Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.

In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.

Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity.

Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.

Insight does not come at random: Individual gray matter networks relate to clinical and cognitive insight in schizophrenia.

BACKGROUND: Impaired clinical and cognitive insight are prevalent in schizophrenia and relate to poorer outcome. Good insight has been suggested to depend on social cognitive and metacognitive abilities requiring global integration of brain signals. Impaired insight has been related to numerous focal gray matter (GM) abnormalities distributed across the brain suggesting dysconnectivity at the global level. In this study, we test whether global integration deficiencies reflected in gray matter network connectivity underlie individual variations in insight. METHODS: We used graph theory to examine whether individual GM-network metrics relate to insight in patients with a psychotic disorder (n = 114). Clinical insight was measured with the Schedule for the Assessment of Insight-Expanded and item G12 of the Positive and Negative Syndrome Scale, and cognitive insight with the Beck Cognitive Insight Scale. Individual GM-similarity networks were created from GM-segmentations of T1-weighted MRI-scans. Graph metrics were calculated using the Brain Connectivity Toolbox. RESULTS: Networks of schizophrenia patients with poorer clinical insight showed less segregation (i.e. clustering coefficient) into specialized subnetworks at the global level. Schizophrenia patients with poorer cognitive insight showed both less segregation and higher connectedness (i.e. lower path length) of their brain networks, making their network topology more "random". CONCLUSIONS: Our findings suggest less segregated processing of information in patients with poorer cognitive and clinical insight, in addition to higher connectedness in patients with poorer cognitive insight. The ability to take a critical perspective on one's symptoms (clinical insight) or views (cognitive insight) might depend especially on segregated specialized processing within distinct subnetworks.

Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis.

BACKGROUND: Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated. OBJECTIVE: To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline. METHODS: We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available. RESULTS: Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = -0.30; 95% CI = -0.51, -0.10; k = 6), and in MCI than in OA (ES = -1.49; 95% CI = -2.69, -0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures. CONCLUSION: Research indicates that RS alpha power decreases with increasing impairment, and could-combined with measures from other frequency bands-become a biomarker of early cognitive decline.

Trait self-reflectiveness relates to time-varying dynamics of resting state functional connectivity and underlying structural connectomes: Role of the default mode network.

BACKGROUND: Cognitive insight is defined as the ability to reflect upon oneself (i.e. self-reflectiveness), and to not be overly confident of one's own (incorrect) beliefs (i.e. self-certainty). These abilities are impaired in several disorders, while they are essential for the evaluation and regulation of one's behavior. We hypothesized that cognitive insight is a dynamic process, and therefore examined how it relates to temporal dynamics of resting state functional connectivity (FC) and underlying structural network characteristics in 58 healthy individuals. METHODS: Cognitive insight was measured with the Beck Cognitive Insight Scale. FC characteristics were calculated after obtaining four FC states with leading eigenvector dynamics analysis. Gray matter (GM) and DTI connectomes were based on GM similarity and probabilistic tractography. Structural graph characteristics, such as path length, clustering coefficient, and small-world coefficient, were calculated with the Brain Connectivity Toolbox. FC and structural graph characteristics were correlated with cognitive insight. RESULTS: Individuals with lower cognitive insight switched more and spent less time in a globally synchronized state. Additionally, individuals with lower self-reflectiveness spent more time in, had a higher probability of, and had a higher chance of switching to a state entailing default mode network (DMN) areas. With lower self-reflectiveness, DTI-connectomes were segregated less (i.e. lower global clustering coefficient) with lower embeddedness of the left angular gyrus specifically (i.e. lower local clustering coefficient). CONCLUSIONS: Our results suggest less stable functional and structural networks in individuals with poorer cognitive insight, specifically self-reflectiveness. An overly present DMN appears to play a key role in poorer self-reflectiveness.

Insight and emotion regulation in schizophrenia: A brain activation and functional connectivity study.

BACKGROUND: Insight is impaired in the majority of schizophrenia patients. The exact neural correlates of impaired insight remain unclear. We assume that the ability to regulate emotions contributes to having good clinical insight, as patients should be able to regulate their emotional state in such a way that they can adapt adequately in order to cope with impaired functioning and negative stigma associated with a diagnosis of schizophrenia. Numerous studies have shown emotional dysregulation in schizophrenia. We investigated the association between insight and brain activation and connectivity during emotion regulation. METHODS: Brain activation during emotion regulation was measured with functional MRI in 30 individuals with schizophrenia. Two emotion regulation strategies were examined: cognitive reappraisal and expressive suppression. Clinical insight was measured with the Schedule for the Assessment of Insight - Expanded, and cognitive insight was measured with the Beck Cognitive Insight Scale. Whole brain random effects multiple regression analyses were conducted to assess the relation between brain activation during emotion regulation and insight. Generalized psychophysiological interaction (gPPI) was used to investigate the relation between task-related connectivity and insight. RESULTS: No significant associations were found between insight and neural correlates of cognitive reappraisal. For clinical insight and suppression, significant positive associations were found between symptom relabeling and activation in the left striatum, thalamus and insula, right insula and caudate, right pre- and postcentral gyrus, left superior occipital gyrus and cuneus and right middle and superior occipital gyrus and cuneus. Furthermore, reduced clinical insight was associated with more connectivity between midline medial frontal gyrus and right middle occipital gyrus. For cognitive insight and suppression, significant positive associations were found between self-reflectiveness and activation in pre- and postcentral gyrus and left middle cingulate gyrus. CONCLUSIONS: Our results suggest an association between the capacity to relabel symptoms and activation of brain systems involved in cognitive-emotional control and visual processing of negative stimuli. Furthermore, poorer self-reflectiveness may be associated with brain systems subserving control and execution.

Association between prefrontal N-acetylaspartate and insight in psychotic disorders.

Insight is impaired in most patients with psychosis and has been associated with poorer prognosis. The exact neural basis of impaired insight is still unknown, but it may involve disrupted prefrontal neural connectivity. Numerous studies have indeed found white matter (WM) abnormalities in psychosis. The association between prefrontal WM abnormalities and insight has not been studied yet by means of proton magnetic resonance spectroscopy (1H-MRS). 1H-MRS can be used to measure N-acetylaspartate (NAA), which is considered to be a marker of neuronal integrity. We measured insight with the Birchwood Insight Scale (BIS) as well as item G12 of the Positive and Negative Syndrome Scale (PANSS) in 88 patients with psychosis. Prefrontal WM concentrations of NAA and ratios of NAA to creatine (Cr) were assessed with 1H-MRS. Nonparametric partial correlational analyses were conducted between NAA concentrations and insight controlling for illness duration, standardized antipsychotic dose, symptom scores, voxel grey matter content and voxel cerebrospinal fluid content. We found a significant correlation between reduced NAA/Cr ratios and poorer insight as measured with the BIS, which remained significant after additional correction for full width at half maximum, signal/noise and age. This is the first study reporting a relationship between lower prefrontal concentrations of a marker of neuronal integrity and impaired insight, providing further evidence that prefrontal pathology may play an important role in impaired insight in psychosis. This may be explained by the involvement of the prefrontal cortex in several executive and metacognitive functions, such as cognitive flexibility and perspective taking.