Neurobiological substrates of Tourette's disorder

OBJECTIVE

This article reviews the available scientific literature concerning the neurobiological substrates of Tourette's disorder (TD).

METHODS

The electronic databases of PubMed, ScienceDirect, and PsycINFO were searched for relevant studies using relevant search terms.

RESULTS

Neuropathological as well as structural and functional neuroimaging studies of TD implicate not only the sensorimotor corticostriatal circuit, but also the limbic and associative circuits as well. Preliminary evidence also points to abnormalities in the frontoparietal network that is thought to maintain adaptive online control. Evidence supporting abnormalities in dopaminergic and noradrenergic neurotransmission remains strong, although the precise mechanisms remain the subject of speculation.

CONCLUSION

Structural and functional abnormalities in multiple parallel corticostriatal circuits may underlie the behavioral manifestations of TD and related neuropsychiatric disorders over the course of development. Further longitudinal research is needed to elucidate these neurobiological substrates.

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.

Brain areas associated with clinical and cognitive insight in psychotic disorders: A systematic review and meta-analysis

In the past years, ample interest in brain abnormalities related to clinical and cognitive insight in psychosis has contributed several neuroimaging studies to the literature. In the current study, published findings on the neural substrates of clinical and cognitive insight in psychosis are integrated by performing a systematic review and meta-analysis. Coordinate-based meta-analyses were performed with the parametric coordinate-based meta-analysis approach, non-coordinate based meta-analyses were conducted with the metafor package in R. Papers that could not be included in the meta-analyses were systematically reviewed. Thirty-seven studies were retrieved, of which 21 studies were included in meta-analyses. Poorer clinical insight was related to smaller whole brain gray and white matter volume and gray matter volume of the frontal gyri. Cognitive insight was predominantly positively associated with structure and function of the hippocampus and ventrolateral prefrontal cortex. Impaired clinical insight is not associated with abnormalities of isolated brain regions, but with spatially diffuse global and frontal abnormalities suggesting it might rely on a range of cognitive and self-evaluative processes. Cognitive insight is associated with specific areas and appears to rely more on retrieving and integrating self-related information.

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.

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.

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.

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.