derivative, thus potentially being less sensitive to head-motion related noise. We found that regardless of substantial head motion differences in the two groups, sFC results were consistent across denoising strategies. Conversely, dFC was extremely sensitive to denoising strategies, particularly for the PD group with the transient-based dFC analyses. Indeed, the use of the peak-based dFC framework enables the detection of time-varying networks but in a way that is highly dependent on the motion correction pipeline. In conclusion, we show that dynamic functional network representations are highly sensitive to both head motion and to fMRI denoising methods. These findings stress the importance of considering and reporting these experimental aspects to help with the reproducibility and interpretation of different studies. Future work is needed to further investigate transient-based dFC strategies that are more robust to head motion.

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bioRxiv2022

How Does White Matter Registration Affect Tractography Alignment?

Tractography is a powerful method to represent the structural connectivity of the brain white matter. Nevertheless, the comparison of these data structures between two individuals is still an open challenge because of their complexity, e.g. digital representation of millions of fibers as polylines. The scientific community spent a meaningful effort to develop new methods of white matter registration aiming to take advantage of diffusion MRI models. Despite the effort to improve the registration of the white matter, little is known about the effect of the registration on tractogram alignment. The main issue for an empirical evaluation is the lack of ground truth, e.g. a sample of data where the correct alignment is validated by experts. This work aims to overcome this drawback by proposing an evaluation framework based on the matching of homologous fiber structures, e.g. known neuroanatomical bundles. The contribution is a quantitative comparison of how the most representative methods of white matter registration affect tractogram alignment.

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bioRxiv2021

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusion Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mappings in neural tissue. However, the specificity of DKI is limited as different microstructural sources can contribute to the total diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to intravoxel diffusion anisotropy (Kaniso), and microscopic kurtosis (K) related to restricted diffusion and/or microstructural disorder. The latter in particular is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible. Recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation and revealed non negligible K in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the kurtosis sources in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of the relative importance of K in the healthy brain. Using this clinical CTI approach, we find that K significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first K maps of the human brain are presented. We find that the spatial distribution of K provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. We further show that ignoring K - as done by many contemporary methods based on multiple gaussian component approximation for kurtosis source estimation - biases the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue. HighlightsO_LICorrelation Tensor MRI (CTI) was recently proposed to resolve kurtosis sources C_LIO_LIWe implemented CTI on a 3T scanner to study kurtosis sources in the human brain C_LIO_LIIsotropic, anisotropic, and microscopic kurtosis sources were successfully resolved C_LIO_LIMicroscopic kurtosis (K) significantly contributes to overall kurtosis in human brain C_LIO_LIK provides a novel source of contrast in the human brain in vivo C_LI

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bioRxiv2021

Structural and functional network-level reorganization in the coding of auditory motion directions and sound source locations in the absence of vision

hMT+/V5 is a region in the middle occipito-temporal cortex that responds preferentially to visual motion in sighted people. In case of early visual deprivation, hMT+/V5 enhances its response to moving sounds. Whether hMT+/V5 contains information about motion directions and whether the functional enhancement observed in the blind is motion specific, or also involves sound source location, remains unsolved. Moreover, the impact of this crossmodal reorganization of hMT+/V5 on the regions typically supporting auditory motion processing, like the human Planum Temporale (hPT), remains equivocal. We used a combined functional and diffusion MRI approach and individual in-ear recordings to study the impact of early blindness on the brain networks supporting spatial hearing, in male and female humans. Whole-brain univariate analysis revealed that the anterior portion of hMT+/V5 responded to moving sounds in sighted and blind people, while the posterior portion was selective to moving sounds only in blind participants. Multivariate decoding analysis revealed that the presence of motion directions and sound positions information was higher in hMT+/V5 and lower in hPT in the blind group. While both groups showed axis-of-motion organization in hMT+/V5 and hPT, this organization was reduced in the hPT of blind people. Diffusion MRI revealed that the strength of hMT+/V5 - hPT connectivity did not differ between groups, whereas the microstructure of the connections was altered by blindness. Our results suggest that the axis-of-motion organization of hMT+/V5 does not depend on visual experience, but that blindness alters the response properties of occipito-temporal networks supporting spatial hearing in the sighted. Significance StatementSpatial hearing helps living organisms navigate their environment. This is certainly even more true in people born blind. How does blindness affect the brain network supporting auditory motion and sound source location? Our results show that the presence of motion directions and sound positions information was higher in hMT+/V5 and lower in hPT in blind relative to sighted people; and that this functional reorganization is accompanied by microstructural (but not macrostructural) alterations in their connections. These findings suggest that blindness alters crossmodal responses between connected areas that share the same computational goals.

