Biology subjects

Dafsari, H.

Publications and source records attributed to Dafsari, H..

3 recordsLinked to original sources

bioRxiv2025

Exploring the association between walking, sleep patterns, brain perivascular spaces, and cognitive function in Parkinson's disease

Perivascular spaces (PVSs) support brain waste clearance and are influenced by sleep. In Parkinsons disease (PD), sleep is often disrupted, and can co-occur with gait dysfunction. This study investigated how sleep disruption, walking difficulties, and cognition relate in PD, considering PVS as a mediator. Our cohort included 348 participants -healthy controls (HC, n=59), prodromal (n=117), and PD patients (n=172)-in the Parkinsons Progression Markers Initiative (PPMI) study. Insomnia and walking issues were assessed by questionnaire, cognition was measured with the Symbol Digit Modalities (SDM) test, and PVS volume fraction in white matter (WM-PVS) and basal ganglia (BG-PVS) was quantified. PVS did not mediate the relationship between sleep or motor problems, on cognition. Walking difficulties were directly linked to poorer cognition, and insomnia to walking deficits. BG-PVS was negatively associated with cognition. PVS burden predicted cognitive function after five years, but not disease progression as indexed by cerebro-spinal fluid (CSF) biomarkers. These findings suggest walking deficits directly relate with cognition in PD, but the relationship is not mediated by PVS.

neuroscience↗

bioRxiv2019

Left prefrontal connectivity links subthalamic stimulation with depressive symptoms in Parkinson’s disease

ObjectiveSubthalamic nucleus deep brain stimulation (STN-DBS) in Parkinsons Disease (PD) not only stimulates focal target structures but also affects distributed brain networks. The impact this network modulation has on non-motor DBS effects is not well characterized. By focusing on the affective domain, we systematically investigate the impact of electrode placement and associated structural connectivity on changes in depressive symptoms following STN-DBS which have been reported to improve, worsen or remain unchanged. MethodsDepressive symptoms before and after STN-DBS surgery were documented in 116 PD patients from three DBS centers (Berlin, Queensland, Cologne). Based on individual electrode reconstructions, the volumes of tissue activated (VTA) were estimated and combined with normative connectome data to identify structural connections passing through VTAs. Berlin and Queensland cohorts formed a training and cross-validation dataset used to identify structural connectivity explaining change in depressive symptoms. The Cologne data served as test-set for which depressive symptom change was predicted. ResultsStructural connectivity was linked to depressive symptom change under STN-DBS. An optimal connectivity map trained on the Berlin cohort could predict changes in depressive symptoms in Queensland patients and vice versa. Furthermore, the joint training-set map predicted changes in depressive symptoms in the independent test-set. Worsening of depressive symptoms was associated with left prefrontal connectivity. InterpretationFibers linking the STN electrode with left prefrontal areas predicted worsening of depressive symptoms. Our results suggest that for the left STN-DBS lead, placement impacting fibers to left prefrontal areas should be avoided to maximize improvement of depressive symptoms.

neuroscience↗

bioRxiv2016

Phase-coherence classification: a new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components

Local field potentials (LFP) reflect the integrated electrophysiological activity of a large group of neurons. To minimize influence of external activity on the analysis, conventionally bipolar recordings are used to eliminate volume-conducted signals. Here we introduce a novel method, called phase-coherence classification (PCC), to separate LFP in time-frequency domain into a volume-conducted, a local incoherent and local coherent signal. The PCC allows to compute the power spectral densities of each signal and to associate each class with possible locations of electro-physiological activity. In order to test the resolution properties and accuracy of the method we generate composite and non-stationary synthetic time series with similar statistical characteristics as measured LFP. The PCC identifies volume-conducted signals with a phase threshold that is determined from probability density functions of non-phase-shifted synthetic time series. We estimate optimal PCC parameters for the analysis of beta band oscillations in LFP and apply the PCC to a test data set obtained from within the subthalamic nucleus of eight patients with Parkinsons disease (PD). We show that PCC can identify activity of multiple local clusters during a tremor episode and quantify the relative power of local and volume-conducted signals. We further analyze the electrophysiological response to an apomorphine injection during rest and show that incoherent activity in the low beta band shows a significant medication-induced decrease. We further find significant movement-induced changes on medication of the local coherent signal, which increased during an isometric hold task and decreased during phasic wrist movement. This indicates a different role of incoherent and coherent signals possibly related to physiologically different networks. This new PCC method can potentially also be applied to EEG and MEG data in order to minimize the influence of spatial leakage on power spectra and coherence estimates.

neuroscience↗