Abnormal brain network community structure related to psychological stress in schizophrenia.

Recent functional imaging studies in schizophrenia consistently report a disruption of brain connectivity. However, most of these studies analyze the brain connectivity during resting state. Since psychological stress is a major factor for the emergence of psychotic symptoms, we sought to characterize the brain connectivity reconfiguration induced by stress in schizophrenia. We tested the hypothesis that an alteration of the brain's integration-segregation dynamic could be the result of patients with schizophrenia facing psychological stress. To this end, we studied the modular organization and the reconfiguration of networks induced by a stress paradigm in forty subjects (twenty patients and twenty controls), thus analyzing the dynamics of the brain in terms of integration and segregation processes by using 3T-fMRI. Patients with schizophrenia did not show statistically significant differences during the control task compared with controls, but they showed an abnormal community structure during stress condition and an under-connected reconfiguration network with a reduction of hub nodes, suggesting a deficit of integration dynamic with a greater compromise of the right hemisphere. These results provide evidence that schizophrenia has a normal response to undemanding stimuli but shows a disruption of brain functional connectivity between key regions involved in stress response, potentially leading to altered functional brain dynamics by reducing integration capacity and showing deficits recruiting right hemisphere regions. This could in turn underlie the hyper-sensitivity to stress characteristic of schizophrenia.

Temporal irreversibility of neural dynamics as a signature of consciousness.

Dissipative systems evolve in the preferred temporal direction indicated by the thermodynamic arrow of time. The fundamental nature of this temporal asymmetry led us to hypothesize its presence in the neural activity evoked by conscious perception of the physical world, and thus its covariance with the level of conscious awareness. We implemented a data-driven deep learning framework to decode the temporal inversion of electrocorticography signals acquired from non-human primates. Brain activity time series recorded during conscious wakefulness could be distinguished from their inverted counterparts with high accuracy, both using frequency and phase information. However, classification accuracy was reduced for data acquired during deep sleep and under ketamine-induced anesthesia; moreover, the predictions obtained from multiple independent neural networks were less consistent for sleep and anesthesia than for conscious wakefulness. Finally, the analysis of feature importance scores highlighted transitions between slow ($\approx$20 Hz) and fast frequencies (>40 Hz) as the main contributors to the temporal asymmetry observed during conscious wakefulness. Our results show that a preferred temporal direction is manifest in the neural activity evoked by conscious mentation and in the phenomenology of the passage of time, establishing common ground to tackle the relationship between brain and subjective experience.

The INSIDEOUT framework provides precise signatures of the balance of intrinsic and extrinsic dynamics in brain states.

Finding precise signatures of different brain states is a central, unsolved question in neuroscience. We reformulated the problem to quantify the 'inside out' balance of intrinsic and extrinsic brain dynamics in brain states. The difference in brain state can be described as differences in the detailed causal interactions found in the underlying intrinsic brain dynamics. We used a thermodynamics framework to quantify the breaking of the detailed balance captured by the level of asymmetry in temporal processing, i.e. the arrow of time. Specifically, the temporal asymmetry was computed by the time-shifted correlation matrices for the forward and reversed time series, reflecting the level of non-reversibility/non-equilibrium. We found precise, distinguishing signatures in terms of the reversibility and hierarchy of large-scale dynamics in three radically different brain states (awake, deep sleep and anaesthesia) in electrocorticography data from non-human primates. Significantly lower levels of reversibility were found in deep sleep and anaesthesia compared to wakefulness. Non-wakeful states also showed a flatter hierarchy, reflecting the diversity of the reversibility across the brain. Overall, this provides signatures of the breaking of detailed balance in different brain states, perhaps reflecting levels of conscious awareness.

Nonequilibrium brain dynamics as a signature of consciousness.

The cognitive functions of human and nonhuman primates rely on the dynamic interplay of distributed neural assemblies. As such, it seems unlikely that cognition can be supported by macroscopic brain dynamics at the proximity of equilibrium. We confirmed this hypothesis by investigating electrocorticography data from nonhuman primates undergoing different states of unconsciousness (sleep, and anesthesia with propofol, ketamine, and ketamine plus medetomidine), and functional magnetic resonance imaging data from humans, both during deep sleep and under propofol anesthesia. Systematically, all states of reduced consciousness unfolded at higher proximity to equilibrium compared to conscious wakefulness, as demonstrated by the computation of entropy production and the curl of probability flux in phase space. Our results establish nonequilibrium macroscopic brain dynamics as a robust signature of consciousness, opening the way for the characterization of cognition and awareness using tools from statistical mechanics.