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bioRxiv2020

Direct structural connections between auditory and visual motion selective regions in humans

In humans, the occipital middle-temporal region (hMT+/V5) specializes in the processing of visual motion, while the Planum Temporale (hPT) specializes in auditory motion processing. It has been hypothesized that these regions might communicate directly to achieve fast and optimal exchange of multisensory motion information. In this study, we investigated for the first time in humans the existence of direct white matter connections between visual and auditory motion-selective regions using a combined functional- and diffusion-MRI approach. We found reliable evidence supporting the existence of direct white matter connections between individually and functionally defined hMT+/V5 and hPT. We show that projections between hMT+/V5 and hPT do not overlap with large white matter bundles such as the Inferior Longitudinal Fasciculus (ILF) nor the Inferior Frontal Occipital Fasciculus (IFOF). Moreover, we did not find evidence for the existence of reciprocal projections between the face fusiform area and hPT, supporting the functional specificity of hMT+/V5 - hPT connections. Finally, evidence supporting the existence of hMT+/V5 - hPT connections was corroborated in a large sample of participants (n=114) from the human connectome project. Altogether, this study provides first evidence supporting the existence of direct occipito-temporal projections between hMT+/V5 and hPT which may support the exchange of motion information between functionally specialized auditory and visual regions and that we propose to name the middle (or motion) occipito-temporal track (MOTT).

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bioRxiv2019

Improving spatial normalization of brain diffusion MRI to measure longitudinal changes of tissue microstructure in human cortex and white matter.

BackgroundFractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter microstructure. Recently, there has been a growing interest in identifying experience-dependent plasticity in gray matter using MD. Improving registration has thus become a major goal to enhance the detection of subtle longitudinal changes in cortical microstructure. PurposeTo optimize normalization to improve registration in gray matter and reduce variability associated with multi-session registrations. Study TypeProspective longitudinal study SubjectsTwenty-one healthy subjects (18-31 years old) underwent 9 magnetic resonance imaging (MRI) scanning sessions each. Field Strength/Sequence3.0T, diffusion-weighted multiband-accelerated sequence, MP2RAGE sequence. AssessmentDiffusion-weighted images were registered to standard space using different pipelines that varied in the features used for normalization, namely the non-linear registration algorithm (FSL vs ANTs), the registration target (FA-based vs T1-based templates), and the use of intermediate individual (FA-based or T1-based) targets. We compared the across-session test-retest reproducibility error from these normalization approaches for FA and MD in white and gray matters. Statistical TestsReproducibility errors were compared using a repeated-measures analysis of variance with pipeline as within-subject factor. ResultsThe registration of FA data to the FMRIB58 FA atlas using ANTs yielded lower reproducibility errors in white matter (p<0.0001) with respect to FSL. Moreover, using the MNI152 T1 template as the target of registration resulted in lower reproducibility errors for MD (p<0.0001), whereas the FMRIB58 FA template performed better for FA (p<0.0001). Finally, the use of an intermediate individual template improved reproducibility when registration of the FA images to the MNI152-T1 was carried out within modality (FA-FA) (p<0.05), but not via a T1-based individual template. Data ConclusionA normalization approach using ANTs to register FA images to the MNI152 T1 template via an individual FA template minimized test-retest reproducibility errors both for gray and white matter.

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bioRxiv2018

White matter connectivity between occipital and temporal regions involved in face and voice processing in hearing and early deaf individuals.

Neuroplasticity following sensory deprivation has long inspired neuroscience research in the quest of understanding how sensory experience and genetics interact in developing the brain functional and structural architecture. Many studies have shown that sensory deprivation can lead to cross-modal functional recruitment of sensory deprived cortices. Little is known however about how structural reorganization may support these functional changes. In this study, we examined early deaf, hearing signer and hearing non-signer individuals using diffusion MRI to evaluate the potential structural connectivity linked to the functional recruitment of the temporal voice area by face stimuli in deaf individuals. More specifically, we characterized the structural connectivity between occipital, fusiform and temporal regions typically supporting voice- and face-selective processing. Despite the extensive functional reorganization for face processing in the temporal cortex of the deaf, macroscopic properties of these connections did not differ across groups. However, both occipito- and fusiform-temporal connections showed significant microstructural changes between groups (fractional anisotropy reduction, radial diffusivity increase). We propose that the reorganization of temporal regions after early auditory deprivation builds on intrinsic and mainly preserved anatomical connectivity between functionally specific temporal and occipital regions.\n\nHighlightsO_LIMacrostructural connectivity of the face-voice system is preserved in early deafness\nC_LIO_LIEarly deafness impacts on the microstructural connectivity of the face-voice system\nC_LIO_LIBoth genetics and experience shape structural connections in the face-voice system\nC_LIO_LIInnate anatomical networks might constrain the expression of cross-modal plasticity\nC_LI\n\nAbbreviations

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