Biology subjects

Jech, R.

Publications and source records attributed to Jech, R..

4 recordsLinked to original sources

bioRxiv2022

Older age is associated with a shift from ventral to dorsal PCC in pathways connecting the DMN with visual and limbic areas and a directionality shift in pathways connecting the DMN with sensorimotor areas

Alterations in the default mode network (DMN) are known to associated with aging and with neurological and psychiatric diseases. We assessed age-dependent changes in interactions within the DMN and between the DMN with other brain areas, and correlations of these interactions with a battery of neuropsychological tests to formulate a macroscopic model of ageing. Using a novel multivariate analysis method, enabling determination of directed pathways, on resting-state functional MRI data from fifty young and thirty-one old healthy individuals, we identified directed intra- and inter-DMN directed pathways that differed between the groups and used their correlations with neuropsychological tests to infer their behavioral meaning. Pathways connecting the DMN with visual and limbic regions in old subjects engaged at BOLD low frequency and involved the dorsal PCC. In young subjects, they were at high frequency and involved the ventral PCC. Pathways combining the sensorimotor (SM) and the DMN, were efferent in young subjects (DMN SM). Most inter-DMN pathways in the old subjects, correlated with reduced speed and working memory and, were DMN efferent (DMN>). We suggest a macroscopic model of aging centered in the DMN. It suggests that the reduced sensorimotor efferent, probably brought about from reduced physical activity, and the increased need to control such activities by the mPFC, causes a higher dependency on external versus internal cues. This results in a shift from ventral to dorsal PCC of inter-DMN pathways. Consequently, the model combines physical activity with ageing.

neuroscience↗

bioRxiv2022

Improving fMRI in Parkinson's Disease by Accounting for Brain Region-Specific Activity Patterns

In functional magnetic imaging (fMRI) in Parkinsons disease (PD), a paradigm consisting of blocks of finger tapping and rest along with a corresponding general linear model (GLM) is often used to assess motor activity. However, this method has three limitations: (i) Due to the strong magnetic field and the confined environment of the cylindrical bore, it is troublesome to accurately monitor motor output and, therefore, variability in the performed movement is typically ignored. (ii) Given the loss of dopaminergic neurons and ongoing compensatory brain mechanisms, motor control is abnormal in PD. Therefore, modeling of patients tapping with a constant amplitude (using a boxcar function) and the expected Parkinsonian motor output are prone to mismatch. (iii) The motor loop involves structures with distinct hemodynamic responses, for which only one type of modeling (e.g., modeling the whole block of finger tapping) may not suffice to capture these structures temporal activation. The first two limitations call for considering results from online recordings of the real motor output that may lead to significant sensitivity improvements. This was shown in previous work using a non-magnetic glove to capture details of the patients finger movements in a so-called kinematic approach. For the third limitation, modeling motion initiation instead of the whole tapping block has been suggested to account for different temporal activation signatures of the motor loops structures. In the present study we propose improvements to the GLM as a tool to study motor disorders. For this, we test the robustness of the kinematic approach in an expanded cohort (n=31), apply more conservative statistics than in previous work, and evaluate the benefits of an event-related model function. Our findings suggest that the integration of the kinematic approach offers a general improvement in detecting activations in subcortical structures, such as the basal ganglia. Additionally, modeling motion initiation using an event-related design yielded superior performance in capturing medication-related effects in the putamen. Our results may guide adaptations in analysis strategies for functional motor studies related to PD and also in more general applications.

neuroscience↗

bioRxiv2021

The sensitivity of ECG contamination to surgical implantation site in adaptive closed-loop neurostimulation systems

BackgroundBrain sensing devices are approved today for Parkinsons, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. ObjectivesInvestigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. MethodsElectric field modelling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.a ResultsThe impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. ConclusionsGiven the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration. HighlightsO_LIChronic embedded brain sensing promises algorithm-based neurostimulation C_LIO_LIAlgorithms for closed-loop stimulation can be impaired by artifacts C_LIO_LIThe relationship of implant location to cardiac dipole has relevant impact on neural signal fidelity; simple models can provide guidance on the sensitivity C_LIO_LIECG artifacts are present in up to 50% of neural signals from left subclavicular DBS systems C_LIO_LIImplanting DBS in a right subclavicular location significantly reduces frequency of ECG artifacts C_LIO_LICranial-mounted implants are relatively immune to artifacts C_LI

neuroscience↗

bioRxiv2020

JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with positron emission tomography derived neurotransmitter maps

Recent studies have shown that drug-induced spatial alteration patterns in resting state functional activity as measured using magnetic resonance imaging (rsfMRI) are associated with the distribution of specific receptor systems targeted by respective compounds. Based on this approach, we introduce a toolbox (JuSpace) allowing for cross-modal correlation of MRI- based measures with nuclear imaging derived estimates covering various neurotransmitter systems including dopaminergic, serotonergic, noradrenergic, and GABAergic (gamma- aminobutric acid) neurotransmission. We apply JuSpace to two datasets covering Parkinsons disease patients (PD) and risperidone-induced changes in rsfMRI and cerebral blood flow (CBF). Consistently with the predominant neurodegeneration of dopaminergic and serotonergic system in PD, we find significant spatial associations between rsfMRI activity alterations in PD and dopaminergic (D2) and serotonergic systems (5-HT1b). Risperidone induced CBF alterations were correlated with its main targets in serotonergic and dopaminergic systems. JuSpace provides a biologically meaningful framework for linking neuroimaging to underlying neurotransmitter information.

neuroscience↗