Generative Embeddings of Brain Collective Dynamics Using Variational Autoencoders.

We consider the problem of encoding pairwise correlations between coupled dynamical systems in a low-dimensional latent space based on few distinct observations. We use variational autoencoders (VAEs) to embed temporal correlations between coupled nonlinear oscillators that model brain states in the wake-sleep cycle into a two-dimensional manifold. Training a VAE with samples generated using two different parameter combinations results in an embedding that encodes the repertoire of collective dynamics, as well as the topology of the underlying connectivity network. We first follow this approach to infer the trajectory of brain states measured from wakefulness to deep sleep from the two end points of this trajectory; then, we show that the same architecture was capable of representing the pairwise correlations of generic Landau-Stuart oscillators coupled by complex network topology.

Disrupted functional connectivity of the locus coeruleus in healthy adults with parental history of Alzheimer's disease.

Prevention and early treatment strategies for Alzheimer's disease (AD) are hampered by the lack of research biomarkers. Neuropathological changes in the Locus Coeruleus (LC) are detected early in AD, and noradrenaline plays a neuroprotective role in LC projecting areas. We assessed functional connectivity (FC) of the brainstem in asymptomatic individuals at familial risk for AD hypothesizing that FC of the LC will be decreased in relation to not-at-risk individuals. Thirty-one offspring of patients with late-onset AD (O-LOAD) (22 females; mean age ± SD = 50.36 ± 8.32) and 28 healthy controls (HC) (20 females; mean age ± SD = 53.90 ± 8.44) underwent a neurocognitive evaluation and a resting-state functional magnetic resonance imaging acquisition. In FC analyses we evaluated whole-brain global connectivity of the brainstem area, and subsequently assessed seed-to-voxel FC patterns from regions showing between-group differences. O-LOAD individuals scored worse in neurocognitive measures of memory and overall functioning (pFDR<0.05). In imaging analyses, we observed that O-LOAD individuals showed decreased global connectivity in a cluster encompassing the left LC (peak = -4, -34, -32, pTFCE<0.05). Seed-to-voxel analyses revealed that this finding was largely explained by decreased connectivity between the LC and the cerebellar cortex. Moreover, FC between the LC and the left cerebellum correlated positively with delayed recall scores. FC between the LC and the cerebellar cortex is decreased in the healthy offspring of patients with LOAD, such connectivity measurements being associated with delayed memory scores. The assessment of FC between the LC and the cerebellum may serve as a biomarker of AD vulnerability.

White matter fiber density abnormalities in cognitively normal adults at risk for late-onset Alzheimer's disease.

Tau accumulation affecting white matter tracts is an early neuropathological feature of late-onset Alzheimer's disease (LOAD). There is a need to ascertain methods for the detection of early LOAD features to help with disease prevention efforts. The microstructure of these tracts and anatomical brain connectivity can be assessed by analyzing diffusion MRI (dMRI) data. Considering that family history increases the risk of developing LOAD, we explored the microstructure of white matter through dMRI in 23 cognitively normal adults who are offspring of patients with Late-Onset Alzheimer's Disease (O-LOAD) and 22 control subjects (CS) without family history of AD. We also evaluated the relation of white matter microstructure metrics with cortical thickness, volumetry, in vivo amyloid deposition (with the help of PiB positron emission tomography -PiB-PET) and regional brain metabolism (as FDG-PET) measures. Finally we studied the association between cognitive performance and white matter microstructure metrics. O-LOAD exhibited lower fiber density and fractional anisotropy in the posterior portion of the corpus callosum and right fornix when compared to CS. Among O-LOAD, reduced fiber density was associated with lower amyloid deposition in the right hippocampus, and greater cortical thickness in the left precuneus, while higher mean diffusivity was related with greater cortical thickness of the right superior temporal gyrus. Additionally, compromised white matter microstructure was associated with poorer semantic fluency. In conclusion, white matter microstructure metrics may reveal early differences in O-LOAD by virtue of parental history of the disorder, when compared to CS without a family history of LOAD. We demonstrate that these differences are associated with lower fiber density in the posterior portion of the corpus callosum and the right fornix.