Biology subjects

Beresniewicz, J.

Publications and source records attributed to Beresniewicz, J..

4 recordsLinked to original sources

bioRxiv2020

Dynamic switching between intrinsic and extrinsic mode networks as demands change from passive to active processing

We here report on the relationship between default and extrinsic mode networks across alternating brief periods of rest and active task processing. We used three different visual tasks: mental rotation, working memory and mental arithmetic in a classic fMRI ON-OFF block design where task (ON) blocks alternated with equal periods of rest (OFF) blocks. By analysing data in two ways, using an ON-OFF contrast, we showed the existence of a generalized task-positive network, labelled the extrinsic mode network (EMN) which was anti-correlated with the default mode network (DMN) as processing demands shifted from rest to active processing. We then identified two key regions of interest (ROIs) in the SMA and Precuneus/PCC regions as hubs for the extrinsic and intrinsic networks, and extracted the time-course from these ROIs. The results showed a close to perfect correlations for the SMA and Precuneus/PCC time-courses for ON-respective OFF-blocks. We suggest the existence of two large-scale networks, an extrinsic mode network and an intrinsic mode network, respectively, which are up- and down-regulated as environmental demands change from active to passive processing.

neuroscience↗

bioRxiv2019

Positive relation between arcuate fasciculus white matter fiber structure and severity of auditory hallucinations: A DTI tractography study

The arcuate fasciculus (AF) has been implicated in the pathology behind schizophrenia and auditory verbal hallucinations (AVHs). White matter tracts forming the arcuate fasciculus can be quantified and visualized using diffusion tensor imaging (DTI) tractography. Although there have been a number of studies on this topic, the results have been conflicting. Studying the underlying white matter structure of the AF could shed light on functional connectivity between temporal and frontal language areas in AVHs. The participants were 66 patients with a schizophrenia diagnosis, where AVHs were defined from the Positive and Negative Syndrome Scale (PANSS), and compared with a healthy control group. DTI was performed on a 3T MR scanner, and tensor estimation was done using deterministic streamline tractography. Statistical analysis of the data showed significantly longer tracts along the AF in patients with severe and frequent AVHs, as well as an overall significant asymmetry with longer fibers on the left side. In addition, there were significant positive correlations between PANSS scores and tract length, tract volume, and number of streamlines for the posterior AF segment on the left side. It is concluded that the present structural results complement previous functional findings of fronto-temporal connectivity in AVH patients.

neuroscience↗

bioRxiv2019

Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods

Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting, and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.

neuroscience↗

bioRxiv2019

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

BackgroundSchizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD, and healthy controls across 10 cohorts. MethodsWe trained six cross-validated models using different combinations of DTI data from 927 healthy controls (HC, 18-94 years), and applied the models to the test sets including 648 SZ (18-66 years) patients, 185 BD patients (18-64 years), and 990 HC (17-68 years), estimating brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=-.29) and BD (d=.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy (FA) based models showed larger group differences than the models based on other DTI-derived metrics. ConclusionsBrain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

neuroscience↗