Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure ...assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have required long acquisition times and thus have been inapplicable for children and adults who are uncooperative, uncomfortable, or unwell. We show that the long scan time requirements are mainly due to disadvantages of classical data processing. We demonstrate how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This modification allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models. We set a new state of the art by estimating diffusion kurtosis measures from only 12 data points and neurite orientation dispersion and density measures from only 8 data points. This allows unprecedentedly fast and robust protocols facilitating clinical routine and demonstrates how classical data processing can be streamlined by means of deep learning.
Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network ...organization of 23 of the world’s most successful memory athletes and matched controls with fMRI during both task-free resting state baseline and active memory encoding. We demonstrate that, in a group of naive controls, functional connectivity changes induced by 6 weeks of mnemonic training were correlated with the network organization that distinguishes athletes from controls. During rest, this effect was mainly driven by connections between rather than within the visual, medial temporal lobe and default mode networks, whereas during task it was driven by connectivity within these networks. Similarity with memory athlete connectivity patterns predicted memory improvements up to 4 months after training. In conclusion, mnemonic training drives distributed rather than regional changes, reorganizing the brain’s functional network organization to enable superior memory performance.
•Memory champions show distributed functional brain network connectivity changes•Mnemonic strategies for superior memory can be learned by naive subjects•Mnemonic training induces similarity with memory champion brain connectivity•Brain network dynamics of this effect differ between task and resting state
Dresler et al. demonstrate that distributed functional brain network connectivity patterns differentiate the world’s leading memory athletes from intelligence-matched controls. Similar connectivity patterns could be induced through intense mnemonic training in naive subjects.
Exposure to chronic stress is frequently accompanied by cognitive and affective disorders in association with neurostructural adaptations. Chronic stress was previously shown to trigger ...Alzheimer’s-like neuropathology, which is characterized by Tau hyperphosphorylation and missorting into dendritic spines followed by memory deficits. Here, we demonstrate that stress-driven hippocampal deficits in wild-type mice are accompanied by synaptic missorting of Tau and enhanced Fyn/GluN2B-driven synaptic signaling. In contrast, mice lacking Tau Tau knockout (Tau-KO) mice do not exhibit stress-induced pathological behaviors and atrophy of hippocampal dendrites or deficits of hippocampal connectivity. These findings implicate Tau as an essential mediator of the adverse effects of stress on brain structure and function.
Falling asleep is paralleled by a loss of conscious awareness and reduced capacity to process external stimuli. Little is known on sleep-associated changes of spontaneously synchronized anatomical ...networks as detected by resting-state functional magnetic resonance imaging (rs-fMRI). We employed functional connectivity analysis of rs-fMRI series obtained from 25 healthy participants, covering all non-rapid eye movement (NREM) sleep stages. We focused on the default mode network (DMN) and its anticorrelated network (ACN) that are involved in internal and external awareness during wakefulness. Using independent component analysis, cross-correlation analysis (CCA), and intraindividual dynamic network tracking, we found significant changes in DMN/ACN integrity throughout the NREM sleep. With increasing sleep depth, contributions of the posterior cingulate cortex (PCC)/retrosplenial cortex (RspC), parahippocampal gyrus, and medial prefrontal cortex to the DMN decreased. CCA revealed a breakdown of corticocortical functional connectivity, particularly between the posterior and anterior midline node of the DMN and the DMN and the ACN. Dynamic tracking of the DMN from wakefulness into slow wave sleep in a single subject added insights into intraindividual network fluctuations. Results resonate with a role of the PCC/RspC for the regulation of consciousness. We further submit that preserved corticocortical synchronization could represent a prerequisite for maintaining internal and external awareness.
Abstract Background Overnight memory consolidation is disturbed in both depression and schizophrenia, creating an ideal situation to investigate the mechanisms underlying sleep-related consolidation ...and to distinguish disease-specific processes from common elements in their pathophysiology. Methods We investigated patients with depression and schizophrenia, as well as healthy control subjects (each n = 16), under a motor memory consolidation protocol with functional magnetic resonance imaging and polysomnography. Results In a sequential finger-tapping task associated with the degree of hippocampal-prefrontal cortex functional connectivity during the task, significantly less overnight improvement was identified as a common deficit in both patient groups. A task-related overnight decrease in activation of the basal ganglia was observed in control subjects and schizophrenia patients; in contrast, patients with depression showed an increase. During the task, schizophrenia patients, in comparison with control subjects, additionally recruited adjacent cortical areas, which showed a decrease in functional magnetic resonance imaging activation overnight and were related to disease severity. Effective connectivity analyses revealed that the hippocampus was functionally connected to the motor task network, and the cerebellum decoupled from this network overnight. Conclusions While both patient groups showed similar deficits in consolidation associated with hippocampal-prefrontal cortex connectivity, other activity patterns more specific for disease pathology differed.
Graph theoretical analysis of functional magnetic resonance imaging (fMRI) time series has revealed a small-world organization of slow-frequency blood oxygen level-dependent (BOLD) signal ...fluctuations during wakeful resting. In this study, we used graph theoretical measures to explore how physiological changes during sleep are reflected in functional connectivity and small-world network properties of a large-scale, low-frequency functional brain network. Twenty-five young and healthy participants fell asleep during a 26.7 min fMRI scan with simultaneous polysomnography. A maximum overlap discrete wavelet transformation was applied to fMRI time series extracted from 90 cortical and subcortical regions in normalized space after residualization of the raw signal against unspecific sources of signal fluctuations; functional connectivity analysis focused on the slow-frequency BOLD signal fluctuations between 0.03 and 0.06 Hz. We observed that in the transition from wakefulness to light sleep, thalamocortical connectivity was sharply reduced, whereas corticocortical connectivity increased; corticocortical connectivity subsequently broke down in slow-wave sleep. Local clustering values were closest to random values in light sleep, whereas slow-wave sleep was characterized by the highest clustering ratio (gamma). Our findings support the hypothesis that changes in consciousness in the descent to sleep are subserved by reduced thalamocortical connectivity at sleep onset and a breakdown of general connectivity in slow-wave sleep, with both processes limiting the capacity of the brain to integrate information across functional modules.
Applying graph theoretical analysis of spontaneous BOLD fluctuations in functional magnetic resonance imaging (fMRI), we investigated whole-brain functional connectivity of 11 healthy volunteers ...during wakefulness and propofol-induced loss of consciousness (PI-LOC). After extraction of regional fMRI time series from 110 cortical and subcortical regions, we applied a maximum overlap discrete wavelet transformation and investigated changes in the brain's intrinsic spatiotemporal organization. During PI-LOC, we observed a breakdown of subcortico-cortical and corticocortical connectivity. Decrease of connectivity was pronounced in thalamocortical connections, whereas no changes were found for connectivity within primary sensory cortices. Graph theoretical analyses revealed significant changes in the degree distribution and local organization metrics of brain functional networks during PI-LOC: compared with a random network, normalized clustering was significantly increased, as was small-worldness. Furthermore we observed a profound decline in long-range connections and a reduction in whole-brain spatiotemporal integration, supporting a topological reconfiguration during PI-LOC. Our findings shed light on the functional significance of intrinsic brain activity as measured by spontaneous BOLD signal fluctuations and help to understand propofol-induced loss of consciousness.
Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute ...stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.
Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.
We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, pperm < .001) and increases in heart rate (R2 = 0.075, pperm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR2 = 0.075, pperm = .030) but not the presence or absence of a mood and anxiety disorder.
Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.
PDF
