Linking Cognitive Integrity to Working Memory Dynamics in the Aging Human Brain.

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and MEG recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuospatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/nonmatch decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared with younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.

Low-Level Visual Features of Window Views Contribute to Perceived Naturalness and Mental Health Outcomes.

Previous studies have shown that natural window views are beneficial for mental health, but it is still unclear which specific features constitute a 'natural' window view. On the other hand, studies on image analysis found that low-level visual features (LLVFs) are associated with perceived naturalness, but mainly conducted experiments with brief stimulus presentations. In this study, research on the effects of window views on mental health was combined with the detailed analysis of LLVFs. Healthy adults rated window views from their home and sent in photographs of those views for analysis. Content validity of the 'ecological' view assessment was evaluated by checking correlations of LLVFs with window view ratings. Afterwards, it was explored which of the LLVFs best explained variance in perceived percentage of nature and man-made elements, and in ratings of view quality. Criterion validity was tested by investigating which variables were associated with negative affect and impulsive decision-making. The objective and subjective assessments of nature/sky in the view were aligned but objective brightness was unreliable. The perceived percentage of nature was significantly explained by green pixel ratio, while view quality was associated with fractals, saturation, sky pixel ratio and straight edge density. The higher subjective brightness of rooms was associated with a lower negative affect, whereas results for impulsive decision-making were inconsistent. The research highlights the validity to apply LLVFs analysis to ecological window views. For affect, subjective brightness seemed to be more relevant than LLVFs. For impulsive decision-making, performance context needs to be controlled in future studies.

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity.

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

The Thought From the Machine: Neural Basis of Thoughts With a Coherent and Diminished Sense of Authorship.

Patients with schizophrenia who experience inserted thoughts report a diminished sense of thought authorship. Based on its elusive neural basis, this functional neuroimaging study used a novel setup to convince healthy participants that a technical device triggers thoughts in their stream of consciousness. Self-reports indicate that participants experienced their thoughts as self-generated when they believed the (fake) device was deactivated, and attributed their thoughts externally when they believed the device was activated-an experience usually only reported by patients diagnosed with schizophrenia. Distinct activations in the medial prefrontal cortex (mPFC) were observed: ventral mPFC activation was linked to a sense of thought authorship and dorsal mPFC activation to a diminished sense of thought authorship. This functional differentiation corresponds to research on self- and other-oriented reflection processes and on patients with schizophrenia who show abnormal mPFC activation. Results thus support the notion that the mPFC might be involved in thought authorship as well as anomalous self-experiences.

Reduced Resting-State Connectivity in the Precuneus is correlated with Apathy in Patients with Schizophrenia.

A diagnosis of schizophrenia is associated with a heterogeneous psychopathology including positive and negative symptoms. The disconnection hypothesis, an early pathophysiological framework conceptualizes the diversity of symptoms as a result of disconnections in neural networks. In line with this hypothesis, previous neuroimaging studies of patients with schizophrenia reported alterations within the default mode network (DMN), the most prominent network at rest. The aim of the present study was to investigate the functional connectivity during rest in patients with schizophrenia and with healthy individuals and explore whether observed functional alterations are related to the psychopathology of patients. Therefore, functional magnetic resonance images at rest were recorded of 35 patients with schizophrenia and 41 healthy individuals. Independent component analysis (ICA) was used to extract resting state networks. Comparing ICA results between groups indicated alterations only within the network of the DMN. More explicitly, reduced connectivity in the precuneus was observed in patients with schizophrenia compared to healthy controls. Connectivity in this area was negatively correlated with the severity of negative symptoms, more specifically with the domain of apathy. Taken together, the current results provide further evidence for a role DMN alterations might play in schizophrenia and especially in negative symptoms such as apathy.

New Graph-Theoretical-Multimodal Approach Using Temporal and Structural Correlations Reveals Disruption in the Thalamo-Cortical Network in Patients with Schizophrenia.

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive, and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modeled independently: Functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural, and topological information of magnetic resonance imaging (MRI) measurements (structural and resting-state imaging) to patients with schizophrenia (n = 35) and matched healthy individuals (n = 41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole-brain connectivity, including functional, structural, and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity, resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting-state functional MRI, and it can be applied to electro-encephalography, magneto-encephalography, etc.